Month: December 2024

What’s that saying? Email marketing is a beast and client segmentation is the leash.

Hmm…well maybe that’s not the saying but you get the idea. 

Targeted emails perform better but really targeted emails perform the best. 

When it comes to email, basic segmentation isn’t cutting it anymore. Brands using advanced segmentation see a 760% increase in revenue from their email campaigns. 

Meanwhile, if you’re still lumping your audience into “past buyers” and “everyone else,” you’re leaving money (and clicks) on the table.

The thing is, this isn’t about reinventing the wheel. It’s about taking what you are already doing and making it better. And that’s what we’re going to do with these advanced client segmentation strategies.

We’re gonna help you crush your email KPIs, maximize ecommerce revenue, and keep customers coming back for more. Let’s break it down.

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What Is Client Segmentation?

Client segmentation is exactly what it sounds like – it’s the process of dividing your audience into smaller, data-driven groups so you can deliver messages that actually resonate. 

In email marketing, that means using data like purchase history, browsing behavior, or even how often they open your emails to create ultra-relevant campaigns. 

For ecommerce, segmentation is a no-brainer. 

Why? Because personalized emails don’t just feel better—they perform better. Segmented campaigns have 14.31% higher open rates and 100.95% higher click-through rates compared to non-segmented campaigns. 

The best part? 

Customers.ai makes segmentation simple and smart. It helps you drill down into your audience to create super-specific segments you can use for email campaigns or ad retargeting. 

I’m getting ahead of myself. We’ll get to that later. For now, let’s touch on why it matters.

Why Segmentation Matters for Email Domination

Segmentation is how you make emails that actually do something. And by something I mean convert.

We all know the days of blasting the same offer to your entire list are long gone and if you’re not breaking your audience into strategic, meaningful groups, you’re playing email marketing on hard mode.

Here’s what client segmentation can do for your ecommerce brand:

1. Send Offers That Hit Home

Not all customers want the same thing and they shouldn’t get the same email. Target frequent buyers with exclusive perks, nudge cart abandoners with reminders, or reward your VIPs with early-access drops. 

2. Retain the Customers You Can’t Afford to Lose

Your best customers are your biggest asset. Segmentation lets you treat them like it. Think loyalty campaigns, surprise rewards, or even just a simple “We appreciate you” email. Keep them engaged and they’ll keep coming back.

3. Turn One-Time Shoppers Into Repeat Buyers

That first purchase is just the start. A well-segmented email strategy can turn a casual buyer into a lifelong fan with tailored product recommendations, cross-sells, and perfectly timed follow-ups.

Segmentation means building a relationship with your customers that feels real. And that’s how you win.

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Client Segmentation Models That Work for Ecommerce Email Campaigns

If you want to dominate inboxes (and your competition), you need to go beyond the basics. 

Let’s break down the client segmentation models that actually drive results and discuss how to turn them into email campaigns that convert.

1. Behavioral Segmentation

Segment customers by how they act. Look at their purchase history, browsing habits, or even how many times they’ve clicked through your emails.

Why it matters: This lets you tailor emails to what customers are actively interested in.

How to use it:

Send “You might also like” recommendations based on past purchases.

Trigger cart abandonment emails 30 minutes after a customer bounces.

Identify serial discount hunters and only send offers when it counts.

2. Demographic Segmentation

Break your audience down by age, location, gender, or income level.

Why it matters: Demographics provide the context behind a customer’s decisions.

How to use it:

Localize your offers with geo-targeted campaigns (“Hey, New Yorkers! This deal’s just for you”).

Promote luxury items to high-income segments while pushing budget-friendly alternatives to others.

Design seasonal campaigns that actually align with your audience’s time zones or local events.

3. Lifecycle Segmentation

Group customers by where they are in their journey: new customers, repeat buyers, or those who’ve ghosted you.

Why it matters: Lifecycle segmentation allows you to target customers when they’re most likely to convert.

How to use it:

Welcome new customers with a drip campaign introducing your brand and bestsellers.

Reward repeat buyers with loyalty points or exclusive offers.

Create win-back campaigns for churned customers with a subject line they can’t ignore: “We miss you (and we brought a gift)!”

4. Psychographic Segmentation

Segment by what your customers care about: their values, interests, or lifestyle choices.

Why it matters: Psychographics tap into the “why” behind purchases.

How to use it:

Appeal to eco-conscious buyers with campaigns that highlight your sustainable practices.

Push product bundles that fit specific interests (like “travel-ready skincare” for frequent flyers).

Show your brand’s personality to align with your customers’ values (“Made for hustlers, dreamers, and night owls”).

Turning Client Segmentation Models Into Revenue

The best campaigns use multiple segmentation layers. For example:

A cart abandonment email tailored to repeat buyers with a “Thanks for being loyal!” twist.

A geo-targeted VIP discount for your top customers in specific cities.

A product recommendation email combining psychographics and behavioral data to match their interests with what they’ve browsed.

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5 Steps to Create a Winning Client Segmentation Strategy 

Ready to build a segmentation strategy that actually works? With Customers.ai, it’s easier than you think. Follow these steps to set up hyper-targeted segments that drive real results:

Step 1: Install the Customers.ai Visitor Identification Pixel

Better segmentation starts with visitor identification and the best tool for that is obviously Customers.ai. 

By adding the Customers.ai pixel to your site, you can not only identify who is on your site but get enriched contact and customer journey data. Track key data points like purchases, browsing behavior, email engagement, and more. 

Here is how to identify your website visitors with Customers.ai.:

1. Sign up for a free account

If you don’t already have a Customers.ai account, sign up here (no credit card is required) and connect your business.

2. Install the x-ray pixel on your site

Installing the website identification x-ray pixel is easy and can be done through Tag Manager, Shopify, WordPress, and more

3. Verify the x-ray pixel is firing

4. Start identifying your website visitors

That’s it! Once the pixel is installed and verified, you can start identifying your website visitors and building audience segments.

Customers.ai is your foundation for building meaningful client segments and even more meaningful campaigns.

Step 2: Build Precise Client Segments

Once the pixel starts collecting data, it’s time to get specific. Use Customers.ai to create segments based on activities, demographics, and behaviors. Examples include:

Big Spenders: Customers who consistently purchase high-ticket items.

One-Time Buyers: First-timers who haven’t returned—yet.

Seasonal Shoppers: Those who only shop during sales or holidays.

Return Visitors: Browsers who come back but haven’t purchased.

Pro Tip: Layer multiple data points (like purchase history and geography) to create even sharper segments.

Step 3: Craft Tailored Email Campaigns

Now that your segments are locked in, it’s time to hit “send.” Create campaigns that speak directly to each group:

Welcome emails for new customers introducing them to your brand.

Exclusive deals for your VIPs to reward loyalty.

Re-engagement emails for churned customers offering an incentive to come back.

Example: Sephora

Sephora is a segmentation powerhouse, leveraging behavioral, lifecycle, and psychographic data to create personalized experiences. Here’s what they do well:

Behavioral: They send recommendations based on past purchases. If you bought foundation, you’ll get follow-ups about complementary products like primers or brushes.

Lifecycle: New customers get an onboarding series with tips on using their products, while repeat buyers receive loyalty rewards and early access to sales.

Psychographics: Sephora segments customers by beauty interests (skincare vs. makeup enthusiasts) and sends tailored content. For instance, skincare fans might receive tutorials on trending ingredients, while makeup lovers get tips on the latest looks.

Sephora’s segmentation strategy translates to emails that feel personal and relevant and have made them the envy of email marketers everywhere.

Step 4: Test, Tweak, and Optimize

We all know about A/B testing but I think it’s important to point out A/B testing isn’t just about trying a different subject line. It’s about figuring out what really resonates with your various client segments.

Here’s how to get your testing right:

Start with one variable: Test one thing at a time, like subject lines, email timing, or CTAs. For example, try “Your VIP Sale Is Here” vs. “Exclusive Sale Just for You” to see what drives opens.

Experiment with timing: Send emails to the same segment at different times of the day or week to see when your audience is most active.

Test content types: Try different formats, like a product showcase email vs. a storytelling approach. Which gets more clicks?

Personalization wins: Add dynamic content (like the recipient’s name or location) in one version and keep it generic in the other. Track which performs better.

Measure what matters: Open rates are great, but focus on metrics that align with your goals—click-through rates, conversions, or revenue.

Pro Tip: Always test your campaigns on smaller subsegments first. Once you find the winner, scale it across the full audience.

Step 5: Take Your Client Segments Beyond Email

Why stop at inboxes? The client segments you create in Customers.ai can do way more than power killer email campaigns – they can be used in ads, too.

Here’s how to make it happen:

Sync Your Client Segments with Facebook and Google AdsTake your carefully crafted audience segments (like “big spenders” or “cart abandoners”) and sync them to Meta or Google (both easily done through Customers.ai). Now you can hit those same people with hyper-targeted ads while they’re scrolling Instagram or Googling their next buy.

Retarget Like a ProGot a segment of email openers who didn’t click? Serve them an ad that picks up right where your email left off. Give them a second chance to care about your message.

Keep It ConsistentThe key here is unified messaging. If your email to VIPs promised early access to a sale, your ads should back it up. When customers see the same message everywhere, it builds trust and makes them way more likely to click buy.

To create a marketing ecosystem you need your emails and ads to work together. With Customers.ai, you’ve got all the tools you need to make it happen

Examples of Winning Segmented Email Campaigns

We’ve looked at the how and why. Now let’s look at some examples of how brands use client segmentation to crush their email marketing.

1. Exclusive Discounts for VIP Customers

The best way to increase LTV is to give special treatment to your best customers.That’s why VIP flows print cash.Steal our framework below.Bookmark this ⤓ pic.twitter.com/XW3hnFtA7h— Konrad | Ecommerce Email & SMS (@EcomKonrad) September 16, 2023

Who doesn’t love feeling special? Reward your best customers with something just for them:

Subject Line: “Because You’re VIP: Your Exclusive 20% Off Awaits”

What Works: It’s targeted, feels exclusive, and shows your top spenders that you appreciate them. Bonus points if you give early access to sales or sneak peeks of new products.

2. “You Left Something Behind” Cart Abandonment Emails

Cart abandonment emails are a classic for a reason – they work. But segmented ones work even better:

Example: Send a gentle nudge to first-time buyers who left a full cart or offer a small discount to frequent shoppers who abandoned mid-checkout.

Subject Line: “Still Thinking About It? Let’s Make It Yours”

What Works: Timing is key. Send within 30 minutes to catch them while they’re still in shopping mode.

3. Personalized Birthday Emails That Actually Convert

All the best birthday emails pic.twitter.com/wuO5jQDBcJ— Sophie Hall (@SophLouiseHall) September 4, 2024

Birthday emails aren’t groundbreaking but the right segmentation can make them irresistible:

Example: Segment customers by purchase history and send them a birthday reward tailored to their favorite category.

Subject Line: “Happy Birthday, [Name]! A Little Gift from Us ”

What Works: Combine their birth month with past buying behavior for maximum impact. Bought skincare? Gift them a discount on their favorite brand.

4. Re-Engagement Emails for Inactive Users

Win-back campaigns don’t have to be boring. Get creative to reignite interest:

Example: Segment based on how long they’ve been inactive (30, 60, or 90 days) and tailor the offer accordingly.

Subject Line: “We Miss You! Here’s 15% to Come Back”

What Works: Acknowledge their absence and give them a reason to return, like an exclusive discount or a new product launch.

Make Your Client Segments Unstoppable

We’ve covered the why, the how, and even taken a peek into what winning segmentation looks like in action. Now it’s your turn.

The more you understand your audience, the better you can connect with them in ways that feel personal, relevant, and timely. It’s how any good brand drives loyalty, conversions, and revenue.

And with Customers.ai? You’ve got everything you need to master client segmentation and dominate your email KPIs.So are you ready to turn insights into action? Let’s make it happen.

Start your free trial of Customers.ai today and see how powerful segmentation can be.

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Client Segmentation FAQs

1. What is the main purpose of client segmentation?

Client segmentation’s primary purpose is to understand your audience on a deeper level and deliver more personalized marketing messages. By grouping clients based on shared traits or behaviors, businesses can send targeted campaigns that resonate, improve customer experiences, and drive higher engagement. For ecommerce, this means better email campaigns, higher ROI, and long-term customer loyalty.

2. How does client segmentation improve ROI in email marketing?

Client segmentation boosts ROI by focusing your efforts on the right audience with the right message. Instead of wasting resources on generic emails, segmented campaigns allow you to tailor offers, timing, and messaging to specific groups, leading to higher open rates, better click-through rates, and more conversions.

3. What are common mistakes businesses make with client segmentation?

Common mistakes include over-segmenting (creating too many small, unusable groups), relying on outdated data, and ignoring customer behaviors. Businesses also fail when they use segmentation without clear goals, like increasing sales or improving retention. Effective segmentation balances precision with practicality.

4. How often should client segments be updated?

Client segments should be updated regularly, typically every quarter or after major campaigns. Behavior and preferences can change over time, so staying current ensures your segments remain relevant. For businesses with frequent customer interactions, consider dynamic segmentation that updates in real time.

5. What is dynamic segmentation, and how is it different from static segmentation?

Dynamic segmentation automatically updates segments based on real-time customer data, like recent purchases or email engagement. Static segmentation, on the other hand, is a one-time setup that doesn’t adapt to changes. Dynamic segmentation is ideal for ecommerce brands looking to stay responsive and relevant.

6. Can client segmentation work for small businesses?

Absolutely. Client segmentation is scalable, meaning small businesses can focus on a few key segments instead of creating dozens. Tools like Customers.ai make it easy for smaller teams to implement segmentation strategies without needing enterprise-level resources.

7. What tools are best for client segmentation?

The best tools depend on your business needs. Options like Customers.ai, Klaviyo, and HubSpot are great for email segmentation, while platforms like Google Analytics can help with behavior tracking. Look for tools that integrate with your existing tech stack and offer automation features to save time.

8. How do you measure the success of client segmentation?

The success of client segmentation is measured through key performance indicators (KPIs) such as open rates, click-through rates, conversion rates, and customer lifetime value (CLV). If segmented campaigns consistently outperform generic ones, your strategy is working.

9. What’s the difference between client segmentation and market segmentation?

Market segmentation focuses on dividing a broader market into targetable groups, often used for product development or branding strategies. Client segmentation is more granular, targeting specific customers within your audience for personalized marketing efforts.

10. Is client segmentation only for email marketing?

Not at all. While email marketing is a popular use case, client segmentation also powers targeted ad campaigns, personalized product recommendations, and loyalty programs. It’s a versatile strategy that can improve customer interactions across channels.

11. How do you use psychographic data for client segmentation?

Psychographic data includes information about customer values, interests, and lifestyles. You can use it to create segments like “eco-conscious shoppers” or “fitness enthusiasts” and tailor marketing messages to their priorities, such as promoting sustainable products or workout gear.

12. What industries benefit the most from client segmentation?

While all industries can benefit, ecommerce, SaaS, and retail often see the highest ROI from segmentation. These sectors rely heavily on personalized marketing to drive sales and retain customers. However, industries like healthcare and finance also use segmentation to improve customer communication.

13. What’s the ideal number of segments for a business?

There’s no one-size-fits-all answer, but most businesses benefit from starting with 3-5 meaningful segments. These can expand as your data and marketing strategies mature. The key is ensuring each segment is actionable and large enough to warrant its own strategy.

14. How does client segmentation improve customer retention?

Segmentation helps businesses understand what keeps customers coming back. By targeting loyal customers with rewards or creating re-engagement campaigns for at-risk ones, you can strengthen relationships and encourage repeat business. Personalized communication is at the heart of retention.

15. How do you collect data for client segmentation?

Data collection starts with tools like CRM platforms, website tracking pixels, email engagement reports, and customer surveys. Each source provides valuable insights—purchase history reveals buying habits, while surveys can capture preferences and psychographics.

16. What is the role of AI in client segmentation?

AI can process large datasets to identify patterns and create smarter segments automatically. For example, AI might group customers based on predictive behaviors, like likelihood to churn, or optimize campaigns in real time by analyzing response data. This makes segmentation faster and more accurate.

17. Can segmentation work without detailed customer data?

Yes, segmentation can start with broad categories like location or purchase frequency. Over time, you can refine these segments by gathering more data through surveys, loyalty programs, or tracking customer interactions on your site and email campaigns.

18. What is the biggest challenge in client segmentation?

The biggest challenge is balancing complexity with usability. Over-complicated segmentation can be hard to implement and track, while overly simple approaches may not deliver meaningful results. The sweet spot is actionable segments based on reliable data.

19. How do you combine segmentation with automation?

Automation tools like Customers.ai allow you to create dynamic workflows for each segment. For example, new customers can automatically receive a welcome series, while cart abandoners trigger reminder emails. Automation ensures your segments are consistently engaged without extra manual effort.

20. What’s the future of client segmentation?

The future lies in hyper-personalization powered by AI and real-time data. Businesses will increasingly use predictive analytics to anticipate customer needs and behaviors, creating experiences that feel one-on-one. Integration across channels will also play a bigger role, ensuring seamless messaging wherever customers interact with your brand.

The post Cracking the Code on Client Segmentation for Email Domination appeared first on Customers.ai.

Did you know that email marketing has an average ROI of $36 for every $1 spent? 

That’s not just impressive, it’s crazy! 

It’s also proof that email is still the king of digital marketing. 

But here’s the thing – king or not, you can’t cash in on that ROI without a killer email list.

That’s where growth hacking comes in. 

Forget the tired, slow, “set-it-and-forget-it” methods of building your email list. Growth hacking is all about getting creative, working smarter (not harder), and using data-driven tools and strategies to grow your list faster than you ever thought possible.

when your sales team is pumped about all the new leads you identified but you’re just a chill guy. pic.twitter.com/UrkJBJ6RgZ— CustomersAI (@CustomersAI) November 22, 2024

So that’s what we are giving you. We’re serving up a fresh set of actionable, insider-approved hacks designed to amp up your subscriber count. 

Ready to build faster, smarter, and bigger? We’ve got 24 email list growth hacks to get you started. 

1. Optimize Your Signup Form Like a Pro

Let’s kick things off with the foundation of any successful email list – your signup form. If it’s not optimized, you ain’t making sales..simple as that. 

Here are three easy but highly effective hacks to get your forms working smarter:

Email List Growth Hack 1: Simplify to Amplify

Think of your signup form as a first date. Keep it simple, because no one wants to commit to a five-minute questionnaire right off the bat. Stick to the essentials—email and maybe a first name if you really need it. The easier the process, the more likely people are to hit that “Sign Up” button.

Email List Growth Hack 2: Pop-Ups That Pop

Love them or hate them, pop-ups work—especially when timed right. Use exit-intent pop-ups to catch visitors before they leave or time-delayed pop-ups to engage them while they’re browsing. The key? Make your pop-up offer irresistible (more on that in a second).

Email List Growth Hack 3: Add Incentives That Convert

Everyone loves a freebie. Whether it’s a discount, an exclusive guide, or a downloadable resource, offering something valuable in exchange for an email is a surefire way to boost signups. Make the incentive clear and front-and-center—your visitors shouldn’t have to guess what they’re getting.

Brand Spotlight: Nike

Nike nails this approach with their clean, minimalistic signup forms. 

They keep it simple – just an email address and a clear incentive like “Get 10% off your first order.” 

Pair that with their strategic use of pop-ups timed to appear when a visitor has been on their site for a few seconds, and it’s no wonder they’re masters at turning site visitors into loyal subscribers.

2. Leverage Your Website as a Growth Machine

Your website isn’t just a place for visitors to browse—it’s your email list’s secret weapon. With the right strategies, you can turn casual visitors into loyal subscribers at every turn. Here’s how to make your site work overtime for you:

Email List Growth Hack 4: Content Upgrades

Not all freebies are created equal. Instead of generic lead magnets, offer content upgrades tailored to the exact page your visitor is on. Think downloadable templates, checklists, or bonus guides that directly relate to your blog post topic. This hyper-relevance makes your offer irresistible.

Email List Growth Hack 5: Sticky Signup Bars

Out of sight, out of mind—that’s the problem with forms buried in footers. Sticky signup bars solve this by staying visible at the top or bottom of the page as visitors scroll. They’re subtle yet effective, always there when someone’s ready to subscribe.

Email List Growth Hack 6: Gamify Engagement

Who doesn’t love a little fun? Tools like spin-to-win wheels or digital scratch-off cards add an interactive element to your site that captures attention and encourages signups. Offering discounts or exclusive perks as rewards keeps visitors eager to participate.

Brand Spotlight: Canva

Canva does an incredible job leveraging their website for email growth. They use sticky signup bars to promote exclusive design tips, templates, and tutorials, ensuring that the value is clear. 

Canva’s blog posts often feature content upgrades like downloadable templates for users looking to elevate their designs. By combining helpful resources with persistent opt-in opportunities, Canva keeps their audience engaged and growing.

3. Turn Social Media Into an Email Funnel

Social media isn’t just for likes and comments. It’s one of the most effective tools to grow your email list. 

With the right approach, you can turn your followers into subscribers who are genuinely interested in your brand. Here’s how:

Email List Growth Hack 7: Exclusive Offers for Followers

Reward your followers with deals they can’t resist, but here’s the twist – make them sign up for your email list to unlock the offer. 

Limited-time discounts, early access to sales, or exclusive content are great ways to get your social audience on your email list.

Email List Growth Hack 8: Instagram Stories & Link Stickers

Instagram Stories are prime real estate for driving signups. Use eye-catching visuals to promote your lead magnet or offer, and don’t forget to add a clear call-to-action with a link sticker to send followers directly to your signup page. 

Bonus: Highlight these stories so they’re always accessible.

Email List Growth Hack 9: Facebook Group Exclusivity

Creating a private Facebook group for your brand? Make email signups a requirement to join. 

This adds an air of exclusivity while building a direct line to your audience. Plus, once they’re in the group, you can use it to share even more value and keep them engaged.

Brand Spotlight: Sephora

Sephora is a master at turning their social followers into email subscribers. Their Instagram Stories often feature exclusive offers that require an email signup to claim, like early access to seasonal collections. 

They also use Facebook groups to foster a community around their beauty products, with group-exclusive deals shared via email. 

Sephora keeps it simple, engaging, and value-packed. Exactly what followers are looking for.

4. Identify Website Visitors to Supercharge Signups

Most website visitors come and go without leaving a trace, but what if you could turn those anonymous lurkers into valuable email leads? 

With the right tools and strategies, you can identify visitors, understand their behaviors, and seamlessly add them to your email list. Here’s how:

Email List Growth Hack 10: Use Visitor Identification Tools

Tools like the Customers.ai visitor identification pixel can reveal who’s visiting your site, even if they don’t fill out a form or checkout. By identifying anonymous visitors, you can target them with relevant campaigns or personalize your offers to nudge them toward signing up.

Email List Growth Hack 11: Personalize Your Outreach

Behavioral data is your best friend. Segment visitors based on actions like cart abandonment, product views, or time spent on specific pages. Tailor your outreach to match their interests (think personalized email offers or retargeting ads that speak directly to their browsing behavior).

Email List Growth Hack 12: Connect CRM Data

Supercharge your email marketing by integrating visitor ID tools with your CRM and email platform (Customers.ai integrates with Klaviyo, Salesforce, Mailchimp, Hubspot, and more!). This allows you to automate targeted campaigns for visitors based on their on-site actions, making your outreach timely, relevant, and effective.

Brand Spotlight: Harvest Hosts

Harvest Hosts exemplifies how to use visitor identification tools effectively. By leveraging tools like Customers.ai, they identify anonymous website visitors and personalize their outreach based on browsing behavior. 

For instance, a visitor exploring their membership options might receive a tailored email showcasing the benefits of joining their RV camping network. This strategic approach turns casual browsers into engaged subscribers and, ultimately, paying members.

Read the full case study >>

5. Partner and Collaborate for Exponential Growth

Why grow your email list solo when you can tap into the power of partnerships? 

Collaborating with other brands, affiliates, or influencers can introduce your business to a whole new audience (and boost signups like crazy). Here’s how to do it:

Email List Growth Hack 13: Cross-Promotions with Brands

Team up with non-competing brands that share your target audience. For example, if you sell fitness gear, partner with a health food company for a co-branded email campaign or exclusive giveaway. It’s a win-win since both brands grow their lists while delivering value to their audiences.

Email List Growth Hack 14: Affiliate-Driven Growth

Affiliates and influencers are pros at reaching niche audiences. Set up a program that rewards them for driving email signups, whether through commissions, perks, or exclusive partnerships. Their credibility helps bring in high-quality leads.

Email List Growth Hack 15: Host Webinars and Co-Branded Events

Educational webinars and events are excellent for attracting signups. Collaborate with a complementary brand to host a session on a topic your shared audience cares about. Make email registration mandatory for attendance, and voilà—you’ve got a fresh batch of engaged subscribers.

Brand Spotlight: Calm

The Calm app has successfully used partnerships to grow its email list. They’ve collaborated with brands like American Express to offer free trials of their premium subscription, requiring an email signup to redeem. 

By aligning with brands that share their wellness-focused audience, Calm grows its reach while delivering exclusive value to new subscribers.

6. Use Automation to Scale Faster

Scaling your email list doesn’t have to mean working harder. The whole point of growth hacking is to work smarter! 

With automation, you can capture leads, nurture relationships, and retarget potential subscribers with minimal effort. Here’s how to put your list growth on autopilot:

Email List Growth Hack 16: Email Capture at Every Interaction

Remember that every touchpoint is an opportunity. Collect emails during checkout, account creation, or even as part of a customer support interaction. And definitely don’t forget those abandoned carts. Follow up with a friendly nudge that not only recovers the sale but also adds the customer to your list.

Email List Growth Hack 17: Set Up Drip Campaigns

Once someone signs up, the real work begins. Drip campaigns keep your brand top-of-mind by sending a series of automated, value-packed emails. Start with a warm welcome, follow up with educational content, and finish with an enticing offer to turn subscribers into customers.

Email List Growth Hack 18: Use Retargeting Ads

Not everyone signs up on the first visit and that’s okay. Use retargeting ads to remind visitors what they’re missing. Pair your ads with a compelling lead magnet, and you’ll bring them back to your site to join your list.

Brand Spotlight: Warby Parker

Warby Parker has mastered email list growth through automation. They collect emails at multiple touchpoints, including virtual try-ons and account signups. Their automated welcome series is a perfect blend of brand storytelling and subtle product promotion, keeping subscribers engaged from day one. 

Plus, their retargeting ads expertly draw back visitors who didn’t sign up, offering compelling reasons to give them a second look.

7. Growth Hacking Through Existing Customers

Your current customers aren’t just a source of revenue…they’re also your secret weapon for growing your email list! 

With the right strategies, you can turn them into your biggest advocates. Here’s how to leverage their loyalty:

Email List Growth Hack 19: Referral Programs That Work

Happy customers are more than willing to spread the word, especially if there’s something in it for them. Create a referral program that rewards subscribers for bringing in new signups. Think exclusive discounts, free products, or VIP perks for both the referrer and the referred.

Email List Growth Hack 20: Shareable Content

The more shareable your content, the faster it spreads. Contests, quizzes, or interactive tools that encourage social sharing can drive massive awareness (and email signups). Make sure there’s a strong call-to-action to subscribe as part of the experience.

Email List Growth Hack 21: Loyalty Perks for New Referrals

Take your referral program up a notch by tying it to your loyalty program. For instance, offer points for every new subscriber they bring in, which can be redeemed for discounts or freebies. It keeps your existing customers engaged and your list growing.

Brand Spotlight: Dropbox

Dropbox’s referral program is legendary for a reason. They offered existing users extra storage space for every friend they referred who signed up. 

The result? Massive growth, fueled entirely by happy customers spreading the word! 

8. Analyze and Optimize Constantly

Growth hacking isn’t a set-it-and-forget-it strategy. It’s an ongoing process of testing, learning, and improving. The more you analyze and optimize your approach, the faster your email list will grow. 

Here’s how to keep the momentum going:

Email List Growth Hack 22: A/B Test Your Forms

Small tweaks can have a big impact. Test everything from your headlines to button colors to call-to-action (CTA) text. Does “Sign Me Up” perform better than “Get Started”? Does a red button outperform blue? Keep experimenting to find what drives the most signups.

Email List Growth Hack 23: Use Heatmaps

Ever wondered where your visitors are clicking or not clicking? Heatmaps reveal the hotspots on your site, helping you adjust the placement of your signup forms for maximum visibility and engagement. For instance, if most activity is above the fold, make sure your form is there too.

Email List Growth Hack 24: Track Conversion Metrics

Don’t just guess what’s working. Track key metrics like form conversion rates, traffic sources, and the effectiveness of your lead magnets. Use this data to double down on what’s driving results and refine what isn’t.

Brand Spotlight: Shopify

Shopify constantly tests and optimizes its website to maximize signups. They’ve used heatmaps to identify high-engagement areas and place their opt-in forms strategically. 

They also run A/B tests on their landing pages, tweaking headlines and CTAs to see what resonates best with their audience. The result? A streamlined, data-driven approach to email list growth.

Let’s Get Growing: Your Email Lists

Building a smarter, faster, and bigger email list doesn’t have to be overwhelming. With these email list growth hacks like optimizing your forms, leveraging your website, turning social media into a funnel, and tapping into automation, you’ve got a roadmap to success. 

Each tip is designed to be actionable, effective, and easy to implement.

The best part? 

You don’t have to do it all at once. Start with just one hack – maybe simplifying your signup form or adding an irresistible lead magnet – and watch how quickly the results start rolling in.

Even better – why not start with visitor identification? 

Start your free trial of Customers.ai and capture 500 emails for free! That’ll certainly send your email list on its way!

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Email Growth Hack FAQs

What are some beginner-friendly email list growth hacks? 

Start with simple strategies like offering a lead magnet, using a pop-up form, and promoting your signup page on social media. These are easy to implement and can quickly boost your subscriber count.

Can email list growth hacks work for small businesses? 

Absolutely. Small businesses can use affordable tools like Mailchimp, free lead magnets, and collaboration with local brands to effectively grow their email list without a big budget.

How do email list growth hacks differ from traditional list-building methods? 

Growth hacks are about speed and creativity, leveraging tools like automation, behavioral tracking, and partnerships to grow faster, while traditional methods focus on steady, organic growth over time.

Are email list growth hacks suitable for ecommerce brands? 

Yes, ecommerce brands can benefit significantly by using hacks like exit-intent pop-ups, cart abandonment recovery emails, and exclusive product discounts to grow their lists and drive conversions.

How do email list growth hacks help with ROI? 

By targeting high-intent subscribers through optimized methods like personalization, referral programs, and retargeting ads, growth hacks ensure your email campaigns are more profitable.

What’s the role of personalization in email list growth hacks? 

Personalization enhances the effectiveness of growth hacks by tailoring offers, forms, and email content to individual user preferences, increasing signups and engagement rates.

Can email list growth hacks improve customer loyalty? 

Yes, growth hacks like referral programs, exclusive loyalty rewards, and personalized onboarding emails don’t just grow your list but also strengthen the bond with your existing audience.

How do visitor identification tools fit into email list growth hacks? 

These tools identify anonymous visitors on your site, allowing you to send targeted campaigns or personalize offers to convert them into subscribers.

Are email list growth hacks scalable for larger businesses? 

Definitely. With tools like automation and CRM integration, businesses of any size can implement scalable growth hacks to continually grow their subscriber base.

How can social media influencers help with email list growth hacks? 

Collaborating with influencers can amplify your growth hacks by driving targeted traffic to your signup pages through authentic recommendations or exclusive offers.

What role does A/B testing play in email list growth hacks? 

A/B testing allows you to optimize every element of your signup strategy, from headlines and CTAs to form placement, ensuring your growth hacks deliver maximum results.

How can content upgrades be considered a growth hack for email lists? 

By offering tailored resources like templates or checklists within blog posts, you can convert readers into subscribers more effectively.

Are email list growth hacks industry-specific? 

While some hacks may be tailored to specific industries, many—like offering lead magnets, using pop-ups, and running referral programs—work across sectors with customization.

How do gamification tools enhance email list growth hacks? 

Tools like spin-to-win wheels or digital scratch cards make signing up fun and interactive, increasing engagement and conversions.

What’s the connection between email list growth hacks and SEO?

Growth hacks like content upgrades and lead magnets placed within high-ranking blog posts can capture organic traffic and convert it into email subscribers.

How can partnerships boost email list growth hacks? 

Partnering with complementary brands allows you to share audiences and execute joint campaigns that grow your email lists faster than working solo.

How do email list growth hacks help with audience segmentation? 

Growth hacks often collect behavioral data during signup, making it easier to segment your audience and deliver personalized campaigns.

How can analytics tools enhance email list growth hacks? 

By tracking form conversion rates, traffic sources, and user behavior, you can identify which growth hacks are working and refine your strategy.

What’s the ROI of implementing email list growth hacks? 

Growth hacks often deliver a high ROI by maximizing conversions from existing traffic and leveraging cost-effective tools like pop-ups and referral programs.

How often should I update my email list growth hacks? 

Regularly review and adjust your hacks to account for changing audience preferences, technological advancements, and performance analytics to keep your list growing efficiently.

The post Email List Growth Hacks: 24 Insider Tactics to Build Faster, Smarter, and Bigger appeared first on Customers.ai.

Guessing is so last year and winging it isn’t a strategy – it’s a cry for help! 

Ecommerce is too competitive to rely on “spray and pray” marketing tactics that leave your ROI up to chance and broad campaigns and generic messaging aren’t just outdated, they’re wasted money.

The truth is, your audience isn’t a monolith. Some are loyal repeat buyers, some are first-timers, and others are just browsing. Treating them all the same? That’s like trying to sell snow boots to someone living in the desert.

That’s why you have to have a few audience segmentation tools in your back pocket. 

Audience segmentation tools take the guesswork out of the equation. They help you identify who your customers are, what they care about, and how they prefer to engage. 

They also prevent you from second-guessing your email campaigns or wondering why your ads aren’t converting. Instead, they give you the data and insights you need to make every move intentional and impactful.

To help ensure your tech stack is filled with the right tools, we’re giving you the practical, innovative, and straight-up best ones out there. 

So whether you’re trying to hyper-target your next Facebook ad or nail that personalized email campaign, these solutions are built for ecommerce marketers who refuse to settle for “meh” results. 

Buckle up—it’s time to start segmenting like a pro.

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Segmentation Made Sexy: What Audience Segmentation Tools Really Do

Let’s cut through the jargon. Audience segmentation tools work by slicing and dicing your customer data into meaningful groups so you can target the right people with the right message at the right time.

For ecommerce, this means you’re not just spamming your entire list with a generic email. Instead, you’re speaking directly to your repeat buyers with exclusive perks, re-engaging cart abandoners with tempting discounts, or wowing your seasonal shoppers with time-sensitive offers.

Let’s look at an example. Say you’re using Klaviyo, a popular audience segmentation tool for ecommerce. 

You notice a segment of customers who’ve purchased twice in the last 90 days but haven’t engaged with your latest sale. With just a few clicks, Klaviyo lets you create a dynamic segment called “Hot but Fading.” Now you can send these customers a targeted email, like: “We see you’ve been loving our stuff—here’s 20% off your next order to keep the streak alive!”

How does this compare to a broad campaign? 

Instead of sending a 10% off discount to your entire list (most of whom might not even be interested), you’re tailoring your offer to a highly engaged group. The result? Higher open rates, better conversion rates, and a stronger connection with your audience.

These tools don’t just organize your audience, they give you the power to take action. Whether it’s tagging high-value customers for a VIP event or automating win-back campaigns for your lapsed shoppers, segmentation turns your data into marketing magic.

Bottom line? Audience segmentation tools make your campaigns smarter and they get results.

The Cool Kids’ Toolkit: 15 Must-Have Audience Segmentation Tools

1. Customers.ai Audience Segmentation Tool

What makes it stand out?Customers.ai’s segmentation tool is all about hyper-targeting. It identifies and segments your audience based on real-time data like website behavior and engagement history. Plus, it integrates directly with your favorite CRMs for seamless action.

Who is it perfect for?Ecommerce marketers who want a tool that combines audience segmentation with automated lead generation.

Pro tip:Use its segmentation to trigger personalized follow-ups for high-intent visitors within minutes of their interaction.

Ratings & Reviews:Customers rave about the tool’s ability to uncover hidden customer segments, with one user stating, “It’s like unlocking a secret map of my audience.”

Average rating: 4.9/5

2. Klaviyo Behavioral Segmentation 

What makes it stand out?Klaviyo’s power lies in its behavioral segmentation. It tracks actions like browsing history, cart abandonment, and purchase frequency to create dynamic audience groups that update in real-time.

Who is it perfect for?Email marketers looking to create hyper-targeted campaigns that drive sales and engagement.

Pro tip:Use Klaviyo’s predictive analytics to identify customers likely to churn and send them win-back offers before it’s too late.

Ratings & Reviews:Marketers praise Klaviyo for its intuitive segmentation features and ROI-focused insights. “It’s my go-to for personalized campaigns,” says one user. Average rating: 4.6/5 on Capterra.

3. MoEngage Omnichannel Segmentation

What makes it stand out?MoEngage excels at omnichannel segmentation, letting you create audience groups across email, SMS, push notifications, and more. It’s perfect for ecommerce brands with a multi-channel approach.

Who is it perfect for?Brands that want to deliver consistent, personalized experiences across multiple customer touchpoints.

Pro tip:Segment customers based on purchase recency and frequency to create VIP loyalty campaigns.

Ratings & Reviews:Users love the tool’s ability to simplify cross-channel segmentation. “It makes omnichannel feel effortless,” notes a reviewer. Average rating: 4.5/5 on Capterra.

4. Segment by Twilio 

What makes it stand out?Segment consolidates customer data from various platforms into one unified profile, allowing for super-specific segmentation. It’s ideal for ecommerce brands juggling multiple tools.

Who is it perfect for?Teams that rely on multiple marketing platforms and need clean, organized data for precise targeting.

Pro tip:Use the “Audience Builder” to create hyper-targeted groups like “holiday gift shoppers” or “last-minute buyers.”

Ratings & Reviews:Users appreciate Segment’s flexibility in handling complex data flows. “It’s the backbone of our segmentation strategy,” says one reviewer. Average rating: 4.4/5 on G2.

5. HubSpot Marketing Segmentation Feature

What makes it stand out?HubSpot’s segmentation features allow you to create lists based on engagement, deal stage, or even specific email clicks. Its visual interface makes complex segmentation easy.

Who is it perfect for?Marketers who want an all-in-one solution for segmentation and lead nurturing.

Pro tip:Combine HubSpot’s segmentation with workflows to automatically re-engage dormant customers.

Ratings & Reviews:HubSpot users love its user-friendly interface and powerful segmentation tools. “A lifesaver for scaling campaigns,” notes a customer. Average rating: 4.5/5 on G2.

6. Amplitude Behavioral Segmentation Tools

What makes it stand out?Amplitude specializes in behavioral segmentation, helping you analyze customer journeys to understand which actions lead to conversions. Its robust cohort analysis allows you to group users based on shared behaviors and track their lifecycle.

Who is it perfect for?Ecommerce marketers focused on improving user engagement and retention.

Pro tip:Use Amplitude’s cohort comparison to test how different audience groups react to promotions or product launches.

Ratings & Reviews:Users love Amplitude’s detailed customer journey insights. “It’s like having a roadmap to understanding your audience,” says a user. Average rating: 4.5/5 on G2.

7. Optimizely Audience Segmentation Testing

What makes it stand out?Optimizely combines segmentation with experimentation, allowing you to test different campaigns for specific audience groups and optimize them in real-time. It’s great for driving conversions with precision.

Who is it perfect for?Marketers who want to experiment and refine campaigns for specific customer segments.

Pro tip:Use segmentation to deliver personalized product recommendations based on browsing habits.

Ratings & Reviews:“The A/B testing paired with audience segmentation makes this a standout,” says a reviewer. Average rating: 4.4/5 on Capterra.

8. Adobe AI-Driven Segmentation Tool

What makes it stand out?Adobe’s advanced AI-driven segmentation provides predictive insights, enabling you to anticipate customer needs before they happen. It’s designed for enterprise-level personalization.

Who is it perfect for?Large ecommerce brands managing complex data and multiple customer touchpoints.

Pro tip:Use Adobe’s real-time personalization feature to dynamically adapt campaigns based on live customer behavior.

Ratings & Reviews:Enterprise users praise its scalability and precision. “It’s incredibly powerful, but expect a learning curve,” warns one user. Average rating: 4.5/5 on Capterra.

9. Sprout Social Audience Segmentation & Listening Tool

What makes it stand out?Sprout Social’s audience segmentation is built into its social listening tools, allowing you to identify key audience groups based on interactions, hashtags, and sentiment.

Who is it perfect for?Social media marketers who want to go beyond basic engagement metrics.

Pro tip:Segment your audience by sentiment analysis to craft campaigns that address pain points or celebrate positive feedback.

Ratings & Reviews:“A must-have for anyone managing social media campaigns,” raves one reviewer. Average rating: 4.6/5 on G2.

10. Clearbit B2B Segmentation

What makes it stand out?Clearbit excels in B2B segmentation by enriching your customer profiles with detailed firmographic and demographic data. Perfect for identifying high-value leads.

Who is it perfect for?B2B marketers looking to personalize outreach based on job title, industry, and company size.

Pro tip:Use Clearbit to pre-segment leads before they enter your sales funnel, saving time and boosting efficiency.

Ratings & Reviews:Users appreciate its data accuracy. “The segmentation capabilities have completely transformed our lead targeting,” notes a user. Average rating: 4.3/5 on G2.

11. Kissmetrics Behavioral Audience Segmentation

What makes it stand out?Kissmetrics dives deep into behavioral segmentation, tracking actions across your website and app to help you understand what drives customer conversions. It’s perfect for creating segments based on engagement history.

Who is it perfect for?SaaS and ecommerce brands aiming to boost retention and increase customer lifetime value.

Pro tip:Use Kissmetrics to segment users who bounce frequently and test targeted retargeting campaigns to win them back.

Reviews:“A game-changer for understanding customer behavior,” says a user. Some note the interface could be more intuitive. Average rating: 4.1/5 on G2.

12. Lexer Advanced Segmentation Tools

What makes it stand out?Lexer specializes in turning raw data into actionable customer profiles, perfect for advanced segmentation. It combines online and offline data, making it ideal for retailers with both ecommerce and brick-and-mortar stores.

Who is it perfect for?Ecommerce brands that want a 360-degree view of their customers and advanced segmentation capabilities.

Pro tip:Use Lexer’s dynamic profiles to identify high-value customers and tailor retention campaigns specifically for them.

Ratings & Reviews:“Lexer bridges the gap between online and offline data beautifully,” says a user. Average rating: 4.5/5 on G2.

13. BlueConic Customer Segmentation Tools

What makes it stand out?BlueConic helps ecommerce businesses unify customer data from multiple touchpoints and use segmentation to create hyper-personalized marketing campaigns. It’s especially good at real-time segmentation for timely customer interactions.

Who is it perfect for?Brands looking to centralize their customer data for precise targeting and seamless cross-channel experiences.

Pro tip:Use BlueConic’s real-time segmentation to adjust offers based on live customer behavior, such as browsing or cart activity.

Ratings & Reviews:“A fantastic tool for centralizing customer insights and acting on them instantly,” says a reviewer. Average rating: 4.4/5 on Capterra.

14. Drip Pre-Built Segmentation Tools

What makes it stand out?Drip is built for ecommerce marketers, with pre-built segmentation templates like “first-time customers” or “VIP buyers” that are easy to implement.

Who is it perfect for?Ecommerce brands that want a straightforward tool for email marketing and segmentation.

Pro tip:Leverage Drip’s product recommendation engine to create segments and send personalized follow-ups that boost sales.

Ratings & Reviews:“Drip makes segmentation feel effortless,” says one user. Average rating: 4.5/5 on Capterra.

15. Mailchimp Intuitive Segmentation 

What makes it stand out?Mailchimp offers intuitive segmentation tools that go beyond basic email lists, allowing you to target customers based on purchase history, engagement, and even predictive demographics.

Who is it perfect for?Small to mid-sized ecommerce businesses that want simple, powerful segmentation for email and ad campaigns.

Pro tip:Use Mailchimp’s predicted demographics feature to craft campaigns tailored to specific age groups or interests.

Ratings & Reviews:“Mailchimp’s segmentation makes it easy to run targeted campaigns without feeling overwhelmed,” says a user. Average rating: 4.5/5 on G2.

​​The Speed Round: How to Choose the Right Tool Without a Headache

Choosing the perfect audience segmentation tool doesn’t have to feel overwhelming. Let’s simplify the process by focusing on what really matters for ecommerce marketers: integrations, pricing, ease of use, and ROI.

Key Factors to Consider

Integrations

Why it matters: Your segmentation tool should seamlessly connect with your existing platforms—think Shopify, WooCommerce, Facebook Ads, email marketing services, and CRM systems.

What to look for: Check if the tool offers native integrations with your current tech stack or supports APIs and third-party connectors.

Top Picks:

Customers.ai: Excellent for integrating with a wide range of marketing tools.

Segment by Twilio: Known for robust integration capabilities across multiple platforms.

Ease of Use

Why it matters: A tool that’s difficult to navigate slows you down and hinders productivity.

What to look for: An intuitive interface, easy setup, and helpful customer support.

Top Picks:

Mailchimp: User-friendly with a straightforward dashboard.

Drip: Simplifies complex segmentation with an easy-to-use platform.

Pricing

Why it matters: You’ll want a tool that fits your budget without sacrificing essential features.

What to look for: Transparent pricing models, scalable plans, and a good balance between cost and functionality.

Top Picks:

Klaviyo: Offers scalable pricing based on the number of contacts.

MoEngage: Provides flexible pricing tailored to your business size.

ROI Potential

Why it matters: The tool should help you increase conversions and revenue, making it a worthwhile investment.

What to look for: Features that directly contribute to sales growth, like advanced targeting, personalization, and analytics.

Top Picks:

Customers.ai: Focuses on intent to boost engagement and sales.

Amplitude: Offers deep insights into user behavior for optimizing marketing strategies.

Quick Decision Guide: Find Your Perfect Match

Ask yourself the following questions to narrow down your options:

What’s your primary goal?

“I need deeper insights into customer behavior.”

Go with: Kissmetrics or Amplitude for advanced behavioral analytics.

“I want to supercharge my email marketing.”

Go with: Klaviyo or Mailchimp for powerful email segmentation.

“I need to unify data from multiple sources.”

Go with: Segment by Twilio or BlueConic for data consolidation.

“I want real-time segmentation for personalized campaigns.”

Go with: Customers.ai or MoEngage for dynamic segmentation.

How big is your team and what’s your tech proficiency?

“We’re a small team looking for simplicity.”

Go with: Drip or Mailchimp for user-friendly interfaces.

“We have technical expertise and need advanced features.”

Go with: Adobe Experience Cloud or Segment by Twilio for robust capabilities.

What’s your budget?

“We’re on a tight budget.”

Go with: Mailchimp or Klaviyo with scalable pricing plans.

“We can invest more for premium features.”

Go with: Adobe Experience Cloud or Customers.ai for comprehensive solutions.

Which channels are most important to you?

“Email is our main focus.”

Go with: Klaviyo or Drip for specialized email marketing segmentation.

“We need strong social media insights.”

Go with: Sprout Social for social media segmentation and analytics.

“We want omnichannel capabilities.”

Go with: MoEngage or BlueConic for cross-channel segmentation.

Final Tips for Choosing an Audience Segmentation Tool

Test Drive First: Most tools offer free trials or demos. Take advantage of these to see which one feels right.

Consider Scalability: Choose a tool that can grow with your business to avoid switching platforms later.

Look for Community and Support: A tool with a strong user community and responsive support can make a big difference.

By focusing on what matters most to your business, you can select an audience segmentation tool that not only meets your needs but also propels your ecommerce marketing to new heights.

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Audience Segmentation Hacks You Won’t Find on the Box

Using segmentation tools the way they’re designed is great, but why stop there? 

Let’s explore some creative, next-level hacks that go beyond the manual. These tips will help you unlock the full potential of your segmentation tools and impress even your savviest customers.

1. Create Micro-Segments for VIP Customers

Your VIP customers deserve the red-carpet treatment, so don’t lump them in with everyone else. Use your segmentation tool to identify your most loyal buyers based on lifetime spend, frequency, or engagement. Then, take it up a notch:

Exclusive Perks: Offer early access to new collections or special “thank you” discounts.

Personalized Outreach: Send a heartfelt “we appreciate you” message—people love a little recognition.

Pro Tip: If you’re using Klaviyo, create a dynamic segment like “Top 5% Spenders” and pair it with predictive analytics to keep them coming back.

2. Tap Into Seasonal Behavior Triggers

Seasonal campaigns are a no-brainer, but here’s the twist: segment customers by their seasonal habits.

Example: Identify customers who always shop during Black Friday but disappear the rest of the year. Create a “Holiday Hustlers” segment and send them early access deals or countdown reminders.

Unexpected Angle: Look at behavior from last year and cross-reference it with their current activity to predict what they’ll want this season.

Pro Tip: Use MoEngage to set up automated campaigns triggered by seasonal events like Valentine’s Day or back-to-school shopping.

3. Sync Across Channels for a Seamless Experience

Nothing screams “I don’t know you” like blasting the same message on every channel. Instead, sync your segments across email, ads, and social media for a cohesive customer journey.

Example: Someone clicks an Instagram ad for your new product but doesn’t purchase. Create a segment called “Browsers, Not Buyers” and retarget them with an exclusive discount via email.

Why it works: Consistency builds trust and nudges customers toward conversion.

Pro Tip: Tools like Customers.ai make cross-channel syncing easy by consolidating data into a single customer profile.

4. Find Your “Almost-There” Shoppers

Cart abandoners get all the love, but what about people who are this close to buying?

Hack: Segment customers who’ve viewed the same product multiple times but haven’t added it to their cart. Call them your “Second Guessers.”

What to do: Send them a quick nudge like, “Still thinking about [Product Name]? Let us sweeten the deal—here’s 15% off.”

Pro Tip: Tools like Customers.ai can track browsing patterns and work with Klaviyo and other tools to automate personalized nudges for these hesitant shoppers.

5. Use Predictive Segments to Spot Future Trends

Why wait for a trend to hit when you can anticipate it? Predictive segmentation tools like those in Adobe Experience Cloud analyze behavior to help you spot emerging customer preferences.

Example: Identify which products are gaining traction among first-time buyers, then double down on promoting them to similar segments.

Pro Tip: Pair predictive insights with seasonal campaigns for maximum impact.

6. Segment Customers by Product Affinity

Sure, everyone loves tacos and cats, but what else can you learn about your customers?

Hack: Group customers based on what they buy together.

Example: “Dog Lovers” buy collars and toys, while “Outdoor Enthusiasts” always add hiking gear.

Why it works: It’s easier to cross-sell or upsell when you understand their preferences.

Pro Tip: Tools like Lexer make it easy to create affinity-based segments that turn insights into targeted recommendations.

7. Combine Lapsed and VIP Segments for Creative Re-Engagement

Sometimes, your lapsed customers were once your best. Combine these two segments to create a group of “Lost Gems,” then run a reactivation campaign tailored to their past loyalty.

What to do: Send a nostalgic message like, “We miss you, [Name]! Remember how much you loved [Product]? Come back for 20% off your next order.”

Pro Tip: Use Klaviyo’s email flows to set up this campaign on autopilot.

8. Re-Engage Return Visitors Klaviyo Doesn’t Catch with Customers.ai Signal

Sometimes, your regular tools miss key opportunities – like visitors who return to your site but don’t leave obvious clues (like signing up or making a purchase). That’s where Customers.ai Signal comes in.

Hack: Use Customers.ai’s ability to identify anonymous return visitors based on behavior and enrich their profiles. Segment these visitors as “Stealth Returnees.”

What to do: Re-engage them with personalized campaigns through Klaviyo or ads. For example, send an email like, “Hey [Name], still thinking about us? Check out what’s new since your last visit!”

Pro Tip: Combine this segment with a dynamic discount code or personalized product recommendations to give them the final push toward conversion.

Stop Guessing, Start Segmenting More Effectively

The days of throwing spaghetti at the wall and hoping something sticks are over. 

With the right audience segmentation tools, you can connect with your customers on a deeper level, deliver personalized experiences, and see results that actually make you proud of your marketing game.

Now it’s time to take action. 

Start by exploring the tools we’ve covered. Many of them offer free trials or demos, so you can test-drive them before committing. Need help comparing your options? 

Check out our Audience Segmentation Tool Checklist to get a side-by-side breakdown of features, pricing, and reviews:

ToolRatingTop PraiseCommon CriticismCustomers.ai4.9/5“Incredible insights and lead targeting capabilities.”“Learning curve for first-time users.”Klaviyo4.6/5“Fantastic for personalized email marketing.”“Pricing can be steep for larger lists.”MoEngage4.5/5“Great for omnichannel campaigns and customer journeys.”“Push notifications can feel limited.”Segment by Twilio4.4/5“Perfect for integrating complex data sources into one system.”“Needs some technical expertise to set up.”HubSpot Marketing Hub4.5/5“Seamless workflows and powerful segmentation tools.”“Can get pricey for small teams.”Amplitude4.5/5“Detailed behavioral analysis that helps improve user retention.”“Steeper learning curve for new users.”Optimizely4.4/5“Amazing for testing and optimizing segmented campaigns.”“Customization can take time to master.”Adobe Experience Cloud4.5/5“Predictive analytics are next level.”“Expensive for smaller businesses.”Sprout Social4.6/5“Social listening and audience segmentation in one tool.”“Limited to social media—no cross-channel.”Clearbit4.3/5“Excellent for lead enrichment and targeting.”“B2B-focused, not ideal for B2C.”Lexer4.5/5“Great for combining online and offline data into actionable profiles.”“Setup can feel complex for small teams.”BlueConic4.4/5“Unified data platform makes segmentation incredibly precise.”“Could use more third-party integrations.”Mailchimp4.5/5“Simple and effective for targeted email campaigns.”“Advanced features may feel limited.”Drip4.5/5“Perfect for ecommerce email segmentation.”“Could use more robust reporting features.”Kissmetrics4.1/5“A game-changer for understanding customer behavior.”“Interface could be more intuitive.”

Now it’s time to stop guessing and start segmenting like the pro you are! Your future self will thank you and your customers might even start thinking you’re psychic.

Don’t forget to start your free Customers.ai trial today and get 500 contacts free!

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Audience Segmentation Tool FAQs

What are audience segmentation tools, and why do ecommerce marketers need them?

Audience segmentation tools are software that analyzes customer data and groups it into segments based on shared traits or behaviors, such as purchase frequency, interests, or demographics. For ecommerce marketers, these tools are essential for delivering personalized experiences, improving campaign ROI, and optimizing marketing strategies. By understanding what motivates specific customer groups, marketers can focus on targeting messages that resonate, rather than taking a one-size-fits-all approach.

How do audience segmentation tools work?

These tools collect data from multiple sources, such as website activity, purchase history, social media engagement, and email interactions. The data is then analyzed to create customer segments based on specific criteria, like shopping habits or demographics. For example, a tool might group customers who abandon their carts but visit the site multiple times, allowing you to target them with reminder emails or discounts. Advanced tools also use machine learning to predict customer behavior and refine segments over time.

What are the main types of audience segments for ecommerce businesses?

Ecommerce businesses often create segments like VIP customers (those who spend the most), first-time buyers (new customers who need nurturing), cart abandoners (potential buyers who didn’t complete their purchase), and dormant users (customers who haven’t engaged recently). These segments help you tailor marketing strategies, such as offering discounts to lapsed customers or early access to new products for VIPs. Seasonal shoppers or users who shop during specific holidays are another common segment for targeted promotions.

What’s the difference between behavioral and demographic segmentation?

Behavioral segmentation focuses on actions customers take, such as frequency of purchases, browsing patterns, or loyalty program engagement. Demographic segmentation categorizes customers by traits like age, gender, income level, or location. While demographics provide a basic understanding of your audience, behavioral data often offers deeper insights into their motivations and how to effectively target them. For example, knowing that a customer regularly abandons their cart tells you more about how to re-engage them than simply knowing their age.

Can audience segmentation tools predict customer behavior?

Yes, many advanced segmentation tools use predictive analytics to forecast customer behavior. By analyzing historical data, these tools can predict outcomes such as purchase likelihood, customer churn, or product preferences. For example, tools like Klaviyo can identify customers who are likely to buy within the next 30 days based on past interactions, allowing marketers to focus efforts on these high-potential segments. This capability helps businesses proactively address customer needs and optimize marketing campaigns.

How do audience segmentation tools integrate with ecommerce platforms like Shopify?

Most tools offer native integrations or API options for platforms like Shopify. This allows the segmentation tool to pull in data such as product views, purchase history, and abandoned cart details. Once integrated, marketers can create dynamic segments that automatically update as customer behavior changes. For instance, you could segment customers who purchased a specific product and target them with upsells or complementary product recommendations directly through Shopify.

Are there free audience segmentation tools available?

Yes, tools like Google Analytics provide free segmentation features, allowing you to analyze audience behaviors and create basic customer groups. While free tools are useful for small-scale needs, they often lack advanced features like real-time updates, predictive analytics, and multi-channel integration. Businesses looking for comprehensive segmentation capabilities may find it worthwhile to invest in premium tools like Customers.ai or MoEngage, which offer advanced segmentation tailored for ecommerce.

How do audience segmentation tools handle privacy concerns?

Reputable tools comply with major data privacy regulations like GDPR and CCPA by anonymizing data, allowing customers to opt out of tracking, and ensuring secure data storage. Many tools also include features to manage customer consent for data collection. For example, Customers.ai enables you to segment users while maintaining compliance by collecting data in ways that respect privacy rules, such as focusing on aggregate trends rather than personal identifiers when required.

What’s the best audience segmentation tool for small businesses?

For small businesses, tools like Mailchimp and Drip are excellent choices. They are affordable, easy to use, and offer robust segmentation features such as dynamic lists based on customer interactions. These tools also provide templates and automation options that help smaller teams execute effective campaigns without requiring extensive technical expertise. Their scalability ensures that as your business grows, your segmentation capabilities can expand with it.

How does segmentation improve email marketing campaigns?

Segmentation allows you to deliver highly targeted email campaigns that speak directly to the recipient’s needs and interests. For instance, instead of sending a general sale email to your entire list, you can target frequent buyers with exclusive perks or re-engage lapsed customers with personalized win-back offers. This not only boosts open and click-through rates but also fosters stronger customer relationships by making your emails feel more relevant and valuable.

Can audience segmentation tools help with ad targeting?

Yes, audience segmentation tools integrate with platforms like Facebook Ads and Google Ads to create highly specific audience groups for campaigns. For example, you could create a segment of cart abandoners who browsed specific product categories and target them with ads offering discounts on those products. This level of precision ensures that your ad budget is spent on users who are most likely to convert, increasing the ROI of your campaigns.

How do I measure the success of my segmentation strategy?

Track metrics like open rates, click-through rates, conversion rates, and customer retention for each segment. Many tools provide detailed analytics dashboards where you can compare the performance of segmented campaigns versus broad ones. For example, a segment-specific email campaign might show a 25% higher conversion rate than your general campaigns, indicating that your segmentation strategy is effective.

Can I use audience segmentation tools for real-time personalization?

Yes, tools like Customers.ai and MoEngage offer real-time segmentation capabilities that allow you to dynamically adjust marketing campaigns based on live customer behavior. For instance, if a customer browses a specific product category but doesn’t purchase, you can immediately trigger a personalized email or ad campaign targeting them with similar items. This immediate response helps keep your brand top of mind and increases conversion potential.

How do segmentation tools handle multi-channel campaigns?

Audience segmentation tools consolidate data from different channels, such as email, social media, and ads, into unified customer profiles. This enables marketers to create seamless, cross-channel experiences. For example, a customer who clicks on a product in an Instagram ad can be targeted with a follow-up email featuring the same product. Tools like Segment by Twilio and BlueConic are particularly strong in this area, offering robust integrations for cross-channel consistency.

What’s the difference between static and dynamic segments?

Static segments are fixed groups created based on a snapshot of customer data, while dynamic segments update automatically as customer behaviors or attributes change. For example, a static segment might include customers who made a purchase last month, whereas a dynamic segment would continuously add customers who meet the same criteria over time. Dynamic segments are more useful for real-time campaigns and ongoing engagement strategies.

How do audience segmentation tools identify high-value customers?

These tools analyze factors like lifetime value, purchase frequency, and average order value to identify high-value customers. They also track engagement metrics such as email open rates or website activity to determine loyalty levels. High-value customer segments can then be targeted with VIP programs, early access to new products, or exclusive offers to maintain their loyalty and encourage repeat purchases.

Can segmentation tools improve customer retention?

Absolutely. By understanding what keeps your customers engaged, segmentation tools allow you to personalize retention strategies. For instance, tools like Klaviyo can identify customers at risk of churning and automate re-engagement campaigns with incentives like discounts or exclusive content. This targeted approach helps you retain customers who might otherwise slip away.

How can audience segmentation tools help during holiday campaigns?

During holidays, segmentation tools can identify seasonal shoppers and create targeted campaigns that align with their shopping behavior. For example, you can use segmentation to send early bird discounts to customers who purchased during the same holiday period last year or upsell related products to customers who bought holiday-specific items. Tools like MoEngage allow you to automate these campaigns for maximum efficiency.

What’s the role of AI in audience segmentation tools?

AI enhances audience segmentation by identifying patterns in data that might not be immediately obvious. It can also predict customer behavior, recommend optimal targeting strategies, and automate updates to segments based on real-time data. For example, Adobe Experience Cloud uses AI to anticipate customer needs and dynamically adjust campaigns accordingly, saving time and improving results.

How do audience segmentation tools support A/B testing?

Segmentation tools enable A/B testing by allowing you to test campaigns on specific audience groups. For instance, you could create two versions of an email targeted at repeat buyers and analyze which version performs better. This helps refine your messaging, design, and overall strategy to maximize impact.
The post 15 Audience Segmentation Tools You Need to Stop Guessing & Start Engaging appeared first on Customers.ai.

Hugging Face is launching a free and open course on machine learning to make artificial intelligence (AI) more accessible to everyone.

The Smöl Course (“Small” Course) guides learners through building, training, and fine-tuning machine learning models. It is based on the SmolLM2 series of models and incorporates insights from the course materials available on GitHub, offering a hands-on approach with open-source tools and real datasets. The course, available on GitHub, includes detailed information on the SmolLM2 models and their practical applications, providing an interactive and engaging learning experience.

Focus on Accessibility and Collaboration

This initiative aligns with Hugging Face’s mission to make AI education accessible to as many people as possible. Traditionally, machine learning has been limited to those with advanced degrees or specialized resources, but Hugging Face offers a practical, cost-free way for anyone to learn.

The GitHub repository for the Smöl Course offers step-by-step instructions, helping users set up their environment, build models, and train them. The content is designed to be simple and practical, with code examples that let learners see AI in action instead of just reading about it.

The course also emphasizes collaboration. It’s a community-driven project, and Hugging Face encourages learners to contribute, share ideas, and ask questions. This kind of collaboration helps participants deepen their understanding and connect with others in the AI community.

Lowering Barriers to AI

Getting into machine learning can be intimidating. The Smöl Course is openly available, welcoming learners of all backgrounds. With no fees and a focus on practical applications, it is a valuable resource for those teaching themselves or looking to transition into AI.

As AI adoption continues to grow, there is a gap between the demand for skilled professionals and the availability of accessible learning resources. By offering accessible AI education, Hugging Face helps bridge that gap and supports the growth of future AI professionals.

How to Get Started

The Smöl Course is available on GitHub. Whether you’re an experienced developer wanting to sharpen your skills or a beginner curious about machine learning, Hugging Face’s approach helps turn curiosity into practical skills.

With this initiative, Hugging Face demonstrates its commitment to making AI education open to all. The Smöl Course is a step toward a more inclusive future for AI.

Check out the Full Course on the GitHub Page. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. If you like our work, you will love our newsletter.. Don’t Forget to join our 60k+ ML SubReddit.

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The post Hugging Face Releases a Free and Open Course on Fine Tuning Local LLMs appeared first on MarkTechPost.

The advancement of AI and machine learning has introduced new capabilities for businesses across industries. From text generation to video synthesis, modern AI models are transforming how organizations operate and innovate. However, large-scale foundation models like GPT-4 and Llama present challenges in achieving advanced intelligence at an accessible cost. Many companies face high computational expenses and limited flexibility, creating a gap between AI potential and practical deployment. How can AI developers balance costs, scalability, and performance?

Another issue is the accessibility of sophisticated models globally. Large language models often require significant infrastructure, making them inaccessible to smaller enterprises or startups. This limits the democratization of AI tools. Organizations need scalable, customizable solutions that balance power, cost, and accessibility—an equilibrium that has been difficult to achieve.

Amazon Introduces Amazon Nova

Amazon introduces Amazon Nova: a new generation of foundation models (FMs) that deliver advanced intelligence and a strong balance of price and performance, available exclusively in Amazon Bedrock. Amazon Nova models aim to bridge the existing gap between high-performing, scalable AI models and practical, cost-effective deployment solutions. These models come in multiple variants tailored to different applications, ranging from text-only capabilities to multimodal functionalities, including image and video generation.

The Nova lineup includes Micro, Lite, Pro, and Premier, each designed to serve distinct requirements. Micro focuses on efficient text-based operations, while Lite extends capabilities to multimodal interactions involving text and images. Pro delivers higher computational power for more complex tasks, and the Premier model—scheduled for a 2025 release—promises additional versatility. Additionally, Amazon has introduced models specifically designed for creative tasks, such as Canvas for image generation and Reel for video generation. These models are available exclusively in Amazon Bedrock, ensuring a secure and seamless integration into existing AWS ecosystems. By providing foundational models optimized for both performance and affordability, Amazon Nova aims to contribute meaningfully to the evolving foundation model landscape.

Technical Details

Amazon Nova offers a variety of models suited to different purposes, providing flexibility to businesses. The Micro model, intended for lightweight applications, handles text-only operations efficiently, offering an economical solution for tasks requiring standard natural language processing (NLP). Meanwhile, the Lite model supports multimodal capabilities, integrating text and images to enable more sophisticated interactions and use cases, such as interactive assistants and content moderation. The Pro model is designed for computationally intensive tasks requiring high capacity, ideal for companies needing advanced NLP or complex data analytics, while the Premier model—expected in 2025—will further enhance processing power and functionality.

One notable feature of the Nova models is their ability to process an extended context length of up to 300K tokens, which surpasses many competing models. This allows Amazon Nova to handle extended texts or dialogues, enhancing its usefulness for large-scale content generation, summarization, and complex document analysis. Moreover, the models are available in over 200 languages, allowing companies to cater to global audiences with minimal integration complexity. Pricing for Nova has been designed to be cost-effective: Micro starts at $0.035 per million input tokens and $0.14 per million output tokens, offering a budget-friendly solution for companies looking to integrate AI without prohibitive costs.

Additionally, Amazon Nova models include watermarking capabilities, providing a layer of security and accountability, which is particularly useful in scenarios where content verification is critical. Fine-tuning options within Amazon Bedrock make it easier for companies to adapt these models to their specific needs without the burden of managing complex model training. Furthermore, their availability within AWS regions in the US ensures that Nova models benefit from AWS’ established infrastructure, providing reliability and scalability from the outset.

Amazon Nova offers businesses advanced AI performance while maintaining affordability, with a price-performance ratio that compares favorably to existing models. Benchmarks show that Nova models perform similarly to Llama 3 at a lower cost, making them accessible to organizations of various sizes.

With models ranging from Micro to Premier, Amazon addresses diverse AI needs, from NLP tasks to creative content generation. Integration with Amazon Bedrock provides a unified, scalable AI infrastructure, reducing deployment complexity. Watermarking capabilities ensure content security, adding trust and reliability for content creators and those handling sensitive information.

The ability to fine-tune Nova models within Amazon Bedrock allows seamless customization to meet industry-specific needs, improving the relevance and impact of AI applications. The extended context length of 300K tokens and AWS integration further enhance scalability, making Nova suitable for a wide range of applications.

Conclusion

Amazon Nova is a balanced advancement in foundation models, offering performance, versatility, and cost-effectiveness through Amazon Bedrock. It caters to diverse needs, from lightweight text analysis to multimodal applications, with an accessible pricing structure and support for extended contexts. The upcoming Premier model and AWS infrastructure support position Nova as a reliable solution for businesses addressing cost, scalability, and accessibility. With fine-tuning options, specialized capabilities, and competitive pricing, Nova aims to make advanced AI technology more attainable, helping businesses leverage AI effectively for both everyday tasks and complex challenges.

Check out the Paper, Details, and Project. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. If you like our work, you will love our newsletter.. Don’t Forget to join our 60k+ ML SubReddit.

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Supporting the health and well-being of diverse global populations necessitates a nuanced understanding of the complex relationships between human behavior and local environments. This requires identifying vulnerable populations and optimizing resource allocation for maximum impact. Traditional methods often rely on manually curated features and task-specific models, making them rigid and challenging to adapt to new or related tasks. Population dynamics models, by contrast, provide a flexible framework for examining how environmental, social, and economic factors influence public health outcomes. The research underscores that local ecological factors can better predict long-term health outcomes than genetics, highlighting the critical role of geospatial modeling in tackling public health challenges, including disease management and climate-related health impacts.

Machine learning has significantly enhanced geospatial modeling by leveraging diverse data sources to increase spatial and temporal resolution. Studies have utilized mobile phone data, web search trends, satellite imagery, and weather information to predict population movement, disease outbreaks, and economic trends. Despite offering actionable insights, these methods often depend on labor-intensive, hand-crafted features and custom models, limiting scalability and interoperability. To address this, recent developments such as GPS2Vec, SatCLIP, and GeoCLIP focus on creating versatile geographic encoders by using geotagged data, satellite imagery, and image-to-GPS alignment. Building on these innovations, newer models aim to integrate human behavior signals with environmental data to produce general-purpose frameworks for improved geospatial inference.

Researchers from Google Research and the University of Nevada, Reno, introduced the Population Dynamics Foundation Model (PDFM), a versatile framework for geospatial modeling. By constructing a geo-indexed dataset incorporating human behavior (e.g., aggregated search trends) and environmental signals (e.g., weather, air quality), PDFM uses graph neural networks to create embeddings for diverse tasks. Benchmarked across 27 health, socioeconomic, and environmental tasks, PDFM achieves state-of-the-art geospatial interpolation, extrapolation, and super-resolution performance. It enhances forecasting models like TimesFM, surpassing supervised methods without fine-tuning. With publicly available embeddings and code, PDFM offers scalable geospatial solutions for research, social good, health, and business applications.

The study curated five datasets at the postal code level within the contiguous US (CONUS) for training and evaluation, focusing on aggregated search trends, maps, busyness, weather, and satellite imagery. Search trends involved the top 1,000 queries from July 2022, scaled and anonymized for privacy. Maps and busyness data provided insights into facilities and activity levels by category. Weather and air quality metrics included climate and pollutant data for July 2022. Satellite embeddings utilized SatCLIP’s Sentinel-2 imagery from 2021–2023. While temporal alignment varied, these datasets covered 28,000 postal codes, representing over 95% of the US population, with exclusions for sparsely populated regions.

To develop PDFM, five datasets covering maps, busyness, search trends, weather, and air quality were collected at postal code and county levels. Using GNNs, PDFM was trained to generate versatile embeddings for solving 27 downstream health, socioeconomic, and environmental tasks. Interpolation and extrapolation experiments simulated missing data scenarios at postal code levels, with PDFM outperforming benchmarks like SatCLIP and GeoCLIP across most tasks. Ablation studies revealed search trends and maps as key contributors. In super-resolution tasks, PDFM showed superior performance, achieving high correlation in postal code-level predictions, highlighting its effectiveness in geospatial forecasting and downstream applications.

In conclusion, The PDFM framework addresses diverse geospatial challenges across the U.S., outperforming existing models like SatCLIP and GeoCLIP on various tasks and enhancing forecasting models such as TimesFM. It integrates diverse datasets, demonstrating adaptability to new tasks, limited data scenarios, and varying resolutions. Future directions include addressing temporal alignment issues, incorporating dynamic embeddings, exploring additional datasets, and leveraging non-spatial graph edges. Limitations include reliance on aggregated data and regional data disparities. The PDFM’s privacy-preserving design ensures broad applicability, with potential global extensions requiring innovative solutions for low-data regions and reliability estimates to enhance predictions in underrepresented areas.

Check out the Paper and GitHub Repo. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. If you like our work, you will love our newsletter.. Don’t Forget to join our 60k+ ML SubReddit.

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The post Google AI Releases Population Dynamics Foundation Model (PDFM): A Machine Learning Framework Designed to Power Downstream Geospatial Modeling appeared first on MarkTechPost.

Amazon Q Business is a generative AI-powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems. Although generative AI is fueling transformative innovations, enterprises may still experience sharply divided data silos when it comes to enterprise knowledge, in particular between unstructured content (such as PDFs, Word documents, and HTML pages), and structured data (real-time data and reports stored in databases or data lakes). Both categories of data are typically queried and accessed using separate tools, from in-product browse and search functionality for unstructured data, to business intelligence (BI) tools like Amazon QuickSight for structured content.
Amazon Q Business offers an effective solution for quickly building conversational applications over unstructured content, with over 40 data connectors to popular content and storage management systems such as Confluence, SharePoint, and Amazon Simple Storage Service (Amazon S3), to aggregate enterprise knowledge. Customers are also looking for a unified conversational experience across all their knowledge repositories, regardless of the format the content is stored and organized as.
On December 3, 2024, Amazon Q Business announced the launch of its integration with QuickSight, allowing you to quickly connect your structured sources to your Amazon Q Business applications, creating a unified conversational experience for your end-users. The QuickSight integration offers an extensive set of over 20 structured data source connectors, including Amazon Redshift, PostgreSQL, MySQL, and Oracle, enabling you to quickly expand the conversational scope of your Amazon Q Business assistants to cover a wider range of knowledge sources. For the end-users, answers are returned in real time from your structured sources, combined with other relevant information found in unstructured repositories. Amazon Q Business uses the analytics and advanced visualization engine in QuickSight to generate accurate and simple-to-understand answers from structured sources.
In this post, we show you how to configure the QuickSight connection from Amazon Q Business and then ask questions to get real-time data and visualizations from QuickSight for structured data in addition to unstructured content.
Solution overview
The QuickSight feature in Amazon Q Business is available on the Amazon Q Business console as well as through Amazon Q Business APIs. This feature is implemented as a plugin within Amazon Q Business. After it’s enabled, this plugin will behave differently than other Amazon Q Business plugins—it will query QuickSight automatically for every user prompt, looking for relevant answers.
For AWS accounts that aren’t subscribed to QuickSight already, the Amazon Q Business admin completes the following steps:

Create a QuickSight account.
Connect your database in QuickSight to create a dataset.
Create a topic in QuickSight, which is then used to make it searchable from your Amazon Q Business application.

When the feature is activated, Amazon Q Business will use your unstructured data sources configured in Amazon Q Business, as well as your structured content available using QuickSight, to generate a rich answer that includes narrative and visualizations. Depending on the question and data in QuickSight, Amazon Q Business may generate one or more visualizations as a response.
Prerequisites
You should have the following prerequisites:

An AWS account where you can follow the instructions in this post.
AWS IAM Identity Center set up to be used with Amazon Q Business. For more information, see Configure Amazon Q Business with AWS IAM Identity Center trusted identity propagation.
At least one Amazon Q Business Pro user that has admin permissions to set up and configure Amazon Q Business. For pricing information, see Amazon Q Business pricing.
An IAM Identity Center group that will be assigned the QuickSight Admin Pro role, for users who will manage and configure QuickSight.
If a QuickSight account exists, then it needs to be in the same AWS account and AWS Region as Amazon Q Business, and configured with IAM Identity Center.
A database that is installed and can be reached from QuickSight to load structured data (or you could create a dataset by uploading a CSV or XLS file). The database also needs credentials to create tables and insert data.
Sample structured data to load into the database (along with insert statements).

Create an Amazon Q Business application
To use this feature, you need to have an Amazon Q Business application. If you don’t have an existing application, follow the steps in Discover insights from Amazon S3 with Amazon Q S3 connector to create an application along with an Amazon S3 data source. Upload the non-structured document(s) to Amazon S3 and sync the data source.
Create and configure a new QuickSight account
You can skip this section if you already have an existing QuickSight account. To create a QuickSight account, complete the following steps:

On the Amazon Q Business console, navigate to your application.
Choose Amazon QuickSight in the navigation pane.

Choose Create QuickSight account.

Under QuickSight account information, enter your account name and an email for account notifications.
Under Assign QuickSight Admin Pro roles, choose the IAM Identity Center group you created as a prerequisite.
Choose Next.

Under Service access, select Create and use a new service role.
Choose Authorize.

This will create a QuickSight account, assign the IAM Identity Center group as QuickSight Admin Pro, and authorize Amazon Q Business to access QuickSight.

You will see a dashboard with details for QuickSight. Currently, it will show zero datasets and topics.

Choose Go to QuickSight.

You can now proceed to the next section to prepare your data.
Configure an existing QuickSight account
You can skip this section if you followed the previous steps and created a new QuickSight account.
If your current QuickSight account is not on IAM Identity Center, consider using a different AWS account without a QuickSight subscription for the purpose of testing this feature. From that account, you create an Amazon Q Business application on IAM Identity Center and go through the QuickSight integration setup steps on the Amazon Q Business console that will create the QuickSight account for you in IAM Identity Center. Remember to delete that new QuickSight account and Amazon Q Business application after your testing is done to avoid further billing.
Complete the following steps to set up the QuickSight connector from Amazon Q Business for an existing QuickSight account:

On the Amazon Q Business console, navigate to your application.
Choose Amazon QuickSight in the navigation pane.

Choose Authorize QuickSight answers.

Under Assign QuickSight Admin Pro roles, choose the IAM Identity Center group you created as a prerequisite.
Under Service Access, select Create and use a new service role.
Choose Save.

You will see a dashboard with details for QuickSight. If you already have a dataset and topics, they will show up here.

You’re now ready to add a dataset and topics in the next section.
Add data in QuickSight
In this section, we create an Amazon Redshift data source. You can instead create a data source from the database of your choice, use files in Amazon S3, or perform a direct upload of CSV files and connect to it. Refer to Creating a dataset from a database for more details.
To configure your data, complete the following steps:

Create a new dataset with Amazon Redshift as a data source.

Configuring this connection offers multiple choices; choose the one that best fits your needs.

Create a topic from the dataset. For more information, see Creating a topic.

Optionally, create dashboards from the topic. If created, Amazon Q Business can use them.

Ask queries to Amazon Q Business
To start chatting with Amazon Q Business, complete the following steps:

On the Amazon Q Business console, navigate to your application.
Choose Amazon QuickSight in the navigation pane.

You should see the datasets and topics populated with values.

Choose the link under Deployed URL.

We uploaded AWS Cost and Usage Reports for a specific AWS account in QuickSight using Amazon Redshift. We also uploaded Amazon service documentation into a data source using Amazon S3 into Amazon Q Business as unstructured data. We will ask questions related to our AWS costs and show how Amazon Q Business answers questions from both structured and unstructured data.
The following screenshot shows an example question that returns a response from only unstructured data.

The following screenshot shows an example question that returns a response from only structured data.

The following screenshot shows an example question that returns a response from both structured and unstructured data.

The following screenshot shows an example question that returns multiple visualizations from both structured and unstructured data.

Clean up
If you no longer want to use this Amazon Q Business feature, delete the resources you created to avoid future charges:

Delete the Amazon Q Business application:

On the Amazon Q Business console, choose Applications in the navigation pane.
Select your application and on the Actions menu, choose Delete.
Enter delete to confirm and choose Delete.

The process can take up to 15 minutes to complete.

Delete the S3 bucket:

Empty your S3 bucket.
Delete the bucket.

Delete the QuickSight account:

On the Amazon QuickSight console, choose Manage Amazon QuickSight.
Choose Account setting and Manage.
Delete the account.

Delete your IAM Identity Center instance.

Conclusion
In this post, we showed how to include answers from your structured sources in your Amazon Q Business applications, using the QuickSight integration. This creates a unified conversational experience for your end-users that saves them time, helps them make better decisions through more complete answers, and improves their productivity.
At AWS re:Invent 2024, we also announced a similar unified experience enabling access to insights from unstructured data sources in Amazon Q in QuickSight powered by Amazon Q Business.
To learn about the new capabilities Amazon Q in QuickSight provides, see QuickSight Plugin.
To learn more about Amazon Q Business, refer to the Amazon Q Business User Guide.
To learn more about configuring a QuickSight dataset, see Manage your Amazon QuickSight datasets more efficiently with the new user interface.
QuickSight also offers querying unstructured data. For more details, refer to Integrate unstructured data into Amazon QuickSight using Amazon Q Business.

About the authors
Jiten Dedhia is a Sr. AIML Solutions Architect with over 20 years of experience in the software industry. He has helped Fortune 500 companies with their AIML/Generative AI needs.
Jean-Pierre Dodel is a Principal Product Manager for Amazon Q Business, responsible for delivering key strategic product capabilities including structured data support in Q Business, RAG. and overall product accuracy optimizations. He brings extensive AI/ML and Enterprise search experience to the team with over 7 years of product leadership at AWS.

Digital experience interruptions can harm customer satisfaction and business performance across industries. Application failures, slow load times, and service unavailability can lead to user frustration, decreased engagement, and revenue loss. The risk and impact of outages increase during peak usage periods, which vary by industry—from ecommerce sales events to financial quarter-ends or major product launches. According to New Relic’s 2024 Observability Forecast, businesses face a median annual downtime of 77 hours from high-impact outages. These outages can cost up to $1.9 million per hour.
New Relic is addressing these challenges by creating the New Relic AI custom plugin for Amazon Q Business. This custom plugin creates a unified solution that combines New Relic AI’s observability insights and recommendations and Amazon Q Business’s Retrieval Augmented Generation (RAG) capabilities, in and a natural language interface for east of use.
The custom plugin streamlines incident response, enhances decision-making, and reduces cognitive load from managing multiple tools and complex datasets. It empowers team members to interpret and act quickly on observability data, improving system reliability and customer experience. By using AI and New Relic’s comprehensive observability data, companies can help prevent issues, minimize incidents, reduce downtime, and maintain high-quality digital experiences.
This post explores the use case, how this custom plugin works, how it can be enabled, and how it can help elevate customers’ digital experiences.
The challenge: Resolving application problems before they impact customers
New Relic’s 2024 Observability Forecast highlights three key operational challenges:

Tool and context switching – Engineers use multiple monitoring tools, support desks, and documentation systems. 45% of support engineers, application engineers, and SREs use five different monitoring tools on average. This fragmentation can cause missed SLAs and SLOs, confusion during critical incidents, and increased negative fiscal impact. Tool switching slows decision-making during outages or ecommerce disruptions.
Knowledge accessibility – Scattered, hard-to-access knowledge, including runbooks and post-incident reports, hinders effective incident response. This can cause slow escalations, uncertain decisions, longer disruptions, and higher operational costs from redundant engineer involvement.
Complexity in data interpretation – Team members may struggle to interpret monitoring and observability data due to complex applications with numerous services and cloud infrastructure entities, and unclear symptom-problem relationships. This complexity hinders quick, accurate data analysis and informed decision-making during critical incidents.

The custom plugin for Amazon Q Business addresses these challenges with a unified, natural language interface for critical insights. It uses AI to research and translate findings into clear recommendations, providing quick access to indexed runbooks and post-incident reports. This custom plugin streamlines incident response, enhances decision-making, and reduces effort in managing multiple tools and complex datasets.
Solution Overview
The New Relic custom plugin for Amazon Q Business centralizes critical information and actions in one interface, streamlining your workflow. It allows you to inquire about specific services, hosts, or system components directly. For instance, you can investigate a sudden spike in web service response times or a slow database. NR AI responds by analyzing current performance data and comparing it to historical trends and best practices. It then delivers detailed insights and actionable recommendations based on up-to-date production environment information.
The following diagram illustrates the workflow.

When a user asks a question in the Amazon Q interface, such as “Show me problems with the checkout process,” Amazon Q queries the RAG ingested with the customers’ runbooks. Runbooks are troubleshooting guides maintained by operational teams to minimize application interruptions. Amazon Q gains contextual information, including the specific service names and infrastructure information related to the checkout service, and uses the custom plugin to communicate with New Relic AI. New Relic AI initiates a deep dive analysis of monitoring data since the checkout service problems began.
New Relic AI conducts a comprehensive analysis of the checkout service. It examines service performance metrics, forecasts of key indicators like error rates, error patterns and anomalies, security alerts, and overall system status and health. The analysis results in a summarized alert intelligence report that identifies and explains root causes of checkout service issues. This report provides clear, actionable recommendations and includes real-time application performance insights. It also offers direct links to detailed New Relic interfaces. Users can access this comprehensive summary without leaving the Amazon Q interface.
The custom plugin presents information and insights directly within the Amazon Q Business interface, eliminating the need to switch between the New Relic and Amazon Q interfaces, and enabling faster problem resolution.
Potential impacts
The New Relic Intelligent Observability platform provides comprehensive incident response and application and infrastructure performance monitoring capabilities for SREs, application engineers, support engineers, and DevOps professionals. Organizations using New Relic report significant improvements in their operations, achieving a 65% reduction in incidents, 10 times more deployments, and 50% faster release times while maintaining 99.99% uptime. When you combine New Relic insights with Amazon Q Business, you can further reduce incidents, deploy higher-quality code more frequently, and create more reliable experiences for your customers:

Detect and resolve incidents faster – With this custom plugin, you can reduce undetected incidents and resolve issues more quickly. Incidents often occur when teams miss early warning signs or can’t connect symptoms to underlying problems, leading to extended service disruptions. Although New Relic collects and generates data that can identify these warning signs, teams working in separate tools might not have access to these critical insights. For instance, support specialists might not have direct access to monitoring dashboards, making it challenging to identify emerging issues. The custom plugin consolidates these monitoring insights, helping you more effectively identify and understand related issues.
Simplify incident management – The custom plugin enhances support engineers’ and incident responders’ efficiency by streamlining their workflow. The custom plugin allows you to manage incidents without switching between New Relic AI and Amazon Q during critical moments. The integrated interface removes context switching, enabling both technical and non-technical users to access vital monitoring data quickly within the Amazon Q interface. This comprehensive approach speeds up troubleshooting, minimizes downtime, and boosts overall system reliability.
Build reliability across teams – The custom plugin makes application and infrastructure performance monitoring insights accessible to team members beyond traditional observability users. translates complex production telemetry data into clear, actionable insights for product managers, customer service specialists, and executives. By providing a unified interface for querying and resolving issues, it empowers your entire team to maintain and improve digital services, regardless of their technical expertise. For example, when a customer service specialist receives user complaints, they can quickly investigate application performance issues without navigating complex monitoring tools or interpreting alert conditions. This unified view enables everyone supporting your enterprise software to understand and act on insights about application health and performance. The result is a more collaborative approach across multiple enterprise teams, leading to more reliable system maintenance and excellent customer experiences.

Conclusion
The New Relic AI custom plugin represents a step forward in digital experience management. By addressing key challenges such as tool fragmentation, knowledge accessibility, and data complexity, this solution empowers teams to deliver superior digital experiences. This collaboration between AWS and New Relic opens up possibilities for building more robust digital infrastructures, advancing innovation in customer-facing technologies, and setting new benchmarks in proactive IT problem-solving.
To learn more about improving your operational efficiency with AI-powered observability, refer to the Amazon Q Business User Guide and explore New Relic AI capabilities. To get started on training, enroll for free Amazon Q training from AWS Training and Certification.
About New Relic
New Relic is a leading cloud-based observability platform that helps businesses optimize the performance and reliability of their digital systems. New Relic processes 3 EB of data annually. Over 5 billion data points are ingested and 2.4 trillion queries are executed every minute across 75,000 active customers. The platform serves over 333 billion web requests each day. The median platform response time is 60 milliseconds.

About the authors
 Meena Menon is a Sr. Customer Solutions Manager at AWS.
Sean Falconer is a Sr. Solutions Architect at AWS.
Nava Ajay Kanth Kota is a Senior Partner Solutions Architect at AWS. He is currently part of the Amazon Partner Network (APN) team that closely works with ISV Storage Partners. Prior to AWS, his experience includes running Storage, Backup, and Hybrid Cloud teams and his responsibilities included creating Managed Services offerings in these areas.
David Girling is a Senior AI/ML Solutions Architect with over 20 years of experience in designing, leading, and developing enterprise systems. David is part of a specialist team that focuses on helping customers learn, innovate, and utilize these highly capable services with their data for their use cases.
Camden Swita is Head of AI and ML Innovation at New Relic specializing in developing compound AI systems, agentic frameworks, and generative user experiences for complex data retrieval, analysis, and actioning.

Today, Amazon SageMaker is excited to announce updates to the inference optimization toolkit, providing new functionality and enhancements to help you optimize generative AI models even faster. These updates build on the capabilities introduced in the original launch of the inference optimization toolkit (to learn more, see Achieve up to ~2x higher throughput while reducing costs by ~50% for generative AI inference on Amazon SageMaker with the new inference optimization toolkit – Part 1).
The following are the key additions to the inference optimization toolkit:

Speculative decoding support for Meta Llama 3.1 models – The toolkit now supports speculative decoding for the latest Meta Llama 3.1 70B and 405B (FP8) text models, allowing you to accelerate inference process.
Support for FP8 quantization – The toolkit has been updated to enable FP8 (8-bit floating point) quantization, helping you further optimize model size and inference latency for GPUs. FP8 offers several advantages over FP32 (32-bit floating point) for deep learning model inference, including reduced memory usage, faster computation, lower power consumption, and broader applicability because FP8 quantization can be applied to key model components like the KV cache, attention, and MLP linear layers.
Compilation support for TensorRT-LLM – You can now use the toolkit’s compilation capabilities to integrate your generative AI models with NVIDIA’s TensorRT-LLM, delivering enhanced performance by optimizing the model with ahead-of-time compilation. You reduce the model’s deployment time and auto scaling latency because the model weights don’t require just-in-time compilation when the model deploys to a new instance.

These updates build on the toolkit’s existing capabilities, allowing you to reduce the time it takes to optimize generative AI models from months to hours, and achieve best-in-class performance for your use case. Simply choose from the available optimization techniques, apply them to your models, validate the improvements, and deploy the models in just a few clicks through SageMaker.
In this post, we discuss these new features of the toolkit in more detail.
Speculative decoding
Speculative decoding is an inference technique that aims to speed up the decoding process of large language models (LLMs) for latency-critical applications, without compromising the quality of the generated text. The key idea is to use a smaller, less powerful, but faster language model called the draft model to generate candidate tokens. These candidate tokens are then validated by the larger, more powerful, but slower target model. At each iteration, the draft model generates multiple candidate tokens. The target model verifies the tokens, and if it finds a particular token unacceptable, it rejects it and regenerates that token itself. This allows the larger target model to focus on verification, which is faster than auto-regressive token generation. The smaller draft model can quickly generate all the tokens and send them in batches to the target model for parallel evaluation, significantly speeding up the final response generation.
With the updated SageMaker inference toolkit, you get out-of-the-box support for speculative decoding that has been tested for performance at scale on various popular open source LLMs. The toolkit provides a pre-built draft model, eliminating the need to invest time and resources in building your own draft model from scratch. Alternatively, you can also use your own custom draft model, providing flexibility to accommodate your specific requirements. To showcase the benefits of speculative decoding, let’s look at the throughput (tokens per second) for a Meta Llama 3.1 70B Instruct model deployed on an ml.p4d.24xlarge instance using the Meta Llama 3.2 1B Instruct draft model.

Given the increase in throughput that is realized with speculative decoding, we can also see the blended price difference when using speculative decoding vs. when not using speculative decoding. Here we have calculated the blended price as a 3:1 ratio of input to output tokens. The blended price is defined as follows:

Total throughput (tokens per second) = NumberOfOutputTokensPerRequest / (ClientLatency / 1,000) x concurrency
Blended price ($ per 1 million tokens) = (1−(discount rate)) × (instance per hour price) ÷ ((total token throughput per second) × 60 × 60 ÷ 10^6)) ÷ 4
Discount rate assuming a 26% Savings Plan

Quantization
Quantization is one of the most popular model compression methods to accelerate model inference. From a technical perspective, quantization has several benefits:

It reduces model size, which makes it suitable for deploying using fewer GPUs with lower total device memory available.
It reduces memory bandwidth pressure by using fewer-bit data types.
If offers increased space for the KV cache. This enables larger batch sizes and sequence lengths.
It significantly speeds up matrix multiplication (GEMM) operations on the NVIDIA architecture, for example, up to twofold for FP8 compared to the FP16/BF16 data type in microbenchmarks.

With this launch, the SageMaker inference optimization toolkit now supports FP8 and SmoothQuant (TensorRT-LLM only) quantization. SmoothQuant is a post-training quantization (PTQ) technique for LLMs that reduces memory and speeds up inference without sacrificing accuracy. It migrates quantization difficulty from activations to weights, which are easier to quantize. It does this by introducing a hyperparameter to calculate a per-channel scale that balances the quantization difficulty of activations and weights.
The current generation of instances like p5 and g6 provide support for FP8 using specialized tensor cores. FP8 represents float point numbers in 8 bits instead of the usual 16. At the time of writing, vLLM and TRT-LLM support quantizing the KV cache, attention, and linear layers for text-only LLMs. This reduces memory footprint, increases throughput, and lowers latency. Whereas both weights and activations can be quantized for p5 and g6 instances (W8A8), only weights can be quantized for p4d and g5 instances (W8A16). Though FP8 quantization has minimal impact on accuracy, you should always evaluate the quantized model on your data and for your use case. You can evaluate the quantized model through Amazon SageMaker Clarify. For more details, see Understand options for evaluating large language models with SageMaker Clarify.
The following graph compares the throughput of a FP8 quantized Meta Llama 3.1 70B Instruct model against a non-quantized Meta Llama 3.1 70B Instruct model on an ml.p4d.24xlarge instance.

The quantized model has a smaller memory footprint and it can be deployed to a smaller (and cheaper) instance type. In this post, we have deployed the quantized model on g5.12xlarge.
The following graph shows the price difference per million tokens between the FP8-quantized model deployed on g5.12xlarge and the non-quantized version deployed on p4d.24xlarge.

Our analysis shows a clear price-performance edge for the FP8 quantized model over the non-quantized approach. However, quantization has an impact on model accuracy, so we strongly testing the quantized version of the model on your datasets.
The following is the SageMaker Python SDK code snippet for quantization. You just need to provide the quantization_config attribute in the optimize() function:

quantized_instance_type = “ml.g5.12xlarge”

output_path=f”s3://{artifacts_bucket_name}/llama-3-1-70b-fp8/”

optimized_model = model_builder.optimize(
    instance_type=quantized_instance_type,
    accept_eula=True,
    quantization_config={
        “OverrideEnvironment”: {
            “OPTION_QUANTIZE”: “fp8”,
            “OPTION_TENSOR_PARALLEL_DEGREE”: “4”
        },
    },
    output_path=output_path,
)

Refer to the following code example to learn more about how to enable FP8 quantization and speculative decoding using the optimization toolkit for a pre-trained Amazon SageMaker JumpStart model. If you want to deploy a fine-tuned model with SageMaker JumpStart using speculative decoding, refer to the following notebook.
Compilation
Compilation optimizes the model to extract the best available performance on the chosen hardware type, without any loss in accuracy. For compilation, the SageMaker inference optimization toolkit provides efficient loading and caching of optimized models to reduce model loading and auto scaling time by up to 40–60 % for Meta Llama 3 8B and 70B.
Model compilation enables running LLMs on accelerated hardware, such as GPUs, while simultaneously optimizing the model’s computational graph for optimal performance on the target hardware. When using the Large Model Inference (LMI) Deep Learning Container (DLC) with the TensorRT-LLM framework, the compiler is invoked from within the framework and creates compiled artifacts. These compiled artifacts are unique for a combination of input shapes, precision of the model, tensor parallel degree, and other framework- or compiler-level configurations. Although the compilation process avoids overhead during inference and enables optimized inference, it can take a lot of time.
To avoid re-compiling every time a model is deployed onto a GPU with the TensorRT-LLM framework, SageMaker introduces the following features:

A cache of pre-compiled artifacts – This includes popular models like Meta Llama 3.1. When using an optimized model with the compilation config, SageMaker automatically uses these cached artifacts when the configurations match.
Ahead-of-time compilation – The inference optimization toolkit enables you to compile your models with the desired configurations before deploying them on SageMaker.

The following graph illustrates the improvement in model loading time when using pre-compiled artifacts with the SageMaker LMI DLC. The models were compiled with a sequence length of 4096 and a batch size of 16, with Meta Llama 3.1 8B deployed on a g5.12xlarge (tensor parallel degree = 4) and Meta Llama 3.1 70B Instruct on a p4d.24xlarge (tensor parallel degree = 8). As you can see on the graph, the bigger the model, the bigger the benefit of using a pre-compiled model (16% improvement for Meta Llama 3 8B and 43% improvement for Meta Llama 3 70B).

Compilation using the SageMaker Python SDK
For the SageMaker Python SDK, you can configure the compilation by changing the environment variables in the .optimize() function. For more details on compilation_config, refer to TensorRT-LLM ahead-of-time compilation of models tutorial.

optimized_model = model_builder.optimize(
    instance_type=gpu_instance_type,
    accept_eula=True,
    compilation_config={
        “OverrideEnvironment”: {
            “OPTION_ROLLING_BATCH”: “trtllm”,
            “OPTION_MAX_INPUT_LEN”: “4096”,
            “OPTION_MAX_OUTPUT_LEN”: “4096”,
            “OPTION_MAX_ROLLING_BATCH_SIZE”: “16”,
            “OPTION_TENSOR_PARALLEL_DEGREE”: “8”,
        }
    },
    output_path=f”s3://{artifacts_bucket_name}/trtllm/”,
)

Refer to the following notebook for more information on how to enable TensorRT-LLM compilation using the optimization toolkit for a pre-trained SageMaker JumpStart model.
Amazon SageMaker Studio UI experience
In this section, let’s walk through the Amazon SageMaker Studio UI experience to run an inference optimization job. In this case, we use the Meta Llama 3.1 70B Instruct model, and for the optimization option, we quantize the model using INT4-AWQ and then use the SageMaker JumpStart suggested draft model Meta Llama 3.2 1B Instruct for speculative decoding.
First, we search for the Meta Llama 3.1 70B Instruct model in the SageMaker JumpStart model hub and choose Optimize on the model card.

The Create inference optimization job page provides you options to choose the type of optimization. In this case, we choose to take advantage of the benefits of both INT4-AWQ quantization and speculative decoding.

For the draft model, you have a choice to use the SageMaker recommended draft model, choose one the SageMaker JumpStart models, or bring your own draft model.

For this scenario, we choose the SageMaker recommended Meta Llama 3.2 1B Instruct model as the draft model and start the optimization job.

When the optimization job is complete, you have an option to evaluate performance or deploy the model onto a SageMaker endpoint for inference.

Pricing
For compilation and quantization jobs, SageMaker will optimally choose the right instance type, so you don’t have to spend time and effort. You will be charged based on the optimization instance used. To learn more, see Amazon SageMaker pricing. For speculative decoding, there is no additional optimization cost involved; the SageMaker inference optimization toolkit will package the right container and parameters for the deployment on your behalf.
Conclusion
To get started with the inference optimization toolkit, refer to Achieve up to 2x higher throughput while reducing cost by up to 50% for GenAI inference on SageMaker with new inference optimization toolkit: user guide – Part 2. This post will walk you through how to use the inference optimization toolkit when using SageMaker inference with SageMaker JumpStart and the SageMaker Python SDK. You can use the inference optimization toolkit with supported models on SageMaker JumpStart. For the full list of supported models, refer to Inference optimization for Amazon SageMaker models.

About the Authors
Marc Karp is an ML Architect with the Amazon SageMaker Service team. He focuses on helping customers design, deploy, and manage ML workloads at scale. In his spare time, he enjoys traveling and exploring new places.
Dmitry Soldatkin is a Senior AI/ML Solutions Architect at Amazon Web Services (AWS), helping customers design and build AI/ML solutions. Dmitry’s work covers a wide range of ML use cases, with a primary interest in Generative AI, deep learning, and scaling ML across the enterprise. He has helped companies in many industries, including insurance, financial services, utilities, and telecommunications. He has a passion for continuous innovation and using data to drive business outcomes.
Raghu Ramesha is a Senior ML Solutions Architect with the Amazon SageMaker Service team. He focuses on helping customers build, deploy, and migrate ML production workloads to SageMaker at scale. He specializes in machine learning, AI, and computer vision domains, and holds a master’s degree in Computer Science from UT Dallas. In his free time, he enjoys traveling and photography.
Rishabh Ray Chaudhury is a Senior Product Manager with Amazon SageMaker, focusing on Machine Learning inference. He is passionate about innovating and building new experiences for Machine Learning customers on AWS to help scale their workloads. In his spare time, he enjoys traveling and cooking. You can find him on LinkedIn.
Lokeshwaran Ravi is a Senior Deep Learning Compiler Engineer at AWS, specializing in ML optimization, model acceleration, and AI security. He focuses on enhancing efficiency, reducing costs, and building secure ecosystems to democratize AI technologies, making cutting-edge ML accessible and impactful across industries.

If you’ve spent any time crafting email flows in Klaviyo, you’ve inevitably dealt with the dreaded low open rates. 

Don’t worry, you’re not alone. 

A quick Google search pulls up hundreds of threads, forums, and comments from frustrated marketers asking the same thing. Why are my flow open rates so low in Klaviyo?

Low open rates are a total vibe killer. 

You put hours into designing the perfect flow, agonize over every pixel and CTA, and then… crickets. 

But here’s the thing. This isn’t just an annoying issue, it can hurt your bottom line.

If your emails aren’t being opened, your messages aren’t getting read, and that means fewer clicks, fewer conversions, and fewer dollars in your pocket.

The good news? 

Low open rates aren’t a death sentence for your Klaviyo flows. They can be fixed and that’s what we’re going to do. 

We’re going to tackle why your open rates might be slumping and, more importantly, how to fix them. From subject line hacks to segmentation strategies, we’ve got the insights you need to level up your email game.

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Understanding Open Rates in Klaviyo Flows

Let’s start with the basics. 

Open rate is the percentage of people who opened your email out of the total number it was delivered to. 

It’s calculated like this:

∗∗OpenRate∗∗=(EmailsOpened÷EmailsDelivered)×100

So, if you sent your flow to 1,000 subscribers and 200 of them opened it, your open rate is 20%.

While some might think open rates are just a vanity metric, I can assure you they are not. They’re your first signal of how effective your email strategy is. 

Low open rates can mean your subject lines aren’t doing their job, your emails are landing in spam, or (worst case) your subscribers just don’t care. 

For ecommerce brands, it’s revenue left on the table. If your customers don’t open your emails, they can’t click through to buy.

Campaign Emails vs. Flow Emails in Klaviyo

Along with understanding the basics of open rates, we also need to clear up a common point of confusion – the difference between campaign emails and flow emails in Klaviyo.

Campaign EmailsThese are your one-off broadcasts. You’re sending a single email to a specific list or segment – maybe a holiday promo, a flash sale, or a product launch. These emails are time-sensitive and usually sent to a broader audience.

Flow EmailsThese are your automated workhorses. Flows are triggered by customer behavior or specific events, like someone signing up for your newsletter, abandoning their cart, or making a purchase. Flows are tailored to the customer journey, meaning they’re highly targeted and more personal.

Why does this matter? 

Open rates for flows and campaigns are measured the same way but the strategies to improve them are totally different. 

Flows are all about relevance and timing, so if your flow open rates are low, it’s a sign that something in your setup isn’t clicking. Maybe the timing feels off or the content isn’t hitting the mark.

By understanding these differences, you’ll be better equipped to pinpoint what’s going wrong and start fixing it.

Why Are My Flow Open Rates So Low in Klaviyo?

Low open rates in Klaviyo flows don’t happen randomly. There’s always a cause and if you know what to look for, you can identify the problem and fix it. 

Let’s look at the most common culprits and how to diagnose and tackle them.

Low Flow Open Rate Issue #1: Poor Subject Lines and Preview Text

We all know the importance of a subject line. If it doesn’t catch attention, if it isn’t interesting, if it isn’t targeted properly…your email won’t even get a glance. 

Image: Milk & Tweed

Similarly, preview text (the snippet of content that appears alongside the subject line) can either add to the intrigue or completely miss the mark.

Common mistakes include:

Subject lines that are vague or too “salesy” (e.g., “Don’t Miss This” or “Big News Inside!”).

Misleading subject lines that create distrust if the email doesn’t deliver on the promise.

Ignoring preview text, resulting in random, unformatted text like “View this email in your browser” showing up in inboxes.

How to Fix It:

Be Specific: Instead of “Special Offer Inside,” try “20% Off Your Favorite Products—Today Only.”

Leverage Curiosity: Use phrases that spark interest without being clickbait, like “You Won’t Believe What’s Back in Stock.”

Optimize Preview Text: This should complement your subject line. Think of it as the next sentence in a conversation.

Diagnosing the Problem:

A/B Testing: Experiment with different subject lines to see which ones resonate.

Analyze Open Rates: Compare emails with varying subject lines to identify patterns in what works. If “Sale” emails get high opens but “Newsletter” emails flop, you’ve found a trend.

Low Flow Open Rate Issue #2: Poor Audience Segmentation

Sending generic messages to a broad audience dilutes your relevance and reduces the chance that individual subscribers feel the email is meant for them. You have to segment your audiences. 

If you aren’t, it could be leading to low open rates in your Klaviyo flows.

Common Segmentation Mistakes:

Using default lists instead of creating segments based on behavior (e.g., recent purchases, browsing history).

Overlooking key traits like geographic location, which can affect relevance (e.g., promoting winter gear to subscribers in tropical climates).

How to Fix It:

Use Klaviyo’s segmentation tools along with Customers.ai audiences to create detailed audience groups based on behavior, preferences, or lifecycle stage.

Test campaigns tailored to these segments. For example:

A cart abandonment flow for shoppers who didn’t complete checkout.

A post-purchase flow for repeat buyers offering loyalty perks.

Return visitors vs. new visitors in Klaviyo

Diagnosing the Problem:

Segment Performance: Review open rates by segment. If one group performs significantly worse, it’s time to refine your targeting.

Compare Engagement Metrics: Compare segmented flow performance against generic campaigns to see the lift segmentation provides.

Low Flow Open Rate Issue #3: Sending Frequency and Timing Issues

Email frequency is a balancing act. If you send too often, you risk overwhelming your audience, leading to unsubscribes and email fatigue. 

On the flip side, sending too infrequently can make subscribers forget about you altogether.

Timing Pitfalls:

Sending emails at odd hours (e.g., midnight on a weekday).

Failing to account for time zones, especially for global audiences.

Overlapping emails from multiple flows, like a welcome series and a cart abandonment email being sent within hours of each other.

How to Fix It:

Use Klaviyo’s smart sending feature to ensure you’re not bombarding your subscribers.

Analyze historical engagement data to identify when your audience is most active, and schedule emails accordingly.

Diagnosing the Problem:

Monitor Engagement: Check open rates by send time. If you notice lower engagement during certain periods, adjust accordingly.

Subscriber Feedback:  Look at unsubscribe rates for flows as higher rates might indicate over-sending.

Low Flow Open Rate Issue #4: Deliverability Challenges

Deliverability issues are often the biggest culprit as they can make your emails invisible, no matter how great your content is. This includes emails landing in spam, getting clipped, or being blocked outright.

Common Deliverability Issues:

Spam Filters: Poor sender reputation, spammy content, or excessive images can send your email straight to spam.

Email Clipping: If your email is too long, it may get clipped by Gmail or other providers, removing the tracking pixel and skewing open rate data.

Domain Issues: If your sending domain isn’t authenticated (e.g., with SPF, DKIM, or DMARC), it can hurt deliverability.

How to Fix It:

Authenticate your domain by setting up SPF, DKIM, and DMARC policies.

Keep emails concise to avoid clipping—aim for 102KB or less.

Regularly clean your email list to remove inactive subscribers and reduce bounce rates.

Diagnosing the Problem:

Check Spam Placement: Use Klaviyo’s deliverability tools or third-party tools (including the Customers.ai Email Deliverability Audit tool) to check where your emails land (inbox, spam, or promotions).

Compare Email Domains: Analyze performance by domain (e.g., Gmail vs. Yahoo) to identify patterns.

Email Clipping: Ensure your emails aren’t too lengthy, causing them to be clipped.

Low Flow Open Rate Issue #5: Content Relevance and Quality

If your email doesn’t provide value, your subscribers won’t bother opening future ones. Content that feels irrelevant or repetitive can erode trust and lead to low engagement over time.

Common Content Mistakes:

Sending the same discount email multiple times without variation.

Using overly generic messaging, like “Check out our products!” without personalization.

Failing to deliver on promises made in the subject line or preview text.

How to Fix It:

Use dynamic content in Klaviyo to personalize emails based on subscriber behavior (e.g., recommending products they’ve browsed).

Experiment with interactive content, like quizzes, polls, or countdown timers, to keep things fresh.

Focus on storytelling. Highlight customer success stories, share behind-the-scenes content, or explain how your products solve specific problems.

Diagnosing the Problem:

Engagement Analysis: Monitor engagement metrics like click-through rates alongside open rates to see if the issue is with content quality.

Subscriber Surveys: Use subscriber feedback (e.g., surveys or direct replies) to learn what type of content they value most.

Diagnose, test, and iterate. That’s the secret to improving your Klaviyo flow open rates. 

By diving into these areas, you’ll not only uncover why your open rates are low but also have actionable steps to turn things around. 

6 Key Strategies to Improve Flow Open Rates in Klaviyo

1. Crafting Compelling Subject Lines

Your subject line is the first thing your subscriber sees and it has a massive impact on flow open rates. 

Image: GlockApps

According to Campaign Monitor, emails with personalized subject lines are 26% more likely to be opened. 

To entice opens:

Keep It Concise: Aim for subject lines between 17-24 characters to ensure full visibility, especially on mobile devices.Campaign Monitor

Personalize: Incorporate the recipient’s name or reference past interactions to create a sense of individual attention.

Create Urgency: Phrases like “Limited Time Offer” or “Only a Few Left” can prompt immediate action.

Avoid Spam Triggers: Steer clear of excessive punctuation, all caps, and overly promotional language to prevent landing in spam folders.

A/B Test: Experiment with different subject lines to identify what resonates with your audience.

Example: Outdoor Gear Retailer

An outdoor gear retailer tested two subject lines for a promotional flow:

“Big Savings on Gear You’ll Love”

“Hiking Boots for Half the Price? Yes, Please!”The second subject line outperformed the first by 40%, proving that specificity and a touch of excitement can make a huge difference.

Tips for Writing Better Subject Lines:

Be clear and direct: “20% Off Back-to-School Essentials—Today Only.”

Use questions to pique interest: “Need a Winter Coat? We’ve Got You Covered.”

Incorporate emojis sparingly to add personality: “ Your Next Adventure Awaits.”

Remember, the subject line sets expectations. Ensure the email content delivers on its promise.

2. Effective Audience Segmentation

Sending tailored content to specific audience segments increases relevance and engagement. In fact, segmented campaigns have an open rate that’s 14.31% higher than non-segmented ones.

Consider:

Behavioral Segmentation: Group subscribers based on actions like past purchases, browsing history, or engagement levels.

Demographic Segmentation: Customize emails according to age, location, or gender to align with subscriber interests.

Lifecycle Segmentation: Address subscribers differently based on their stage in the customer journey—new subscribers, repeat customers, or lapsed users.

Example: Skincare Brand

A skincare brand used segmentation to target two distinct groups:

Subscribers who browsed anti-aging products but hadn’t purchased.

Customers who bought anti-aging products in the last 30 days.

The first group received an email titled, “Discover the Secret to Younger Skin,” with a special discount on anti-aging serums. 

The second group got a different email: “Keep Your Glow Going—Here’s How to Maximize Your Serum Results.” 

The result? The segmented emails achieved a 28% higher open rate compared to their generic campaigns.

How to Segment:

Behavior: Target cart abandoners, frequent buyers, or those who’ve clicked on specific categories.

Demographics: Segment by age, location, or gender for more personalized messaging.

Engagement Level: Create “VIP” flows for your most active subscribers or win-back emails for lapsed customers.

By delivering content that speaks directly to each segment’s interests, you enhance the likelihood of opens and interactions.

3. Optimizing Send Times

When you send your email matters as much as what you send. Studies show that the best time to send emails is typically midweek, with Tuesday and Thursday mornings performing best. 

But don’t take this as gospel as your audience’s habits may vary. Time emails to align with when YOUR subscribers are most likely to check their inboxes. 

Strategies include:

Analyze Engagement Data: Review past campaign data to identify peak engagement times.

Consider Time Zones: Segment your list by geographic location to send emails at appropriate local times.

Test and Refine: Conduct A/B tests to determine optimal send times for different segments.

Example: Fitness Apparel Brand

A fitness apparel brand noticed their global open rates were lagging. By reviewing engagement data, they discovered most of their U.S. customers opened emails around 8 a.m., while European subscribers preferred mid-afternoon. 

They adjusted their flow timing using Klaviyo’s time zone settings and saw a 17% lift in open rates within a month.

Align your send schedule with subscriber habits and you will increase the chances of your emails being opened and read. Simple enough, right?

4. Ensuring Email Deliverability

According to HubSpot, 21% of legitimate emails never reach their destination. Deliverability issues like landing in spam or getting clipped are often caused by poor sender reputation or technical missteps.

To maintain a good sender reputation and avoid spam filters:

Authenticate Your Domain: Implement SPF, DKIM, and DMARC protocols to verify your sending domain’s legitimacy.

Maintain List Hygiene: Regularly clean your email list by removing inactive subscribers and correcting invalid addresses.

Monitor Engagement: Keep an eye on open and click rates; low engagement can signal to ISPs that your emails aren’t wanted.

Avoid Spammy Content: Use a balanced text-to-image ratio and steer clear of trigger words that might flag your email as spam.

Ensuring your emails are technically sound and desired by recipients helps maintain deliverability and open rates.

5. Enhancing Content Quality

Delivering valuable and relevant content keeps subscribers engaged and encourages them to open future emails. Data from Adobe shows that 39% of consumers want emails that are more informative and less promotional.

Focus on:

Provide Value: Share content that addresses your subscribers’ needs, interests, or pain points.

Be Consistent: Maintain a regular sending schedule so subscribers know when to expect your emails.

Use Engaging Visuals: Incorporate high-quality images and a clean design to make your emails visually appealing.

Include Clear CTAs: Guide your readers on the next steps with prominent and compelling calls to action.

Example: DTC Coffee Company

A DTC coffee company saw their engagement drop when they sent the same “10% Off Your Next Order” email three times in a month. 

They pivoted, creating a flow with personalized brewing tips, user-generated content, and a discount code buried in a helpful guide. 

The result? A 22% increase in open rates and a boost in click-throughs.

How to Enhance Content Quality:

Offer value first: Share tips, tutorials, or exclusive behind-the-scenes content.

Use dynamic content to tailor emails based on subscriber behavior or preferences.

Include strong CTAs that guide the reader to the next step.

By consistently delivering quality content, you build trust and anticipation, leading to higher open rates over time.

Look, when it comes to implementing these strategies, it requires ongoing analysis and adaptation. 

Regularly review your email performance metrics to understand what’s working and where there’s room for improvement.

6. Monitoring and Analyzing Performance

Improving your flow open rates isn’t just about implementing a few strategies and hoping for the best. You have to monitor your performance, analyze trends, and refine your approach based on what the data tells you. 

Start With the Right Metrics

When it comes to improving open rates, you need to focus on the numbers that matter:

Open Rates: The percentage of subscribers who opened your email—your baseline for success.

Click-Through Rates (CTR): How many people clicked links inside your email, a sign of engaging content and clear CTAs.

Unsubscribe Rates: Spikes here indicate something’s off—maybe your content isn’t hitting the mark, or you’re emailing too often.

Bounce Rates: High bounce rates mean deliverability issues, often caused by invalid emails or poor sender reputation.

Use A/B Testing to Optimize

Data-driven decision-making is essential for continuous improvement, and A/B testing is one of the best tools to identify what works. By testing one variable at a time, you can make informed changes that boost engagement.

What to Test:

Subject lines: Try something straightforward like “New Arrivals Are Here” vs. something more intriguing like “What’s New? Click to See.”

Timing: Send emails at different times of day to see when your audience is most responsive.

Content: Test a simple text-focused email against one with more visuals to see which performs better.

CTAs: Experiment with different wording, such as “Shop Now” vs. “Learn More.”

Example:A pet supply company A/B tested two subject lines for a cart abandonment flow:

“You Forgot Something in Your Cart ”

“Ready to Check Out? We’ve Got a Treat for You!”The second subject line increased open rates by 18%, proving that playful, pet-themed language resonated better with their audience.

Identify Trends Over Time

Email marketing is not a “set it and forget it” operation. Trends in your data can reveal opportunities or signal problems before they snowball. 

For example:

Are certain flows (like your welcome series) outperforming others?

Does seasonality impact engagement, with higher open rates during certain months?

Are open rates dropping for a specific audience segment?

By reviewing these trends regularly, you can make proactive adjustments rather than scrambling to fix issues after they’ve hurt performance.

Make Tracking a Habit

Set aside time each month to review your email performance. 

It doesn’t need to be a deep dive every time. Sometimes even a quick glance at your flow performance dashboard in Klaviyo is all it takes to spot trends or outliers.

And don’t forget that your audience evolves. What worked six months ago might not resonate now. Regular tracking and testing ensure you stay aligned with your subscribers’ preferences.

By making performance monitoring part of your workflow and pairing it with the other strategies outlined in this guide, you’ll have everything you need to keep your Klaviyo flows improving and your open rates climbing.

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Say Goodbye to Low Flow Open Rates in Klaviyo

Low open rates in Klaviyo flows don’t have to be the bane of your ecommerce marketing efforts. 

With the right strategies and a commitment to improvement, you can turn things around and make your email flows a powerful tool for driving engagement and revenue.

Here’s a quick recap of the 6 key strategies we covered:

Craft Compelling Subject Lines: Hook your readers with subject lines that are clear, engaging, and relevant. Test, tweak, and repeat.

Segment Your Audience: Send the right message to the right people at the right time.

Optimize Send Times: Align your emails with when your audience is most active for maximum impact.

Ensure Deliverability: Keep your emails out of spam folders and in front of your subscribers with technical best practices.

Enhance Content Quality: Deliver value, stay consistent, and always provide a clear call to action.

Monitor and Analyze Performance: Regularly track your results, test new ideas, and refine your approach based on the data.

Remember, improving open rates isn’t a one-and-done deal. It’s an ongoing process that requires you to adapt to your audience’s evolving preferences and behaviors. What works today might need a refresh tomorrow so keep experimenting, learning, and optimizing.

The best part? 

Each small improvement adds up, leading to stronger engagement, happier customers, and more conversions over time. So, start implementing these strategies today. 

Your inbox (and your revenue) will thank you for it.

Ready to improve your flow rates in Klaviyo? Get a free Klaviyo signal audit and see how we can help improve your email performance today!

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FAQs About Low Flow Open Rates in Klaviyo

What are open rates in Klaviyo flows?

Open rates in Klaviyo flows refer to the percentage of recipients who open your automated emails. It’s calculated by dividing the number of unique opens by the total number of emails delivered. This metric helps you understand how effective your subject lines and preview text are in grabbing attention.

What is a good open rate for Klaviyo flows?

A good open rate for Klaviyo flows typically falls between 20-30%, depending on your industry and audience. Flows, being behavior-triggered, often perform better than broadcast campaigns because they’re more personalized and relevant to the recipient’s actions.

Why are my Klaviyo flow open rates so low?

Low open rates can result from several factors, including unengaging subject lines, poor audience segmentation, deliverability issues (like landing in spam folders), or irrelevant content. Reviewing these areas and testing improvements can help boost your rates.

How does Klaviyo calculate open rates?

Klaviyo calculates open rates by dividing the number of unique email opens by the number of emails delivered and multiplying by 100. This percentage reflects the effectiveness of your email in capturing the recipient’s attention.

What factors affect open rates in Klaviyo flows?

Open rates are influenced by subject lines, preview text, send times, audience segmentation, email deliverability, and content relevance. Even external factors, like inbox clutter during busy seasons, can play a role.

How can I improve the subject lines in my Klaviyo flows?

To improve subject lines, make them concise, clear, and relevant. Personalize them with the recipient’s name or preferences, create urgency with time-sensitive language, and avoid spam triggers like excessive punctuation or all caps. A/B testing is key to finding what resonates with your audience.

Does segmentation impact open rates in Klaviyo flows?

Yes, segmentation has a huge impact. Sending targeted emails based on customer behavior, demographics, or lifecycle stage ensures the content is relevant, which significantly increases the likelihood of an open.

What is A/B testing in Klaviyo, and how does it help improve open rates?

A/B testing in Klaviyo involves creating two versions of an email (varying one element, like the subject line) and sending them to different segments of your audience. By comparing the results, you can identify which approach drives higher open rates and optimize future flows accordingly.

What are the most common deliverability issues in Klaviyo?

Common deliverability issues include emails landing in spam folders, high bounce rates due to invalid addresses, poor sender reputation, and overly large email files that get clipped. Ensuring proper domain authentication (SPF, DKIM, DMARC) and maintaining list hygiene can help.

How does email clipping affect open rates in Klaviyo flows?

Email clipping occurs when the size of an email exceeds 102KB, leading to part of it being hidden in certain email clients. This can remove tracking pixels, making opens harder to track and skewing your open rate data.

What is the role of time zones in Klaviyo email open rates?

Time zones play a critical role. If you send an email at 9 a.m. for a global audience, some recipients may receive it in the middle of the night. Using Klaviyo’s time zone settings ensures emails arrive at optimal times for engagement.

Why does my welcome flow have a higher open rate than other flows?

Welcome flows often have higher open rates because they’re sent to new subscribers who are highly engaged. These emails also set the tone for your relationship, making recipients more likely to open them.

How often should I monitor Klaviyo flow open rates?

Regular monitoring is key. Check your flow analytics monthly to track performance trends, identify underperforming emails, and test new strategies. Frequent reviews allow you to make timely adjustments.

What is the best time to send Klaviyo flow emails?

The best time to send emails depends on your audience. Studies suggest midweek mornings (like Tuesday at 10 a.m.) often perform well, but your own audience data might reveal different peak times. A/B testing can help pinpoint optimal times.

How does sender reputation impact open rates in Klaviyo?

Sender reputation affects whether your emails reach the inbox or get flagged as spam. A high reputation is built by consistent engagement, low bounce rates, and good email practices. Poor reputation can result in low deliverability and poor open rates.

Can cleaning my email list improve open rates in Klaviyo?

Yes, regularly removing inactive subscribers and invalid email addresses improves your open rates by focusing your efforts on engaged recipients. This also enhances your sender reputation.

What is Smart Send Time in Klaviyo, and how does it work?

Smart Send Time is a Klaviyo feature that identifies the best time to send emails based on your audience’s engagement patterns. By analyzing historical data, it helps you optimize timing for better open rates.

Why do my promotional flows have lower open rates than transactional ones?

Transactional emails like order confirmations or shipping updates are expected and highly relevant, leading to higher open rates. Promotional flows can feel less urgent, so crafting compelling subject lines and relevant offers is essential.

How can I use dynamic content to improve open rates in Klaviyo?

Dynamic content personalizes emails based on the recipient’s behavior or preferences. For example, including product recommendations based on past browsing can make emails more relevant and increase open rates.

Does Klaviyo track opens on mobile devices?

Yes, Klaviyo tracks opens on both desktop and mobile devices. This data can help you understand how your audience engages with your emails and optimize designs for mobile-first experiences.

What are some examples of bad subject lines that hurt open rates?

Examples include vague phrases like “Check This Out!” or overly aggressive ones like “BUY NOW!!!”. These lack relevance or feel spammy, leading recipients to ignore or delete the email.

How do emojis in subject lines affect Klaviyo open rates?

Emojis can increase open rates by adding personality and visual interest, but they should be used sparingly. Overusing emojis or selecting irrelevant ones can make your email appear unprofessional.

Why is personalization important for Klaviyo flow open rates?

Personalization, such as including the recipient’s name or tailored content, makes the email feel relevant and engaging. Emails with personalized subject lines have been shown to achieve 26% higher open rates.

How can I identify underperforming flows in Klaviyo?

Use Klaviyo’s flow analytics to compare open rates, click-through rates, and conversions across different flows. Look for flows with below-average performance and focus on testing improvements.

What’s the biggest mistake marketers make with Klaviyo flows?

The biggest mistake is “set it and forget it.” Flows need regular monitoring, testing, and optimization to stay effective as your audience evolves and expectations shift.
The post Why Your Klaviyo Flow Open Rates Are Low (And How to Fix Them) appeared first on Customers.ai.

Black Friday might be over, but the party doesn’t have to stop. 

This year, Black Friday brought in a jaw-dropping $74.4 billion globally – a 5% increase year over year! 

While you might have cashed in on some of that, there’s a good chance a lot of that traffic just browsed, hesitated, or abandoned their carts.

The good news?

They’re still out there and they’re still interested. 

That means now is the time to use smart retargeting strategies to turn those window shoppers into buyers and keep the momentum rolling through the holiday season. 

Stick with me and I’ll show you exactly how to bring these ready-to-buy shoppers back, convert them, and start 2024 strong. Let’s go!

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Step 1: Identify Your Black Friday Audience Segments

Alright, so we know why retargeting is a must, now let’s talk about the how. 

The key to turning Black Friday traffic into post-holiday sales gold is all about audience segmentation. 

Not everyone who visited your site is in the same boat, so blasting the same ad to everyone? Fake news. 

Instead, let’s break your audience into three juicy, conversion-ready segments:

Category 1: The Click-and-Bail Crowd

These are the folks who browsed your site, clicked on a product (or maybe a few), and then peaced out before hitting “Buy Now.” 

They’re curious, but they need a little more convincing. 

For this group, your retargeting ad could feature the products they clicked on with a nudge like, “Still thinking about this? Don’t wait too long—it’s selling fast!”

Category 2: Cart Abandoners (aka So Close, Yet So Far)

These shoppers took things a step further. They added items to their cart but didn’t follow through. Maybe they got distracted, or maybe they needed a push to seal the deal. 

Retargeting these customers with dynamic ads showing their abandoned cart items can work wonders. Throw in an extra incentive, like free shipping or a limited-time discount, to sweeten the deal.

Category 3: First-Time Buyers Turned VIP Potentials

Let’s not forget about those who did buy during Black Friday. 

Sure, they converted, but don’t let that be the end of their journey. 

This group is perfect for upselling or introducing complementary products. Did they buy a sweater? Show them the scarf that goes with it. Just bought a skincare kit? Hit them with a deal on your matching night cream.

Step 2: Segment Like a Pro Using Customers.ai and Meta Ads

Now that you know who your segments are, it’s time to identify and build those segments. 

That’s where Customers.ai and Meta Ads come in. 

Unlike the Facebook Pixel, which can miss key visitors, Customers.ai tracks and enriches your audience data, giving you the insights you need to create targeted, high-converting segments and campaigns. 

Here’s your step-by-step breakdown:

Capture More Visitor Data with Customers.ai

Forget relying solely on the Facebook Pixel, which only picks up a portion of your site visitors. Customers.ai website visitor identification identifies up to 30% more visitors, including return users and those not logged in, thanks to advanced tracking technology. 

This gives you a bigger pool to retarget and more opportunities to convert.

What it does: Tracks customer journeys from first touchpoint to final interaction, identifying behavior, demographics, and repeat visits.

Why it matters: You can retarget visitors who would otherwise remain anonymous to Meta Ads.

Build Your Black Friday Audience Segments

Customers.ai takes the guesswork out of audience segmentation. Here’s how to divide your traffic into actionable groups:

Clickers Who Didn’t Convert:

Identify visitors who browsed specific products or categories but didn’t make a purchase.

Create a segment in Customers.ai for “product viewers” and sync it directly to Meta Ads as a Custom Audience.

Abandoned Cart Shoppers:

Customers.ai tracks who added items to their cart but didn’t complete checkout.

Segment these users, then push this data to Meta Ads for dynamic cart abandonment campaigns.

First-Time Buyers:

Segment new customers from Black Friday and use Customers.ai to analyze their purchase behavior.

Build an upsell strategy for complementary products and sync it to Meta Ads for personalized follow-up ads.

Sync Your Audience Segments Directly to Meta Ads

With Customers.ai, there’s no need for clunky manual uploads. It seamlessly integrates with Meta Ads, allowing you to sync your audience segments in real-time. Here’s how:

Go to your Customers.ai dashboard and select your audience segment.

Click the “Sync to Facebook” button to push the data directly into your Meta Ads account.

Your audiences are now ready to use in Custom Audiences, Lookalike Audiences, or Retargeting campaigns.

Personalize Your Campaigns

Once your segments are synced, craft campaigns that speak directly to each group. 

Customers.ai helps you identify key demographics and behavioral patterns to tailor your messaging.

For Clickers: Show carousel ads featuring the products they browsed, paired with enticing offers like, “Still thinking about this? Grab it now with 15% off!”

For Cart Abandoners: Use dynamic product ads that show their abandoned items with a message like, “Your cart misses you! Complete your purchase today and enjoy free shipping.”

For First-Time Buyers: Highlight complementary products or offer exclusive discounts for their next purchase.

Track, Optimize, and Scale

Customers.ai doesn’t stop at segmentation and when combined with Meta, you can get detailed analytics on campaign performance. Use this data to:

Monitor Conversion Rates: See which audience segments are delivering the highest ROI.

Optimize Your Ads: Test different creatives, offers, and messaging for each segment.

Scale Your Winners: Use Lookalike Audiences in Meta Ads to find more customers like your best performers.

When you use Customers.ai alongside Meta Ads, you’re not just running ads, you’re making your campaigns sharper, more strategic, and fully maximizing all that hard-earned Black Friday traffic!

Now let’s get into how to craft those winning post-holiday offers. 

Step 3: Craft Irresistible Post-Holiday Offers

The post-holiday period is prime time to re-engage shoppers and squeeze every ounce of value out of your Black Friday traffic. 

But customers may be a little fatigued after the shopping frenzy. That’s why your offers need to be tailored, enticing, and timed just right. 

Here are some ideas to make sure your post-holiday deals hit the sweet spot.

1. Limited-Time Discounts on Products They Loved

Remember those click-and-bail shoppers from Black Friday? 

They were interested enough to check out your products, but something held them back. Now’s your chance to close the deal with a targeted offer that feels personal.

What to do: Offer a limited-time discount (e.g., 10–15%) on the exact product they viewed or similar items.

How to position it: “Don’t miss out on what caught your eye! Get 15% off your favorite picks, but only for the next 48 hours.”

Why it works: By keeping the discount small but time-sensitive, you create urgency without devaluing your products.

Example: Sephora frequently sends targeted emails to customers who browsed specific products but didn’t buy, offering a 15% discount with a clear time limit. Their emails also include product reviews or top-rated tags to build trust and encourage conversions.

2. Free Shipping on Abandoned Cart Items

For cart abandoners, the barrier to checkout might have been something as simple as shipping costs. Removing that hurdle can be the nudge they need to finish their purchase.

What to do: Retarget these customers with dynamic ads showcasing their cart items and offering free shipping.

How to position it: “Your favorites are waiting! Complete your order now and enjoy free shipping—today only.”

Why it works: Free shipping is a low-cost incentive for you that can feel like a huge value to the customer, especially after the holidays when budgets are tight.

Example: ASOS sends cart abandoners a personalized email featuring their exact items left in the cart, paired with a free shipping offer. They include a bright call-to-action button that says, “Get it now with free delivery!” to make the process effortless.

3. Bundles and Complementary Products for Past Buyers

Your Black Friday buyers are a goldmine for post-holiday sales. They’ve already shown they love your brand, so why not offer them products that pair perfectly with what they bought?

What to do: Create bundles or cross-sell complementary items (e.g., “Complete Your Set” deals).

How to position it: “Loved your new boots? Pair them with our bestselling cozy socks and save 20% on the bundle!”

Why it works: Offering complementary items adds value to their purchase while increasing your average order value.

Example: Apple is the master of this. After a customer purchases an iPad, Apple follows up with recommendations for compatible accessories like the Apple Pencil or Magic Keyboard, often bundling them with discounts or free engraving options.

4. Post-Holiday Clearance Sales

The post-holiday period is a great time to clear out seasonal inventory while keeping your customers engaged.

What to do: Offer exclusive discounts on leftover holiday items or other seasonal products.

How to position it: “Our post-holiday clearance is here! Snag your favorites before they’re gone—up to 40% off.”

Why it works: Customers love a good deal, and you get to free up inventory space for the new year.

Example: Target’s post-holiday sales are legendary, often offering steep discounts on holiday decor, wrapping paper, and seasonal snacks. They also highlight the limited availability, encouraging shoppers to act fast.

5. Reward Programs and VIP Perks

Turn first-time or repeat buyers into loyal customers by introducing a post-holiday reward program or exclusive perks.

What to do: Offer discounts, early access to new products, or loyalty points for future purchases.

How to position it: “We’re spreading the holiday cheer! Earn double rewards points on all purchases this week.”

Why it works: Loyalty programs build a long-term relationship with your customers and incentivize repeat purchases.

Example: Starbucks regularly rewards its loyalty program members with bonus stars during specific promotional periods, encouraging customers to visit more frequently and earn rewards faster.

6. Create a Sense of Urgency Without Being Overwhelming

Post-holiday shoppers are in a delicate spot. They’ve just navigated the chaos of Black Friday, and their inboxes are still recovering from an avalanche of “last chance” emails. 

The key to using urgency is to strike the right balance between motivating action but not being pushy or exhausting. Here’s how to nail it.

Clear Timeframes: Specify the duration of the offer (e.g., “Offer ends in 48 hours”) to create a sense of urgency.

Highlight Scarcity: If applicable, mention limited stock to encourage prompt action.

Avoid Overcommunication: Limit the frequency of urgent messages to prevent customer fatigue.

Provide Value: Ensure that the urgency aligns with genuine value to the customer, enhancing trust and satisfaction.

With tailored offers, a thoughtful approach, and the right sense of urgency, you can turn one-time buyers into loyal customers and drive those end-of-year sales sky-high!

Step 4: Put It All Together for Killer Post-Holiday Retargeting Campaigns

By now, you’ve segmented your audience, tailored your offers, and know exactly how to approach post-Black Friday shoppers. 

But how do you bring it all together into a killer retargeting campaign? Let’s recap the key elements to ensure your strategy is airtight and your ads hit all the right notes.

1. Leverage Your Custom Audiences

Your Custom Audiences are the foundation of your retargeting efforts. 

These include your click-and-bail shoppers, cart abandoners, and first-time buyers. Make sure you’ve uploaded or synced your segmented data into Meta Ads, so you’re ready to target each group with laser precision.

Pro Tip: Don’t forget to expand your reach with Lookalike Audiences. Meta Ads can help you find new customers who closely resemble your best performers, turning your existing data into even more opportunities.

2. Nail Your Ad Placements

Where you show your ads is just as important as who you’re targeting. 

Meta Ads offers a variety of placements, and choosing the right ones for each audience can make or break your campaign.

Facebook Feed: Great for eye-catching carousel ads or product videos. Ideal for cart abandoners or first-time buyers who are already familiar with your brand.

Instagram Stories: Perfect for dynamic ads that feel casual and native to the platform. Works well for click-and-bail shoppers.

Facebook and Instagram Reels: Highly engaging for showcasing products in action or creating a sense of urgency with quick, visually compelling offers.

Audience Network: Expand your reach by targeting users across Meta’s partner apps and websites—ideal for Lookalike Audiences.

3. Test Creatives. Then Test Again.

No matter how good your strategy is, you’ll never know what resonates most until you test it. A/B testing is your best friend here.

What to Test:

Headlines and ad copy (e.g., “Your cart misses you!” vs. “Still thinking about these?”).

Visuals (static images, videos, or carousels).

Call-to-action buttons (e.g., “Shop Now” vs. “Claim Your Discount”).

Why It Matters: Testing helps you refine your approach and ensures you’re not wasting budget on ads that don’t convert.

4. Use Creative Formats That Drive Action

Your audience segments have unique needs, so your ad creatives should reflect that. Here are some winning formats to consider:

Dynamic Ads: Show users the exact products they viewed or abandoned in their carts. Pair these with a time-sensitive offer to drive conversions.

Carousel Ads: Highlight multiple products or complementary items. Great for first-time buyers or showcasing bundles.

Video Ads: Engage users with short, visually appealing clips that highlight product benefits or create a sense of urgency.

Example: A carousel ad might show a customer their abandoned cart items alongside complementary products, while a video ad could showcase glowing customer reviews to build trust and excitement.

5. Analyze, Optimize, and Scale

This is not a set it and forget it kind of thing. After all, your campaign doesn’t stop once it’s live. 

Keep an eye on performance metrics like click-through rates (CTR), conversion rates, and return on ad spend (ROAS). Use this data to optimize your campaigns:

Pause underperforming ads and reallocate budget to your top-performing creatives.

Test new segments or offers to keep things fresh.

Scale successful campaigns by increasing budgets or expanding to Lookalike Audiences.

When you put all of these elements together, you’re creating a can’t-miss retargeting strategy that is relevant, personal, and intentional. 

Don’t Let Your Black Friday Traffic Go Cold

Black Friday might be over but your opportunity to capitalize on that traffic is just getting started. 

Retargeting gives you the chance to reconnect with shoppers who showed interest, boost your post-holiday revenue, and even turn one-time buyers into loyal customers. 

But remember – the clock is ticking and those shoppers won’t wait forever. 

Whether it’s through dynamic ads, tailored offers, or upselling to your Black Friday buyers, now’s the time to act. Don’t let all that valuable holiday traffic slip through your fingers.

Start setting up your retargeting campaigns today and turn Black Friday browsers into loyal buyers tomorrow. Your next big win is just a click away!

Ready to take your Black Friday Meta retargeting strategies to the next level? Start your free trial today and get 500 contacts free!

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Advance your marketing performance with Sales Outreach School, a free tutorial and training area for sales pros and marketers.

The post Post-Holiday Power Moves: Meta Retargeting Strategies to Bring Back Your Black Friday Shoppers appeared first on Customers.ai.

Recommender systems (RS) are essential for generating personalized suggestions based on user preferences, historical interactions, and item attributes. These systems enhance user experience by helping individuals discover relevant content, such as movies, music, books, or products tailored to their interests. Popular platforms like Netflix, Amazon, and YouTube leverage RS to deliver high-quality recommendations that improve content discovery and user satisfaction. Collaborative Filtering (CF), a widely used technique, analyzes user-item interactions to identify patterns and similarities. However, CF faces challenges such as scalability, data sparsity, and the cold-start problem, which limit its effectiveness. Addressing these issues is crucial for improving recommendation accuracy and ensuring consistent performance.

Research on RS has increasingly incorporated advanced deep learning (DL) techniques to overcome traditional limitations. Studies have explored various approaches, such as CNNs, RNNs, and hybrid models, that combine collaborative filtering with DL architectures. Techniques like autoencoders, GNNs, and reinforcement learning have also been applied to improve recommendation relevance and adaptability. Recent works focus on privacy-aware RS, multimodal analysis, and time-sensitive recommendations, demonstrating the potential of DL to handle sparse data, enhance personalization, and adapt to dynamic user preferences. These innovations address critical gaps in RS, paving the way for more efficient and user-centric recommendation systems.

Researchers from Mansoura University have introduced the HRS-IU-DL model, an advanced hybrid recommendation system that integrates multiple techniques to enhance accuracy and relevance. The model combines user-based and item-based CF with Neural Collaborative Filtering (NCF) to capture non-linear relationships, RNN for sequential pattern analysis, and CBF using TF-IDF for detailed item attribute evaluation. Evaluated on the Movielens 100k dataset, the model demonstrates superior performance across metrics like RMSE, MAE, Precision, and Recall, addressing challenges such as data sparsity and the cold-start problem while significantly advancing recommendation system technologies.

The study enhances RS by integrating NCF with CF and combining RNN with Content-Based Filtering (CBF). The hybrid model (HRS-IU-DL) leverages user-item interactions, item attributes, and sequential patterns for accurate, personalized recommendations. Using the Movielens dataset, the approach incorporates matrix factorization, cosine similarity, and TF-IDF for feature extraction, alongside deep learning techniques to address cold-start and data sparsity challenges. Privacy-preserving methods ensure user data security. The model effectively captures complex user behaviors and temporal dynamics, improving recommendation accuracy and diversity across e-commerce, entertainment, and online platforms.

The proposed hybrid model (HRS-IU-DL) was evaluated on the Movielens 100k dataset, split 80–20 for training and testing, and compared against baseline models. Initial data exploration included rating distribution and statistical analysis to address sparsity and imbalance—preprocessing steps involved normalization, privacy-preserving techniques, and filtering user and movie IDs. The model combines CF, NCF, CBF, and RNN to leverage user-item interactions and item properties. Hyperparameter tuning enhanced performance metrics, achieving RMSE of 0.7723, MAE of 0.6018, Precision of 0.8127, and Recall of 0.7312. It outperformed baseline models in accuracy and efficiency, demonstrating superior recommendation capabilities.

In conclusion, the HRS-IU-DL hybrid model integrates CF, CBF, NCF, and RNN to improve recommendation accuracy by addressing limitations like data sparsity and the cold-start problem. The system delivers personalized recommendations by leveraging user-item interactions and item properties. Experiments on the Movielens 100k dataset highlight its superior performance, achieving the lowest RMSE and MAE alongside improved Precision and Recall. Future research will incorporate advanced architectures like Transformers, contextual data, and test scalability on larger datasets. Efforts will also focus on enhancing computational efficiency and scalability for real-world applications.

Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. If you like our work, you will love our newsletter.. Don’t Forget to join our 55k+ ML SubReddit.

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In the quest to develop new drugs, the journey from laboratory research to clinical application is complex and expensive. The drug discovery process involves multiple stages, including target identification, drug screening, lead optimization, and clinical trials. Each stage requires a substantial investment of time and resources, leading to a high risk of failure. More specifically, the challenge of predicting a drug candidate’s absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties represents a crucial bottleneck. Without efficient methods for accurately predicting these properties, promising compounds often fail at later stages of development, leading to significant financial losses. Machine learning (ML) offers an opportunity to accelerate drug discovery by predicting properties and behaviors without the need for expensive and lengthy experiments. However, successfully implementing ML in drug discovery requires knowledge across multiple domains, including chemistry, biology, and data science, posing a high barrier to entry for non-experts.

Researchers from the University of Southern California, Carnegie Mellon University, and Rensselaer Polytechnic Institute introduced DrugAgent, a multi-agent framework aimed at automating machine learning (ML) programming in drug discovery. DrugAgent seeks to address the challenges involved in utilizing ML for drug discovery by providing a structured and automated approach. Specifically, DrugAgent leverages Large Language Models (LLMs) to perform tasks autonomously, from data acquisition to model selection, thereby enabling pharmaceutical scientists to benefit from AI without needing extensive coding expertise. DrugAgent systematically explores various ideas and builds domain-specific tools that cater to the unique needs of drug discovery, bridging the gap between theoretical ML potential and practical applications in pharmaceutical research.

DrugAgent consists of two main components: the LLM Instructor and the LLM Planner. The LLM Instructor identifies specific requirements that need domain-specific knowledge and creates suitable tools to meet these requirements. This ensures that the ML tasks align with the complexities of drug discovery, from proper data preprocessing to the correct usage of chemistry-specific libraries. Meanwhile, the LLM Planner manages the exploration and refinement of ideas throughout the ML workflow, enabling DrugAgent to evaluate multiple approaches and converge on the most effective solution. By systematically managing the exploration of diverse ideas, the LLM Planner ensures that DrugAgent is capable of generating and filtering out infeasible solutions based on real-time observations. This automated workflow allows DrugAgent to complete an end-to-end ML pipeline for ADMET prediction, from dataset acquisition to performance evaluation. In a case study using the PAMPA dataset, DrugAgent achieved an F1 score of 0.92 when using a random forest model to predict absorption properties, demonstrating the effectiveness of the framework.

The importance of DrugAgent lies in its ability to lower the barrier for applying ML in drug discovery. The pharmaceutical industry is characterized by highly specialized knowledge requirements, and ML-based drug discovery is no different. General-purpose LLMs, though powerful, often fall short when it comes to the nuances of drug discovery tasks, such as selecting the correct APIs for domain-specific libraries or accurately preprocessing chemical data. This is where DrugAgent excels; it integrates workflows to identify the steps that require specialized expertise and builds the necessary tools to handle them. Additionally, DrugAgent employs a dynamic idea space management system that generates multiple approaches at the beginning and iteratively updates them based on experimental outcomes. By adopting this structured workflow, DrugAgent can automatically determine the most suitable approach for a given task. For instance, in the ADMET prediction case study, DrugAgent evaluated different models, including graph neural networks and pretrained models like ChemBERTa, ultimately selecting the random forest model due to its superior performance. This systematic exploration and tool-building process ensures that DrugAgent can effectively navigate the complexities of drug discovery.

The introduction of DrugAgent represents a significant advancement in the application of AI to pharmaceutical research. By automating complex ML programming tasks, DrugAgent allows pharmaceutical scientists to focus on the strategic aspects of drug discovery, such as hypothesis formulation and result interpretation, rather than dealing with technical implementation challenges. The framework’s ability to achieve high prediction accuracy, as seen in the ADMET prediction task, highlights its potential to improve drug candidate screening and reduce the risk of late-stage failures. The researchers conducted a comparison between DrugAgent and ReAct, a general-purpose LLM-based reasoning and action framework, in automating the ADMET prediction task. The comparison revealed that ReAct struggled with domain-specific integration, such as incorrect API calls and a lack of self-debugging capabilities. On the other hand, DrugAgent systematically addressed these issues, ensuring the successful completion of the entire pipeline without human intervention. These results highlight DrugAgent’s ability to enhance efficiency, reduce costs, and increase the success rate in drug discovery.

In conclusion, DrugAgent offers an automated solution for leveraging machine learning in drug discovery, addressing several key challenges that have traditionally hindered the integration of AI into this field. By incorporating domain-specific knowledge and systematically refining multiple ideas, DrugAgent bridges the gap between general AI capabilities and the specialized needs of pharmaceutical research. The initial success demonstrated by DrugAgent, particularly its ability to autonomously complete an ML pipeline and achieve strong prediction performance, suggests a promising future for AI-driven drug discovery. As the field continues to evolve, DrugAgent provides a foundation for further advancements, ultimately contributing to more efficient, accurate, and cost-effective drug development pipelines.

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Mesh generation is an essential tool with applications in various fields, such as computer graphics and animation, computer-aided design (CAD), and virtual and augmented reality. Scaling mesh generation for converting simplified images into higher-resolution ones requires substantial computational power and memory. Additionally, maintaining intricate details while managing computational resources is challenging. Specifically, models with more than 8000 faces in their 3D structure pose quite a challenge. To address these issues, Researchers at the South China University of Technology, ShanghaiTech University, University of Hong Kong, and

Tencent Hunyuan has developed the Blocked and Patchified Tokenization (BPT) framework, marking a significant advancement in various industries that require scaling mesh generation. The BPT framework aims to achieve high computational efficiency output fidelity. 

Traditional approaches for mesh generation include Delaunay triangulation, heuristic optimization and various machine learning models. To successfully generate a mesh, these conventional models sacrifice detail or resolution when dealing with large-scale datasets due to memory constraints compromising the fidelity of the design. BPT is a novel framework that transforms the mesh generation problem into a token-based framework. Comprehensive tokenization can effectively conserve the essential structural details while reducing the mesh data dimensionality. Moreover, token-based generation is much faster and quickly processes large-scale mesh data while maintaining high fidelity. 

First, BPT breaks down the large mesh into smaller and manageable blocks, which are converted into tokens. These tokens represent various essential features of the mesh. Similar blocks are grouped as patches to further reduce the dimensionality of our data. The next step includes feeding this reduced data to a transformer-based neural network, which generates the 3D mesh iteratively. Focusing on tokenized features rather than raw data minimizes memory usage and improves processing speed without sacrificing fidelity. 

BPT achieves a reduction in sequence lengths of about 75% compared to the original sequences, thus enabling the processing of meshes that have more than 8,000 faces. This large reduction in data volume allows for the creation of much more detailed and topologically accurate 3D models. The work demonstrates significant speed and accuracy improvements over the state-of-the-art techniques. In practice, this is not without its limitations: the research may demand further validation of the approach on a larger set of 3D datasets as well as pose challenges pertaining to its direct integration into existing workflows besides a sizable computational cost with regard to training the neural network.

This research work introduces a new approach to mesh generation, solving severe scalability problems by innovative tactics. BPT marks the emergence of a critical improvement in the processing of large-resolution three-dimensional models. Its impact is wide-ranging because it has the potential to change industries that rely on detailed 3D modeling and simulation. Further research may make it more suitable for a range of applications and reduce any drawbacks identified. This work has been a major milestone in computational geometry and has provided new avenues for advanced capabilities in 3D modeling.

Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. If you like our work, you will love our newsletter.. Don’t Forget to join our 55k+ ML SubReddit.

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