Month: December 2024

Tons of people visit your website daily, but most of them leave without saying a word. No sign-ups, no purchases, not even a friendly “hello.” Just silence. That’s revenue walking out the door!

Now imagine knowing exactly who those visitors are – their names, emails, company info, and more – all before they bounce. Sounds pretty awesome, right?

Here’s the catch. Most visitor identification tools stop short. They give you some data but don’t offer an API to seamlessly connect it with your workflows. 

That’s where Customers.ai comes in. 

With a fully-featured visitor identification API, you can stop guessing and start taking action. 

Let’s break it down.

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What Is a Website Visitor Identification API?

A website visitor identification API gives you direct access to data about who’s visiting your website. 

It pulls details like names, emails, and company information from your traffic and makes it available for use in other tools and platforms you rely on.

Here’s how it works – the API collects visitor data in real-time and integrates it into your CRM, marketing software, or custom workflows. This allows you to act on that information instantly. Whether that’s personalizing a campaign, scoring leads, or alerting your sales team.

Use cases include:

Real-time alerts: Notify your team the moment a high-value prospect visits.

Personalization: Tailor your outreach based on visitor profiles.

Lead qualification: Focus your efforts on the most promising opportunities.

An API makes the process seamless, giving you the power to turn traffic into tangible results without manual effort.

The Big Problem: Most Visitor Identification Solutions Don’t Have APIs

Did you know that none of the major visitor identification providers offer an API? It’s true!

These visitor identification tools look great on the surface but fall short where it really matters. 

While they may give you some basic data about who’s visiting your site, they stop there. 

Why do so many tools skip the API? 

Simple: building and maintaining a reliable API takes resources, expertise, and a commitment to giving users full control over their data. 

Image: MobileAppDaily

Unfortunately, many providers would rather keep things locked into their platforms, forcing you to work around their limitations.

Without an API, you’re left dealing with:

Manual exports: Constantly downloading and uploading spreadsheets – time you’ll never get back.

Lack of customization: You’re stuck using their system, even if it doesn’t fit your workflow.

Siloed data: Your information can’t talk to other tools, leaving you with disconnected processes.

This is where Customers.ai changes the game. 

Unlike other tools, Customers.ai offers a fully-featured visitor identification API that puts you in control. 

It integrates directly with your CRM, marketing software, and other systems so you can stop wasting time and start turning data into results.

What Makes a Great Visitor Identification API?

When it comes to visitor identification APIs, you need one that meets your specific needs. Here’s what to look for in a top-notch solution:

Real-time Data Access

The best APIs deliver anonymous visitor data the moment someone lands on your site. Waiting hours (or even minutes) means missed opportunities. 

Real-time access ensures you can act instantly. Whether it’s notifying your sales team or personalizing a visitor’s experience, time is of the essence.

Easy Integration

A great API plays well with others. It should connect seamlessly with your existing tools (think CRMs, marketing platforms, analytics dashboards, etc.) without requiring a degree in rocket science to set up.

Security and Compliance

Handling visitor data comes with responsibility. Look for APIs that prioritize compliance with regulations like GDPR and CCPA to protect both your business and your customers.

Scalability

As your business grows, so should your API. A solid solution can handle increasing traffic and data without breaking a sweat, ensuring it supports your long-term goals.

The term ‘API’ is thrown around a lot but remember that they are more than just a convenient feature – they’re the key to unlocking the full potential of your visitor data. They eliminate silos, streamline workflows, and make sure you’re getting the most value from every click on your website. 

And a great visitor identification API doesn’t just capture data. It takes that data, enriches it, and transforms it into opportunities you can act on immediately

How to Choose the Right Visitor Identification API for Your Business

Not all APIs will be the right fit for your business, so choosing the best one requires a thoughtful approach. Here’s what to consider:

Assess your needs: Start by looking at your business size, goals, and existing tech stack. Are you focused on lead generation, sales, or customer insights? Do you need a solution that integrates with specific tools or scales as you grow? Understanding your requirements will help narrow down your options.

Evaluate API documentation and support: Clear, detailed documentation is non-negotiable. It should be easy for your developers to get started without hitting roadblocks. Bonus points if the provider offers active support to troubleshoot issues or guide you through complex integrations.

Prioritize updates and reliability: A solid API isn’t static. It evolves with the times. Look for a provider that actively updates their API to keep pace with new tech and regulations. Reliability is just as crucial. You need an API that performs consistently, even during peak traffic.

By considering these factors, you’ll be able to choose an API that fits seamlessly into your business, keeps your data flowing, and delivers the results you need. 

When in doubt, go for a solution like Customers.ai, where reliability, flexibility, and developer support are baked in.

Spotlight: The Customers.ai Website Visitor Identification API

When it comes to visitor identification APIs, Customers.ai is in a league of its own. 

While most solutions stop short of offering robust API functionality, Customers.ai gives you the tools you need to connect, act, and grow…all without breaking a sweat.

Here’s what makes it stand out:

Fully-featured API for developers: The Customers.ai API isn’t just a bolt-on feature, it’s a comprehensive solution built to adapt to your needs. Whether you’re syncing data with your CRM or integrating with custom workflows, it’s designed to give developers full flexibility.

Seamless integrations: Customers.ai plays nice with the tools you already use, from CRMs to marketing platforms. Its API ensures smooth, hassle-free connections, eliminating data silos and manual processes.

Real-time data sync: With Customers.ai, you’ll never miss a moment. The API captures visitor information in real time, giving you the power to act instantly – whether that means scoring a lead or triggering personalized outreach.

How Businesses Are Using Customers.ai

Lead scoring: Automatically prioritize high-value leads based on visitor activity and profile details.

Personalized outreach: Send tailored messages to prospects at the perfect moment, increasing engagement and conversions.

Sales team alerts: Notify your team in real-time when key prospects visit, so they’re ready to close deals faster.

Want to see it in action? Check out the API page to explore its full capabilities and start turning your website traffic into sales.

Why Visitor Identification APIs Are a Must-Have for Staying Competitive

If you’re serious about getting the most out of your website traffic, an API isn’t optional – it’s essential. 

A great visitor identification API lets you turn anonymous clicks into meaningful connections, enabling you to personalize your outreach and automate your workflows.

The Customers.ai API takes it a step further, giving you real-time access to actionable data and seamless integration with your tools. 

It’s not just about knowing who’s on your site, it’s about using that knowledge to grow your business.

Ready to stop guessing and start connecting? Talk to our team about getting access to our API.

See Who Is On Your Site Right Now!

Get names, emails, phone numbers & more.

Try it Free, No Credit Card Required

Start Your Free Trial

Important Next Steps

See what targeted outbound marketing is all about. Capture and engage your first 500 website visitor leads with Customers.ai X-Ray website visitor identification for free.

Talk and learn about sales outreach automation with other growth enthusiasts. Join Customers.ai Island, our Facebook group of 40K marketers and entrepreneurs who are ready to support you.

Advance your marketing performance with Sales Outreach School, a free tutorial and training area for sales pros and marketers.

Website Visitor Identification API FAQs

What is the primary benefit of a visitor identification API?

A visitor identification API lets you go beyond anonymous traffic data, providing actionable insights like visitor names, email addresses, and company details. This enables you to personalize outreach, score leads, and automate workflows, ultimately increasing conversions and reducing manual work.

How does a visitor identification API collect data?

Visitor identification APIs collect data in real time using methods like IP matching, cookies, or third-party integrations. The API processes this data, matches it to detailed visitor profiles, and delivers it directly to your preferred tools. This ensures your team can act instantly, without delays or manual processes.

Is a visitor identification API secure?

Yes, if the API follows strict data protection protocols. Look for solutions that comply with GDPR and CCPA, offer encrypted connections, and provide clear policies on data storage and usage. These features ensure that visitor information is handled responsibly, protecting both your business and your customers.

What industries benefit most from visitor identification APIs?

Ecommerce, B2B sales, SaaS, and even healthcare companies can benefit. In ecommerce, for instance, identifying visitors can lead to personalized product recommendations and higher conversions. For B2B, the API can connect website traffic to decision-makers, speeding up the sales cycle.

What kind of data can a visitor identification API provide?

Basic details: Names, email addresses, phone numbers.

Company information: Industry, size, location, and revenue.

Behavioral data: Pages visited, time spent, and actions taken.This breadth of information allows teams to understand and act on visitor intent effectively.

How does an API improve lead scoring?

APIs deliver precise, real-time data that can be integrated into lead scoring systems. For example, if a visitor from a high-value company views your pricing page, the API can alert your team immediately, assigning a higher score to prioritize outreach.

How do APIs support personalization?

With real-time access to visitor data, you can dynamically adjust website content, emails, or ad campaigns. For example, 80% of consumers are more likely to make a purchase when brands personalize their experiences, and APIs are the backbone of making that happen.

Are visitor identification APIs scalable?

Yes, most APIs are designed to grow with your business. They can handle increasing traffic volumes without compromising performance, ensuring you maintain the same level of data accuracy and integration no matter how much your website scales.

What does real-time data mean in the context of APIs?

Real-time data means you’re getting visitor insights as they happen. This is critical for businesses looking to engage visitors before they leave. For example, using real-time data, a sales team can instantly receive alerts about high-value leads and act on them without delay.

How do APIs handle compliance with data privacy laws?

Reputable APIs follow strict guidelines to remain compliant with GDPR, CCPA, and other regulations. This includes offering opt-out options for visitors, encrypting data transmissions, and ensuring that any collected data is stored and used responsibly.

How do APIs compare to standalone visitor identification tools?

While standalone tools often trap data in their platforms, APIs allow you to integrate visitor information directly into your existing systems. This eliminates manual exports, ensures data consistency, and supports real-time actions, making them far more versatile and effective.

How do APIs improve marketing automation?

By integrating visitor data into automation tools, APIs let you trigger workflows based on visitor behavior. For example:

Send a follow-up email to visitors who abandon their cart.

Notify sales when a high-value lead visits your site.

Add visitors to segmented retargeting campaigns automatically.

What role do APIs play in retargeting?

APIs allow you to collect and send visitor data to advertising platforms for retargeting. For instance, visitors who view a product but don’t purchase can be retargeted with personalized ads across Facebook or Google, increasing the likelihood of conversion by up to 70%.

What should I look for in API documentation?

Good API documentation should include:

Clear setup instructions with examples.

Comprehensive endpoint details.

Error-handling guidelines.

Contact information for developer support.

These features ensure smooth implementation and minimize downtime during integration.

How can APIs reduce manual workflows?

APIs eliminate repetitive tasks like downloading data or importing spreadsheets. Instead, they automate the transfer of information between tools, freeing up time for your team to focus on strategy rather than admin work.

How does a visitor identification API support sales teams?

By providing real-time alerts about visitor activity, APIs ensure sales teams never miss a lead. For example, if a key decision-maker from a target account visits your pricing page, the API can instantly notify the appropriate sales rep to follow up.

How do APIs impact ROI?

Businesses using APIs for visitor identification see significant returns. For example, personalized campaigns powered by APIs have been shown to drive up to 760% more revenue than generic campaigns. This level of optimization turns traffic into measurable results.

What are the risks of not using an API?

Without an API, businesses risk losing valuable time and opportunities. Manual data exports, siloed information, and delayed responses to visitor behavior can lead to missed conversions and reduced efficiency.

How does an API fit into an omnichannel strategy?

APIs unify visitor data across platforms, ensuring consistent messaging and personalization. For example, they allow you to sync data between your website, email campaigns, and ads, creating a seamless customer experience that boosts engagement and loyalty.

Why is Customers.ai’s API unique?

Customers.ai offers one of the few fully-featured visitor identification APIs on the market. It integrates seamlessly with CRMs, marketing tools, and workflows while providing real-time data, robust documentation, and excellent developer support, making it a standout solution for businesses serious about turning traffic into results.

The post Website Visitor Identification APIs: What You Need to Know appeared first on Customers.ai.

It’s a sad fact that 98% of your website visitors will leave without doing anything. No purchases, no sign-ups, nothing. 

That’s a massive chunk of potential revenue slipping through the cracks and most brands don’t even know who those visitors are.

Now throw in privacy updates that block cookies, limit tracking, and make data harder to gather. Suddenly, knowing who’s on your site and what they want feels impossible.

This is where real-time visitor identification comes in. 

Visitor identification is not just about collecting data – it’s about spotting the humans behind the clicks, learning what they want, and giving it to them before they bounce. 

And the best part? You don’t need to break any privacy laws or feel like you’re sneaking around. With the right tools and strategies, you can keep it ethical and effective. 

Ready to finally meet your visitors in real-time? Let’s dive in.

Unlock High-Intent Leads Hiding on Your Site

Book a demo of Customers.ai’s U.S. website visitor identification, customer journey insights and remarketing platform to skyrocket conversions and sales.

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The Visitor Identification Crisis

Hundreds, maybe thousands, of people are browsing your site every day. They’re clicking around, checking out products, and maybe even adding items to their cart. But you have no idea who they are.

Without knowing who’s visiting, you’re left guessing. 

Are they returning customers looking for their next purchase? First-timers trying to make up their minds? High-intent shoppers or just window browsers? Who knows?!

This lack of visibility leads to some big missed opportunities:

Anonymous browsers who never convert: You’re losing potential buyers because you don’t know how to guide them to the checkout.

Lost leads: Visitors leave and you don’t even have the chance to follow up with a well-timed nudge.

Generic marketing: Without real insights, you’re stuck serving the same, uninspiring messaging to everyone, regardless of their interests.

And that’s the thing – today’s customers expect more than generic messaging. They want experiences tailored to their needs, not a one-size-fits-all approach. 

In fact, 71% of consumers expect companies to deliver personalized interactions and 76% get frustrated when this doesn’t happen.

Real-time visitor identification flips the script though! 

Instead of chasing anonymous visitors, you’re identifying real people in real-time. You’re understanding intent, you’re tracking journeys, you’re seeing engagement – you are getting real data that allows you to engage your visitors and give them the experience they so crave.

How Real-Time Visitor Identification Changes the Game

Let’s ditch the guesswork. With real-time visitor identification, you’re making moves while the visitor is still on your site. 

Here’s what that can look like:

Spotting a repeat visitor and making them feel like a VIP

Imagine Sarah, who’s been on your site twice this week eyeing the same handbag. As soon as she comes back, your system recognizes her as a return visitor.

A personalized email is sent 30 minutes later – “Welcome back, Sarah! Take 10% off your favorite bag today only.” 

That’s the kind of nudge that turns indecision into action.

Leveraging return visitor data with Customers.ai and Klaviyo:

Customers.ai can identify a visitor who’s been on your site multiple times and automatically sync that data with Klaviyo. 

For example, Klaviyo can trigger a “We missed you!” email with a curated list of products they’ve browsed. Better yet, pair it with a time-sensitive discount to bring them back before they lose interest.

Catching a high-intent shopper at the perfect moment

Meet Jake. He’s spent five minutes scrolling through your pricing page so he’s clearly considering a purchase. 

Instead of letting him bounce, your live chat automatically kicks in: “Hi Jake, have any questions about our plans? Here’s a quick guide to help you decide.” 

Now you’re not just a website – you’re a helpful guide steering him toward the finish line.

Saving an abandoned cart before it’s too late

Emma just loaded up her cart with three skincare products but hasn’t clicked ‘checkout.’ She’s hovering. 

Before she clicks away, a pop-up appears: “Complete your order in the next 10 minutes and get free shipping!” Real-time insights mean you’re offering exactly what she needs to seal the deal, right when she’s about to leave.

This isn’t about overwhelming visitors with flashy pop-ups or gimmicks. It’s about being in the right place, at the right time, with the right message.

Real-time visitor identification gives you the ability to act now. 

Whether it’s rewarding loyalty, answering questions, or overcoming hesitation, you’re meeting visitors where they are.

Features That Define a Killer Visitor Identification Strategy

If you’re going to invest in a strategy to identify, understand, and engage your site visitors, you need real-time visitor identification tools with features that deliver real results. 

Here’s what separates the good from the great:

1. Blazing-Fast, Accurate Data Collection

Speed and precision are everything. When someone lands on your site, you only have seconds to capture their attention. Your tools should:

Process visitor data instantly—no lagging or delayed insights.

Accurately track key metrics like session duration, location, device, and click behavior.

Handle spikes in traffic without missing a beat (because when a campaign goes viral, you don’t want to drop the ball).

Without real-time data, you’re stuck playing catch-up, and in ecommerce, “later” is often too late.

2. Seamless Integration with Personalization Tools

Visitor data is only as valuable as what you do with it. The best tools integrate effortlessly with platforms like Klaviyo or HubSpot, enabling you to:

Trigger real-time personalized emails based on browsing behavior (e.g., “Still thinking about those sneakers?”).

Create dynamic pop-ups tailored to each visitor (like offering a 10% discount to new users).

Sync data across your stack for a unified customer experience, ensuring email offers align with on-site actions.

When your tools talk to each other, your marketing becomes smarter, faster, and more effective.

3. Predictive Insights for Proactive Engagement

The most powerful tools help you predict what’s coming next. Look for features that:

Identify high-value leads based on behavior, such as time spent on pricing pages or repeat visits.

Use AI to recommend the most relevant products or actions for each visitor.

Detect potential churn or cart abandonment before it happens, so you can intervene with an offer or reminder.

With predictive insights, you give yourself the ability to act before opportunities slip away.

4. Privacy-First Tracking That Builds Trust

With evolving privacy laws and consumer expectations, compliance is a must. A great tool ensures:

Full compliance with GDPR, CCPA, and other privacy regulations.

Transparency in how data is collected and used, so visitors feel secure.

Options for cookie-less tracking, giving you insights without compromising trust.

When your customers feel their data is handled ethically, they’re more likely to engage and come back.

5. Actionable Dashboards for Real-Time Decision Making

Data shouldn’t feel like a puzzle you need a degree to solve. Your tools should provide:

Intuitive dashboards that display visitor data in an easy-to-understand format.

Real-time updates so you can act instantly when an opportunity arises.

Customizable views that prioritize the metrics most important to your business.

A quick glance should tell you everything you need to know – who’s on your site, what they’re doing, and how you can engage them before they bounce.

With these features in place, you’re collecting data that helps you actually understand your visitors and make decisions  and leads to more purchases, fewer abandoned carts, and happier customers who stick around.

Actionable Steps to Implement Real-Time Visitor Identification

Getting started with real-time visitor identification doesn’t have to be complicated but it does require the right steps to set yourself up for success. Here’s a detailed plan to kick things off:

Choose a Visitor Identification Tool That Fits Your Business

Selecting the right visitor identification tool is the foundation of your strategy. 

The tool should align with your goals – whether it’s increasing conversions, enhancing personalization, or reducing cart abandonment. 

Here’s what to look for when evaluating your options:

Real-Time Data Collection: The tool should capture visitor data instantly and accurately, giving you the ability to act while the visitor is still on your site. Delays or incomplete data can mean missed opportunities.

Data Accuracy: While many visitor identification tools claim to be able to identify 20% of visitors, that doesn’t mean those 20% are accurate. In fact, it turns out that only 30% of that 20% is correct. 

Integration Capabilities: Make sure the tool integrates seamlessly with your existing tech stack, including email platforms like Klaviyo, CRMs, and analytics tools. This ensures data flows smoothly across systems for a consistent customer experience.

Predictive Features: Look for advanced capabilities such as AI-driven insights that help predict visitor intent, identify high-value leads, and detect potential cart abandonment.

Ease of Use: The tool should be intuitive enough for your team to use without needing constant IT support. Prioritize a clean interface and accessible features that allow quick setup and customization.

Scalability and Budget Fit: Consider your current needs and how the tool will grow with your business. Ensure it fits your budget without sacrificing essential functionality.

A tool like Customers.ai stands out for its accuracy, ability to deliver actionable insights, sync with platforms like Klaviyo for personalized email campaigns, and support ecommerce brands with targeted, data-driven strategies. 

Set Up Real-Time Triggers for High-Value Actions

Real-time triggers allow you to engage visitors when it matters most – while they’re still on your site. 

Setting these up ensures you’re not just reacting to data but actively using it to improve engagement and conversions. Here’s how to make your triggers impactful:

Cart Abandonment: Configure your tool to detect when a visitor leaves items in their cart without completing a purchase. Trigger actions like:

An on-site pop-up offering a discount or free shipping to encourage checkout.

A personalized email sent minutes after they leave, reminding them of their cart and offering support or incentives.

Repeat Visits: Recognize visitors who return to your site and tailor their experience. Examples include:

Showing a personalized banner that acknowledges their return, like “Welcome back! Still interested in [product they browsed]?”

Using tools like Customers.ai integrated with Klaviyo to trigger loyalty campaigns or exclusive offers via email.

High-Intent Actions: Identify behaviors that signal strong purchase intent, such as spending time on pricing pages or viewing multiple product reviews. Respond by:

Activating a live chat to answer questions or provide personalized recommendations.

Highlighting social proof, like customer testimonials or best-seller badges, in real time.

The key to real-time triggers is to make them timely and relevant, ensuring they guide the visitor closer to conversion without feeling intrusive.

Analyze Visitor Behavior to Build Better Segments

Understanding how visitors interact with your site is essential for creating effective, personalized marketing strategies. Real-time visitor data allows you to segment your audience based on meaningful patterns.

Here’s how to use this data for segmentation:

Identify Purchase Intent: Look at behaviors like time spent on specific product pages, repeat visits, and add-to-cart actions. Use this to separate high-intent shoppers from casual browsers.

Segment by Engagement Level: Divide visitors into groups based on how they engage with your site, such as:

Highly engaged users who explore multiple pages or interact with live chat.

Low-engagement users who bounce quickly, so you can target them with re-engagement campaigns.

Track Entry Points: Determine where your visitors are coming from (email, ads, social media, etc.) and tailor your messaging accordingly. For example, visitors from an ad campaign might respond better to urgency-driven offers.

Refining your segments over time ensures your marketing stays relevant and impactful, targeting the right people with the right message.

Regularly Test and Adjust Your Strategies

Visitor behavior evolves and your strategy should too. Testing and adjusting your real-time identification efforts help you stay ahead of changing trends. Here’s how to keep your approach fresh:

Experiment with Offers and Messaging: Test different incentives, like discounts, free shipping, or loyalty rewards, to see which resonate most with specific segments.

Optimize Timing: A/B test when triggers are activated—immediately after a cart is abandoned or after a short delay—to find the sweet spot for engagement.

Monitor Performance Metrics: Keep a close eye on conversion rates, average order values, and bounce rates to measure the effectiveness of your triggers and campaigns.

Adjust Based on Data: Use insights from your tests to refine your strategies. For example, if a free shipping offer performs better than a percentage discount, prioritize that in your campaigns.

This iterative process ensures your visitor identification strategy stays effective and continues driving results.

By setting up these strategies, you’ll turn visitor data into meaningful actions that keep people engaged and ready to buy. 

Real-time visitor identification is all about staying adaptable and making every interaction count.

Ready to Meet Your Visitors?

Real-time visitor identification is about about connecting with the people behind the clicks and giving them exactly what they need, when they need it. 

It’s a huge opportunity to turn passive browsing into meaningful engagement and real results.

Are you ready to stop guessing and start building better experiences for your visitors?

Then get your free trial of Customers.ai and start identifying your anonymous visitors today. 500 contacts free!

See Who Is On Your Site Right Now!

Get names, emails, phone numbers & more.

Try it Free, No Credit Card Required

Start Your Free Trial

Important Next Steps

See what targeted outbound marketing is all about. Capture and engage your first 500 website visitor leads with Customers.ai X-Ray website visitor identification for free.

Talk and learn about sales outreach automation with other growth enthusiasts. Join Customers.ai Island, our Facebook group of 40K marketers and entrepreneurs who are ready to support you.

Advance your marketing performance with Sales Outreach School, a free tutorial and training area for sales pros and marketers.

The post Real-Time Visitor Identification: Meet the Buyers Behind Every Click appeared first on Customers.ai.

Ah, the thrill of new contacts hitting your email list. It’s like opening a mystery box – Could be treasure, could be… a headache waiting to happen?

Unfortunately, not all new contacts are good for business and some could tank your email deliverability faster than you can say “unsubscribe.” Think invalid emails, spam traps, or worse — people who never wanted to hear from you in the first place.

In fact, around 20% of email addresses collected online are either fake or risky. That’s one in five! 

If you’re not careful, these rogue entries can send your bounce rates soaring, drag your sender reputation through the mud, and ensure your perfectly crafted email campaigns never see the light of an inbox.

But don’t panic — we’ve got your back. Why? Because it’s a concern we hear from potential customers all the time (and a problem we actually solve!). 

Let’s dig into why new contacts can spell email deliverability trouble and, more importantly, how to handle them like a pro.

Email Deliverability Hacks:

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HOSTED BY

Larry Kim

Founder and CEO, Customers.ai

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What Does Email Deliverability Trouble Really Look Like?

Email deliverability trouble isn’t just “bad metrics”. It’s a cascade of chaos that starts with your emails ghosting inboxes and ends with your revenue crying in a corner.

image: Phonexa

Here’s what actually happens:

The Bounce Rate Spiral: Say you’ve got a bunch of fake or invalid addresses on your list. Your emails bounce harder than a basketball at a pickup game. ISPs notice and think, “Hmm, sketchy sender?” Now your sender score takes a hit.

Spamville, Population: You: Even if your content is pure gold, low engagement or spammy-looking addresses can land you in the junk folder. And good luck convincing Gmail you’re legit once you’re flagged.

The Dreaded Cold Shoulder: Ever sent a killer sale email and heard crickets? That’s low open rates telling ISPs, “Nobody cares.” The result? They stop prioritizing your emails.

Why This Is a Disaster for Ecommerce

Imagine spending hundreds (or thousands) on ads to build your list. You’re dreaming of skyrocketing sales, but instead, your emails end up in spam folders, or worse, never get delivered. That’s money down the drain.

But it’s more than wasted ad dollars:

Your automated flows (think: welcome series, cart reminders) stop converting.

Customer relationships suffer because they’re not even seeing your emails.

Your sender reputation goes downhill, and climbing back up takes time you don’t have.

The worst part? 

It’s a slow bleed. 

You might not even realize deliverability is the problem until your numbers nosedive. But don’t worry, we’ll show you how to fix this mess.

Why New Contacts Can Be a Recipe for Email Deliverability Trouble

Once upon a time, in the glory days of email marketing, buying a list of 10,000 emails was like striking gold. Big list = big sales, right? No one cared too much about where those emails came from, and platforms like Yahoo and Google hadn’t yet declared war on spam.

Fast forward to today, and those days are long gone. ISPs are way savvier, cracking down hard on anything that even sniffs of spam. 

Google’s algorithms? Ruthless. Yahoo’s filters? Unforgiving. And if your emails don’t meet their standards, you’ll be lucky if they even land in the junk folder.

And it’s not just about bought lists anymore. Even the contacts you collect through legit channels can hurt you if they’re unverified or disengaged. For example, invalid emails make up about 10-15% of most email lists, which means you’re almost guaranteed to hit a few that bounce — and ISPs don’t look kindly on that.

Let’s break down why new leads can cause chaos and how to keep your campaigns running smoothly.

1. Unverified Addresses: A Direct Line to Bounces

You know those irresistible giveaways and flashy pop-ups promising discounts? They’re great for growing your list, but they’re also magnets for fake, mistyped, or disposable email addresses. 

If you’re not verifying these, you’re asking for trouble: bounce rates over 2% can signal ISPs that your emails are low-quality, putting your sender reputation at risk.

2. Engagement Rate Drops Like a Rock

New contacts are a wildcard. They’re not warmed up to your brand yet, and if they don’t open or engage with your emails right away, your metrics take a hit. 

Fun fact: ISPs expect at least 15-20% open rates as a sign of healthy engagement. Anything lower, and they might start filtering your emails into spam folders.

3. Spam Traps: The Silent Killers

Here’s something sneaky – some email addresses on purchased or outdated lists are actually designed to catch spammers! 

These “spam traps” might look like legit contacts, but when you hit them, your sender reputation nosedives. Even worse, you might not know they’re there until it’s too late.

4. High Expectations, Low ROI

You’ve got a fresh batch of leads and you’re dreaming of conversions. Unfortunately for you, cold contacts rarely perform as well as warm, engaged ones. 

Studies show that new leads are 40% less likely to convert than returning or re-engaged contacts, making them a less reliable revenue source.

The bottom line? New contacts are risky business if you’re not strategic. Without proper filtering and nurturing, they can hurt your sender reputation, waste your ad spend, and clog your list with dead weight. 

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Email Deliverability Red Flags to Watch For

New contacts can be risky business but spotting the red flags early can save your deliverability (and your sanity). 

Here are the top warning signs that something’s off:

1. Instant High Bounce Rates

If your bounce rate spikes right after adding new contacts, that’s a neon sign you’ve got unverified or fake emails on your list. 

ISPs use high bounce rates as a signal to question your sender reputation, making it harder for all your emails to reach inboxes.

Pro Tip: A healthy bounce rate should stay below 2%. If you’re seeing anything higher, it’s time to start validating your email addresses before hitting send.

2. Low Engagement from the Start

New contacts who don’t open, click, or engage within the first few campaigns? 

That’s a bad sign. 

These disengaged addresses drag down your open and click-through rates, making ISPs think your emails aren’t worth delivering.

Red Flag Metric: Contacts who don’t open a single email within the first 30 days are likely dead weight. Segment them out for re-engagement efforts or consider cutting them loose.

3. Unusual Spam Complaints

Even one or two spam complaints from new contacts can set off alarms. Whether they didn’t mean to sign up, forgot they opted in, or just don’t like your vibe, complaints hurt your sender reputation and put you at risk of ending up on a blocklist.

Warning Sign: If spam complaints creep above 0.1%, it’s time to dig into where your new contacts are coming from and whether your opt-in process is clear.

These are all early indicators that your list might be more trouble than it’s worth. Catch them early, and you’ll keep your email deliverability (and your revenue) on track. 

How to Handle New Contacts Without Wrecking Your Deliverability

If you handle them right, new contacts don’t have to spell doom for your email campaigns. With the right strategies (and email deliverability tools like Customers.ai in your corner), you can turn risky leads into revenue-driving superstars. 

Here’s how:

1. Verify, Verify, Verify

Before a single email leaves your outbox, make sure those addresses are legit. Email validation tools like ZeroBounce and NeverBounce can weed out invalid, fake, or disposable addresses before they tank your deliverability.

Pro Tip: Customers.ai takes email validation a step further. Not only do we scrub your list for invalid addresses, but we also send a “signs of life” email to ensure the contacts on your list are active and engaged. No ghost recipients here!

2. Double Opt-In: Quality Over Quantity

A double opt-in process might feel like an extra step but for many, it’s worth it. When new contacts confirm their subscription, you filter out the uninterested or accidental sign-ups before they hit your main list.

Why It Works: Only the most interested users make it through, which means better engagement rates and fewer spam complaints.

3. Segment First, Email Later

Don’t dump new contacts into your primary email campaigns right away. Instead, segment them into a nurturing sequence designed to warm them up to your brand.

Start Here:

Create a dedicated welcome flow with tailored content.

Monitor engagement metrics before moving them to your main campaigns.

Customers.ai makes this process even smoother by helping you focus on high-intent visitors from the start, so you’re not wasting time on leads that won’t convert.

Shopper Identified Welcome email flow started $3800 purchase madeWe all know welcome flows are money makers so getting more people into them is key. That’s where visitor ID comes in. 20% more people into your welcome flows = more sales faster! pic.twitter.com/Rg3vokjjau— CustomersAI (@CustomersAI) November 1, 2024

4. Set Expectations Early

First impressions matter. Your welcome email should make it crystal clear what your subscribers can expect: frequency, content type, and value. A clear and engaging introduction reduces confusion and spam complaints.

Quick Tips for a Stellar Welcome Email:

Reiterate what they signed up for (e.g., “Here’s your 10% discount code!”).

Provide immediate value.

Highlight what’s coming next, so they’re ready to engage.

By combining proper validation, thoughtful segmentation, and strategic onboarding, you’re protecting your deliverability and setting up your email campaigns for long-term success. 

The Long Game: Building Trust with Your Contacts

Email marketing isn’t about blasting your list and crossing your fingers. It’s about building trust, driving engagement, and making sure your emails actually hit the inbox. 

Here’s how to keep the relationship strong:

1. Prioritize Quality Over Quantity

Forget bloated lists. The days of “more is better” are long gone. 

A smaller, engaged list is worth ten times more than a massive one full of unresponsive addresses. 

Why? 

Because ISPs pay attention to your engagement rates. If too many people ignore your emails, it’s game over for your sender reputation.

Focus on the people who actually care about your brand. They’re the ones who will open, click, and convert.

2. Monitor and Adjust

Don’t just set it and forget it. Keep tabs on your metrics (bounce rates, open rates, click-throughs, etc.) and watch for anything that seems off. 

A dip in engagement? It might be time for a re-engagement campaign. Bounce rates creeping up? Check for unverified addresses slipping through the cracks.

Regular list maintenance isn’t optional. It’s how you keep your campaigns healthy and your emails landing in the inbox.

3. Engage Smarter, Not Harder

Not everyone is ready for your emails. Some leads need a little more warming up. 

At Customers.ai, we take a smarter approach — we recommend sending low-intent visitors to retargeting ad campaigns instead of blasting them with emails.

Why? 

Because people who aren’t ready to buy are more likely to ignore your emails or, worse, mark them as spam. 

Retargeting ads help you stay on their radar without risking your sender reputation. Once they’re warmed up, you can bring them into your email funnel with confidence.

4. Keep It Legal

Let’s not forget the legal side of things. GDPR, CCPA, and other regulations are there for a reason. 

Make sure you’re only emailing people who’ve given clear consent, and give them an easy way to opt out. The last thing you want is to end up on the wrong side of compliance.

Building trust with your contacts isn’t complicated, but it does take effort. Focus on quality over quantity, keep your list clean, and engage your audience the smart way.

Final Send-Off: Keeping Your Emails on Point

New contacts are a double-edged sword. They can either boost your campaigns or sink your email deliverability if you’re not careful. 

The good news? You’re in control!

By validating your list, segmenting your audience, engaging wisely, and keeping a close eye on your metrics, you can turn potential problems into opportunities. 

At Customers.ai, we make it easy to not only avoid the risks but also set your email marketing up for success with high-intent contacts and smart engagement strategies.

A 40% increase in email clicks!!! That’s what one of our customers saw after implementing our new email validator and anti-spam tool.Why? Because it removes emails from the promotion tab and puts it in the inbox Killer right???See how it’s done … pic.twitter.com/iMfwSvV6VM— CustomersAI (@CustomersAI) November 15, 2024

Remember, email deliverability trouble doesn’t have to be the end of the story. With the right steps, you can flip the script and turn it into deliverability triumph. 

Keep your list clean, your strategy sharp, your inbox placement strong, and the rest will follow. Let’s make email work for you, not against you.

Get your free Customers.ai trial and start building email lists that work smarter and don’t risk your deliverability.

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FAQs: Email Deliverability Trouble

1. What is email deliverability trouble?

Email deliverability trouble happens when your emails fail to reach your recipients’ inboxes, often getting stuck in spam folders or blocked altogether. This isn’t just an inconvenience; it’s a signal that something is wrong with your email strategy, whether it’s poor sender reputation, high bounce rates, or disengaged recipients.

Why does this matter? Because deliverability trouble directly impacts your ability to connect with your audience. If your emails aren’t getting through, you’re missing opportunities to nurture leads, drive sales, and grow your business. For ecommerce brands, where email campaigns often mean the difference between an abandoned cart and a completed sale, poor deliverability can hit your bottom line hard.

The good news is that most deliverability trouble is preventable. By keeping your email lists clean, engaging your audience with relevant content, and following email authentication protocols, you can avoid these issues and keep your campaigns on track.

2. Why do emails end up in spam instead of the inbox?

Emails land in spam because spam filters are designed to protect users from irrelevant or harmful messages. Common triggers include poor sender reputation, lack of email authentication (SPF, DKIM, DMARC), or content that mimics spam (e.g., too many links or excessive caps).

Even small mistakes can hurt your chances—like sending bulk emails to unverified contacts or neglecting engagement metrics. ISPs monitor how recipients interact with your emails, so low open rates or frequent spam complaints are red flags that push your messages into the junk folder.

To stay out of spam, focus on list hygiene, deliver valuable content, and avoid spammy tactics like misleading subject lines or overuse of promotional language.

3. How do I know if I’m facing email deliverability trouble?

Signs of deliverability trouble include rising bounce rates, declining open and click-through rates, and increased spam complaints. You may also notice emails not being delivered at all or ending up in spam folders when you test them yourself. Using tools like Google Postmaster or Sender Score can help you monitor your sender reputation and identify deliverability issues. Another tell? If recipients start reporting your emails as spam, ISPs will take notice, and your deliverability will spiral downward. The sooner you catch these warning signs, the easier it is to fix the problem and prevent further damage to your reputation.

4. What are the most common causes of email deliverability trouble?

Deliverability issues often stem from poor sender practices. Common causes include sending to outdated or purchased email lists, lack of email authentication (SPF, DKIM, or DMARC), high bounce rates from invalid or mistyped addresses, low engagement from your recipients (e.g., unopened emails), and content that looks like spam, such as overly promotional language or deceptive subject lines. Addressing these issues through better email practices and tools can prevent many problems before they start.

5. How does sender reputation impact deliverability?

Sender reputation is like a credit score for your email domain or IP address. ISPs use it to determine whether your emails are trustworthy. A strong sender reputation means your emails are more likely to reach inboxes, while a poor one can lead to emails being filtered into spam or blocked entirely. Your reputation is influenced by factors like bounce rates, spam complaints, and recipient engagement. To maintain a healthy sender reputation, regularly clean your list, monitor metrics like open and click-through rates, and send relevant, valuable content.

6. What is a bounce rate, and why does it matter for deliverability?

Bounce rate refers to the percentage of emails that cannot be delivered to recipients. High bounce rates can harm your sender reputation and signal to ISPs that you’re sending to invalid or outdated addresses. There are two types of bounces: soft bounces, which are temporary issues like a full inbox, and hard bounces, which occur when an email address is invalid or non-existent. Keeping your bounce rate under 2% is critical for maintaining good deliverability. Regularly validating your email list can help reduce bounce rates and protect your reputation.

7. How can I reduce email bounce rates?

Reducing bounce rates starts with good list hygiene. Avoid purchasing email lists, use double opt-in methods to confirm new subscribers, and validate your email list with tools like ZeroBounce or NeverBounce. Additionally, segment your audience to ensure you’re sending relevant emails, and monitor your metrics regularly to catch potential issues early. By focusing on quality over quantity, you’ll not only lower your bounce rates but also improve your engagement rates and deliverability overall.

8. What are spam traps, and how do they harm deliverability?

Spam traps are email addresses designed to catch spammers. There are two main types: pristine traps, which are never used for legitimate purposes, and recycled traps, which were once valid addresses but have been repurposed. Sending emails to spam traps can significantly damage your sender reputation and result in blacklisting. These traps often end up on your list through purchased or outdated email lists. To avoid spam traps, never buy email lists, validate new subscribers, and keep your database up to date.

9. How can I prevent emails from being marked as spam?

Preventing spam complaints starts with setting clear expectations for your subscribers. Use a double opt-in process to ensure people want to receive your emails, and make it easy for recipients to unsubscribe if they no longer want your content. Create engaging, relevant emails that provide value, and avoid spammy practices like misleading subject lines or excessive use of caps and exclamation marks. Monitor your complaint rates—keeping them below 0.1% is ideal—and regularly clean your list to remove inactive users.

10. What is email authentication, and why is it important for deliverability?

Email authentication verifies that your emails are sent by an authorized sender, helping ISPs trust your messages. Authentication protocols like SPF, DKIM, and DMARC protect your domain from being spoofed and ensure your emails are less likely to be flagged as spam. Without authentication, ISPs may block your emails or send them to spam, even if your content is legitimate. Setting up these protocols is essential for maintaining a strong sender reputation and improving deliverability.

11. How do engagement rates impact email deliverability trouble?

Engagement rates, like opens, clicks, and replies, are key indicators ISPs use to assess whether your emails are valuable to recipients. Low engagement signals that people aren’t interested in your messages, which can result in emails being filtered into spam or blocked. To boost engagement, segment your audience so each recipient gets content tailored to their interests. For example, targeting engaged customers with exclusive offers can significantly improve open and click-through rates. High engagement shows ISPs that your emails are welcome, improving your overall deliverability.

12. Why is email list cleaning critical for avoiding deliverability trouble?

Over time, even the best email lists accumulate inactive users, invalid addresses, and spam traps. Sending to these contacts can result in high bounce rates and poor engagement, both of which harm your sender reputation. Regular list cleaning ensures you’re only emailing people who want to hear from you. Tools like NeverBounce and ZeroBounce can help remove invalid addresses, while segmenting inactive subscribers for re-engagement campaigns can keep your list healthy. A clean list leads to better deliverability and higher ROI on your campaigns.

13. What role do subject lines play in email deliverability?

Subject lines are one of the first things both recipients and spam filters evaluate. Overly aggressive or misleading subject lines (e.g., “GET RICH NOW!!!”) can trigger spam filters or discourage recipients from opening your emails. To improve deliverability, keep subject lines clear, relevant, and aligned with the content of your email. A/B test different approaches to see what resonates with your audience. For example, personalized subject lines (like “Hey [Name], your exclusive offer is here!”) often perform better in terms of both engagement and deliverability.

14. How does sending frequency affect deliverability?

Sending emails too frequently can overwhelm your subscribers, leading to higher unsubscribe and spam complaint rates. On the other hand, sending too infrequently can result in disengagement and forgotten subscriptions, which also harm deliverability. Finding the right balance depends on your audience and content. For example, ecommerce brands might send promotional emails weekly, while B2B companies could send bi-weekly updates. Monitor engagement metrics to determine the optimal cadence and adjust if you notice a spike in unsubscribes or complaints.

15. What is a sender score, and how does it relate to email deliverability?

A sender score is a numerical value (from 0 to 100) that reflects the health of your email-sending practices. It’s calculated based on factors like bounce rates, spam complaints, and engagement. A high score (above 80) indicates good sender reputation and better deliverability, while a low score can result in emails being filtered or blocked. You can check your sender score using tools like SenderScore.org. Maintaining a high sender score requires consistent list hygiene, engaging content, and adherence to best practices like authentication protocols.

16. Can purchased email lists cause deliverability trouble?

Yes, purchased email lists are a major risk to deliverability. These lists often contain outdated or fake addresses, spam traps, and uninterested recipients. Sending to these contacts can lead to high bounce rates, low engagement, and spam complaints, all of which harm your sender reputation. Additionally, many email marketing platforms prohibit the use of purchased lists to protect their own reputation with ISPs. Building your list organically through opt-in methods is the only way to ensure long-term deliverability success.

17. What is a re-engagement campaign, and how does it help deliverability?

A re-engagement campaign targets inactive subscribers to either win them back or confirm they want to remain on your list. This not only helps improve engagement metrics but also cleans up your list by identifying users who are no longer interested. For example, you might send a series of emails with subject lines like “We Miss You!” or offer an incentive like a discount to encourage interaction. If contacts don’t engage after multiple attempts, consider removing them to maintain list health and boost overall deliverability.

18. How can I monitor email deliverability performance?

Monitoring deliverability requires tracking key metrics like open rates, bounce rates, and spam complaints. Tools like Google Postmaster Tools, Microsoft SNDS, and email service provider dashboards can provide insights into how ISPs are treating your emails. For example, a sudden spike in bounces or complaints indicates a problem that needs immediate attention. Regularly reviewing these metrics helps you identify trends and address issues before they escalate, ensuring your deliverability stays on track.

19. How do high unsubscribe rates affect deliverability?

High unsubscribe rates can signal to ISPs that your emails aren’t relevant or welcome, which can harm your sender reputation. While some unsubscribes are natural, frequent or sudden spikes should prompt a review of your email content and strategy. To reduce unsubscribes, set clear expectations during sign-up about the type and frequency of emails you’ll send. Additionally, segment your audience to ensure recipients receive emails that match their interests and preferences.

20. What is IP warming, and why is it important for email deliverability?

IP warming is the process of gradually increasing the volume of emails sent from a new IP address to establish trust with ISPs. Sending too many emails too quickly from a new or cold IP can raise red flags and result in emails being filtered or blocked. For example, start by sending emails to your most engaged subscribers, then gradually scale up to your full list over several weeks. This approach builds a positive sender reputation and improves inbox placement for future campaigns.

21. How does personalization improve email deliverability?

Personalized emails not only improve engagement but also signal to ISPs that your messages are valuable to recipients. Adding personalization, such as using the recipient’s name or tailoring content based on their past interactions, increases the likelihood of opens and clicks. For example, instead of sending a generic promotion, segment your audience and create personalized offers, like “20% off your favorite category!” Higher engagement rates from personalization boost your sender reputation and overall deliverability.

22. What are soft bounces, and how should I handle them?

Soft bounces occur when an email is temporarily undeliverable due to issues like a full inbox or a server problem. Unlike hard bounces, which indicate a permanent issue, soft bounces don’t immediately harm your deliverability. However, repeated soft bounces for the same address can signal a problem. Most email service providers automatically stop sending to addresses with repeated soft bounces, but you should regularly review these contacts and remove them if the issue persists.

23. How do abandoned cart emails affect deliverability?

Abandoned cart emails are highly effective for ecommerce but need to be handled carefully to avoid deliverability trouble. Sending too many reminders can lead to spam complaints, while failing to personalize the content can reduce engagement. Limit your follow-ups to 2-3 well-timed emails and include personalized details, like the items left in the cart. High engagement with these emails can boost your sender reputation, but overdoing it may backfire.

24. How does mobile optimization impact deliverability?

Emails that aren’t optimized for mobile can lead to poor user experiences, causing recipients to ignore or delete them. Since ISPs monitor engagement, low interaction with non-mobile-friendly emails can negatively impact deliverability. Ensure your emails use responsive design, concise subject lines, and clear CTAs that are easy to interact with on smaller screens. Mobile optimization doesn’t just improve deliverability—it enhances the recipient experience, increasing conversions.

25. Can retargeting campaigns help reduce email deliverability trouble?

Retargeting campaigns are an effective way to engage low-intent visitors without risking email deliverability. Instead of adding unengaged users directly to your email list, use retargeting ads to keep your brand on their radar. Platforms like Customers.ai specialize in targeting high-intent visitors, ensuring you’re focusing your email campaigns on contacts who are ready to engage. This approach reduces spam complaints, improves list quality, and ensures your emails are hitting the right inboxes.
The post Why New Contacts Can Cause Email Deliverability Trouble (And How to Handle Them) appeared first on Customers.ai.

Black Friday and Cyber Monday? Done and dusted. And wow, this year didn’t disappoint!

Shoppers in the U.S. spent a record-breaking $13.3 billion online during Cyber Monday alone, up 7.3% from last year. 

At Customers.ai, we also saw our ecommerce clients crush it on Black Friday, with 12.5 million emails captured and 30 million emails sent. 

But now comes the tricky part – transitioning from the high-stakes, big-budget energy of BFCM to the steadier rhythm of post-holiday campaigns. 

Here’s the good news—people are still shopping. The holidays aren’t over and there’s plenty of opportunity to keep the momentum rolling.

The key? 

Sustainable Facebook ad strategies that balance ROI with consistency, keeping your audience engaged without burning through your ad budget. 

In this post, we’re spilling the tips to help you scale smart, build year-round campaigns, and make sure the holiday cheer lasts well into January.

Ready to make it happen? Let’s dive in.

Why Post-Holiday Facebook Ad Campaigns Need a Shift in Strategy

The holiday season is like the Super Bowl for ecommerce – high stakes, massive ad budgets, and a short window to make a big impact. 

From the urgency of Black Friday to the record-breaking online shopping on Cyber Monday, brands pull out all the stops to get in front of eager shoppers.

But as soon as Black Friday and Cyber Monday end, the game changes. 

The competition starts to cool down and the audience’s mindset shifts. No more doorbuster deals or FOMO-fueled shopping frenzies. 

Instead, consumers move into a more deliberate mode – finishing their holiday lists, prioritizing their budgets, and even scouting for post-season bargains.

This shift in behavior is reflected in ad trends, too. 

Did you know Facebook Ads campaigns typically see a 15% lower CPM in January compared to Q4? 

That’s right. Once the frenzy dies down, the cost to reach your audience becomes more affordable. 

This is the perfect time to adjust your strategy and stretch your ad dollars further.

So, what does this mean for your Facebook ad campaigns? 

It’s time to pivot from high-intensity seasonal marketing to sustainable, everyday strategies. 

Let’s break down the key differences between these two approaches and why a shift in focus is critical to maintaining your ROI post-holidays.

Seasonal Campaigns vs. Everyday Marketing: The Key Differences

1. Competition Levels

Seasonal: During Black Friday and Cyber Monday, the competition is intense. Every brand, big or small, is fighting for attention, driving up CPMs (cost per thousand impressions) and CPCs (cost per click).

Everyday: Post-holidays, the competition drops, giving you an opportunity to capture audiences at a lower cost—if you play your cards right.

2. Consumer Mindset

Seasonal: Shoppers are primed to buy—urgency, discounts, and FOMO drive immediate conversions.

Everyday: After the holiday buzz, consumers slow down. They’re more budget-conscious and deliberate in their purchasing decisions.

3. Campaign Goals

Seasonal: The focus is on driving quick sales, often through discounts and time-sensitive offers.

Everyday: It’s all about consistency. You’ll need to prioritize engagement, loyalty, and repeat purchases to keep revenue steady.

Shifting to Evergreen Strategies

Once the holiday hype fades, your approach has to shift, too. 

Seasonal campaigns rely on urgency, but sustainable growth requires campaigns that are always relevant, no matter the time of year.

What are evergreen strategies? 

These are campaigns designed to drive consistent results year-round. They focus on timeless messaging, products, and offers that aren’t tied to specific holidays or events. Think of them as the steady engine that keeps your business moving long after the holiday fuel runs out.

The holidays might be winding down, but that doesn’t mean your momentum has to. Let’s talk about scaling down budgets without losing results.

Scaling Down Facebook Ad Budgets Without Losing Momentum

The holiday rush might be over but that doesn’t mean your ad spend should come to a screeching halt. 

Instead of going from 100 to zero, it’s all about recalibrating your strategy. Scaling down your budget thoughtfully can help you maintain visibility, engage your audience, and keep driving sales – all without burning through your marketing dollars.

Here’s how to optimize your ad spend post-holidays:

1. Focus on High-ROI Audiences

Instead of casting a wide net, zero in on the groups most likely to convert:

Repeat Customers: These are your MVPs. They’ve already bought from you, and with a well-timed follow-up offer, they’re more likely to come back.

Warm Leads: Retarget shoppers who browsed your site (hello visitor identification) or added items to their carts but didn’t complete their purchase. Use Facebook Ads to bring them back with personalized reminders or discounts.

Pro Tip: Use Customers.ai Custom Audiences to create highly targeted segments that can be synced to Facebook. For example, you could retarget customers who purchased during Black Friday with a complementary product ad or a bounce-back offer (“20% off your next purchase!”).

2. Shift from Awareness to Retargeting and Engagement Ads

During the holiday season, awareness campaigns are essential to grab attention. Post-holidays, your strategy should shift toward:

Retargeting Ads: These keep you top of mind for audiences who’ve already interacted with your brand.

Engagement Ads: Encourage post-purchase actions like following your page, leaving a review, or engaging with your content.

Example: Say you ran broad awareness campaigns during Black Friday to attract new shoppers. Now, take those same shoppers and retarget them with product-specific ads based on their browsing history. If someone looked at winter boots but didn’t buy, show them a carousel ad featuring the boots with a “New Year Special” discount.

3. Gradually Reduce Spending While Maintaining Visibility

Scaling down doesn’t mean turning off the lights. The goal is to adjust your budget while staying visible to key audiences.

Here’s a budget reallocation framework to guide you:

Awareness (Top of Funnel): Reduce this to 10–15% of your total spend. Focus on organic reach and let paid ads target high-value audiences.

Consideration (Middle of Funnel): Allocate around 25–30%. Use these campaigns to retarget warm audiences with tailored offers or content.

Conversion (Bottom of Funnel): Keep this at 55–60%. Prioritize ads designed to drive purchases, such as retargeting cart abandoners or upselling to recent buyers.

Pro Tip: Monitor your performance metrics closely. If your ROAS is strong in a particular audience segment, you can shift more budget there to maximize returns.

Scaling down your ad budget doesn’t mean scaling back your results. Next up, let’s talk about how to find the evergreen elements from your seasonal campaigns and use them to keep your Facebook Ads working year-round.

Finding the Evergreen Gold in Seasonal Campaigns

Seasonal campaigns might revolve around the holiday hype, but that doesn’t mean their success has to be limited to a specific timeframe. 

The best brands know how to extract evergreen value from their seasonal efforts, transforming short-term wins into long-term gains.

Here’s how you can find the golden nuggets in your holiday campaigns and use them year-round:

1. Analyze Your Top-Performing Creatives, Messaging, and Offers

Take a close look at what worked during your Black Friday and Cyber Monday campaigns:

Creatives: Which ad visuals drove the highest click-through rates? Were carousel ads more effective than videos?

Messaging: Did your audience respond better to urgency-driven language (“Hurry, ends tonight!”) or value-based messaging (“Save big on must-haves!”)?

Offers: Which discounts or bundles brought in the most sales?

Pro Tip: Use Facebook Ads Manager to break down performance metrics by audience segment, placement, and ad format. This helps you identify patterns that can guide your future campaigns.

If there was one thing I could have a marketer obsess about, it would be this 2×2 matrix. Ad creative is a power law game: 90% of your results will come from <10% of ads (the “hits”). The brands that win in 2025 will be the ones that effectively move from the left side of the 2×2… pic.twitter.com/tAP2z8EIWj— Sam (@DigitalSamIAm) December 10, 2024

2. Highlight Products or Services That Gained Traction

Your holiday campaigns likely spotlighted a mix of products but not all of them will perform equally. Pinpoint the ones that resonated most with your audience.

Best-Sellers: Which products flew off the shelves? Keep these front and center in your evergreen campaigns.

Hidden Gems: Did a lower-profile product unexpectedly gain traction? This could signal untapped demand worth exploring further.

Example: A beauty brand might discover their holiday gift sets sold exceptionally well, even beyond Black Friday. They could repurpose these bundles as “self-care kits” for a January wellness campaign.

3. Repurpose Holiday Content for Year-Round Use

Seasonal doesn’t mean disposable. With a few tweaks, your holiday creatives and messaging can stay relevant long after the holidays:

Adjust the Language: Swap out holiday-specific phrases like “Holiday Special” for broader terms like “Limited-Time Offer” or “Winter Sale.”

Reuse Visuals: If your ads featured cozy, wintry aesthetics, they can still resonate in January and February.

Highlight Year-Round Benefits: Shift the focus from gifting to personal use. For example, a fitness product promoted as “The Perfect Gift for Him” can be repurposed as “Start Your New Year Strong.”

As Courtney Fritts pointed out in our 2024 Holiday Guide, Advanced Meta Ads Strategies to “Sleigh” Black Friday, use your best performing creative from the year to guide your ads. It works both ways!

Brand Spotlight: Starbucks

Starbucks does an incredible job of repurposing seasonal elements for long-term campaigns. 

Their famous holiday cups and festive drinks create buzz during the holidays, but they don’t stop there. By January, they pivot to highlighting year-round best-sellers like lattes and teas while introducing “winter warmers” with similar cozy vibes. 

This seamless transition keeps the brand relevant without reinventing the wheel.

4. Example: Turning a “Holiday Special” into a “Limited-Time Offer”

Let’s say you ran a “Holiday Special” campaign for a premium product bundle. Instead of shelving it after the holidays:

Rename it: Rebrand it as a “New Year’s Deal” or “Limited-Time Offer.”

Refresh the messaging: Replace gift-giving language with self-care or start-of-the-year themes.

Extend the life cycle: Run the campaign through January or even position it as a quarterly promotion.

By analyzing what worked, highlighting key products, and repurposing your content, you can turn the success of your seasonal campaigns into evergreen gold. 

Keeping Audience Engagement High After the Holidays

The holiday frenzy might be over, but this is not the time to let your audience forget about you. 

To keep the post-holiday momentum alive, you need to stay visible, relevant, and most importantly, engaging. Here’s how to do it:

Spotlight New Arrivals and Promotions

Shoppers love a reason to stay excited. Keep your brand top of mind by showcasing:

Fresh inventory: Highlight products that align with January vibes, like fitness gear, planners, or cozy essentials for winter.

Post-holiday deals: Run campaigns that offer discounts on leftover inventory or “start the new year right” promotions.

Example: A home goods store can pivot from “Holiday Hosting Essentials” to “New Year Home Refresh” ads, promoting stylish storage solutions or home decor updates.

Get Personal with Data-Driven Messaging

Use the purchase history from your holiday campaigns to create hyper-relevant ads:

You bought X, you might like Y: Recommend complementary items or accessories for their recent purchase.

Exclusive loyalty rewards: Offer returning customers a personalized discount or early access to new arrivals.

Facebook Ads Pro Tip: Dynamic Ads let you automate personalized recommendations so every shopper sees products they’re most likely to buy.

Turn Engagement into a Two-Way Street

Post-holiday isn’t just about selling—it’s about connecting:

Run polls or quizzes in your Facebook Ads to make customers feel involved (“What’s your 2024 goal: fitness, travel, or self-care?”).

Share user-generated content (UGC) to showcase real customers using your products.

Host Q&A sessions or post tips that add value, like “5 Ways to Style Your Winter Wardrobe.”

Example: A beauty brand could feature customer reviews or repost holiday makeup looks with captions like, “Here’s how our customers are glowing into the new year!”

Stay Consistent with Retargeting and Loyalty Campaigns

Retarget shoppers who visited your site or purchased during the holidays:

Use subtle reminders to encourage repeat purchases, like “Time to restock your favorites!” ads.

Offer loyalty perks, such as points for future discounts or VIP early access to sales.

Consistency is key. Keep your campaigns active so your brand stays on their radar, even as their shopping habits slow down.

The post-holiday period isn’t just about wrapping up (get it?). I’s about setting the stage for what’s next. 

Whether you’re promoting new arrivals, building community, or nurturing loyalty, consistent engagement is the key to turning one-time holiday shoppers into lifelong customers.

Unlock High-Intent Leads Hiding on Your Site

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Using Customers.ai to Build Sustainable Facebook Ad Campaigns

Building sustainable Facebook ad campaigns requires two key ingredients: knowing your audience and delivering ads that feel relevant and timely. 

That’s where Customers.ai comes in. 

By combining advanced visitor insights, smart segmentation, and personalization, Customers.ai empowers ecommerce brands to get more out of their Facebook ad spend year-round.

Segment Smarter, Not Harder

The key to post-holiday success is knowing who to target and Customers.ai’s segmentation tools make it simple:

Group audiences by behavior, demographics, or intent.

Retarget high-value customers with tailored offers or complementary products.

Build Lookalike Audiences of your top shoppers to expand your reach with confidence.

Know Your Visitors Like Never Before

Insights matter. With Customers.ai’s visitor tracking, you can dig into:

Which products or pages are getting the most attention.

How users navigate your site, helping you refine both your ads and your messaging.

What content sparks engagement so you can replicate that success in future campaigns.

Automate Personalization at Scale

Personalization isn’t optional anymore, it’s essential. Customers.ai makes it effortless to:

Deliver dynamic product ads tailored to individual shoppers.

Sync email follow-ups and Facebook Ads for a seamless, multi-channel experience.

Keep your audience engaged with messaging that feels relevant and timely.

Retarget with Precision

Not all retargeting is created equal. Customers.ai helps you:

Re-engage cart abandoners with offers that convert.

Retarget warm leads based on their browsing habits or wishlist activity.

Combine retargeting with SMS or email campaigns for maximum impact.

Customers.ai equips you with the tools to turn holiday momentum into lasting success. 

Whether you’re segmenting smarter, personalizing better, or retargeting with precision, it’s all about keeping your Facebook ad campaigns sustainable.

From Seasonal Peaks to Steady Growth

Black Friday and Cyber Monday may have been the highlight of the holiday season but they’re just the beginning when it comes to building long-term success. 

Transitioning from seasonal peaks to steady, sustainable growth requires thoughtful Facebook ad strategies – ones that focus on segmentation, personalization, and consistent engagement.

Now’s the time to take a step back and reflect on your holiday campaigns. What worked? What didn’t? 

Use those insights to refine your approach, identify evergreen opportunities, and map out a plan for consistent ROI. 

By leveraging smarter segmentation, better retargeting, and ongoing audience engagement, you can turn your holiday highs into year-round wins.

Ready to level up your Facebook ad campaigns? Customers.ai is here to help. With tools that make audience insights, personalization, and automation simple, you’ll have everything you need to grow sustainably and profitably – all year long.

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Important Next Steps

See what targeted outbound marketing is all about. Capture and engage your first 500 website visitor leads with Customers.ai X-Ray website visitor identification for free.

Talk and learn about sales outreach automation with other growth enthusiasts. Join Customers.ai Island, our Facebook group of 40K marketers and entrepreneurs who are ready to support you.

Advance your marketing performance with Sales Outreach School, a free tutorial and training area for sales pros and marketers.

The post Holiday Rush vs. Everyday Sales: Building Sustainable Facebook Ad Campaigns appeared first on Customers.ai.

Quantum computing has long been seen as a promising avenue for advancing computational capabilities beyond those of classical systems. However, the field faces a persistent challenge: error rates. Quantum bits, or qubits, are inherently fragile, and minor disturbances can lead to computational errors. This sensitivity has limited the scalability and practical application of quantum systems. Addressing these issues is crucial for quantum computing to achieve broader utility, enabling progress in fields like cryptography, material science, and artificial intelligence.

Google Quantum AI introduces Willow, a new quantum computing chip designed to reduce errors as the system scales. Willow represents a significant step in addressing error rates, a problem that has challenged researchers for decades. By integrating advanced error correction techniques with novel hardware design, Willow reduces error rates while increasing the number of qubits. This development positions Google as a leader in quantum research, moving the field closer to realizing practical quantum computing applications.

At the core of Willow’s design is a combination of advanced hardware and software. The chip incorporates a fault-tolerant architecture, a key improvement over earlier designs. By employing surface codes and optimized qubit connectivity, Willow mitigates noise interference and enhances qubit coherence times. Its ability to reduce errors is enabled by advances in qubit stability and error correction algorithms. Furthermore, Willow’s architecture is designed to scale effectively, ensuring that increases in the number of qubits do not result in disproportionately higher error rates. These improvements enhance computational accuracy and allow quantum systems to address increasingly complex problems.

Results from benchmark testing highlight Willow’s capabilities. In one test, Willow solved a computational problem in under five minutes that would take a leading classical supercomputer an estimated 10^25 years to complete. This performance demonstrates the potential of quantum computing to address challenges that are infeasible for classical systems. Willow’s ability to reduce errors addresses a significant limitation in quantum computing, enabling the development of systems that are both scalable and reliable.

In conclusion, the introduction of Willow by Google Quantum AI represents a meaningful advancement in quantum computing. By addressing the longstanding challenge of error rates, Willow provides a foundation for scalable and practical quantum systems. Its performance in benchmark tests underscores the transformative potential of quantum computing across various domains. As the field evolves, innovations like Willow will play a critical role in shaping a future where quantum computing drives scientific discovery and technological progress.

Check out the Details here. 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. Don’t Forget to join our 60k+ ML SubReddit.

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The post Google Quantum AI Introduces Willow: A New State-of-the-Art Quantum Computing Chip with a Breakthrough that can Reduce Errors Exponentially appeared first on MarkTechPost.

OpenAI has unveiled Sora, its new text-to-video generation tool, a major step forward in AI-powered content creation. However, the launch comes with a notable exception: users in the European Union and the United Kingdom won’t have access for now, highlighting ongoing challenges between innovation and regulation.

Sora is OpenAI’s answer to simplifying video production. It takes written prompts and transforms them into videos, all while offering tools to fine-tune the results. At its core is the Turbo architecture, designed to prioritize speed and user-friendliness. The dedicated UI Studio introduces a storyboard feature that feels familiar to anyone who has used platforms like TikTok or Instagram Reels, making it intuitive for creators looking to dive into short-form video content.

Starting today, Sora will be available to ChatGPT Pro and Plus subscribers without any extra fees. Yet, its absence in the EU and UK is a striking reminder of how regulatory landscapes can shape technology adoption. While users in these regions wait, the rest of the world gets to experiment with this powerful tool.

Sora’s storyboard function makes it particularly appealing for creating quick, engaging videos tailored to social media trends. This ease of use could lead to a wave of AI-generated content dominating platforms like YouTube Shorts and TikTok. While this lowers the barrier to entry for creators, it also raises questions about how we’ll navigate a world where synthetic media becomes the norm. Ensuring transparency in content origins may soon become a key issue.

For creators, Sora offers a chance to streamline their workflow. It reduces the time and effort needed to produce polished videos, giving individuals more room to focus on storytelling and creativity. Businesses, on the other hand, can leverage Sora for efficient content generation—whether for ads, promotions, or social media strategies.

The tool’s Turbo architecture ensures it can handle the demands of both casual creators and enterprises looking for scalable solutions. Whether you’re a small startup or a big brand, Sora has the potential to redefine how you approach video marketing.

As with any groundbreaking tool, Sora’s introduction isn’t without its challenges. The potential for misuse—like creating misleading or harmful content—underscores the need for responsible AI usage. OpenAI will need to implement clear guidelines and safeguards to minimize these risks.

Additionally, the rise of AI-generated media could blur the line between authentic and synthetic content. Platforms and creators alike may need to adopt practices to ensure transparency, such as labeling AI-generated videos.

The release of Sora signals a new era in video content creation. For most users, it represents an exciting opportunity to explore what’s possible with AI. For those in the EU and UK, it’s a reminder of how regulations can impact access to cutting-edge tools.

OpenAI’s decision to make Sora free for Pro and Plus users is a clear step toward democratizing AI technologies. As more people start using the tool, its potential to shape the future of media and marketing will become increasingly evident.

Sora is more than just a new tool; it’s a glimpse into the evolving landscape of AI in creative industries. While it opens doors for creators and businesses to push boundaries, it also invites reflection on how to responsibly integrate AI into our lives. The absence of Sora in certain regions is a testament to the complexities of balancing innovation with regulatory compliance.

As the world embraces Sora, its impact on video creation, social media, and broader content strategies will be closely watched. This marks not just a milestone for OpenAI but also a turning point for how we think about the intersection of AI and creativity.

Try Sora here. 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.

[Must Attend Webinar]: ‘Transform proofs-of-concept into production-ready AI applications and agents’ (Promoted)
The post OpenAI Just Released Sora: The Most Awaited AI Video-Generation Tool appeared first on MarkTechPost.

Inspired by human cognitive processes, large language models (LLMs) possess an intriguing ability to interpret and represent abstract world states, which are specific snapshots of the situation or context (basically the environment) described in the text, such as the arrangement of objects or tasks in a virtual or real-world scenario. The research explores this potential by examining whether LLMs construct goal-oriented abstractions, focusing on task-relevant details, rather than capturing a comprehensive and detailed world model, i.e., a structured framework that helps the AI understand the current situation and predict how it might change.

A vital challenge in AI is determining the level of abstraction required for solving specific tasks effectively. Balancing between intricate, highly detailed world models and minimalistic abstractions is essential. More complex models can help decision-making efficiency, while excessively abstract representations may omit critical information necessary for task completion. Researchers have attempted to unravel whether LLMs can achieve this balance, particularly when tasked with understanding and acting on textual descriptions of the world. These investigations have resulted in contradictory findings, prompting the need for a more systematic approach.

The study identifies limitations in current methods for probing LLMs. Existing research often seeks to recover the complete world state encoded in LLM representations. However, this approach must differentiate between general abstractions, which provide a broad understanding of the world, and goal-oriented abstractions, which prioritize task-specific information. For instance, some models excel in retrieving semantic relations between entities, while others struggle with tasks requiring nuanced recovery of world dynamics. These inconsistencies highlight the necessity of a framework capable of distinguishing varying levels of abstraction in LLMs.

Mila, McGill University, and Borealis AI researchers proposed a new framework grounded in state abstraction theory from reinforcement learning to address these gaps. This theory emphasizes creating simplified representations by aggregating similar states without compromising task-specific objectives. The framework was tested through a custom-designed “REPLACE” task, which challenges LLMs to manipulate objects in a textual environment to achieve a predefined goal. By varying task requirements and probing different levels of abstraction, the researchers aimed to understand whether LLMs prioritize detailed or goal-directed representations. The study also evaluated the impact of fine-tuning and advanced pre-training on the models’ abstraction capabilities.

The results revealed critical insights into how LLMs process world states. Models fine-tuned for specific tasks demonstrated a strong preference for goal-oriented abstractions. For example, in the REPLACE task, fine-tuned versions of Llama2-13b and Mistral-13b achieved success rates of 88.30% and 92.15%, respectively, far surpassing their pre-trained counterparts. Also, these models exhibited optimal action selection rates of 84.02% and 87.36%, indicating their ability to prioritize task-relevant information efficiently. Notably, fine-tuned models consistently outperformed pre-trained models in preserving task-specific abstractions, demonstrating that task-oriented training enhances LLMs’ ability to prioritize actionable insights over irrelevant world details.

Advanced pre-training was found to enhance LLMs’ reasoning capabilities but primarily for task-specific objectives. For example, pre-trained models like Phi3-17b identified necessary actions well but needed help capturing broader world details. In the REPLACE task, pre-trained models demonstrated high proficiency in tracking critical relationships, such as the relative position of objects and the agent’s required next actions. However, these models had lower success rates in maintaining comprehensive world representations, such as detailed object locations across the environment. This gap underscores that while pre-training improves goal-oriented abstraction, it must fully equip models for tasks demanding holistic understanding.

An important observation from the study is how LLMs process information during task execution. Fine-tuned models largely discarded details irrelevant to completing their goals. For instance, they ignored information about static elements in the environment (e.g., naming conventions for containers) unless it directly influenced the task. This focus allowed the models to streamline decision-making processes but limited their ability to handle tasks requiring detailed world knowledge. Researchers noted that LLMs simplified object relationships to essential terms, such as determining proximity or identifying the next critical action to perform, rather than preserving intricate world dynamics.

The study’s key takeaways can be summarized below:

LLMs, particularly those fine-tuned for specific tasks, excel in prioritizing actionable details over broader world representations. Models like Llama2-13b demonstrated an 88.30% success rate in achieving task objectives, highlighting their ability to focus on relevant information.

Pre-training improves task-relevant reasoning but has a limited impact on understanding broader world states. For instance, Phi3-17b accurately identified critical next actions but needed help comprehensively encoding all object locations.

Fine-tuned LLMs significantly simplify their representation of the world, discarding unnecessary information to optimize decision-making. However, this approach limits their versatility for tasks requiring a more detailed understanding of the environment.

Fine-tuning proved critical for enhancing task success and optimality, with fine-tuned models achieving efficiency rates exceeding 84%. This improvement indicates that tailored training is necessary for maximizing LLMs’ utility in specific applications.

In conclusion, this research underscores the strengths and limitations of LLMs in representing and reasoning about the world. Fine-tuned models are adept at focusing on actionable insights, effectively abstracting away irrelevant details to achieve task-specific goals. However, they often need to capture the broader dynamics of the environment, limiting their ability to handle more complex or multifaceted tasks.

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 60k+ ML SubReddit.

[Must Attend Webinar]: ‘Transform proofs-of-concept into production-ready AI applications and agents’ (Promoted)
The post How Fine-Tuned Large Language Models Prioritize Goal-Oriented Reasoning Over Comprehensive World Representations: Insights From the REPLACE Framework appeared first on MarkTechPost.

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker, users want a seamless and secure way to experiment with and select the models that deliver the most value for their business. In the initial stages of an ML project, data scientists collaborate closely, sharing experimental results to address business challenges. However, keeping track of numerous experiments, their parameters, metrics, and results can be difficult, especially when working on complex projects simultaneously. MLflow, a popular open-source tool, helps data scientists organize, track, and analyze ML and generative AI experiments, making it easier to reproduce and compare results.
SageMaker is a comprehensive, fully managed ML service designed to provide data scientists and ML engineers with the tools they need to handle the entire ML workflow. Amazon SageMaker with MLflow is a capability in SageMaker that enables users to create, manage, analyze, and compare their ML experiments seamlessly. It simplifies the often complex and time-consuming tasks involved in setting up and managing an MLflow environment, allowing ML administrators to quickly establish secure and scalable MLflow environments on AWS. See Fully managed MLFlow on Amazon SageMaker for more details.
Enhanced security: AWS VPC and AWS PrivateLink
When working with SageMaker, you can decide the level of internet access to provide to your users. For example, you can give users access permission to download popular packages and customize the development environment. However, this can also introduce potential risks of unauthorized access to your data. To mitigate these risks, you can further restrict which traffic can access the internet by launching your ML environment in an Amazon Virtual Private Cloud (Amazon VPC). With an Amazon VPC, you can control the network access and internet connectivity of your SageMaker environment, or even remove direct internet access to add another layer of security. See Connect to SageMaker through a VPC interface endpoint to understand the implications of running SageMaker within a VPC and the differences when using network isolation.
SageMaker with MLflow now supports AWS PrivateLink, which enables you to transfer critical data from your VPC to MLflow Tracking Servers through a VPC endpoint. This capability enhances the protection of sensitive information by making sure that data sent to the MLflow Tracking Servers is transferred within the AWS network, avoiding exposure to the public internet. This capability is available in all AWS Regions where SageMaker is currently available, excluding China Regions and GovCloud (US) Regions. To learn more, see Connect to an MLflow tracking server through an Interface VPC Endpoint.
In this blogpost, we demonstrate a use case to set up a SageMaker environment in a private VPC (without internet access), while using MLflow capabilities to accelerate ML experimentation.
Solution overview
You can find the reference code for this sample in GitHub. The high-level steps are as follows:

Deploy infrastructure with the AWS Cloud Development Kit (AWS CDK) including:

A SageMaker environment in a private VPC without internet access.
AWS CodeArtifact, which provides a private PyPI repository so that SageMaker can use it to download necessary packages.
VPC endpoints, which enable the SageMaker environment to connect to other AWS services (Amazon Simple Storage Service (Amazon S3), AWS CodeArtifact, Amazon Elastic Container Registry (Amazon ECR), Amazon CloudWatch, SageMaker Managed MLflow, and so on) through AWS PrivateLink without exposing the environment to the public internet.

Run ML experimentation with MLflow using the @remote decorator from the open-source SageMaker Python SDK.

The overall solution architecture is shown in the following figure.

For your reference, this blog post demonstrates a solution to create a VPC with no internet connection using an AWS CloudFormation template.
Prerequisites
You need an AWS account with an AWS Identity and Access Management (IAM) role with permissions to manage resources created as part of the solution. For details, see Creating an AWS account.
Deploy infrastructure with AWS CDK
The first step is to create the infrastructure using this CDK stack. You can follow the deployment instructions from the README.
Let’s first have a closer look at the CDK stack itself.
It defines multiple VPC endpoints, including the MLflow endpoint as shown in the following sample:
vpc.add_interface_endpoint(
“mlflow-experiments”,
service=ec2.InterfaceVpcEndpointAwsService.SAGEMAKER_EXPERIMENTS,
private_dns_enabled=True,
subnets=ec2.SubnetSelection(subnets=subnets),
security_groups=[studio_security_group]
)
We also try to restrict the SageMaker execution IAM role so that you can use SageMaker MLflow only when you’re in the right VPC.
You can further restrict the VPC endpoint for MLflow by attaching a VPC endpoint policy.
Users outside the VPC can potentially connect to Sagemaker MLflow through the VPC endpoint to MLflow. You can add restrictions so that user access to SageMaker MLflow is only allowed from your VPC.
studio_execution_role.attach_inline_policy(
iam.Policy(self, “mlflow-policy”,
statements=[
iam.PolicyStatement(
effect=iam.Effect.ALLOW,
actions=[“sagemaker-mlflow:*”],
resources=[“*”],
conditions={“StringEquals”: {“aws:SourceVpc”: vpc.vpc_id } }
)
]
)
)
After successful deployment, you should be able to see the new VPC in the AWS Management Console for Amazon VPC without internet access, as shown in the following screenshot.

A CodeArtifact domain and a CodeArtifact repository with external connection to PyPI should also be created, as shown in the following figure, so that SageMaker can use it to download necessary packages without internet access. You can verify the creation of the domain and the repository by going to the CodeArtifact console. Choose “Repositories” under “Artifacts” from the navigation pane and you will see the repository “pip”.

ML experimentation with MLflow
Setup
After the CDK stack creation, a new SageMaker domain with a user profile should also be created. Launch Amazon SageMaker Studio and create a JupyterLab Space. In the JupyterLab Space, choose an instance type of ml.t3.medium, and select an image with SageMaker Distribution 2.1.0.
To check that the SageMaker environment has no internet connection, open the JupyterLab space and check the internet connection by running the curl command in a terminal.

SageMaker with MLflow now supports MLflow version 2.16.2 to accelerate generative AI and ML workflows from experimentation to production. An MLflow 2.16.2 tracking server is created along with the CDK stack.
You can find the MLflow tracking server Amazon Resource Name (ARN) either from the CDK output or from the SageMaker Studio UI by clicking “MLFlow” icon, as shown in the following figure. You can click the “copy” button next to the “mlflow-server” to copy the MLflow tracking server ARN.

As an example dataset to train the model, download the reference dataset from the public UC Irvine ML repository to your local PC, and name it predictive_maintenance_raw_data_header.csv.
Upload the reference dataset from your local PC to your JupyterLab Space as shown in the following figure.

To test your private connectivity to the MLflow tracking server, you can download the sample notebook that has been uploaded automatically during the creation of the stack in a bucket within your AWS account. You can find the an S3 bucket name in the CDK output, as shown in the following figure.

From the JupyterLab app terminal, run the following command:
aws s3 cp –recursive <YOUR-BUCKET-URI> ./
You can now open the private-mlflow.ipynb notebook.
In the first cell, fetch credentials for the CodeArtifact PyPI repository so that SageMaker can use pip from the private AWS CodeArtifact repository. The credentials will expire in 12 hours. Make sure to log on again when they expire.
%%bash
AWS_ACCOUNT=$(aws sts get-caller-identity –output text –query ‘Account’)
aws codeartifact login –tool pip –repository pip –domain code-artifact-domain –domain-owner ${AWS_ACCOUNT} –region ${AWS_DEFAULT_REGION}
Experimentation
After setup, start the experimentation. The scenario is using the XGBoost algorithm to train a binary classification model. Both the data processing job and model training job use @remote decorator so that the jobs are running in the SageMaker-associated private subnets and security group from your private VPC.
In this case, the @remote decorator looks up the parameter values from the SageMaker configuration file (config.yaml). These parameters are used for data processing and training jobs. We define the SageMaker-associated private subnets and security group in the configuration file. For the full list of supported configurations for the @remote decorator, see Configuration file in the SageMaker Developer Guide.
Note that we specify in PreExecutionCommands the aws codeartifact login command to point SageMaker to the private CodeAritifact repository. This is needed to make sure that the dependencies can be installed at runtime. Alternatively, you can pass a reference to a container in your Amazon ECR through ImageUri, which contains all installed dependencies.
We specify the security group and subnets information in VpcConfig.
config_yaml = f”””
SchemaVersion: ‘1.0’
SageMaker:
PythonSDK:
Modules:
TelemetryOptOut: true
RemoteFunction:
# role arn is not required if in SageMaker Notebook instance or SageMaker Studio
# Uncomment the following line and replace with the right execution role if in a local IDE
# RoleArn: <replace the role arn here>
# ImageUri: <replace with your image if you want to avoid installing dependencies at run time>
S3RootUri: s3://{bucket_prefix}
InstanceType: ml.m5.xlarge
Dependencies: ./requirements.txt
IncludeLocalWorkDir: true
PreExecutionCommands:
– “aws codeartifact login –tool pip –repository pip –domain code-artifact-domain –domain-owner {account_id} –region {region}”
CustomFileFilter:
IgnoreNamePatterns:
– “data/*”
– “models/*”
– “*.ipynb”
– “__pycache__”
VpcConfig:
SecurityGroupIds:
– {security_group_id}
Subnets:
– {private_subnet_id_1}
– {private_subnet_id_2}
“””
Here’s how you can setup an MLflow experiment similar to this.
from time import gmtime, strftime

# Mlflow (replace these values with your own, if needed)
project_prefix = project_prefix
tracking_server_arn = mlflow_arn
experiment_name = f”{project_prefix}-sm-private-experiment”
run_name=f”run-{strftime(‘%d-%H-%M-%S’, gmtime())}”
Data preprocessing
During the data processing, we use the @remote decorator to link parameters in config.yaml to your preprocess function.
Note that MLflow tracking starts from the mlflow.start_run() API.
The mlflow.autolog() API can automatically log information such as metrics, parameters, and artifacts.
You can use log_input() method to log a dataset to the MLflow artifact store.
@remote(keep_alive_period_in_seconds=3600, job_name_prefix=f”{project_prefix}-sm-private-preprocess”)
def preprocess(df, df_source: str, experiment_name: str):

mlflow.set_tracking_uri(tracking_server_arn)
mlflow.set_experiment(experiment_name)

with mlflow.start_run(run_name=f”Preprocessing”) as run:
mlflow.autolog()

columns = [‘Type’, ‘Air temperature [K]’, ‘Process temperature [K]’, ‘Rotational speed [rpm]’, ‘Torque [Nm]’, ‘Tool wear [min]’, ‘Machine failure’]
cat_columns = [‘Type’]
num_columns = [‘Air temperature [K]’, ‘Process temperature [K]’, ‘Rotational speed [rpm]’, ‘Torque [Nm]’, ‘Tool wear [min]’]
target_column = ‘Machine failure’
df = df[columns]

mlflow.log_input(
mlflow.data.from_pandas(df, df_source, targets=target_column),
context=”DataPreprocessing”,
)

…

model_file_path=”/opt/ml/model/sklearn_model.joblib”
os.makedirs(os.path.dirname(model_file_path), exist_ok=True)
joblib.dump(featurizer_model, model_file_path)

return X_train, y_train, X_val, y_val, X_test, y_test, featurizer_model
Run the preprocessing job, then go to the MLflow UI (shown in the following figure) to see the tracked preprocessing job with the input dataset.
X_train, y_train, X_val, y_val, X_test, y_test, featurizer_model = preprocess(df=df,
df_source=input_data_path,
experiment_name=experiment_name)
You can open an MLflow UI from SageMaker Studio as the following figure. Click “Experiments” from the navigation pane and select your experiment.

From the MLflow UI, you can see the processing job that just run.

You can also see security details in the SageMaker Studio console in the corresponding training job as shown in the following figure.

Model training
Similar to the data processing job, you can also use @remote decorator with the training job.
Note that the log_metrics() method sends your defined metrics to the MLflow tracking server.
@remote(keep_alive_period_in_seconds=3600, job_name_prefix=f”{project_prefix}-sm-private-train”)
def train(X_train, y_train, X_val, y_val,
eta=0.1,
max_depth=2,
gamma=0.0,
min_child_weight=1,
verbosity=0,
objective=’binary:logistic’,
eval_metric=’auc’,
num_boost_round=5):

mlflow.set_tracking_uri(tracking_server_arn)
mlflow.set_experiment(experiment_name)

with mlflow.start_run(run_name=f”Training”) as run:
mlflow.autolog()

# Creating DMatrix(es)
dtrain = xgboost.DMatrix(X_train, label=y_train)
dval = xgboost.DMatrix(X_val, label=y_val)
watchlist = [(dtrain, “train”), (dval, “validation”)]

print(”)
print (f’===Starting training with max_depth {max_depth}===’)

param_dist = {
“max_depth”: max_depth,
“eta”: eta,
“gamma”: gamma,
“min_child_weight”: min_child_weight,
“verbosity”: verbosity,
“objective”: objective,
“eval_metric”: eval_metric
}

xgb = xgboost.train(
params=param_dist,
dtrain=dtrain,
evals=watchlist,
num_boost_round=num_boost_round)

predictions = xgb.predict(dval)

print (“Metrics for validation set”)
print(”)
print (pd.crosstab(index=y_val, columns=np.round(predictions),
rownames=[‘Actuals’], colnames=[‘Predictions’], margins=True))

rounded_predict = np.round(predictions)

val_accuracy = accuracy_score(y_val, rounded_predict)
val_precision = precision_score(y_val, rounded_predict)
val_recall = recall_score(y_val, rounded_predict)

# Log additional metrics, next to the default ones logged automatically
mlflow.log_metric(“Accuracy Model A”, val_accuracy * 100.0)
mlflow.log_metric(“Precision Model A”, val_precision)
mlflow.log_metric(“Recall Model A”, val_recall)

from sklearn.metrics import roc_auc_score

val_auc = roc_auc_score(y_val, predictions)

mlflow.log_metric(“Validation AUC A”, val_auc)

model_file_path=”/opt/ml/model/xgboost_model.bin”
os.makedirs(os.path.dirname(model_file_path), exist_ok=True)
xgb.save_model(model_file_path)

return xgb
Define hyperparameters and run the training job.
eta=0.3
max_depth=10

booster = train(X_train, y_train, X_val, y_val,
eta=eta,
max_depth=max_depth)
In the MLflow UI you can see the tracking metrics as shown in the figure below. Under “Experiments” tab, go to “Training” job of your experiment task. It is under “Overview” tab.

You can also view the metrics as graphs. Under “Model metrics” tab, you can see the model performance metrics that configured as part of the training job log.

With MLflow, you can log your dataset information alongside other key metrics, such as hyperparameters and model evaluation. Find more details in the blogpost LLM experimentation with MLFlow.
Clean up
To clean up, first delete all spaces and applications created within the SageMaker Studio domain. Then destroy the infrastructure created by running the following code.
cdk destroy
Conclusion
SageMaker with MLflow allows ML practitioners to create, manage, analyze, and compare ML experiments on AWS. To enhance security, SageMaker with MLflow now supports AWS PrivateLink. All MLflow Tracking Server versions including 2.16.2 integrate seamlessly with this feature, enabling secure communication between your ML environments and AWS services without exposing data to the public internet.
For an extra layer of security, you can set up SageMaker Studio within your private VPC without Internet access and execute your ML experiments in this environment.
SageMaker with MLflow now supports MLflow 2.16.2. Setting up a fresh installation provides the best experience and full compatibility with the latest features.

About the Authors
Xiaoyu Xing is a Solutions Architect at AWS. She is driven by a profound passion for Artificial Intelligence (AI) and Machine Learning (ML). She strives to bridge the gap between these cutting-edge technologies and a broader audience, empowering individuals from diverse backgrounds to learn and leverage AI and ML with ease. She is helping customers to adopt AI and ML solutions on AWS in a secure and responsible way.
Paolo Di Francesco is a Senior Solutions Architect at Amazon Web Services (AWS). He holds a PhD in Telecommunications Engineering and has experience in software engineering. He is passionate about machine learning and is currently focusing on using his experience to help customers reach their goals on AWS, in particular in discussions around MLOps. Outside of work, he enjoys playing football and reading.
Tomer Shenhar is a Product Manager at AWS. He specializes in responsible AI, driven by a passion to develop ethically sound and transparent AI solutions.

Can you believe that TikTok, the app that turned dance challenges into a cultural phenomenon and ad revenue powerhouse, is now at the center of a heated Supreme Court battle? 

How did we get here? 

It all started with rising concerns about data security and TikTok’s connection to China’s ByteDance, sparking calls for a nationwide ban. Fast forward and the debate’s now escalated to the highest court in the land.

Now, it’s worth nothing the ban is far from a done deal but it is certainly closer than ever before. 

And while the ban’s future hangs in the balance, its potential impact is already sending shockwaves through the business world. Experts predict a $1.3 billion loss for small U.S. businesses in the first month alone if TikTok goes dark. 

That’s a gut punch to ecommerce brands and advertisers who’ve built TikTok into their go-to revenue generator.

So what does this mean for advertisers? 

Well, whether you’re running viral campaigns or just dipping your toes into TikTok ads, this shake-up could flip your ad strategy upside down. 

The reality is that for advertisers, the TikTok ban isn’t just a news story – it’s a warning shot that is forcing us to ask – what’s Plan B?

TikTok’s Meteoric Rise: From Dance App to Advertising Giant

Let’s rewind a few years. 

TikTok started as a quirky app for lip-sync videos and dance trends, but it didn’t take long to become the it platform for creators, brands, and yes, advertisers. 

Fast forward to today, and TikTok is a cultural juggernaut, boasting over 1 billion monthly active users. And guess what? That number keeps climbing.

Advertisers quickly caught on. 

In 2023, businesses spent a whopping $2.5 billion globally on TikTok ads, cementing the app as the #2 app by ad revenue, just behind—you guessed it—Meta. 

TikTok wasn’t just a new option – it was the must-have platform, especially for reaching younger, highly engaged audiences.

It wasn’t always like this, though. Back in the day (read: a few years ago), Facebook ruled the ad game. If you wanted to reach anyone, you threw your dollars at Zuckerberg’s empire. But as TikTok, YouTube, and other platforms exploded, advertisers diversified their budgets, spreading the love across multiple channels.

Now here’s the twist – with TikTok’s future in limbo, we might see a boomerang effect. If TikTok is banned, all those ad dollars could come rushing back to Facebook. But is putting all your eggs back in Meta’s basket really the move?

Probably not because the real issue has to do with data. 

The Data Dilemma: Why First-Party Data Is Your Lifeline

Whether TikTok survives or not, the real issue for advertisers runs deeper than one platform. 

Social media giants like TikTok and Facebook hold onto their user data like it’s a winning lottery ticket. 

Sure, they’ll give you some insights to play with, but are they really giving you everything? Not a chance.

It’s actually getting worse. 

Between privacy updates (looking at you, iOS 14) and platforms tightening their grip on audience data, advertisers are losing visibility. You’re left running campaigns in the dark, hoping the algorithm works its magic. Sound familiar?

This is where first-party data comes in. 

First-party data is data you collect directly from your customers. It’s yours to keep, analyze, and use however you want. No middleman. No gatekeepers. It’s the antidote to being at the mercy of platforms constantly changing the rules.

Here’s why first-party data is a must:

Control: You decide how to use it, not TikTok, Facebook, or any other platform.

Accuracy: It’s straight from your customers, not some inferred guesswork from an algorithm.

Adaptability: If one platform tanks (hello, TikTok ban), your strategy doesn’t implode.

The takeaway? 

Platforms may come and go but your data is forever. 

Building a first-party data strategy isn’t just smart – it’s survival. 

So whether you’re running TikTok ads or gearing up for a post-ban world, the real question is are you owning your TikTok ads data or is it owning you?

Owning Your TikTok Ads Data with Customers.ai

Even if TikTok dodges the ban hammer, here’s the big question – are you really getting the most out of your TikTok ads data? 

Probably not. 

Platforms like TikTok only give you surface-level data – enough to run ads but not enough to own your audience. 

That’s where Customers.ai and visitor identification come in.

Here’s what you can do with Customers.ai:

Capture and own TikTok ad data: We’re not just talking engagement metrics or generic audience insights. You’ll know exactly who’s clicking your ads and landing on your site.

Go beyond the platform: Get the kind of data TikTok won’t give you, like demographics, psychographics, and a detailed view of your customer journey. You’ll see where your customers came from, what they did, and most importantly, what they’re ready to do next.

Smarter retargeting: Someone clicks on your TikTok ad but doesn’t convert? No problem. Retarget them on Meta or even email. Customers.ai lets you follow your audience wherever they go, making it easy to hit those multiple touchpoints big-ticket items often need.

Why this matters now more than ever:

For TikTok advertisers, small-ticket items (like that impulse-buy phone case) are easy wins. 

But if you’re selling something bigger, you need more than one interaction to seal the deal. That means catching your audience where they are and when they’re ready to buy.

And guess what? 

TikTok doesn’t hand you that kind of data on a silver platter. But with Customers.ai, you get that data! 

You’re essentially building a future-proof strategy that keeps you in control, no matter what happens to TikTok or any other platform.

How It Works: Owning Your TikTok Ad Data with Customers.ai

So, how does Customers.ai actually deliver all this magic? Let’s break it down:

1. Capture and Own TikTok Ad Data

When someone clicks on your TikTok ad, Customers.ai captures their information as they interact with your site. This isn’t just anonymous clicks and views – we’re talking real, actionable data. 

Customers.ai integrates with your campaigns to track visitors from the ad all the way to your website. This means you’re not relying on TikTok’s limited reporting.

2. Go Beyond the Platform

Platforms like TikTok might give you surface-level data, but Customers.ai digs deeper. By capturing data directly on your site, you get:

Demographics: Who they are—age, location, interests.

Psychographics: What motivates them to act.

Customer Journey Insights: See exactly how they arrived, what they browsed, and how close they are to making a purchase.

This level of detail lets you segment your audience in ways TikTok never could.

Whether someone’s ready to buy now or needs a little extra nudge, you’ll know exactly how to move them forward.

3. Smarter Retargeting

Here’s the beauty of owning your data: you’re not stuck retargeting on TikTok alone. 

If someone clicks your TikTok ad but doesn’t convert, Customers.ai helps you retarget them across other platforms, like Meta, Google Ads, or even email.

For example:

TikTok user clicks your ad but leaves without buying.

Customers.ai captures their visit and stores their data.

You use this data to serve a retargeting ad on Instagram or send a personalized email offer.

The result? You create multiple touchpoints, ensuring you stay top-of-mind, especially for those big-ticket items that require a longer buying cycle.

Remember – it’s all about owning your data so that you can use it in more impactful ways. And this is pretty impactful. 

The Future of Advertising Isn’t on One Platform

Whether TikTok weathers the storm or faces a nationwide ban, one thing is crystal clear – relying on a single platform is a risky game. 

The real lesson here isn’t just about TikTok. It’s about taking control of your advertising strategy and future-proofing your business.

Stop letting platforms dictate what data you can access. 

Start capturing and owning your own data so you can reach your audience on your terms, wherever they are, and whenever they’re ready to buy.

Platforms will come and go but first-party data is the constant you can’t afford to ignore. 

Whether you’re retargeting TikTok clicks on Meta or using Customers.ai to supercharge your campaigns, the takeaway is simple – it’s time to think beyond the platform and take control of your marketing destiny.

Ready to stop guessing and start owning your data? 

Let Customers.ai show you how to turn your ad clicks into real, actionable insights. Your future self (and your ROI) will thank you.

Start your free trial today and get 500 free contacts!

See Who Is On Your Site Right Now!

Get names, emails, phone numbers & more.

Try it Free, No Credit Card Required

Start Your Free Trial

Important Next Steps

See what targeted outbound marketing is all about. Capture and engage your first 500 website visitor leads with Customers.ai X-Ray website visitor identification for free.

Talk and learn about sales outreach automation with other growth enthusiasts. Join Customers.ai Island, our Facebook group of 40K marketers and entrepreneurs who are ready to support you.

Advance your marketing performance with Sales Outreach School, a free tutorial and training area for sales pros and marketers.

The post TikTok Ban Drama: What It Means for Your TikTok Ads Data appeared first on Customers.ai.

Welcome to our DTC Next Ecommerce Growth Virtual Summit Series where we are recapping all of the goodness from our big-time event. Miss the show? Catch the replay here and get the hottest tips from the top ecommerce pros in the industry along with an exclusive gift!

Don’t have time to watch? Get the full recap below:

Ecommerce success isn’t about having the flashiest ads or the trendiest products. 

It’s about knowing your customers. No – really knowing them – and tailoring their experience at every stage of the journey.

Maryna Hradovich from Maestra has seen what works and what doesn’t. With years of experience helping brands like L’Oréal thrive, she’s sharing her top three lifecycle marketing strategies to help you stay ahead in 2025. 

And these are no pie-in-the-sky, you’ll never really be able to do them ideas. They’re actionable, proven tactics that can deliver results.

Let’s break them down.

1. Make Real-Time Personalization Your Secret Weapon

Ever walked into a store where the staff knew your name and exactly what you were looking for? 

That’s the vibe your website needs to deliver! 

And guess what? It pays off. Real-time website personalization can drive a 10% revenue boost.

How to Pull It Off:

Collect Those Emails: Use tactics like product browsing suggestions, educational content, or “notify me when it’s on sale” options.

Source-Based Discounts: Traffic from Meta? Google? Different shoppers, different behavior—tailor those offers!

Dynamic Pricing: Not all customers are created equal. Offer deeper discounts to the deal-hunters while maintaining full price for those ready to splurge.

Boost AOV: Sweeten the deal by encouraging customers to add just one more thing to their cart.

Why Maryna Says it Matters: A personalized experience isn’t just a “nice-to-have”. It’s the difference between a bounce and a checkout.

2. Supercharge Your Abandonment Flows

Sure, you’ve set up cart abandonment emails. Congrats! You’re doing the bare minimum. 

Maryna’s advice? Take it further. Optimized abandonment flows can increase your revenue by 5x.

Expand Beyond the Cart:

Session, Search, and Category Abandonment: If someone browses, searches, or checks out a category but doesn’t commit, follow up.

Cross-Device Recognition: People shop everywhere—on their phones at lunch, their laptop at work, their tablet on the couch. Make sure their cart follows them.

Scarcity Works: “Only 1 left in stock!” notifications can be the nudge someone needs to hit “Buy Now.”

Loyalty Rewards: Skip the discounts. Offer loyalty points for purchases completed within 24 hours to drive urgency while boosting retention.

Pro Tip from Maryna Hradovich: Don’t just focus on recovering lost revenue. Use these flows to create loyalty and long-term customers.

3. Ditch Manual Campaigns and Bulk Discounts

Manual email campaigns? Bulk discounts? That’s so 2010. 

Maryna suggests leaving these outdated tactics behind and embracing the magic of automation.

Why Manual Campaigns are Out:

Segmentation often falls flat when it’s done manually. Either you’re sending irrelevant emails, or you’re spending way too much time trying to get it right.

Bulk discounts eat into margins and attract price-sensitive shoppers who might never come back.

What to Do Instead:

Dynamic Templates: One template, infinite possibilities. Personalize subject lines, product recommendations, and offers based on user behavior.

AI-Driven Segmentation: Let AI handle the heavy lifting. Target customers with exactly what they want, when they want it.

Case Studies that Prove It Works:

Magnum Bike automated their sales emails based on previous views and purchases, saving 8 hours per campaign while increasing conversions.

For Ocean dynamically personalized email content for each user, skyrocketing engagement.

The Results: Up to 20% incremental email revenue and hours of time saved.

Your 2025 Lifecycle Marketing Playbook Starts Here

2025 is around the corner (no really…we are talking days) and the stakes are higher than ever. 

The brands that thrive won’t just be the ones with the best products – they’ll be the ones delivering unforgettable customer experiences.

Personalize your website, fine-tune your abandonment flows, and embrace automation. Follow these strategies from Maryna Hradovich you will be leading the pack.

Ready to see how Customers.ai can help? Start your free trial today and get 500 free contacts!

See Who Is On Your Site Right Now!

Get names, emails, phone numbers & more.

Try it Free, No Credit Card Required

Start Your Free Trial

Important Next Steps

See what targeted outbound marketing is all about. Capture and engage your first 500 website visitor leads with Customers.ai X-Ray website visitor identification for free.

Talk and learn about sales outreach automation with other growth enthusiasts. Join Customers.ai Island, our Facebook group of 40K marketers and entrepreneurs who are ready to support you.

Advance your marketing performance with Sales Outreach School, a free tutorial and training area for sales pros and marketers.

The post 3 Lifecycle Marketing Strategies from Maryna Hradovich to Elevate Your Ecommerce Game appeared first on Customers.ai.

The field of natural language processing (NLP) has grown rapidly in recent years, creating a pressing need for better datasets to train large language models (LLMs). Multilingual models, in particular, require datasets that are not only large but also diverse and carefully curated to capture the nuances of many different languages. Existing resources like CC-100, mC4, CulturaX, and HPLT provide useful starting points but come with notable drawbacks. These include scalability issues, incomplete language coverage, and noisy data that can undermine model training.

Hugging Face researchers released FineWeb2, a dataset that sets a new benchmark for multilingual training resources. Spanning 8 terabytes of compressed text data—roughly equivalent to 3 trillion words—FineWeb 2 draws from 96 CommonCrawl snapshots collected between 2013 and April 2024. This dataset is the result of extensive processing and refinement using the Datatrove library, ensuring high-quality text content organized into 1,893 language-script pairs. Released under the permissive ODC-By 1.0 license, FineWeb 2 is accessible for both research and commercial applications, making it a versatile resource for the NLP community.

What sets FineWeb2 apart is its consistent performance across multilingual tasks. It surpasses other popular datasets like CC-100, mC4, CulturaX, and HPLT, and in some cases, even outperforms datasets specifically curated for individual languages. These results underscore FineWeb 2’s potential as a one-stop solution for multilingual model pretraining.

Technical Details

FineWeb2’s foundation lies in the Datatrove library, a powerful tool for large-scale data processing. This library extracts and processes text from CommonCrawl snapshots, a rich source of diverse web data. By employing advanced deduplication methods, the dataset minimizes redundancy and removes low-quality text, leaving only meaningful content. Its rigorous filtering ensures that the dataset maintains linguistic relevance and coherence across languages.

With coverage of over 1,000 languages, FineWeb2 offers a unique resource for building models that can handle low-resource languages—a historically underserved area in NLP. The dataset’s organization into language-script pairs further enhances its utility for multilingual research. Moreover, the commercially permissive license allows organizations to use FineWeb 2 in a wide range of projects, bridging the gap between academic research and practical applications.

Performance Insights and Results

FineWeb2 has been tested extensively using FineTasks, a benchmark suite designed to evaluate linguistic and semantic capabilities. The results are compelling: FineWeb 2 consistently outperforms datasets like CC-100, mC4, CulturaX, and HPLT across tasks such as machine translation, text classification, and language modeling. Importantly, it also holds its own against single-language specialized datasets in several scenarios, demonstrating its ability to generalize effectively across languages.

These results reflect not just the scale of FineWeb 2 but also the quality of its data and the thoughtful design of its processing pipeline. With nearly 3 trillion tokens, researchers and developers have access to a dataset that balances size, quality, and diversity, enabling robust training for a wide range of multilingual tasks.

Key Takeaways from FineWeb2

FineWeb2 comprises 8TB of compressed text data, equivalent to nearly 3 trillion words, sourced from 96 CommonCrawl snapshots spanning 2013 to 2024.

It covers over 1,000 languages, organized into 1,893 language-script pairs, supporting research and applications in low-resource languages.

Processed using the Datatrove library, the dataset is meticulously deduplicated and filtered to ensure high quality and relevance.

It outperforms leading multilingual datasets like CC-100, mC4, CulturaX, and HPLT on diverse tasks and even rivals some single-language specialized datasets.

Available under the ODC-By 1.0 license, FineWeb 2 is suitable for both research and commercial use.

Conclusion

Hugging Face’s FineWeb2 represents a significant step forward in the development of multilingual datasets. By addressing common challenges like noisy data and incomplete language coverage, it provides a high-quality resource that can support a wide range of NLP tasks. Its scale, careful curation, and accessibility make it an essential tool for researchers and developers alike. As the need for inclusive and effective language models grows, FineWeb 2 offers a robust foundation for advancing multilingual NLP in both academia and industry.

Check out the Dataset. 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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Web-crawled image-text datasets are critical for training vision-language models, enabling advancements in tasks such as image captioning and visual question answering.  However, these datasets often suffer from noise and low quality, with inconsistent associations between images and text that limit the capabilities of the models. This limitation prevents achieving strong and accurate results, particularly in cross-modal retrieval tasks. Moreover, the computational costs of handling such large datasets are very prohibitive, making it very important to have a better methodology for training.

To address these limitations, researchers have explored synthetic captions generated by multimodal large language models (MLLMs) as replacements for raw web-crawled captions. Synthetic captions improve models’ performance, such as that demonstrated by VeCLIP and Recap-DataComp-1B. Still, current approaches face significant problems: the computational costs for processing whole captions, the issue of scalability especially with complex architectures, and inefficiency in making use of the entire information in synthetic captions.

Researchers from UC Santa Cruz and the University of Edinburgh introduce CLIPS, an enhanced vision-language training framework that maximizes the utility of synthetic captions through two innovative designs. It uses a strategy that focuses on partial synthetic captions for contrastive learning. Through the sampling of a part of synthetic captions, CLIPS shortens the input token length while either improving or retaining performance, consistent with principles derived from the inverse scaling law observed during CLIP training. This methodology not only improves retrieval accuracy but also significantly reduces computational costs. In addition, CLIPS incorporates an autoregressive caption generator that generates whole synthetic captions based on web-crawled captions and their corresponding images. This method follows the recaptioning mechanism found in MLLMs and ensures that synthetically captioned content is well utilized, enriching the semantic alignment between image and text.

The technical implementation involves preprocessing synthetic captions using a sub-caption masking strategy, retaining approximately 32 tokens—about one or two sentences—for the text encoder. This approach is coupled with a multi-positive contrastive loss, aligning both original and shortened captions for improved efficiency and effectiveness. In parallel, the generative framework uses an autoregressive decoder that takes web-crawled image attributes and captions as input, guided by a specially designed combination mask to allow for optimal token interaction. The decoder produces outputs that align with complete synthetic captions, and this training is consistent with using a generative loss function. This training is carried out on extensive datasets like DataComp-1B, and evaluations are made against benchmarks like MSCOCO and Flickr30K. Performance metrics include recall at 1 (R@1) for retrieval tasks and zero-shot classification accuracy.

Evaluations show that CLIPS achieves state-of-the-art performance on a range of tasks. For MSCOCO, it achieves an improvement of more than 5% in text-to-image retrieval accuracy and more than 3% in image-to-text retrieval compared to previous approaches. Similarly, on Flickr30K, the model shows better retrieval accuracy in both directions compared to competing frameworks. The effectiveness of this framework is further emphasized by its scalability, where smaller models trained using CLIPS outperform larger models obtained from competing approaches. In addition to retrieval tasks, the incorporation of the CLIPS visual encoder within multimodal large language models markedly improves their efficacy across various benchmarks, highlighting the flexibility and adaptability of this training framework. Moreover, ablation studies provide further corroboration of the generative modeling method’s effectiveness, demonstrating significant improvements in both alignment and retrieval metrics while preserving computational efficiency.

In conclusion, CLIPS transforms vision-language training over the challenges of previous attempts. It establishes new high benchmarks in cross-modal retrieval tasks by using synthetic captions and novel learning methodologies, providing scalability, computational efficacy, and improved multimodal understanding. This framework works as a major step that has been taken in attempting to pursue artificial intelligence through multimodal applications.

Check out the Paper, Code, and Model on Hugging Face. 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 This AI Paper from UC Santa Cruz and the University of Edinburgh Introduces CLIPS: An Enhanced CLIP Framework for Learning with Synthetic Captions appeared first on MarkTechPost.

Code intelligence has grown rapidly, driven by advancements in large language models (LLMs). These models are increasingly utilized for automated programming tasks such as code generation, debugging, and testing. With capabilities spanning multiple languages and domains, LLMs have become crucial tools in advancing software development, data science, and computational problem-solving. The evolution of LLMs is transforming how complex programming tasks are approached and executed.

One significant area for improvement in the current landscape is the need for comprehensive benchmarks that accurately reflect real-world programming demands. Existing evaluation datasets, such as HumanEval, MBPP, and DS-1000, are often narrowly focused on specific domains, like advanced algorithms or machine learning, failing to capture the diversity required for full-stack programming. Moreover, these datasets could be more extensive in assessing the multilingual and domain-spanning capabilities necessary for real-world software development. This gap poses a major obstacle to effectively measuring and advancing LLM performance.

Researchers from ByteDance Seed and M-A-P have introduced FullStack Bench, a benchmark that evaluates LLMs across 11 distinct application domains and supports 16 programming languages. The benchmark includes data analysis, desktop and web development, machine learning, and multimedia. Further, they developed SandboxFusion, a unified execution environment that automates code execution and evaluation in multiple languages. These tools aim to provide a holistic framework for testing LLMs in real-world scenarios and overcoming the limitations of existing benchmarks.

The FullStack Bench dataset contains 3,374 problems, each accompanied by unit test cases, reference solutions, and easy, medium, and hard difficulty classifications. Problems were curated using a combination of human expertise and LLM-assisted processes, ensuring diversity and quality in question design. SandboxFusion supports the execution of FullStack Bench problems by enabling secure, isolated execution environments that accommodate the requirements of different programming languages and dependencies. It supports 23 programming languages, providing a scalable and versatile solution for benchmarking LLMs on datasets beyond FullStack Bench, including popular benchmarks like HumanEval and MBPP.

The researchers conducted extensive experiments to evaluate the performance of various LLMs on FullStack Bench. Results revealed marked differences in performance across domains and programming languages. For example, while some models demonstrated strong basic programming and data analysis capabilities, others needed help with multimedia and operating system-related tasks. Pass@1, the primary evaluation metric, varied across domains, highlighting models’ challenges in adapting to diverse and complex programming tasks. SandboxFusion proved to be a robust and efficient evaluation tool, significantly outperforming existing execution environments in supporting a wide range of programming languages and dependencies.

Scaling laws were also analyzed, showing that increasing parameters generally improves model performance. However, researchers observed a performance decline for some models at higher scales. For example, the Qwen2.5-Coder series peaked at 14B parameters but showed a drop in performance at 32B and 72B. This finding underscores the importance of balancing model size and efficiency in optimizing LLM performance. Researchers observed a positive correlation between code compilation pass rates and test success rates, emphasizing the need for precise and error-free code generation.

The FullStack Bench and SandboxFusion collectively represent significant advancements in evaluating LLMs. By addressing the limitations of existing benchmarks, these tools enable a more comprehensive assessment of LLM capabilities across diverse domains and programming languages. This research lays the groundwork for further innovations in code intelligence and emphasizes the importance of developing tools that accurately reflect real-world programming scenarios.

Check out the Paper, FullStack Bench, and SandboxFusion. 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 Bytedance AI Research Releases FullStack Bench and SandboxFusion: Comprehensive Benchmarking Tools for Evaluating LLMs in Real-World Programming Scenarios appeared first on MarkTechPost.

Snowflake recently announced the launch of Arctic Embed L 2.0 and Arctic Embed M 2.0, two small and powerful embedding models tailored for multilingual search and retrieval. The Arctic Embed 2.0 models are available in two distinct variants: medium and large. Based on Alibaba’s GTE-multilingual framework, the medium model incorporates 305 million parameters, of which 113 million are non-embedding parameters. The large variant builds on a long-context adaptation of Facebook’s XMLR-Large and houses 568 million parameters, including 303 million non-embedding parameters. Both models support context lengths of up to 8,192 tokens, making them versatile for applications requiring extensive contextual understanding.

The innovation behind Arctic Embed 2.0 lies in its ability to provide high-quality retrieval across multiple languages while retaining its predecessors’ superior English retrieval capabilities. Snowflake’s team carefully balanced these multilingual demands, enabling Arctic Embed 2.0 to outperform even English-only models in English-language benchmarks such as the MTEB Retrieval benchmark. Also, these models demonstrated remarkable performance on multilingual benchmarks, including CLEF and MIRACL, achieving higher nDCG@10 scores across languages like German, French, Spanish, and Italian.

Despite their compact size relative to other frontier models, Arctic Embed 2.0 models deliver rapid embedding throughput. Testing on NVIDIA A10 GPUs revealed the large model’s capacity to process over 100 documents per second with sub-10ms query embedding latency. This efficiency facilitates deployment on cost-effective hardware, a crucial advantage for enterprises managing large-scale data. The release also includes advanced features such as Matryoshka Representation Learning (MRL), a technique designed for scalable retrieval. With MRL, users can compress embeddings to as little as 128 bytes per vector, a compression ratio 96 times smaller than the uncompressed embeddings of some proprietary models like OpenAI’s text-embedding-3-large. 

Arctic Embed 2.0, released under the Apache 2.0 license, allows organizations to modify and deploy models, ensuring wide applicability across various industries and use cases. This move underscores Snowflake’s dedication to democratizing AI tools, as highlighted by Clément Delangue, CEO of Hugging Face, who praised the contribution of these models to the global AI community. The models excel in in-domain evaluations like MIRACL and out-of-domain scenarios tested through CLEF benchmarks. This generalization is a critical improvement over earlier models, which often showed overfitting tendencies toward specific datasets.

Compared with other open-source and proprietary models, Arctic Embed 2.0 is a leader in multilingual and English-language retrieval quality. While some existing models force users to choose between maintaining high English retrieval performance or adding operational complexity for multilingual support, Arctic Embed 2.0 offers a unified solution. Its multilingual embeddings eliminate the need for separate models, simplifying workflows while achieving top-tier results. Another highlight of this release is its support for enterprise-grade retrieval at scale. The models’ compact embeddings and robust performance make them ideal for businesses aiming to handle vast document repositories efficiently.

In conclusion, Arctic Embed L 2.0 and Arctic Embed M 2.0 represent a leap in multilingual embedding models. With their unparalleled efficiency, scalability, and quality, these models set a new standard for global-scale retrieval tasks. Snowflake’s release empowers organizations to address multilingual challenges effectively and reinforces its role as a trailblazer in the AI landscape.

Check out the Arctic Embed L 2.0 and Arctic Embed M 2.0. 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.

[Must Attend Webinar]: ‘Transform proofs-of-concept into production-ready AI applications and agents’ (Promoted)
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Language Agents (LAs) have recently become the focal point of research and development because of the significant advancement in large language models (LLMs). LLMs have demonstrated significant advancements in understanding and producing human-like text. LLMs perform various tasks with great performance and accuracy. Through well-designed prompts and carefully selected in-context demonstrations, LLM-based agents, such as Language Agents, can be endowed with different environmental interaction and task-solving mechanisms.

Current agents typically use fixed mechanisms or a list of mechanisms activated in some predetermined order, greatly limiting their adaptability to the many different potential structures of solutions to tasks. Existing Language Agents (LAs) use other mechanisms, such as Reflexion with its Reflection mechanism for refinement and ReAct with External-Augmentation for evidence-based solutions. However, these approaches depend on manual mechanism activation, restricting adaptability in dynamic environments. ReAct, Reflexion, and Multi-Agent Debate have been developed to improve autonomous task-solving. Still, they require labor-intensive prompt designs and rely on proprietary, opaque foundation models, which hinder research into intrinsic mechanisms. Open-source LLMs have been adapted into LAs through imitation fine-tuning, using high-reward trajectories from golden rationales or distilled outputs from ChatGPT. This adaptation allowed smaller models to develop skills such as reasoning and planning. While these LAs are promising, they don’t support interactive self-improvement, so exploration fine-tuning to enhance adaptability is highly sought after.

To mitigate this, researchers from the University of Chinese Academy of Sciences and the Institute of Automation proposed Adaptive Language Agent Mechanism Activation Learning with Self-Exploration (ALAMA), which optimized mechanism activation adaptability without relying on expert models.

ALAMA introduced a unified agent framework (UniAct) to integrate diverse mechanisms as specific actions within a shared action space, enabling adaptive mechanism activation. The method leveraged self-exploration to generate diverse training trajectories, reducing reliance on manual annotation and expensive proprietary models.

The study focused on equipping an agent with five essential mechanisms—Reason, Plan, Memory, Reflection, and External-Augmentation—each enhancing task-solving performance through various prompts and demonstrations. ALAMA utilized the UniAct framework to unify diverse mechanisms into a shared action space. By leveraging self-exploration, the agent generated diverse solution trajectories, reducing the reliance on manual annotation. Through Implicit Mechanism Activation Optimization (IMAO) and Mechanism Activation Adaptability Optimization (MAAO), the agent was fine-tuned to adaptively activate appropriate mechanisms based on task characteristics, enhancing its ability to solve tasks with mechanism sensitivity.

The experiment used GPT-3.5-turbo0125 as the baseline and MetaLlama3-8B-Instruct for ALAMA. Datasets included GSM8K and HotpotQA for training and testing, with NumGLUE, SVAMP, TriviaQA, and Bamboogle for evaluating generalization. Several baselines were selected for comparison:  (1) Fixed single mechanisms, with manually constructed in-context demonstrations to activate different mechanisms; (2) Average performance across mechanisms; (3) Majority Voting, which selected the most consistent answer; and (4) Self-Adapt Consistency, applying the self-consistency technique to ALAMA.

The method showed notable improvements over traditional fixed manual mechanisms, particularly when combined with supervised learning (IMAO) and preference learning (MAAO), leading to better performance on tasks like GSM8K and HotpotQA. ALAMA’s adaptability was driven by behavior contrastive learning, enabling the model to choose the most suitable mechanism for each task. It was also less demanding regarding training data than prior methods but still delivered state-of-the-art results, making it more data-efficient. ALAMA performed very well on held-out tasks and showed better zero-shot performance than previous models, including majority voting-based ones. The method showed better results when more than one mechanism was used instead of a single mechanism. 

In conclusion, the proposed method greatly improved agent performance through mechanism sensitivity optimization and self-exploration. Despite its limitations, such as not addressing multi-mechanism activation and restricted data evaluation, the proposed method laid a foundation for future research to explore complex mechanism combinations and integrate diverse data to enhance adaptive learning capabilities!

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