Medical researchers are awash in a tsunami of clinical data. But we need major changes in how we gather, share, and apply this data to bring its benefits to all, says Leo Anthony Celi, principal research scientist at the MIT Laboratory for Computational Physiology (LCP).
One key change is to make clinical data of all kinds openly available, with the proper privacy safeguards, says Celi, a practicing intensive care unit (ICU) physician at the Beth Israel Deaconess Medical Center (BIDMC) in Boston. Another key is to fully exploit these open data with multidisciplinary collaborations among clinicians, academic investigators, and industry. A third key is to focus on the varying needs of populations across every country, and to empower the experts there to drive advances in treatment, says Celi, who is also an associate professor at Harvard Medical School.
In all of this work, researchers must actively seek to overcome the perennial problem of bias in understanding and applying medical knowledge. This deeply damaging problem is only heightened with the massive onslaught of machine learning and other artificial intelligence technologies. “Computers will pick up all our unconscious, implicit biases when we make decisions,” Celi warns. Sharing medical data
Founded by the LCP, the MIT Critical Data consortium builds communities across disciplines to leverage the data that are routinely collected in the process of ICU care to understand health and disease better. “We connect people and align incentives,” Celi says. “In order to advance, hospitals need to work with universities, who need to work with industry partners, who need access to clinicians and data.”
The consortium’s flagship project is the MIMIC (medical information marked for intensive care) ICU database built at BIDMC. With about 35,000 users around the world, the MIMIC cohort is the most widely analyzed in critical care medicine.
International collaborations such as MIMIC highlight one of the biggest obstacles in health care: most clinical research is performed in rich countries, typically with most clinical trial participants being white males. “The findings of these trials are translated into treatment recommendations for every patient around the world,” says Celi. “We think that this is a major contributor to the sub-optimal outcomes that we see in the treatment of all sorts of diseases in Africa, in Asia, in Latin America.”
To fix this problem, “groups who are disproportionately burdened by disease should be setting the research agenda,” Celi says.
That’s the rule in the “datathons” (health hackathons) that MIT Critical Data has organized in more than two dozen countries, which apply the latest data science techniques to real-world health data. At the datathons, MIT students and faculty both learn from local experts and share their own skill sets. Many of these several-day events are sponsored by the MIT Industrial Liaison Program, the MIT International Science and Technology Initiatives program, or the MIT Sloan Latin America Office.
Datathons are typically held in that country’s national language or dialect, rather than English, with representation from academia, industry, government, and other stakeholders. Doctors, nurses, pharmacists, and social workers join up with computer science, engineering, and humanities students to brainstorm and analyze potential solutions. “They need each other’s expertise to fully leverage and discover and validate the knowledge that is encrypted in the data, and that will be translated into the way they deliver care,” says Celi.
“Everywhere we go, there is incredible talent that is completely capable of designing solutions to their health-care problems,” he emphasizes. The datathons aim to further empower the professionals and students in the host countries to drive medical research, innovation, and entrepreneurship. Fighting built-in bias
Applying machine learning and other advanced data science techniques to medical data reveals that “bias exists in the data in unimaginable ways” in every type of health product, Celi says. Often this bias is rooted in the clinical trials required to approve medical devices and therapies.
One dramatic example comes from pulse oximeters, which provide readouts on oxygen levels in a patient’s blood. It turns out that these devices overestimate oxygen levels for people of color. “We have been under-treating individuals of color because the nurses and the doctors have been falsely assured that their patients have adequate oxygenation,” he says. “We think that we have harmed, if not killed, a lot of individuals in the past, especially during Covid, as a result of a technology that was not designed with inclusive test subjects.”
Such dangers only increase as the universe of medical data expands. “The data that we have available now for research is maybe two or three levels of magnitude more than what we had even 10 years ago,” Celi says. MIMIC, for example, now includes terabytes of X-ray, echocardiogram, and electrocardiogram data, all linked with related health records. Such enormous sets of data allow investigators to detect health patterns that were previously invisible.
“But there is a caveat,” Celi says. “It is trivial for computers to learn sensitive attributes that are not very obvious to human experts.” In a study released last year, for instance, he and his colleagues showed that algorithms can tell if a chest X-ray image belongs to a white patient or person of color, even without looking at any other clinical data.
“More concerningly, groups including ours have demonstrated that computers can learn easily if you’re rich or poor, just from your imaging alone,” Celi says. “We were able to train a computer to predict if you are on Medicaid, or if you have private insurance, if you feed them with chest X-rays without any abnormality. So again, computers are catching features that are not visible to the human eye.” And these features may lead algorithms to advise against therapies for people who are Black or poor, he says.
Opening up industry opportunities
Every stakeholder stands to benefit when pharmaceutical firms and other health-care corporations better understand societal needs and can target their treatments appropriately, Celi says.
“We need to bring to the table the vendors of electronic health records and the medical device manufacturers, as well as the pharmaceutical companies,” he explains. “They need to be more aware of the disparities in the way that they perform their research. They need to have more investigators representing underrepresented groups of people, to provide that lens to come up with better designs of health products.”
Corporations could benefit by sharing results from their clinical trials, and could immediately see these potential benefits by participating in datathons, Celi says. “They could really witness the magic that happens when that data is curated and analyzed by students and clinicians with different backgrounds from different countries. So we’re calling out our partners in the pharmaceutical industry to organize these events with us!”
This is a guest post co-authored by Nafi Ahmet Turgut, Mutlu Polatcan, Pınar Baki, Mehmet İkbal Özmen, Hasan Burak Yel, and Hamza Akyıldız from Getir. Getir is the pioneer of ultrafast grocery delivery. The tech company has revolutionized last-mile delivery with its “groceries in minutes” delivery proposition. Getir was founded in 2015 and operates in Turkey, the UK, the Netherlands, Germany, France, Spain, Italy, Portugal, and the United States. Today, Getir is a conglomerate incorporating nine verticals under the same brand. Predicting future demand is one of the most important insights for Getir and one of the biggest challenges we face. Getir relies heavily on accurate demand forecasts at a SKU level when making business decisions in a wide range of areas, including marketing, production, inventory, and finance. Accurate forecasts are necessary for supporting inventory holding and replenishment decisions. Having a clear and reliable picture of predicted demand for the next day or week allows us to adjust our strategy and increase our ability to meet sales and revenue goals. Getir used Amazon Forecast, a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts, to increase revenue by four percent and reduce waste cost by 50 percent. In this post, we describe how we used Forecast to achieve these benefits. We outline how we built an automated demand forecasting pipeline using Forecast and orchestrated by AWS Step Functions to predict daily demand for SKUs. This solution led to highly accurate forecasting for over 10,000 SKUs across all countries where we operate, and contributed significantly to our ability to develop high scalable internal supply chain processes. Forecast automates much of the time-series forecasting process, enabling you to focus on preparing your datasets and interpreting your predictions. Step Functions is a fully managed service that makes it easier to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function helps you scale more easily and change applications more quickly. Step Functions automatically triggers and tracks each step and retries when there are errors, so your application executes in order and as expected. Solution overview Six people from Getir’s data science team and infrastructure team worked together on this project. The project was completed in 3 months and deployed to production after 2 months of testing. The following diagram shows the solution’s architecture.
The model pipeline is executed separately for each country. The architecture includes four Airflow cron jobs running on a defined schedule. The pipeline starts with feature creation which first creates the features and loads them to Amazon Redshift. Next, a feature processing job prepares daily features stored in Amazon Redshift and unloads the time series data to Amazon Simple Storage Service (Amazon S3). A second Airflow job is responsible for triggering the Forecast pipeline via Amazon EventBridge. The pipeline consists of Amazon Lambda functions, which create predictors and forecasts based on parameters stored in Amazon S3. Forecast reads data from Amazon S3, trains the model with hyperparameter optimization (HPO) to optimize model performance, and produces future predictions for product sales. Then the Step Functions “WaitInProgress” pipeline is triggered for each country, which enables parallel execution of a pipeline for each country. Algorithm Selection Amazon Forecast has six built-in algorithms (ARIMA, ETS, NPTS, Prophet, DeepAR+, CNN-QR), which are clustered into two groups: statististical and deep/neural network. Among those algorithms, deep/neural networks are more suitable for e-commerce forecasting problems as they accept item metadata features, forward-looking features for campaign and marketing activities, and – most importantly – related time series features. Deep/neural network algorithms also perform very well on sparse data set and in cold-start (new item introduction) scenarios. Overall, in our experimentations, we observed that deep/neural network models performed significantly better than the statistical models. We therefore focused our deep-dive testing on DeepAR+ and CNN-QR One of the most important benefits of Amazon Forecast is scalability and accurate results for many product and country combinations. In our testing both DeepAR+ and CNN-QR algorithms brought success in capturing trends and seasonality, allowing us to obtain efficient results in products whose demand changes very frequently. Deep AutoRegressive Plus (DeepAR+) is a supervised univariate forecasting algorithm based on recurrent neural networks (RNNs) created by Amazon Research. Its main advantages are that it is easily scalable, able to incorporate relevant co-variates into the data (such as related data and metadata), and able to forecast cold-start items. Instead of fitting separate models for each time series, it creates a global model from related time series to handle widely-varying scales through rescaling and velocity-based sampling. The RNN architecture incorporates binomial likelihood to produce probabilistic forecasting and is advocated to outperform traditional single-item forecasting methods (like Prophet) by the authors of DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. We ultimately selected the Amazon CNN-QR (Convolutional Neural Network – Quantile Regression) algorithm for our forecasting due to its high performance in the backtest process. CNN-QR is a proprietary ML algorithm developed by Amazon for forecasting scalar (one-dimensional) time series using causal Convolutional Neural Networks (CNNs). As previously mentioned, CNN-QR can employ related time series and metadata about the items being forecasted. Metadata must include an entry for all unique items in the target time series, which in our case are the products whose demand we are forecasting. To improve accuracy, we used category and subcategory metadata, which helped the model understand the relationship between certain products, including complementary and substitutes. For example, for beverages, we provide an additional flag for snacks since the two categories are complementary to each other. One significant advantage of CNN-QR is its ability to forecast without future related time series, which is important when you can’t provide related features for the forecast window. This capability, along with its forecast accuracy, meant that CNN-QR produced the best results with our data and use case. Forecast Output Forecasts created through the system are written to separate S3 buckets after they are received on a country basis. Then, forecasts are written to Amazon Redshift based on SKU and country with daily jobs. We then carry out daily product stock planning based on our forecasts. On an ongoing basis, we calculate mean absolute percentage error (MAPE) ratios with product-based data, and optimize model and feature ingestion processes. Conclusion In this post, we walked through an automated demand forecasting pipeline we built using Amazon Forecast and AWS Step Functions. With Amazon Forecast we improved our country-specific MAPE by 10 percent. This has driven a four percent revenue increase, and decreased our waste costs by 50 percent. In addition, we achieved an 80 percent improvement in our training times in daily forecasts in terms of scalability. We are able to forecast over 10,000 SKUs daily in all the countries we serve. For more information about how to get started building your own pipelines with Forecast, see Amazon Forecast resources. You can also visit AWS Step Functions to get more information about how to build automated processes and orchestrate and create ML pipelines. Happy forecasting, and start improving your business today!
About the Authors Nafi Ahmet Turgut finished his Master’s Degree in Electrical & Electronics Engineering and worked as graduate research scientist. His focus was building machine learning algorithms to simulate nervous network anomalies. He joined Getir in 2019 and currently works as a Senior Data Science & Analytics Manager. His team is responsible for designing, implementing, and maintaining end-to-end machine learning algorithms and data-driven solutions for Getir. Mutlu Polatcan is a Staff Data Engineer at Getir, specializing in designing and building cloud-native data platforms. He loves combining open-source projects with cloud services. Pınar Baki received her Master’s Degree from the Computer Engineering Department at Boğaziçi University. She worked as a data scientist at Arcelik, focusing on spare-part recommendation models and age, gender, emotion analysis from speech data. She then joined Getir in 2022 as a Senior Data Scientist working on forecasting and search engine projects. Mehmet İkbal Özmen received his Master’s Degree in Economics and worked as Graduate Research Assistant. His research area was mainly economic time series models, Markov simulations, and recession forecasting. He then joined Getir in 2019 and currently works as Data Science & Analytics Manager. His team is responsible for optimization and forecast algorithms to solve the complex problems experienced by the operation and supply chain businesses. Hasan Burak Yel received his Bachelor’s Degree in Electrical & Electronics Engineering at Boğaziçi University. He worked at Turkcell, mainly focused on time series forecasting, data visualization, and network automation. He joined Getir in 2021 and currently works as a Lead Data Scientist with the responsibility of Search & Recommendation Engine and Customer Behavior Models. Hamza Akyıldız received his Bachelor’s Degree of Mathematics and Computer Engineering at Boğaziçi University. He focuses on optimizing machine learning algorithms with their mathematical background. He joined Getir in 2021, and has been working as a Data Scientist. He has worked on Personalization and Supply Chain related projects. Esra Kayabalı is a Senior Solutions Architect at AWS, specializing in the analytics domain including data warehousing, data lakes, big data analytics, batch and real-time data streaming and data integration. She has 12 years of software development and architecture experience. She is passionate about learning and teaching cloud technologies.
Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. To make it simpler to evaluate the capabilities of Amazon Textract, we have launched a new Bulk Document Uploader feature on the Amazon Textract console that enables you to quickly process your own set of documents without writing any code. In this post, we walk through when and how to use the Amazon Textract Bulk Document Uploader to evaluate how Amazon Textract performs on your documents. Overview of solution The Bulk Document Uploader should be used for quick evaluation of Amazon Textract for predetermined use cases. By uploading multiple documents simultaneously through an intuitive UI, you can easily gauge how well Amazon Textract performs on your documents. You can upload and process up to 150 documents at once. Unlike the existing Amazon Textract console demos, which impose artificial limits on the number of documents, document size, and maximum allowed number of pages, the Bulk Document Uploader supports processing up to 150 documents per request and has the same document size and page limits as the Amazon Textract APIs. This makes it more efficient for you to evaluate a larger set of documents. The Bulk Document Uploader outputs a standard Amazon Textract JSON response and CSV file. The results are provided in JSON format for easy programmatic analysis. Additionally, a human-readable CSV file with confidence scores is provided for simple comparison and evaluation of the extracted information. When using this feature, keep in mind the following:
The Bulk Document Uploader processes documents via asynchronous operations. You can track the status of the processing on the Amazon Textract console. Only DetectDocumentText (OCR), AnalyzeDocument (Tables, Queries, Forms, and Signatures), and AnalyzeExpense APIs are currently supported. The Bulk Document Uploader provides JSON results of the API operations and formatted CSV reports. You may need to rely on external tools for visualization of the data, such as displaying bounding box highlights on the document using the JSON results. Using this feature to process documents incurs the same charges as regular Amazon Textract usage (depending on which feature is used), and is subject to the TPS (transactions per second) limits for APIs that are set for the account and Region. For more information on pricing, refer to Amazon Textract pricing. To learn more about Amazon Textract limits, refer to Quotas in Amazon Textract. Accepted file formats for bulk uploader are JPEG, PNG, TIF, and PDF. JPEG 2000-encoded images within PDFs are also supported. JPEG and PNG files have a 10 MB size limit, whereas PDF and TIF files have a 500 MB size limit. Multi-page PDF and TIF files have a 3,000 page limit.
Use the Bulk Document Uploader The Bulk Document Uploader is intended to help you quickly evaluate how Amazon Textract performs on a set of your own documents, without needing to write any code. You can use the Bulk Document Uploader to process as many as 150 documents instead of uploading and processing documents individually. You can bulk upload documents directly from your computer or import documents from an existing Amazon Simple Storage Service (Amazon S3) bucket. The Bulk Document Uploader provides results that you can download later for offline review. Each downloadable ZIP file contains the Amazon Textract API response in JSON file format and a human-readable CSV file of the output containing the extracted data and confidence scores. The output results are available for download for 7 days after processing. After 14 days, documents are cleared from the Submitted documents section. To use the Bulk Document Uploader, complete the following steps:
On the Amazon Textract console, under Demos in the navigation pane, choose Bulk Document Uploader. Choose Upload documents. Specify the source of your documents.
You have two options to upload documents:
Import documents from S3 bucket – If you’re using an S3 bucket for your documents, provide the bucket URL and (optionally) the prefix where your documents reside, in s3://your-bucket/prefix/ format. Alternatively, choose Browse S3 to browse and select the desired location of your documents. If the Amazon S3 location you specified contains more than 150 documents, then only the first 150 documents will be sent to Amazon Textract for processing. Upload documents from your computer – If you’re uploading documents from your computer, you can upload up to 50 documents at a time by choosing Upload Documents. To upload additional documents (up to the maximum of 150), choose Add documents after your initial documents are uploaded.
In this case, your documents are first uploaded to an S3 bucket in your account that is created on your behalf, therefore it’s important to ensure that you have permissions to access and upload documents to Amazon S3. This is a one-time action, and the same bucket will be used for all subsequent uploads from your computer. If you want to upload and process the same set of documents, you can use the path to this S3 bucket using the Import documents from S3 bucket option. The S3 bucket created on your behalf will be visible after the bucket gets created.
Next, specify the Amazon Textract feature you want to use to process your documents.
You may select only one feature at a time to process your documents. If you need to evaluate additional features, you must create a separate request by selecting the desired feature and uploading the documents again. If the AnalyzeDocument – Queries feature is selected, you need to provide the queries you want to test against your documents. You can specify up to 30 queries at a time. If the uploaded documents contain multi-page (PDF or TIF) files, queries are only applied to the first page of each document. Refer to Best Practices for Queries to learn about how to construct queries.
Choose Start processing to submit the documents to Amazon Textract for processing.
You can track the document status and download the output results of processed documents in the Submitted documents section. This section updates periodically, and you can manually refresh it to see if the processing is complete. Each document is processed individually, so you can either select the document with Ready to download status or wait for all documents to complete processing to download the results. The output of the processed documents will remain available for up to 7 days for download, after which they will expire. Expired documents will be cleared from the Submitted documents section after 7 additional days (14 days from the processed date). We suggest downloading and preserving the outputs within the 7-day period.
Conclusion In this post, we announced the new Amazon Textract Bulk Document Uploader feature, which allows you to quickly process a large number of documents for evaluation purposes. You can use this feature to evaluate Amazon Textract for a predetermined use case with your documents. To learn more about how you can use Amazon Textract in your intelligent document processing workload, visit Amazon Textract features and Getting started with Amazon Textract.
About the Authors Shashwat Sapre is a Senior Technical Product Manager with the Amazon Textract team. He is focused on building machine learning-based services for AWS customers. In his spare time, he likes reading about new technologies, traveling and exploring different cuisines. Anjan Biswas is a Senior AI Services Solutions Architect with a focus on AI/ML and Data Analytics. Anjan is part of the world-wide AI services team and works with customers to help them understand and develop solutions to business problems with AI and ML. Anjan has over 14 years of experience working with global supply chain, manufacturing, and retail organizations, and is actively helping customers get started and scale on AWS AI services.
Welcome to the future, where businesses are in a high-stakes poker game. Artificial intelligence (AI) is the ace up their sleeves! In this blog post, we’ll take a wild ride through the magical realm of AI tools for business.
We’ll uncover the secret sauce that’s spicing up industries, learn how these tools are like having your own crystal ball for decision-making, and even dive into the nitty-gritty of AI adoption (hint: it’s not always a walk in the park).
So buckle up, and let’s explore the exciting world of AI tools that can help your business soar to new heights and leave the competition eating your digital dust!
AI Tools for Marketing
AI Tools for Customer Service
AI Tools for Sales
AI Tools for Productivity
AI Tools for Image Generation
AI Tools for Data Analysis
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Best AI Tools for Business: Marketing
Email Generator
Personalized, eye-catching emails are key to beating the competition to the customer. But for them to be effective you have to really nail them.
It’s difficult! And it can be a lot of work!
Or we should say: it used to be difficult and a lot of work. Now, it’s easy!
Customers.ai has an email generator built right into our email automation. Using it couldn’t be any simpler!
Plug in your URL so it can learn what your company does.
Type in any other information you’d like it know.
Profit!
A great AI tools for business
Signs of Life Detector
Writing a great email only helps your business if your potential customer sees it. That’s why we’ve built got email deliverability tools powered by AI built into our automations.
Our tools are designed to improve campaign performance, reduce spam complaints, and make your life easier.
Here’s how the tool works:
Automated Email Campaigns
Our automated email campaigns do the boring part for you so you can focus on what you do best.
Everything from simple scheduled cadences to flows with multiple senders triggered by and exporting to multiple external sources is possible with Customers.ai.
Here’s how to find integrations so you can make the most out of Customers.ai’s features:
Setting up integrations
To get the most out of automations, we recommend using round robin sending, which allows you to send from multiple addresses. This means you can send more emails in a day!
Here’s how to set up round robin sending:
How to set up Round Robin sending
Unlike other email automations, sending round robin emails won’t mess up your analytics.
We aggregate the stats from all the senders on your campaign right into your automation so you can always see how you’re performing.
Best AI Tools for Business: Customer Service
Ava
Ava is a chatbot that can answer questions and resolve issues.
Jeeves
Jeeves is a chatbot. It can help book appointments and track orders.
Katie
Katie is a chatbot that provides customer support.
Best AI Tools for Business: Sales
Customers.ai
Customers.ai’s email extractor tool generates better leads at lower costs than digital ads and other sales prospecting softwares.
When these high quality leads are combined with our SOLD and AI email automation tools, converting leads is easier than ever.
Chorus.ai
Chorus.ai helps salespeople record, transcribe, and analyze their sales calls.
Best AI Tools for Business: Productivity
Grammarly
Grammarly is an AI tool that can help businesses improve their writing.
Hemingway Editor
Hemingway Editor is an AI Tool that can help businesses identify and fix bad writing.
ProWritingAid
ProWritingAid is an AI tool that helps business improve their grammar.
ChatGPT for Sheets
This tool integrates with ChatGPT to clean up Google Sheets, generate tags, generate ad copy, and more.
ChatGPT for Docs
This tool integrates with ChatGPT so you can generate an outline, blog post, or speech with GPT in Google Docs.
Fiscal Notes + ChatGPT
Fiscal notes added integration with ChatGPT so users can match its regulatory database with the power and convenience of AI.
Kayak + ChatGPT
Use ChatGPT to help book business trips via Kayak.
Expedia + ChatGPT
If you prefer Expedia, you can use ChatGPT to help book business trips through them as well.
AI Tools for Business: Image Generators
DALL-E
DALL-E creates images from language prompts.
Midjourney
Midjourney also generates images from language prompts.
AI Tools for Business: Data Analysis
Google Cloud AI, Microsoft Azure, and Amazon SageMaker are all AI tools that can help businesses analyze data and build machine learning models.
Using AI Tools for Business
Sales & Marketing
Using AI Tools for Business like Customers.ai’s suite of prospecting and outreach automations saves time and money while getting better results. What’s better than more revenue from less work?
Decision Making and Data Analysis
Embracing AI tools in decision making and data analysis can revolutionize the way businesses operate, making them more agile and efficient. AI-powered solutions can analyze vast amounts of data at lightning speed, generating actionable insights that enable companies to make more informed, strategic decisions.
Customer Service and Support
AI tools are revolutionizing customer service and support by automating tasks, improving response times, and personalizing interactions. Implementing AI solutions in this area can lead to higher customer satisfaction and increased loyalty.
Conclusion
AI Tools for Business are going to change the way businesses operate. In fact, they already have. Be on the forefront by trying Customers.ai today!
FAQs about AI Tools for Business
What are AI tools? AI tools are software applications that use artificial intelligence to perform tasks. AI tools can be used for a variety of purposes, including customer service, marketing, sales, and more. What are the benefits of using AI tools for business? The are many benefits of to using AI tools for business. These include automating tasks, improving efficiency, boosting productivity, increasing personalization, and making better decisions. What are some top AI tools for business? Some of the top AI tools for business include Customers.ai, ChatGPT, Dall-E, Midjourney, and more. How can I find the right AI tools for my business? There are a few things you can do to find the right AI tools for your business. First, you need to identify your needs. What tasks do you need to automate? What areas of your business could use improvement? Once you know your needs, you can start researching AI tools. There are a number of resources available to help you find the right AI tools, including online reviews, industry publications, and trade shows. How can I use AI tools to improve my business? AI tools can help you automate customer service, personalize marketing, and more. How much do AI tools cost? The cost of AI tools varies depending on the tool and the features it offers. Some AI tools are free to use, while others can cost thousands of dollars per month. What is the future of AI tools for business? AI tools are still in their early stages of development, but they have the potential to revolutionize the way businesses operate. In the future, AI tools are likely to become more powerful, more affordable, and more widely used. As AI tools continue to develop, businesses will need to adapt to the changing landscape and find ways to use AI tools to their advantage. The post AI Tools for Business: Top 21 in 2023 appeared first on Customers.ai.
Email marketing is a tried and true tactic for businesses. It helps them connect with their customers, build relationships, and drive sales. AI email marketing is the next frontier.
With the sheer volume of emails flooding inboxes every day, it’s becoming increasingly difficult to stand out and make an impact.
By leveraging the power of AI, businesses can create more personalized and targeted email campaigns. These are more likely to resonate with their audience.
In this blog post, we’ll explore what AI email marketing is, the benefits it offers, and some of the tools and technologies that make it possible. Join us as we dive into this exciting world of and discover how it can help take your email campaigns to the next level.
What is AI Email Marketing?
Benefits of AI Email Marketing
Examples of AI Email Marketing
Best Tools for AI Email Marketing in 2023
Future of AI Email Marketing
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What is AI Email Marketing?
It is the use of artificial intelligence, machine learning, and other advanced technologies to optimize and automate email marketing campaigns.
At its core, it’s about using data and algorithms to create personalized and targeted emails that are more likely to resonate with the recipient.
Basically: its role is to make your life easier and your company more successful at the same time.
One of the key features is its ability to analyze large amounts of data about customer behavior and preferences.
By tracking how customers interact with emails (such as which links they click, when they open them, and how long they spend reading), AI algorithms can identify patterns and insights that human marketers might miss. These make your emails more deliverable.
Using this data, you can then create highly personalized email content that speaks directly to the recipient’s interests and needs.
For example, an AI-powered email might include product recommendations based on the recipient’s purchase history or browsing behavior.
You can also automate various aspects of the email marketing process, such as segmentation, scheduling, and A/B testing. By taking care of these tasks automatically, businesses can save time and resources while still achieving better results.
This offers a powerful way to improve the effectiveness and efficiency of email marketing campaigns, while also creating a more personalized and engaging experience for customers. Better results with less work!
Benefits of AI Email Marketing
Increased personalization
One of the biggest benefits is its ability to personalize emails at scale. By analyzing customer data and behavior, AI algorithms can create highly targeted and relevant email content that speaks directly to the recipient’s interests and needs. This level of personalization can lead to higher open rates, click-through rates, and ultimately, conversions.
Improved targeting and segmentation
AI can help businesses segment their email lists more effectively. By analyzing customer data, AI algorithms can identify different segments of customers based on factors such as demographics, behaviors, and preferences. This allows businesses to send more targeted and relevant emails to each segment. Which leads to, you guessed it, higher engagement and conversions.
Higher open rates and click-through rates
Email marketing campaigns using AI are more personalized and targeted. Because of this, they tend to have higher open rates and click-through rates than traditional email marketing. By sending emails that are more likely to resonate with the recipient, businesses can increase the chances that they will take action and engage with the content.
Greater efficiency and cost savings
AI can automate can automate many aspects of the email marketing process, such as segmentation, scheduling, and A/B testing. This can save businesses time and resources, allowing them to focus on other areas of their marketing strategy.
Examples of AI Email Marketing
Some of the most successful and cutting-edge businesses already ahead. Better to be like them than their competitors, yeah?
Netflix
Courtesy of Netflix
Netflix uses AI algorithms to personalize their email content based on each subscriber’s viewing history and preferences. For example, if a subscriber has been watching a lot of action movies, they might receive an email recommending a new action movie release.
Sephora
Sephora uses AI-powered chatbots to provide personalized makeup recommendations to customers via email. By analyzing customer data and preferences, the chatbot can suggest products that are tailored to each individual’s needs.
Spotify
Courtesy of MIH83
Spotify uses AI algorithms to create personalized playlists for each user based on their listening history and preferences. They then send personalized emails to each user with recommendations for new songs and playlists based on their listening habits.
Best AI Email Marketing Tools for 2023
AI Email Marketing Tool #1: X-Ray Email Extractor
What’s the most important part of email marketing? Getting the emails in the first place!
Customers.ai’s X-Ray email extractor makes getting high-quality leads a snap. All you have to do is install a bit of code to your site. Your reward? The names and email addresses of up to 20% of the anonymous visitors to your website.
These are leads who already know who you are and are already interested.
Here’s how to install it on your site:
To get the most out of the tool, we recommend setting it up on high-intent pages. Then, you can set up email automations targeted by the pages they visited. Here’s how to set that up:
From there, you can start turning your website traffic into revenue!
AI Email Marketing Tool #2: Automated Email Generator
What’s the most important part of email marketing? The emails!
But it can be a real pain. Coming up with personalized campaigns for every page? Every type of customer? It’s exhausting!
That’s why introduced our AI email writer, which will write your emails for you! Here’s a glimpse of it in action:
And it was able to do all of that just off our homepage! It only gets better the more information you give it!
This is an essential time saving tool. It’s even better when combined with our other targeting tools
AI Email Marketing Tool #3: Signs of Life Detector
What’s the most important part of email marketing? Getting the emails delivered!
That’s why we designed our Signs of Life Detector. It uses the power of AI to create better performing campaigns with fewer spam complaints, and less work for you!
The SOLD tool analyzes recipient responses to your emails to make this happen! Get more engagement as customers move through your automation, not less!
AI Email Marketing Tool #4: Automated Email Campaigns
Our campaigns are simple, customizable, and powerful.
Just need to set up a simple cadence? No problem.
Need to export some information to a Google Sheet? Easy!
Need to run some info through a Zapier-integrated tool? No sweat.
We know you need an eye on how your campaigns are performing. So we’ve got analytics at your fingertips. They’re compatible with our email deliverability tools like round robin sending. This means you don’t have to sacrifice analytics for deliverability!
Future of AI Email Marketing
The use of AI in email marketing is still in its early stages, but it is clear that this technology has the potential to transform the way businesses communicate with their customers. Here are a few trends that are likely to shape the future:
Increased personalization
As AI algorithms become more sophisticated and businesses collect more data on their customers, email marketing is likely to become even more personalized. Emails may be tailored to individual preferences, behaviors, and interests, creating a more engaging and relevant experience for customers.
Greater automation
AI technology can automate many aspects of email marketing, including content creation, segmentation, scheduling, and performance analysis. As businesses become more comfortable with using AI in their email campaigns, they may increasingly rely on automation to streamline their processes and improve their ROI.
Integration with other technologies
AI is likely to become more integrated with other technologies, such as chatbots, voice assistants, and social media. This integration will allow businesses to create more cohesive and omnichannel experiences for their customers.
Enhanced creativity
While AI algorithms can generate content, they are not yet able to replace the creativity and ingenuity of human marketers. However, as AI technology develops, it may become more capable of assisting marketers in the creative process, such as by suggesting new ideas or providing inspiration.
Conclusion
AI email marketing has the potential to revolutionize the way businesses communicate with their customers. By leveraging the power of machine learning algorithms, businesses can create more personalized, relevant, and engaging email campaigns that drive conversions and build customer loyalty.
However, while the benefits are clear, there are also potential drawbacks and limitations that must be considered. Businesses must be mindful of issues such as privacy, cost, and algorithmic bias, and work to mitigate these issues wherever possible.
Looking to the future, AI is likely to become even more sophisticated and integrated with other technologies. As businesses continue to collect more data on their customers and AI algorithms become more advanced, we can expect to see even greater levels of personalization, automation, and creativity in email marketing campaigns.
Ultimately, the key to success in AI email marketing is to use this technology in a thoughtful and strategic manner. You can do that with Customers.ai!
FAQs about AI Email Marketing
What is AI email marketing? AI email marketing is the use of artificial intelligence (AI) technologies such as machine learning algorithms to optimize and personalize email marketing campaigns. What are the benefits of AI email marketing? The benefits of AI email marketing include increased personalization, automation, efficiency, and relevance of email campaigns. This can lead to higher open rates, click-through rates, and ultimately, higher ROI. What tools are available for AI email marketing? There are a variety of tools, including email extractors, outreach tools, and automated writers. These tools increase personalization and deliverability. What is the future of AI email marketing? The future of AI email marketing is likely to involve increased personalization, automation, integration with other technologies, enhanced creativity, and ethical considerations around responsible and transparent use of AI technology. How does AI improve email marketing? AI can improve email marketing by using machine learning algorithms to analyze customer data, optimize email content, personalize campaigns, and automate certain tasks, such as segmentation and send times. How can businesses get started with AI email marketing? Businesses can get started with AI email marketing identifying their goals and target audiences. Then they can look into the available products and pick the right one for them. The post AI Email Marketing in 2023 appeared first on Customers.ai.
Artificial intelligence (AI) is rapidly transforming the world of marketing. AI-powered tools are being used to automate tasks, personalize marketing messages, and create more effective and engaging content. As a result, AI is helping marketers reach their target audiences more effectively and efficiently.
In this blog post, we will discuss the benefits of using AI in marketing and explore the different types of AI tools that are available to marketers. We will also provide tips on how to choose the right AI tools for your marketing needs.
So, what are you waiting for? Read on to learn more about how AI can help you take your marketing to the next level.
Types of AI Tools for Marketing
Best AI Tools for Marketing
Picking AI Tools for Marketing
Benefits of AI Tools for Marketing
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Types of AI Tools for Marketing
AI-Powered Writing Assistants
These tools help marketers generate content, such as blog posts, social media posts, and email campaigns. Using writing assistants helps saves time and creates more engaging content.
AI-Powered Content Marketing Platforms
AI-powered content marketing platforms can help marketers manage their content marketing campaigns. These platforms can help marketers track their results, identify trends, and optimize their content for search engines.
AI-Powered SEO Tools
These tools help marketers improve their website’s search engine ranking. These tools can help marketers identify keywords, track their progress, and make changes to their website to improve their rankings.
AI-Powered Marketing Automation Tools
AI-powered marketing automation tools can help marketers automate tasks, such as sending email campaigns, creating social media posts, and managing customer relationships. These tools can help marketers save time and improve their efficiency.
AI-Powered Personalization Tools
AI-powered personalization tools can help marketers personalize their marketing messages for each individual customer. These tools can help marketers target ads based on a user’s interests, send personalized email campaigns, and create personalized content.
Best AI Tools for Marketing
Best AI Tools for Marketing: Customers.ai
What does it do?
Customers.ai is an all-in-one AI-powered sales prospecting and marketing automation suite. Our bevy of tools makes it a must-use for B2C businesses of all sizes.
X-Ray Email Extractor
Customers.ai’s X-Ray tool identifies up to 20% of anonymous visitors to your website.
Even better, adding it to high-intent pages on your website is easy!
Adding this AI Tool for Marketing is simple!
Once you have it set up on specific pages, you can easily run targeted outreach automations based on what the user visited.
Setting up the targeting on this AI Tool for Marketing is super easy!
Email Deliverability Tools
We’ve got email deliverability tools built right into our automation to protect your reputation.
All our email automations begin with our Signs of Life Detector.
After your email contacts are validated, we send an opening email. If your leads open the email, we put them through the rest of your automation. If they don’t, we don’t. Easy!
This reduces spam complaints and gets your emails in front of the people who matter.
Round Robin Sending
Another tool for protecting your deliverability is round robin sending.
We automatically send emails from multiple accounts. This means you can send more emails a day without catching the eye of email providers.
Jasper.ai
What does it do?
Jasper.ai is an AI writing assistant. It can help you with everything from writing blog posts and social media posts to creating email campaigns and ad copy.
How does it use AI?
Jasper.ai uses AI to understand your audience and their needs, and then generate content that is tailored to their interests.
Anyword
What does it do?
Anyword is a data-driven copywriting tool. It helps you create more effective and engaging marketing content.
How does it use AI?
Anyword uses AI to analyze your target audience and the competitive landscape, and then generate copy that is more likely to convert
Scalenut
What does it do?
It is a content marketing platform that can help you with everything from generating ideas to writing and editing content.
How does it use AI?
Scalenut uses AI to generate content, edit content, and identify keywords.
Semrush
What does it do?
Semrush is a comprehensive marketing toolkit. It includes tools for SEO, social media marketing, and content marketing.
How does it use AI?
Semrush uses AI to track website rankings, improve your SEO performance, and find new keywords to target.
Pro Rank Tracker
What does it do?
An SEO tool that tracks your website rankings.
How does it use AI?
Pro Rank Tracker uses AI to identify opportunities to improve your SEO performance
INK
What does it do?
INK is a writing assistant.
How does it use AI?
It uses AI to improve writing style, grammar, and clarity.
Seventh Sense
What does it do?
Seventh Sense is a marketing platform. They help you improve your conversion rates and build stronger relationships with customers.
How does it use AI?
It uses AI to personalize marketing messages and content for each individual customer.
Acrolinx
What does it do?
It is a content editing platform designed to improve the quality of the content.
How does it use AI?
Acrolinx uses AI to fix errors in grammar, spelling, and style. It also uses AI to improve your content generally.
MarketMuse
What does it do?
It is a content research and writing platform.
How does it use AI?
MarketMuse uses AI to help you find the right topics to write about. It generates high-quality content that is optimized for search engines.
Picking AI Tools for Marketing
Here are some tips on how to choose the right AI tools for your marketing needs:
Consider your budget
AI tools can range in price from free to thousands of dollars per month. It’s important to set a budget before you start shopping for AI tools.
Consider your marketing goals
What are you hoping to achieve with AI? Do you want to increase website traffic, generate more leads, or improve customer satisfaction? Once you know your goals, you can start looking for AI tools that can help you achieve them.
Consider the size of your team
If you have a small team, you may want to choose AI tools that are easy to use and don’t require a lot of training. If you have a large team, you may want to choose AI tools that can scale with your business.
By following these tips, you can choose the right AI tools for your marketing needs and start using AI to improve your marketing results.
Benefits of AI Tools for Marketing
Increased personalization
AI can be used to personalize marketing messages for each individual customer. This can help marketers build stronger relationships with their customers and increase conversion rates.
Improved efficiency
AI can be used to automate marketing tasks, such as sending email campaigns, creating social media posts, and managing customer relationships. This can free up marketers to focus on more creative and strategic tasks.
Reduced costs
AI can help marketers save money on marketing costs. For example, AI-powered marketing automation tools can help marketers save time and money on sending email campaigns.
Improved results
AI can help marketers improve their marketing results. For example, AI-powered personalization tools can help marketers increase conversion rates.
As AI continues to develop, we can expect to see even more benefits of using AI in marketing. AI has the potential to revolutionize the way marketers reach their target audiences and achieve their marketing goals.
Conclusion
In conclusion, AI is a powerful tool that can be used to improve marketing results. By automating tasks, personalizing marketing messages, and creating more effective and engaging content, AI can help marketers reach their target audiences more effectively and efficiently.
FAQs about AI Tools for Marketing
What are AI tools for marketing? They are software programs that use artificial intelligence to automate tasks and improve the effectiveness of marketing campaigns. These tools can be used for a variety of tasks, such as generating content, sending emails, managing social media, and tracking results. How can AI tools for marketing help my business? They can help your business in a number of ways. It can help you save time, improve accuracy, increase efficiency, and boost result. What are some of the benefits of using AI tools for marketing? There are many benefits to using AI tools for marketing. Tools can improve efficiency by automating tasks that would otherwise be time-consuming and labor-intensive, freeing up your time to focus on other aspects of your business. They can also increase accuracy by providing insights and recommendations. Through this, they can boost results. What are some of the challenges of using AI tools for marketing? There are a few challenges associated with using AI tools for marketing. First, cost: AI tools can be expensive to purchase and implement. Second, expertise: It can take time and effort to learn how to use AI tools effectively. Third, data privacy: AI tools collect and analyze a lot of data, which can raise concerns about privacy. How do I choose the right AI tools for my business? When choosing AI tools for marketing, it is important to consider your specific needs and goals. Some factors to consider include, the type of marketing tasks you need help with, the size of your business, your budget, and your level of expertise. Where can I learn more about AI tools for marketing? You can learn more about AI tools for marketing by watching webinars and reading blogs. The post Top AI Tools for Marketing in 2023 appeared first on Customers.ai.
“In my opinion, AutoGPT / babyAGI / AgentGPT is the real disruption point for the use of AI for complex problem-solving. Keep an eye out for these terms popping up in the news in the coming months.”
Autonomous AI agents will introduce a new world of research and finding solutions, with unprecedented methods and approaches, unlike anything we would deem intuitive as humans.
Let’s break it down:
🤔 Autonomous AI agents are based on a generative agent architecture that consists of three main components: memory streams, reflection, and planning. This architecture enables the agents to learn from and interact with each other, similar to how humans do. Memory streams provide agents with long-term memory, recording their experiences in natural language. Reflection helps agents draw conclusions about themselves and others, guiding their behavior and interactions. Planning allows agents to translate their conclusions and current environment into high-level action plans, which are then recursively broken down into detailed behaviors for action and reaction.
🤔 Autonomous AI agents can potentially apply their reasoning to broader, more complex problems that require long-term planning and multiple steps. For example, developers are trying to create an autonomous system by stringing together multiple instances of OpenAI’s large language model (LLM) GPT that can do a number of things on its own, such as execute a series of tasks without intervention, write, debug, and develop its own code, and critique and fix its own mistakes in written outputs. One such application is Auto-GPT, which autonomously develops and manages businesses to increase net worth.
🤔 Those agents on autopilot can also take uncommon paths that may not be intuitive to humans but may lead to better outcomes. For example, generative AI models can produce novel and creative outputs that may surprise or inspire human users. Generative AI models can also optimize for objectives that may not be obvious or conventional to humans, such as minimizing energy consumption or maximizing diversity.
All of this suggest that 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐰𝐢𝐥𝐥 𝐢𝐧𝐝𝐞𝐞𝐝 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐞 𝐚 𝐧𝐞𝐰 𝐰𝐨𝐫𝐥𝐝 𝐨𝐟 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐚𝐧𝐝 𝐟𝐢𝐧𝐝𝐢𝐧𝐠 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬, with unprecedented methods and approach, unlike anything we would deem intuitive as humans. However, this also raises some ethical and societal challenges that need to be addressed, such as ensuring the safety and accountability of these agents, respecting the rights and values of human users, and fostering a collaborative and beneficial relationship between humans and AI.
What do you think — just hype? Or more behind?
Either way, here are some more articles you may find useful in this context.
Note:This article is written based on the Flutter SDK version 2.10.0.
Flutter’s RaisedButton widget has been deprecated since the release of Flutter 2. Material Design guidelines recommend that if you hire Flutter developers, they will use the ElevatedButton widget instead of the deprecated RaisedButton. This article will provide migration instructions for experts who need to replace RaisedButton with ElevatedButton.
Why is RaisedButton deprecated?
RaisedButton has been deprecated because it is not compliant with the updated Material Design guidelines. According to the updated guidelines, buttons should have a consistent elevation across different platforms, and the raised appearance of RaisedButton widget is not consistent across all platforms. As a result, ElevatedButton was introduced as a replacement, which has a more consistent elevation across different platforms.
Migration Instructions
To migrate from RaisedButton to ElevatedButton, follow these steps:
Replace all references to RaisedButton with ElevatedButton in your code.
If you used the RaisedButton.icon constructor, replace it with the ElevatedButton.icon constructor.
Update any properties that have changed. The following properties have been renamed or replaced:
Color is now style.backgroundColor
textColor is now style.foregroundColor
If you were using the disabledColor property, replace it with the style property and set the foregroundColor and backgroundColor properties separately.
Import the material.dart package if you haven’t already done so:
import 'package:flutter/material.dart';
8. Update the code where you use RaisedButton to use the ElevatedButton widget instead. For example:
Before:
RaisedButton( onPressed: () { // Do something when the button is pressed }, child: Text('Click me'), )
After:
ElevatedButton( onPressed: () { // Do something when the button is pressed }, child: Text('Click me'), )
7. If you want to style the ElevatedButton, you can use the style property. For example, to change the button’s background color, you can use:
Example
ElevatedButton( onPressed: () { // Do something when the button is pressed }, aElevatedButton( onPressed: () { // Do something when the button is pressed }, style: ElevatedButton.styleFrom( primary: Colors.red, // Set the button's background color ), child: Text('Click me'), ) )
Conclusion
In conclusion, RaisedButton has been deprecated, and coders should use ElevatedButton to comply with the updated Material Design guidelines. The migration process involves replacing all references to RaisedButton with ElevatedButton and updating any styling as required. By following these instructions, the development team of Flutter can ensure that their code is up to date and compliant with the latest Material Design guidelines.
If you want to know the usage of RaisedButton Depreciation and Migration in Flutter app development, then you connect with a leading app development company Flutter Agency who will give you complete guidance and knowledge of Flutter with their skills. Don’t hesitate, and feel free to connect with us!
Frequently Asked Questions (FAQs)
1. Why do we use an elevated button?
An elevated button is labeled child viewed on the Material widget whose Material. elevation raises when the Button is clicked. RaisedButton was utilized for this task earlier, which has now been deprecated.
2. What is the significant difference between the elevated and raised buttons in Flutter?
Elevated buttons are the un-deprecated raised buttons with no explicitly defined button stying. However, elevated buttons can not be styled, meaning you can not change the color of a button, Buttontyle, etc., just like raised buttons.
3. How do you provide elevated button width?
The complete width is set to the Elevated Button by including the style parameter; then, use the ElevatedButton. Styleform class to give the size of Button uButtonn property known as the minimum size.
We all remember conversing with a Chatbot at some point in our lives.
And we also remember having to then connect with a Human because the chatbot couldn’t understand our query. It was simply too complex for the Bot to decipher. The customer support executive, however, could easily understand our intent and satisfy us with an appropriate solution.
Conversational AI is the use of Machine learning and advanced algorithms where you interact with a computer naturally using audio, and the machine/computer understands the intent behind your query and responds accordingly. The response is usually in the form of audio, which mimics an actual human conversation and is the closest you can get to “speaking to a machine.” Ever used Amazon Alexa to know what time of the day it is or asked Apple’s Siri for directions? Then you have used Conversational AI, and these platforms are just scratching the surface of what the technology is capable of.
The technology behind Conversational AI is something called reinforcement learning, where the bot need not have a script to read off a response from. Traditional chatbots need to have scripts written by human agents behind the scenes, and they are told specifically what to do in response to specific keywords. A conversational AI chatbot progressively learns the responses it needs to give to carry out a successful conversation.
Now imagine a Chatbot who can play the customer support executive as naturally as a human can! It can understand our intent. Go through the multiple layers of a question. And give personalized solutions in a language we understand! That is the perfect example of high-level conversational AI!
#DYK, there is an entire discipline dedicated to Conversational Design?
Conversational Design concerns designing flows that seem real & human-like.
How does Conversational AI work?
Simply put, It allows computers to process text or voice into a language they understand. The machines then are able to understand the questions and respond to them aptly.
There are 4 major components of a smart conversational AI:
NLP (Natural Language Processing)
NLU (Natural Language Understanding)
Machine Learning
Speech Recognition
All these features work in a constant loop to understand, analyze, and respond to humans. Additionally, machine learning helps computers remember new words, phrases, and contexts so that they continue building their database. And get better at responding in a more personalized manner.
The whole purpose of developing it is to give users the same kind of conversation experience with machines as they have with real humans.
Why Is Conversational AI Important?
Conversational AI gives greater insight into the habits of the customer, which in turn, helps speed up the responses of the chatbot. As customer queries get more and more complex, it is Conversational AI that helps companies deal with a wide array of customers.
But this raises the issue of whether conversational AI is that significant. According to the numbers:-
More than 2.5 billion individuals use messaging services, and there are around a dozen main platforms that cater to different geographic and demographic groups.
Messaging applications make up five of the top ten most popular apps of all time, and 75% of smartphone users use at least one chat app.
Voice assistant systems such as Siri, Alexa, and Google Assistant are becoming increasingly popular. Almost 47% of individuals worldwide are more interested in adopting these technologies in their day-to-day activities.
Companies are routinely recruiting more AI employees to improve the consumer experience of AI technology. Big tech giants such as Microsoft, Google, Amazon, and Apple are working hard to democratize powerful artificial intelligence so that businesses can use it to create increasingly complicated experiences.
Conversational experiences will play a significant part in producing these one-to-one encounters and using the information they give, providing companies with an edge regardless of their core product in a world where mass marketing is progressively giving way to one-to-one brand creation.
What Are The Use Cases Of Conversational AI?
Lead generation: Companies currently use a chatbot to connect with potential consumers browsing their products and services on their websites.
Product promotion: Chatbots may communicate with potential consumers, present the offer, answer commonly asked questions, and even close the deal. All of this is done automatically and to as many clients as your website has at the moment.
Launch of new products: Introducing new items to your clients may be done with the aid of a chatbot, much like promotions. The chatbot will be ready to greet potential customers and advertise your new product or service at all times. In this manner, no matter what time of day or night it is, all of your clients will be informed about your new items and given precise and consistent information.
Assisting in making purchases: To improve the overall purchase process, chatbots answer questions and recommend items or services to all consumers. They provide a sense of companionship to your consumers, display photographs and videos from your inventory, and complete the buying process.
Nurturing the leads: Chatbots let clients learn more about your products and services by communicating with them at various sales funnel phases. Promptly, the chatbot will offer each consumer the information they require.
Faster customer query redressal: Chatbots can answer your clients’ most common queries 24 hours a day, 7 days a week. Thanks to chatbots, customer support personnel will be able to focus their attention on claims that require human-to-human interaction.
Making appointments: If your company wants to schedule appointments or make bookings. Using a chatbot to automate the act of arranging appointments will streamline important operations in your firm.
Product recommendations: A chatbot’s capacity to retrieve data from databases is one of its most significant characteristics. The consumer will be able to locate items faster and more efficiently thanks to the usage of a chatbot in customer service. A chatbot will also be able to suggest other related goods to enhance the user experience.
Order follow-up: Chatbots streamline the purchase confirmation process and keep customers updated on the status of their orders. Rather than speaking with one of your representatives, your client will opt to write to the chatbot for this information.
Evaluation of customer satisfaction: Another scenario in which your consumers would rather deal with a chatbot than a human representative is when they are asked to rate their level of pleasure. A chatbot can provide you with more precise replies.
What is an example of Conversational AI
As is evident, conversational AI can be used for a host of features, from recommending products and services appointment scheduling, and even boosting customer engagement. One example of conversational AI being used to make customer’s life easy is to schedule appointments through SmartAction.
SmartAction is a conversational AI tool that allows for intelligent appointment booking using a combination of voice and text. Missed appointment dates are a thing of the past with this super intelligent conversational AI tool. SmartAction understands that booking an appointment is not as straightforward as it sounds and involves a continuous back and forth between both parties before they come to a mutually agreed date and time.
The natural language capabilities of SmartAction are top-notch, thanks to a vast database of scheduling-related data. Think of just about any type of scheduling-related task, and SmartAction can take care of it for you. From hair salons to car repair shops, appointment scheduling is present just about anywhere, and with a conversational AI tool such as a SmartAction, you can rest assured that all the customers to your website will leave with a superior customer experience.
What is the difference between Conversational AI and a Chatbot?
Usually, chatbots are these basic software programs that answer people’s questions through a chat-based interface. Websites install them with predesigned questions & answers flow to navigate visitors to the desired action.
Whereas conversational AI is more context-based. It is designed to give users a conversational experience with computers. Like how you would have with another human! They use various artificial intelligence technologies to make computers talk with us in a smarter and more natural way.
The most basic difference between the two is that Conversational AI is AI-based, and chatbots are rule-based.
What is the key differentiator of Conversational Artificial Intelligence?
Chatbots and Conversational Artificial Intelligence converge on a host of features but are fundamentally different due to the fact that conversational AI uses Machine Learning and Natural Language Processing to understand the exact intent behind a user query and then give an appropriate response. Traditional chatbots often function on predefined workflows, where they understand only text inputs and commands. Conversational AI, on the other hand, understands even voice inputs in addition to text inputs.
If scalability is an issue for your brand, then a conversational AI tool can help you overcome this problem easily. Conversational AI chatbots are also much more efficient at transferring calls to an agent if they find it difficult to resolve a conversation since they use concepts of NLP and NLU to put human conversations into proper context. There are advanced computing algorithms at work here, and conversational AI is the perfect example of technology solving a very “human” problem.
Benefits of using conversational AI tools
A conversational AI platform may help everyone from small to medium businesses to giant corporations. The following are some of the most important advantages of employing conversational AI tools:
Conversational AI aids in the delivery of quick replies to a variety of clients. In an ideal world, every customer would receive a comprehensive customer service experience. A chatbot or virtual assistant is an excellent approach to guarantee that everyone’s demands are met without overworking yourself or your staff. Simple customer support concerns can be handled by AI chatbots, freeing you and your staff to handle more sophisticated issues. It also cuts down on both sides’ wait times.
Through useful hints and probing queries, conversational AI may potentially teach people. Customer service representatives frequently provide lessons to their clients. These courses can use conversational AI to automatically use a client’s profile data to guarantee that clients receive individualized assistance.
Conversational AI has been a useful guide for clients who are unsure where they should go. It can aid in the resolution of customer support queries. It can help in the creation and modification of sales. Customers may not be aware of product or add-on recommendations made by these platforms.
Language boundaries are no longer an issue thanks to conversational AI. Language translation software is included with most chatbots and virtual assistants. This enables them to identify, understand, and produce practically any language efficiently. Consequently, linguistic issues no longer hold up any customer service engagement. It makes your company more inviting and accessible to many clients.
Conversational AI overcomes the challenge of availability 24 hours a day, seven days a week. If a consumer needs assistance outside of typical business hours, a chatbot can assist them. It addresses a logistical issue and demonstrates how chatbots may save time, but there’s more to it.
Another advantage of Conversational AI, in terms of supporting clients in making buying decisions, is its accessibility. One of the most appealing aspects of starting a business online is that sales may happen at any time. The only thing that may get in the way is if consumers have shipping, sales, or product queries while there aren’t any personnel accessible. This is readily fixed with a chatbot or virtual assistant. It can help anyone waiting to respond to a query before completing their checkout because it is available at all hours. It implies those purchases will happen sooner — and you won’t have to worry about buyers losing interest in their purchase before it’s completed.
How to build Conversational AI?
Building your own Conversational AI chatbot is not as tedious a process as it is thought to be. Here are the steps involved in building your own chatbot:
Find the purpose of your chatbot.
Decide where you want your chatbot to appear.
Choose how you want to build your bot (using frameworks or using a platform).
Design chatbot conversation.
Test if your bot is working as per the plan.
Train your bot to talk like a human.
Monitor how your chatbot is performing.
We have listed all these steps in detail in our blog post here, which you can check out and start building your bot (without any code). Moving on.
How do you implement conversational AI?
There are a lot of factors that organizations need to consider before implementing Conversational AI as part of their tech stack. Here are some of the basic guidelines:
1. Start with a clear and focused use case
2. Understand the objective of your business to determine the interaction model
3. Carefully plan out to make clear choices and decide the answers that are to be given for repeated queries
4. Understand your customers and their queries to make sustained interactions
5. Try to understand the emotions of the customers and then decide that replies to your chatbots should be made to them
6. Finally, promote your conversational AI tool
A conversational AI tool can be implemented in the following ways:-
Chatbots — Chatbots may be found on websites, Facebook Messenger, iMessage, display advertising, and possibly additional channels in the future. They’re responding to more than simply support inquiries in most of these cases; they’re helping users to discover things they like and want to buy. This isn’t the only solution to the plethora of options available to today’s customers, but it’s one of the better ones since it allows individuals to converse and think things through with the assistance of a professional assistant.
Voice Assistants — Voice assistants are similar to chatbots, but because individuals must speak out to connect with them, the industry has evolved to include several non-transactional tasks. The important thing to remember is that while companies can profit from using voice assistants, they won’t be able to generate full-funnel engagement on their own. Although voice-based conversational AI has experienced little acceptance for transactions, it does provide a compelling entry point for customers to begin product searches, ask contemplation questions, and lay out in their minds the features of certain goods they’re interested in.
Mobile Assistants — Mobile assistants such as Siri, Google Now, and others, like Home Voice Assistants such as Amazon Alexa, Google Home, and Apple HomePod, can work similarly for brands, except that in most cases, consumers are using mobile assistants to perform tasks that they need doing quickly but when their hands are full. Text-to-speech functions while driving, sending quick messages, asking about the weather, or getting the results of a search engine query are all examples of this.
Interactive Voice Recognition Systems — Incoming callers can obtain information through a voice response system of pre-recorded instructions without speaking to an agent and use menu selections through touch-tone keypad selection or speech recognition to have their call routed to specified departments or specialists. A well-designed IVR software system can help improve contact center operations and KPIs while also increasing customer satisfaction. An efficient interactive voice response system can assist consumers in locating answers and doing simple activities on their own, especially during times of heavy call volume.
Importance of NLU in Conversation AI
NLU stands for natural language understanding. A question changes meaning with changing contexts. While a human can understand the intent behind a question, machines usually fail to do the same.
NLU comes under the bracket of NLP (Natural Language Processing and conversational AI. It helps conversational AI mark patterns in human language, understand users’ intent, and give them apt solutions accordingly. Conversational AI is developed so that computers can understand the intent of the user in both written and spoken.
Conversational AI, NLU, & NLP together help computers to interpret human language by understanding the basic speech parts.
Different versions of Conversational AI
From Facebook Messenger to WhatsApp to websites, brands are deploying conversational AI everywhere! Making visitors aware of their products/services, learning about their numerous benefits, and reaching a positive decision. Also, it is, by responding aptly and on time, helping brands curate & deliver a “WOW” customer experience. Every time!
Conversational AI software
Conversational AI software can be used to help customers solve common problems and automate repetitive tasks using natural language commands. Examples of Conversational AI Software include Kommunicate.io (Chatbot), Amelia, LivePerson, Haptik, Ada, and ServiceNext, among others.
So, here are the 3 different types of Conversational AI that are great for your business:
Voice Assistants: Think of Alexa! How she recognizes & responds to your voice commands. Usually, you have to speak out loud to the voice assistant to have it perform a function. Such as “Play Blinding Lights by The Weekend” or “Set the Alarm for Tomorrow”. So, Businesses can use them to answer customers’ questions related to their brand.
Mobile Assistant- Mobile assistants like Siri or Google are no different than Alexa or any other voice assistant. However, they are present in one’s mobile and act by converting speech to text. People might use them to place orders from their phones when they are busy. Brands have to stay prepared for that!
Chatbots: Chatbot is different from all kinds of conversational AI because it actually converses with customers. It keeps asking them relevant questions until they have reached the solution that they were looking for. Developers are continuously trying to make chatbots more contextual so they can give smart, personalized solutions.
Industries that are using Conversational AI
According to a report by marketsandmarkets “The global conversational AI market size is expected to grow from USD 4.8 billion in 2020 to USD 13.9 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 21.9% during the forecast period”.
Presently, businesses around the world are using it mostly in the form of chatbots only. However, there still are many other forms in which different industries are deploying this technology for benefit.
Some of the industries employing are:
Conversational AI in healthcare
AMREF is one of Africa’s largest healthcare organizations, headquartered in Nairobi, Kenya. When the coronavirus pandemic hit in 2020, AMREF was tested to its capacity, with the doctors and healthcare workers fighting an unknown virus. And then, there was a different kind of problem that AMREF had to tackle.
The educational institutes that come under AMREF’s purview had a lot of inquiries coming in, most of them repetitive queries about admission deadlines, course fees, and scholarship details. The management was already short—staffed, thanks to the pandemic, and were soon sifting through email after email, looking for the important ones which were drowned in the student inquiries. This is where the Kommunicate Conversational AI chatbot came in. Within a matter of days after implementation, the chatbot was able to deal with most of the queries that the students threw at it, and soon, the institute was able to provide a satisfactory customer experience to everyone who interacted with the bot.
Conversational AI in BFSI
Taxbuddy is an online tax filing service that helps you file your tax returns and also provides a plethora of other tax-related services in India, making it one of the most trusted brands when it comes to tax filing. Taxbuddy was launched in 2019, and the website soon grew in popularity, leaving behind a very peculiar problem.
Filing tax returns in India is a cumbersome process, and there were a lot of questions that customers asked the Chartered Accountants (CAs) before filing their returns. Taxbuddy felt that a chat interface was the best way to prevent the CAs from being overburdened. Taxbuddy looked for a Conversational AI chatbot solution and found the perfect partner in Kommunicate. With Kommunicate, Taxbuddy was able to save close to 2000+ hours and saw an increase of 13x in its productivity. This is a classic case of Conversational AI solving an everyday problem, and you can read the full story here.
Conversational AI in eCommerce
The ECommerce market, especially in the US, is quite mature when it comes to the number of players, the customer base, and the technology used. So when Epic Sports, a US-based eCommerce firm that specializes in sports apparel and accessories in the US, wanted to scale its customer base, they looked at one solution — chatbots.
Epic Sports was using Google’s Dialogflow ( which seamlessly integrates with Kommunicate), and when they started re-directing all their customer requests to the Kommunicate chatbot, they were now leveraging the best-of-breed technology. The Kommunicate chatbot helped Epic Sports contain up to 60% of their incoming service requests. Now THAT is an impressive application of conversational AI.
Challenges of Conversational AI
No support for native languages: Since a large portion of the world’s population does not speak English yet, one of the major challenges of Conversational AI is support for non-native speakers. Building conversational AI capabilities and support in non-native languages is crucial to spreading awareness of conversational AI, and this still remains a challenge for the widespread adoption of the technology.
Language comprehension: Conversational AI gets its raw input in the form of language, and this language can be in different dialects and can even contain background noises. In addition to this, there can be different slang that the AI engine will find difficult to comprehend.
Handling simultaneous conversations: Voice assistants that are the prime use case of Conversational AI are often placed in the living rooms of customers, where they must distinguish between a set of voice commands to the voice that is originally programmed. This comprehension is a major challenge to develop since the voices can sometimes be similar, and also the AI has to filter out the background noise too.
Conversational AI best practices
Building Conversational AI is different from building traditional software, and here are 3 best practices that one should follow before setting out to build a Conversational AI solution.
The first step in building a fully functional chatbot is to build a working prototype, and this can be as simple as building an FAQ bot. With your MVP in place, you should be able to gauge how well your Conversational AI model is working and what improvements need to be made. If you want to offer a greater level of personalization, you must integrate your bot to different databases. A good VA bot drives the conversation by intelligently leveraging AI and automation to suggest the next best course of action for users.
Businesses are continuously evolving, and what is relevant today may not be relevant six months down the road. Hence, conducting very extensive user research and then creating five to six versions of your Conversational AI tool before going into production can actually hurt your business. The trick here is to stay agile and iterate often according to changing business needs. Defining a clear roadmap for your product and pivoting at the right time can mean the difference between your VA surviving or ultimately sinking into the abyss.
Conversational AI should always be designed with the goal of serving the end users. Product teams should focus on high-volume tickets that often require minimum development efforts before trying to tackle the more complex use cases.
Wrapping up
With more interactions with humans, Conversational AI will continue to move toward perfection. It is quite possible that in the coming future, this technology becomes as effective as a human representative. It might even converse or provide solutions based on the emotional state of the consumer. From Healthcare to Human resources to Food, every industry today can use & experiment with conversational AI to grow multi-folds.
At Kommunicate, we are envisioning a world-beating customer support solution to empower the new era of customer support. We would love to have you on board to have a first-hand experience of Kommunicate. You can signup here and start delighting your customers right away.
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