How to Supercharge Customer Segmentation with AI and ML Solutions

Some tips and tricks for retail companies and MarTech teams looking to achieve hyper-personalization and capitalize on customer data-based insights 

Over the past few years, marketing teams in retail companies have been looking to leverage customer data to improve their campaigns and conversions. Customer segmentation has proven to be an effective way of achieving personalization and improving targeting.

However, traditional customer segmentation has a lot of challenges related to data quality and management that can result in wrong decisions and wasted marketing budgets. That's where artificial intelligence (AI) technologies such as machine learning (ML), deep learning (DL), and computer vision come in as a "rescue ranger".

At the dawn of the fourth industrial revolution, general data analytics solutions in marketing simply don’t cut it. Nor do they help deliver the experiences that your target customers are looking for these days.

This article explores how retail companies can benefit from AI-powered customer segmentation and bespoke Customer Data Platforms and what needs to be done to better understand your customer behavior trends and sentiments.


What is customer segmentation?

Customer segmentation describes the act of separating customers into groups based on shared characteristics.

Traditional customer segmentation protocols tend to be broad and divide the audience by age, sex, personal interests, socioeconomic background, geographical locations, etc.

They also divide the target audience further into categories like a first-time customer and a repeat customer. However, the level of segmentation often ends there.

To take it to the next level with custom AI tools, you must first determine if you have access to the correct data for the project. You should also have the ability to seamlessly connect data from disparate sources, scale on demand to provide targeted recommendations and leverage machine and deep learning (ML/DL) techniques.

77% of marketing ROI comes from segmented, targeted, and triggered campaigns

What's the difference between personalization and segmentation?​

While segmenting your audience is key to achieving your marketing campaign goals, it doesn’t allow you to personalize your offering to build strong brand loyalty and boost conversions. But at the same time, personalization and segmentation aren’t too different.

Segmentation provides the foundation by grouping customers based on specific (general) characteristics. Personalization helps brands connect with them individually based on their unique needs, desires, and motivations. We can perceive segmentation as “macro-segmentation” and personalization as a “micro-segmentation.”

However, we only achieve true personalization when we deploy AI and ML algorithms into marketing campaigns. This is because these advanced solutions help marketing teams continuously capture and analyze each brand interaction. Upon completion, you can use these insights to target specific customers with personalized messages on a massive scale.

Segmentation alone can’t deliver insights with the same level of detail or sophistication. As such, you can’t target customers with unique messages on a large scale without AI-powered data analytics.

Segmentation patterns (even micro-segmentation) are not personalization. Only personalization can offer unique messages and value propositions that exactly match many individual customers’ needs, preferences, and desires. Brands using segmentation fail to tailor marketing to individual needs, which is critical in today’s competitive world. And with a technology solution that uses artificial intelligence and machine learning, marketers can create high-volume messages and offers that reflect quality, 1: 1 personalization.

Key challenges of customer segmentation

Data quality

One of the biggest issues with customer segmentation is data quality. Many marketing databases are not maintained or cleaned regularly, and inaccurate data in source systems usually results in poor segmentation quality.

Data management

Effective customer segmentation is based on tagging data with precise terms and phrases. Many users entering data into systems do not understand segmentation definitions and use them incorrectly. Database users need to be trained to understand the different customer segments that have been identified, the actual data in the segments that categorizations represent, and when to use the correct customer segmentation for the proper analysis scenarios.

Time drain

It takes a significant amount of time to identify the target audience, analyze research data and develop advertising campaigns. Most small companies use market segmentation to identify their target customers. Market segmentation entails the development of customer profiles based on demographic, behavioral, technographic, and other types of data. Marketers then determine if their target audience is large enough to generate significant revenue. Subsequently, they spend time looking for channels that will help them reach their core customer base.

AI-driven customer segmentation

When you add AI to data analytics, your customer targeting becomes more accurate, dynamic and capable of boosting conversions. Powered with machine/deep (ML/DL) learning algorithms, you can analyze customer data more thoroughly and generate in-depth results about the targeted segments. You can also use this information to automate personalized marketing campaigns for each group.

This approach will generate superior results compared to traditional analytics-driven marketing campaigns.  For example, custom AI solutions can eliminate human bias when analyzing data and identify hidden trends and patterns that you never thought of before.

Businesses also benefit from automatic segment updates that help accurately reflect changes in the marketplace. Other advantages include increased personalization in real-time and limitless scalability.

To benefit from AI-powered customer segmentation, businesses must first change their investments to understand customer sentiments. It starts with turning qualitative comments into quantitative data with specialized custom AI solutions.

This approach demands companies to bring together data from call center notes, customer relationship management systems, emails, reviews, social media engagements, chatbots, and more.

Once you have all this data in a centralized warehouse, custom AI models and tools will go to work and look beyond essential customer sentiments (positive or negative).

For example, to analyze customer experiences, you have to extract and map keywords related to the following:

  • Activities (ordering, service delivery, etc.)
  • Available resources (knowledge, product, skills, systems, and more)
  • Content (or situation that impact experiences (like time of day)
  • Customer role (neutral or provide suggestions)
  • Engagement (call center and chatbot interactions)


You can also break down customer emotion into categories such as love, joy, anger, surprise, and more. Cognitive responses are things like suggestions, complaints, and compliments. Your AI tool will transform all this information into predictive variables that can train AI models to pinpoint when customers are neutral, unsatisfied, satisfied, or have a complaint.

In addition, you can create segments by traffic source (for example, Google Ads, social media, and email campaigns), search behavior, time spent on the platform, content viewed, type of device (mobile or laptop), likes, and preferences.

There isn’t a “right” way to segment your audience. It really depends on your business, industry vertical, goals, customers, location, and so on. So, turn-key AI solutions can fall short as they didn’t build it specifically for your unique business (and audience).

Are you looking to build an AI-powered Customer Data Platform to take your MarTech/AdTech campaigns to the next level? is featured by Clutch among Top 15 AI development companies in Romania. We help global enterprises and SMEs jump fast on the AI tech bandwagon and capitalize on custom ML/DL models thanks to our R&D Center, access to Europe's largest pool of AI dev talent, tribal knowkedge, and proven methodologies.

Supervised and unsupervised ML-based segmentation

There are two types of machine learning-based customer segmentation: supervised and unsupervised.

In the case of supervised ML, the marketer sets the rules first, and ML is used to sort the data based on those rules. For example, you can select options to sort customers by the number of products ordered, average profitability, and/or average cost.

When it comes to unsupervised ML, an algorithm is used to find different “clusters” based on similarities between customers, which may not be obvious at first sight. These clusters tend to be very small, so marketers can better identify specific customer groups, providing more personalized offerings and better targeting. For instance, unsupervised machine learning can identify the most targeted customer cluster, like those who ordered the most products, spent the most money, and never came back to the site. 

Non-targeted campaigns show a 50% lower CTR than segmented campaigns

Key benefits of AI-powered customer segmentation​​

The advantages of using custom AI-based solutions to segment your target audience include the following:

AI highlights what’s missing

AI takes the guesswork out of the equation. You’ll know exactly what your customers want and the touchpoints that really matter to them. Whenever business expectations don’t match those of the target audience, AI will alert your marketing team and provide them with an opportunity to fix it.

Quickly identify the root cause

If there’s a problem with a marketing campaign, customer service, or customer experience, smart algorithms will quickly alert you to it. AI tools will tell you what the problem is, where it is, and what’s causing it. This approach can help companies identify product defects quickly, problems with their marketing messages, and limit customer churn.

Real-time emotional and cognitive responses

In real-time, AI also helps enterprises understand their customers’ feelings about a product or service (even discreet emotions). It’s crucial because your customers won’t remember these feelings at a later date when you target them with a survey. Knowing what they are feeling in real-time also allows businesses to intervene at the right moment to avert abandonment.

Stop or Prevent Tumbling Sales

Whenever your sales numbers are starting to plummet, AI will provide an opportunity to adapt your campaigns to help stop it. For example, if a specific segment is likely to return or fail to pay in full, you can avoid targeting that segment or not offer credit.

These insights can also help companies hold on to their customers, reducing the costs associated with customer churn and new acquisitions.

As we can see, the key to efficient and accurate customer segmentation is:

  • Have a lot of accurate and complete data (the more and cleaner – the better).
  • Invest in developing the skills, technology, and knowledge base needed to work with data and segmentation strategies.

Determine new market opportunities

Dividing the customer base into smaller groups can allow marketers to identify segments they are not yet reaching. While this is true, there are still differences between consumers in any segment, so targeting one person in that segment may differ from the best way to target another person in the same segment.

Improve distribution strategy

Companies need to know where and when customers buy their products and services to better shape their distribution strategies. Unfortunately, traditional customer segmentation is not enough to provide this information on an individual level.

If you have a robust customer view of pure data and detailed customer information in the company’s data repositories, then you are in good shape.

Real-world case: how a leading European retail company achieved a 90% increase in customer insights by harnessing Facial Recognition ​​

Some time ago, a leading European retail company turned to for help building an advanced CCTV analytics dashboard to better define customer clusters, understand customer behavior, and improve informed decision-making.

Using stream cameras and video analytics servers (including Facial Recognition, Queue Detector, People Counter, Activity Visualizer and heatmap processing), our software engineering team built and delivered a dashboard solution to map each customer cluster’s needs and address their most sophisticated requests right away.

Our team delivered the custom-built solution with the following main features:

  • people counting,
  • gender and age breakdown,
  • in-depth visitor analysis,
  • foot traffic interval comparison and selection,
  • direction and heatmap analysis,
  • timeline comparison,
  • queue time and foot traffic analytics.


As a result, our retail client enjoyed:

  • 90% increase in customer insights generated;
  • 50% increase in the immediate response rate;
  • 60% more accurate customer patterns.


Check out full project case story here.

Customer data platform maturity stages

Now let’s take a closer look at customer data maturity stages.

Identity resolution. Data/GDPR compliance. First-party data access.

Development of custom dashboards and KPIs. Deployment and integration of data analytics tools, systems, and capabilities.

Development of triggers and personalization. Channel context.

Custom audiences. Retargeting. Media/bidding optimization with machine and deep learning (ML/DL) algorithms.

Real-time decisioning/personalization. Ai-based website + eCommerce conversion rate optimization. Testing and validation.

Building customer cross-channel journeys. Online/offline integrations.

Building AI/ML predictive models. Attribution.

In summary

The best part about all this is that the whole process can be codified and automated. It’ll also require little human intervention to manage it. Custom AI-based segmentation also provides all stakeholders with a comprehensive view of the entire customer journey to anticipate potential issues and respond accordingly.

Data helps businesses better understand their customers. Custom AI-powered data tools help enable more accurate customer segmentation, more meaningful engagements, and enhanced customer experiences—all of which have a direct impact on your bottom line.

Check out some of our custom AI projects

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