FD Buyer: Your Shopper Isn’t a Zip Code
Through a special arrangement, presented here for discussion is a summary of an article from Frozen & Dairy Buyer magazine.
For decades, the consumer industry has relied upon demographics, consumer segmentation models or focus groups to understand shopper motivation and make decisions at retail. But today, data-driven shopper insights let retailers approach decisions such as promotions and assortment with granular, shopper-inspired sophistication.
Embracing a customer-driven model requires shifting from “you are where you live” (demographics) and “you are what you say” (attitudes) to the “you are what you do” (behaviors) model.
While demographic and attitudinal data have value, they are limited and imprecise and should not be the central conduits for understanding shoppers. Here’s why:
- They drive generalizations. Small sample sizes can be unreadable and lack precision. Further, they reinforce a single view of an “average” shopper.
- They are not “who” driven. They don’t correlate to behaviors and are thus a poor proxy for targeting. They also don’t recognize your best shoppers and ignore the ability to reward these shoppers.
- They are not integrated. They aren’t joined up and thus provide incomplete knowledge of the customer. Without a closed loop, you can’t understand the impact of your actions.
- They are not unique to you. Demographic and attitudinal data can be replicated, providing you with no competitive advantage.
When we look at shopper behavior, we quickly see the failure of classic demographics. For example, dunnhumby recently worked with a brand that shifted its positioning from mainstream to organic. We analyzed the response in shopper behaviors through purchase weight changes (think brand increasers, maintainers and decreasers). Through the lens of demographics, the reaction of all these groups looked almost identical.
But when we use behavioral purchase markers (think buys lowest price, buys small pack size, buys kids’ brands) we see that while the brand increasers were customers who over-index in healthy product purchases, the maintainers were mainstream customers and the decreasers were calorie loaders with a price sensitivity. The behavioral view provides greater depth on what customers did and enables enhanced thinking about merchandising tactics. Based on these insights, the brand was able to look at other products that the healthy shoppers were buying and the price point and depth of discount of those purchases.
This type of understanding is essential for you to make the best use of new mechanics and vehicles to reach shoppers. Understanding high-value shoppers through the behavioral lens requires looking at both the value they bring to your brand and their behaviors based on what they buy.
Discussion Questions: How important is integrating a behavior model into the analysis used to make decisions on promotions, SKU selection/rationalization, launches and other areas? What are the challenges of using shopper data to drive more behavior-based decision-making?