Customer Segmentation Models and How AI is Enhancing Them 

Personalization is no longer a luxury. It’s an expectation. And, at the heart of personalization is customer segmentation, the practice of dividing consumers into distinct groups to provide targeted services and communication.  

While our earlier blog explores how AI is reshaping retail at scale, this post zooms in on the customer segmentation models and how AI is enhancing them.  

Why customer segmentation matters 

Understanding your customers is key to staying ahead in this retail market. Customer segmentation helps brands tailor strategies according to customer preferences instead of taking a one-size-fits-all approach or guesswork. 

• Customer Segmentation enables targeted marketing and personalized promotions 
• It supports better inventory and supply chain planning/management  
• Enhances both in-store and digital shopping experiences 

AI customer segmentation can find patterns in real-time customer data, predict future behaviors, and demands.  

Types of customer segmentation models  

There are multiple customer segmentation models. Their importance varies depending on your industry and business. Here are some of the commonly used segmentation models:   

Demographic segmentation  

When the segmentation is based on the various demographic characteristics of the customers like age, gender, income, marital status, etc., it’s called demographic segmentation.   

How is it implemented by brands? Here’s an example.  

A home improvement retailer uses demographic segmentation to target new homeowners in the 30–40 age group with DIY starter kits and renovation bundles. Meanwhile, older customers nearing retirement receive promotions on home safety upgrades and energy-efficient appliances. 

Geographic segmentation  

It is based on where your customers are physically located and is typically more useful for B2C brands. Let’s look at a use case.  

A clothing retailer offers raincoats and waterproof boots in coastal cities like Seattle and Boston, where rainfall is frequent. In warmer cities like Phoenix and Miami, it promotes sandals, shorts, and lightweight fabrics. Store inventory and marketing are tailored to match the local climate and customer needs. 

RFM segmentation  

Recency, Frequency, Monetary (RFM) segmentation is a powerful segmentation model to group customers according to their purchase history. It considers three main parameters:  

• Recency: How recently a customer has made a purchase.  
• Frequency: How often a customer makes a purchase.  
• Monetary: How much money the customer spends on purchases. 

It helps brands differentiate high-value, long-term customers from occasional buyers and align communication according to the brand goals.       

Behavioral segmentation  

Behavioral segmentation clusters consumers based on their behavior like purchase history and content preferences instead of external factors. Here’s how Sephora is using behavioral segmentation:   

Sephora identifies customers who regularly buy skincare but not makeup. It sends them personalized offers on beginner makeup kits and how-to videos. This boosts cross-selling and increases customer lifetime value. 

Psychographic segmentation  

It dives deeper into the psychology of the customers to understand how they think about your brand and products. Psychographic segmentation groups customers by values, hobbies, habits, interests, and views.   

Patagonia, an American retailer of outdoor clothing and gear, targets environmentally conscious consumers who value sustainability and outdoor lifestyles. It highlights eco-friendly materials, fair trade practices, and repair services to align with their values and build brand loyalty. 

Value-based segmentation  

Here, the customers are segmented based on the revenue they bring to the business. The cost of building and maintaining a relationship with these segments is also taken into account.  

This helps to align your marketing strategy according to the most profitable customer groups leading to higher ROI. However, it ignores the non-monetary value that a customer brings to your business.  

Needs-based segmentation  

Needs-based segmentation models divide customers according to their needs and pain points. It works best to find out the challenges customers are facing and tailor your products and marketing initiatives according to those needs.  

Technographic segmentation  

It groups consumers according to the technology they use. For example, a tech company can use technographic segmentation to promote the latest smart home devices and wearables to tech-savvy early adopters, while offering simplified tech bundles and setup assistance to less tech-experienced shoppers. 

How is AI enhancing customer segmentation?  

AI significantly enhances accuracy in customer segmentation by precisely analyzing vast amounts of data, identifying key patterns in real-time, and predicting future actions.  

Here’s how AI does that:  

• Advanced data processing: Processes structured and unstructured, complex and large datasets from several touchpoints and identifies key patterns that humans can easily miss.  
• Golden customer record creation: AI can integrate data from all sources, including social media interactions to POS purchase history, and create a single view of the customer, providing a deeper understanding of customer behavior.      
• Real-time update: Automatically updates customer segments based on real-time changes in customer behavior, making segments more effective. AI can accurately track marketing endeavors as per your customer segments and automatically update the segments for higher ROI. 
• Predictive analysis: AI uses Machine Learning to predict future customer actions and market conditions, allowing businesses not only to anticipate future demands but also to take proactive actions.   

Wrapping it up 

Instead of depending on a single model, combining different segmentation models according to your business goals helps create a more accurate picture of your customer groups. With AI customer segmentation, you can easily take your marketing efforts to the next level by creating precise customer segments and forecasting more accurately.  

To explore the full possibility of AI in customer segmentation and how IXO, a global AI and data analytics company is leveraging it for designing next-gen customer analytics solutions, get in touch with our team of experts.   

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