Behavioral Segmentation Through Path Analysis

Ryan Garrett
Ryan Garrett
2017年11月1日 3 最小阅读

Do you ever receive surveys that ask you to check off a bunch of little boxes to describe yourself? What is your age range? What is your income range? What is the highest level of education you completed? What’s your favorite ice cream flavor?

Somewhere in an office building, a marketer is crunching the numbers, trying to figure out who their customers “are” and how to reach them – through which channels should they market, and which messages should they send.

“Wait,” you’re probably thinking. “Just because I’m 45 and have a bachelor’s degree, that doesn’t mean I want to buy the same soda – or sign up for the same checking account – as every other 45-year-old with a bachelor’s degree.”


While there is indeed value to be gained from this type of demographic segmentation, it’s time for marketers and companies as a whole to take the next step – behavioral segmentation.

What is behavioral segmentation?

Behavioral segmentation groups customers based on behavioral patterns and purchasing decisions, allowing producers to personalize their marketing approach to each group.

Behavioral segmentation in the mobile age

A quick search around the web tells you behavioral segmentation is broadly applicable beyond purchase decisions, and path analysis is often an indispensable element of behavioral segmentation. In the mobile age, behavioral segmentation is useful in identifying the paths of groups that leave your app before making a purchase, download your app in the first place, or read reviews via your app before making a purchase in-store, for a short list of examples.

Let’s look at some other ways you could apply this type of path-based behavioral segmentation to improve your marketing, continuing our focus on mobile-related use cases.
  • Retail: Who opens my app and goes straight to the coupons? Should I change the order in which the app displays various coupons and discounts based on whether someone is a coupon-first app user or a user who just wants to browse?
  • Financial services: Who walks into my bank branch shortly after engaging with the mobile app? Are there ways I could better serve this behavior-based segment in the mobile channel to reduce expensive branch visits? Are there offers I could promote to this segment in-branch to make their visit more profitable for me and a better long-term value for them?
  • Telecommunications: What are my most common paths to churn? Should I offer the same discount to segments who had three or more dropped calls this month as segments with family members who canceled their service last month? Or do these different behavior-based segments on different paths require unique approaches to save their accounts?

Path analysis and behavioral segmentation

I don’t mean to downplay the value of demographic segmentation. In fact, demographic and behavioral segmentation are most powerful in concert. Consider the financial services use case above. With path-based behavioral segmentation, you can identify the customers who visit the branch of using your mobile app. By layering demographic segmentation on top of that, you can further segment this group by account value, location, age and more. It’s easy to see how you can significantly personalize your marketing outreach based on a combination of behavior and demographic segmentation.

This path-based list, built in the Path Analysis Guided Interface, includes data such as account value and ZIP code, which can be used for additional demographic segmentation.

Maybe you don’t drink the same soda as your neighbor. Then you know it’s time to take your segmentation to the next level. It’s time to start using path analysis to identify and explore behavior-based segments. Contact us, we can make it easy.

It’s time for marketers and companies as a whole to take the next step – behavioral segmentation.

关于我们 Ryan Garrett

Ryan Garrett is senior business development manager for Teradata Field Applications. His goal is to help organizations derive value from data by making advanced analytics more accessible, repeatable and consumable. He has a decade of experience in big data at companies large and small, an MBA from Boston University and a bachelor’s degree in journalism from the University of Kentucky.
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