Scale Your Behavioral Segmentation to Drive Customer Loyalty

Scale Your Behavioral Segmentation to Drive Customer Loyalty

Consumer shopping behavior continues to shift dramatically as global events and emerging technologies impact how, when, and why they purchase from a brand. Customer loyalty is no longer a guarantee in this evolving market, and brands must find new ways to understand and engage their customers.

This article will help you understand what behavioral segmentation is, where behavioral segmentation fits into your loyalty marketing programs, and what other solutions need to be combined with it in order to create personalized offers and experiences that actually drive customer loyalty and create incremental revenue.

What is behavioral segmentation?

As the name implies, behavioral segmentation in marketing is a method of grouping customers by their behavior patterns. These behaviors may relate to the customer lifecycle, such as getting married, having a baby, or buying a home, or to seasonal patterns like holiday shopping and summer vacations. You can also segment by customers’ behavior with your brand, such as purchase history or how they responded to specific marketing messages.

Marketers and data science teams can use behavioral segmentation variables in combination with standard demographic and geographical data in an effort to make their offers and communications more effective. They will try to find correlations between specific customer behavior data and demographic categories like age, income, and location in order to validate their strategies or predictive modeling.

The Problem with Modern Segmentation Practices

Many brands have tried to solve the issue of shifting consumer behavior with additional segmentation models, like behavioral. However, consumers are demanding a level of personalization that segmentation alone can’t solve.

According to McKinsey & Company, 75% of US consumers changed their shopping behavior and shifted to new brands during the COVID-19 pandemic, despite the fact that most brands were using some level of segmentation to engage customers.

In many cases, segmentation techniques such as 1) micro-segmentation, which categorize customers based on a complex set of attributes, or 2) smart segments, which use AI tools to examine available data and use that to decide how to divide customers, create unnecessary

complexities for loyalty marketers and confusion for brands, rather than creating the personalized experiences consumers crave.

How Can I Use Behavioral Segmentation in Loyalty?

Today’s wealth of customer data allows brands to identify their audiences in a number of ways. By tracking the customer behavior of their most loyal customers, brands can identify larger trends and habits that will help drive deeper loyalty from other customers. Brands and marketers can use behavioral segmentation to:

  • Identify their most valuable customers. Tracking purchase behavior can reveal which customers are spending the most money, purchasing most often, and visiting your website or store most often. These loyal customers can then be encouraged to turn these behaviors into ongoing habits. They can also identify high value behaviors in non loyalty-program customers, and promote these behaviors in order to increase customer lifetime value (CLV) and create ongoing loyalty.
  • Uncover roadblocks on the customer journey. Is there a point at which certain users are abandoning their carts, leaving a website or disengaging from a loyalty program, while other users continue on their journey? Examining the differences between these audiences can help marketers determine why the low-performing ones are not converting and create programs that help move customers deeper into a relationship with the brand.
  • Improve program design. Tracking customer interaction with a company’s digital channels can help improve the user experience within them. For example, if a company identifies that users are abandoning their loyalty program via desktop, but engaging regularly on their mobile app, they know they need to create a more uniform user experience across both channels, and identify where the desktop version is failing their loyal customers.

Why Segment Customers by Behavior?

As you pursue a deeper emotional relationship between your brand and your customers, you’re probably looking to personalize messages and offers any way you can. Behavioral segmentation can help get you closer to this goal—although not necessarily across the finish line. You can use the behavioral segmentation strategy to:

  • Identify your customers preferences, likes and dislikes
  • Determine what your customers are most likely to buy, and when they’re likely purchase
  • Build customer audiences based on their response to your products and promotions
  • Monitor the online behavior of your customers using data collection and tracking technologies (cookies, beacons, pixels, etc.) to gain a 360 view of your customers’ journey
Segmentation examples graphic

Behavioral segmentation can help make your marketing more personalized than other consumer segmentation techniques, but it still can’t fully drill down to achieve 1:1 personalization. And here’s why:

Where Behavioral Segmentation Falls Short

Behavior is dynamic, as are the data points that track that behavior. Behavioral segmentation models aren’t. Consumer behavior changes constantly, and human-made segmentation models simply can’t be fine-tuned often enough to keep up, nor are they built to constantly ingest new data points.

Consumer behaviors can be tracked, but that doesn’t necessarily tell you the motivations behind them. It’s hard to measure and gain insights. As mentioned above, more granular segmentation requires a lot of human capital, and behavioral segmentation involves complex data constructs. You’ll need a large team of data scientists to build and explain the segment insights, and a large team of marketers to take action on those insights via a number of differentiated programs.

How to Improve Behavioral Segmentation For Loyalty, Using AI and Machine Learning

When you are trying to achieve 1:1 personalization, behavioral segmentation alone can’t push you over the finish line—for that, you’ll need an additional MarTech solution that utilizes AI and machine learning.

This ensures there is continuous, updated data being ingested through all customer channels, and then optimizing the programs based on those new insights. It's impossible for humans to understand and use all of those data points for millions of loyal customers, but for a ML platform, it takes a matter of minutes to create personalized offers and experiences for every single customer. By inputting behavioral data into a machine learning powered offer platform, brands can:

1. Create More Effective Offers - An AI and ML solution can take the behavioral data points you’ve collected through behavioral segmentation and personalize each individual offer based on what the specific recipient has searched, browsed and shopped for in the past.

2. Drive the Right Actions - An ML and AI platform can use behavioral details, such as average order value, frequency of purchase, and frequency of app use to ensure your customers are being asked to take the right actions that are valuable to a company. For example, a frequent shopper on the website can now receive an offer to download the app, as the brand knows that app users are 2x times more likely to be loyal to their brand.

How to Leverage AI to Optimize Large Business Segments and Behavioral Segmentation

Instead of an ever increasing number of micro-segments, brands would be better served by creating large business segments, with ties to behaviors, to organize campaigns and broader corporate objectives. By clearly defining large segments such as net-new customers, brand advocates, and lapsed customers, marketers and business leaders can focus on long-term strategies to improve business results and design programs that help achieve them.

With dynamic offer optimization platforms, machine learning does the work of optimizing campaigns based on previous behaviors and personalizing offers at the customer level. Dynamic offers allow brands to focus on their larger business objectives, because marketers can use their time more strategically to develop the campaign concepts and program ideas that will realize those goals.

This approach means marketers no longer need to tweak campaigns and achieve incremental improvements. Optimization solutions run in the background and automate the manual work so brands and their loyalty marketers can focus on the big picture of deepening emotional connections with consumers.

3 Ways to Use Behavioral Segmentation to Improve Loyalty

1. Customer Journey Stages

Behavioral segmentation by stage of the buyer’s journey with your brand—or around the customer loyalty ladder—can help you create conversions at more points along the customer journey, or identify points where your efforts are not working in order to optimize efforts.

Sound challenging? It is, because marketers often make the error of relying on a single point of data to indicate the customer’s journey stage, when in reality, a customer at any given stage will interact with content targeted to many different stages, on many different channels.

To make the best use of customer journey data, use a machine learning algorithm to collect all of a customer’s data points across all channels. Based on the customer’s behavior over time, a ML solution will be able to “weigh” all the different data points to determine which are the largest indicators of the journey stage. If your solution utilizes an ML platform, like Formation, the engine will be able to make these learnings actionable.

2. Usage Segmentation

This refers to behaviorally segmenting consumers based on how often they use, or purchase, your product or service. Customers are usually segmented into three categories:

  • Heavy Users or Super Users - Those who purchase the product or service most frequently and are most engaged
  • Medium or Average Users - Customers that purchase the product or service regularly but not necessarily frequently
  • Light Users - Single-use customers or customers who purchase the product or service significantly less than other customers

Segmenting by usage can be a strong predictor of customer loyalty, churn, and customer lifetime value (CLV). The right AI and ML solution will be able to determine where customers fall along this spectrum, and also stretch their behavior to move them to a higher usage category.

3. Customer Loyalty

Your most loyal customers generate the largest amount of your revenue. Studies indicate that as much as 40% of an eCommerce company’s revenue is generated by the 8% of customers that are the most loyal—so it’s critical to know who those customers are. This data can help you answer questions like what behaviors along the customer journey are key indicators of loyalty, how to divide your customers into tiers for your customer loyalty program, and which customers are likely to have the highest CLV..

By importing your customer loyalty data into an AI and ML solution that can make it actionable, you can further maximize customer CLV.

Make Behavioral Segmentation a Starting Point, Not an End Goal

Behavioral segmentation can be a useful tool for starting to get value from your behavioral data. But remember that like any type of segmentation, it’s still generalizing a group of people, so it won’t be able to achieve relevance for every customer.

Using a MarTech solution like Formation that automates the ingestion of those behavioral data segments, and then uses ML to optimize programs based on that data will create personalized offers and experiences that create long lasting customer loyalty. Ready to build more personalization into your loyalty program? Check out our how-to guide: Modernizing Loyalty Offers for the Digital Consumer

Behavioral Segmentation FAQ

What is the Key to Using Behavioral Segmentation Successfully in Consumer Markets?

The key to successful behavioral segmentation is to understand the needs and benefits sought by different groups of consumers. Companies can gain this understanding by collecting and evaluating customer behavior data. The data can come from first-party sources, like a company’s website, mobile app and customer loyalty program, and from third party data, like a social media platform, digital campaign platform or search engine.

What is the Difference Between Behavioral Segmentation and Personalization?

Behavioral segmentation and personalization are both ways to increase the relevance of marketing to potential customers.

Behavioral segmentation divides customers into groups based on their behavior patterns or behavioral data, so that marketers can target specific behaviors that can make their desired outcomes more likely. Marketing to behavioral segments is likely to create more relevance than marketing by demographics or geography, but still won’t necessarily be relevant to every customer in the segment.

Personalization is a strategic approach that tries to make marketing individually relevant to every customer. Because adjusting a marketing offer to be unique to each customer would take infinite human resources, the most effective personalization is accomplished with a combination of marketing technology (MarTech) and customer data.

How Can AI and Machine Learning Improve Behavioral Segmentation Using Customer Data?

When marketing to behavioral segments, AI and machine learning (ML) can help make that marketing even more relevant, and therefore more effective. An AI and ML solution, such as an offer optimization platform, can:

  • Serve individually targeted content using the behavioral data that has been gathered on each customer
  • Deliver individually personalized marketing offers where the action and reward are both tailored for each customer and where they are on their customer journey
  • Guide customers to new behaviors by creating individualized cross-sell or upsell opportunities

What are Some Behavioral Segmentation Examples and Strategies?

1. Purchasing Behavior

Behavioral segmentation by customers by their purchasing behaviors and transaction history can help you understand how customers make their purchasing decisions, what complexities are involved, what barriers stand in their way and which behaviors are most likely to predict a purchase. Purchasing behaviors can usually be subcategorized into four categories:

Complex - The customer has an important decision to make and is highly involved in the decision-making and purchasing process, perhaps doing a significant amount of research. The brands they are choosing between will have many key differences, such as an Apple laptop versus a high-end PC laptop that runs Microsoft Windows.

Variety-Seeking - The brands or products vary significantly, but the

customer is not very involved in the decision-making process because the purchase decision is not very consequential in their lives. For example, if a customer is choosing between an organic or conventional frozen pizza at the grocery store, the brands are quite different, but there is little risk to choosing one over the other.

Dissonance-Reducing - The customer is making an important decision, but choosing between two parity products or brands with very similar features. Think of a customer choosing a new vanity for a bathroom remodel—if several options use the same materials and fit with their decorating scheme, they are likely to choose the option with the lowest price.

Habitual - These are purchase decisions of low importance where the customer is choosing between many different brands that all offer essentially the same product. Habitual behaviors are usually a matter of personal preference or brand loyalty—for example, there is little difference between a roll of Charmin toilet paper and a roll of Cottonelle, but if given the option, some consumers would consistently choose one over the other.

Behavioral segmentation by purchasing behavior may seem straightforward, but pulling actionable insights from these behaviors is challenging without significant data science resources.

2. Occasion Purchasing

Occasion purchasing as a form of behavioral segmentation is when customers make purchase decisions because of specific timing or circumstances in their lives, allowing brands to segment them by these decisions. There are three types of occasion purchasing that are useful in behavioral segmentation:

Life Stage - A customer is making purchase decisions due to reaching a key milestone in life. Getting married, having children, moving, home purchases and retirement are some of these major milestones, but purchase drivers can also be as simple as a milestone birthday.

Seasonal - Purchase decisions are driven by time of year factors such as holidays, the start or end of the school year and changes in weather.

Day-Part - Habitual purchase decisions that are driven by time of day, like when a customer gets a coffee every weekday morning or joins friends for brunch every Sunday. For restaurant and foodservice brands especially, day-part can be a critical element in leveraging customer behavior to increase revenue, particularly if they have an AI and machine learning (ML) solution in place to detect changes in these patterns.

3. Customer Journey Stage

Behavioral segmentation by stage of the buyer’s journey with your brand—or around the customer loyalty ladder—can help you create conversions at more points along the journey, or identify points where your efforts are not working in order to optimize those efforts.

Sound challenging? It is, because marketers often make the error of relying on a single point of data to indicate the customer’s journey stage, when in reality, a customer at any given stage will interact with content targeted to many different stages, on many different channels.

To make the best use of customer journey data, use a machine learning algorithm to collect all of a customer’s data points across all channels. Based on the customer’s behavior over time, a ML solution will be able to “weigh” all the different data points to determine which are the largest indicators of the journey stage. If your solution utilizes AI and machine-learning, like Formation, the engine will be able to make these learnings actionable.

4. Usage Rate Segmentation

This refers to behaviorally segmenting consumers based on how often they use, or purchase, your product or service. Usage rate segmentation by usage looks at behaviors like how the customer uses the product, how frequently, how long they spend using it and what features they use. Customers are usually segmented into three categories:

Heavy Users or Super Users - Those who use the product or service most frequently and are most engaged

Medium or Average Users - Customers that use the product or service regularly but not necessarily frequently

Light Users - Single-use customers or customers who use the product or service significantly less than other customers

Segmenting by usage can be a strong predictor of customer loyalty, churn, and customer lifetime value (CLV). The right AI and ML solution will be able to determine where customers fall along this spectrum, and also stretch their behavior to move them to a higher usage category.

5. Customer Loyalty

Your most loyal customers generate the largest amount of your revenue. Studies indicate that as much as 40% of a eCommerce company’s revenue is generated by the 8% of customers that are the most loyal—so it’s critical to know who those customers are.

Behavioral segmentation by customer loyalty goes hand-in-hand with the other types of segmentation mentioned here: purchasing behavior, timing/occasion, customer journey stage and usage. This data can help you answer questions like what behaviors along the customer journey are key indicators of loyalty, how to divide your customers into tiers for your customer loyalty program, and which customers are likely to have the highest CLV.

By importing your customer loyalty data into an AI and ML solution that can make it actionable, you can further maximize customer CLV.


Behavioral Segmentation: Keep Learning

Behavioral segmentation can also be titled marketing segmentation. Either way, you're learning valuable data about your customers, which allows you to implement the best ways to interact and communicate with them. Click below to download our free guide and learn how you can modernize your loyalty program to get the most out of your behavioral and marketing segments.


Are you ready to put behavioral segmentation into action? Learn how you can modernize your loyalty program to get the most out of your customer segments and loyalty marketing efforts.