Customer behavioral data has become a high-value asset to marketing teams, brands and companies large and small. But what exactly are they all doing with this data? One common use is behavioral segmentation, a segmentation strategy that enables marketing to customers based on their behaviors. Because this segmentation strategy is growing in popularity, we’re delving deeper into how it works—and when it doesn’t.
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’ interactions with your brand, including their purchase history or how they responded to specific marketing messages.
Marketers and data science teams usually 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 behavioral data and demographic categories like age, income and location in order to validate their strategies or predictive modeling.
As you pursue a deeper 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 this segmentation strategy to:
Behavioral segmentation can help make your marketing more personalized than other consumer segmentation techniques, but it still can’t fully drill down to achieve individual marketing and hyper-personalized offers—what we call 1:1 personalization. And here’s why:
Behavior is dynamic. 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.
It’s hard to measure and gain insights. Consumer behaviors can be tracked, but that doesn’t necessarily tell you the motivations behind them. If a loyal customer’s purchase volume falls off, but you don’t know the reason behind that change, you can’t optimize your marketing accordingly.
It requires a lot of human capital. Behavioral segmentation involves complex data constructs. You’ll need a large team of data scientists to build and explain them, and a large team of marketers to make them actionable.
To determine if behavioral segmentation would be beneficial for your brand, it’s helpful to understand the different ways you can use it.
Segmenting 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.
Occasion purchasing 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 3 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.
Behavioral segmentation by stage of the buyer’s journey with your brand—or around the customer loyalty loop—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 also includes an AI engine, like Formation, the engine will be able to make these learnings actionable.
This refers to behaviorally segmenting consumers based on how often they use, or purchase, your product or service. 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 3 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 (LTV). 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.
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 LTV.
By importing your customer loyalty data into an AI and ML solution that can make it actionable, you can further maximize customer LTV.
Behavioral segmentation is one of the most effective types of customer segmentation in marketing. But 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 some added help from a MarTech solution with an AI and ML engine. Here are some tips to help you personalize and optimize with behavioral data:
1. Serve Content That’s Better Targeted - Using behavioral data, you can segment your customers by their interests, average order value, frequency of purchase, and frequency of app use, then use that information to make your messaging more likely to be relevant to customers. However, segmentation alone still can’t guarantee relevance to every customer—but an ML and AI platform can use that behavioral data to target your communications at an individual level.
2. Create More Effective Offers - You can segment customers by categories or products they like, browse and shop for to make your offers more valuable to them. An AI and ML solution can further improve this value by personalizing each individual offer to what the specific recipient has searched, browsed and shopped for in the past.
3. Cross Sell or Upsell - Behavioral segmentation will allow you to run cross sell or upsell remarketing campaigns based on your customer segment’s prior behaviors. An AI and ML solution will make these campaigns more effective by individually personalizing your campaign to each customer, and providing multi-step behavioral offers to guide them to the new goal behavior.
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. It also requires a large data science team to manage your behavioral segmentation models, with no guarantee that these models will get increasingly effective.
Using a MarTech solution that incorporates ML and AI can push the benefits of behavioral data beyond segmentation, to make your marketing campaigns individually relevant and 1:1 personalized. It also allows your campaigns to get continually more effective over time as the machine learning engine learns the motivations behind each customer’s behaviors—something that behavioral segmentation alone can never accomplish.
Learn more about the Formation hyper-personalization platform
Read more about why segmentation falls short: