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
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?
I guess I’m surprised that this is being (a) questioned and (b) treated as new. Companies like Burke (when they had AdTel), IRI, and Nielsen have been doing this forever (or at least since the early ’80s, when I started in business). Demographics and psychographics were shown to predict little to nothing in the way of purchase behavior back in the ’70s. And we’ve been focusing on who is impacted when you make merchandising changes since the early ’90s in our VR work.
So the answer is, “of course it’s important.” Even more so as we continue to move to more granular targeting of advertising, promotion, and merchandising.
Traditional demographics and psychographics are the baseline. They’re not going away. I think the point is that we now have the potential to gain additional insights beyond the baseline:
– market basket analysis – you really can’t do SKU rationalization right without understanding affinities. Just playing “lifeboat” in a category is woefully inadequate
– sentiment analysis – we get to find out, through the parsing of unstructured data found on social media, blogs, and reviews what our customers REALLY think.
The customer is more willing to give us her point of view than ever before. We might as well make use of it.
Happy Thanksgiving everybody! Over and out.
I see the value and the importance of integrating a behavior model. I think on the other hand that attitudinal data can be very valuable, as it provides insight into the emotional connection between the shopper and the brand. Attitudinal data could be aggregated with behavioral data; it’s up to practitioners to enable this integration.
Sadly, this article trivializes the work of marketing research to help understand consumers. Worse it discusses a limited number of measures that are not used in isolation or at least should not be. Finally, the author is still stuck looking in the rear view mirror when current shopper insights is focused on predicting what will happen in the future…a better platform for making investment decisions.
Social demographic are a great tool to help understand the consumer. The problem is most of our demographic conclusions are not founded in reality. They have been developed on the observation that if the consumer says or does A then they must be B. A better documentation of the social demographic variable is required and an understanding of the interactions as well.
It is certainly important to integrate shopper behavior into an analysis when it is appropriate and available. But it should not be a replacement for attitudinal and descriptive data. Rather, these are all components of a comprehensive approach to developing needed insights.
Depending exclusively on behavioral analysis can have many of the same problems as depending on demographics alone. We learn the what but not the who or the why for the shopper.
It is very important. Utilizing the transactional data available will help you to key on habits and behaviors within your customers buying patterns. Having the knowledge of when customers come and purchase product X, 80% of the time they are purchasing product Y. This type of association within your very own data will help drive better business decisions around marketing campaigns, creative, and the merchandising mix.
The best thing you can do is overlay this information with demographics. Using demographics and a behavioral model will help segment your customer database into cohorts, especially if you have large ticket items that require a longer buying cycle.
There is no “one size fits all” response or answer to this question. I have found throughout the years that behavioral consumer research is only as valuable as the process and accuracy that goes into the endeavor. Most of my clients are small brands, and I always caution my clients not to draw conclusions from improper and incomplete research.
The short answer? Behavioral targeting is crucial to any consumer analysis, as are demographics, focus groups, etc. They’re all important, and I’m not sure incorporating behavior analysis into the mix is any new news. Marketers understand that they’re just looking at a piece of the pie, and before making any adjustments, all variables need to be accounted for.
How does digital marketing play into this? Social media, for instance, provides significant consumer behavior data…and it comes right from the consumer, him/herself. I think, perhaps, marketers should look to new forms of nontraditional marketing for market research.
Gotta take the emotion out of these analyses. There are great tools available today to help better predict demand down to the SKU in each retail location. Demand signal repositories are just the start. Marketers will continue to refine these behavioral measures, and linked with technology, the end result will be more effective inventory management and more satisfied customers.
The Internet is the great leveler of data. Zip code demarcation is no longer relevant. In today’s environment the basics of marketing are king; product, price, performance, and lastly place. As more retailers drive their prices lower to maximize this exposure, they generate a new awareness of their products in our technology based model.
Many years back I was in Florida attending a corporate sales course. This lasted for two weeks and during the weekend layover, I visited a store owned by a large national clothing company. On my way through to men’s shirts, I saw a circular display of gloves and scarfs. This stopped me in my tracks as I continued to then search for more miscues. Today I see millions of dollars being spent putting merchandise that will not sell where it does not belong. Does spending the time to understand a marketplace and then placing the products wanted there make so much sense that it is no longer necessary?!
Behavior is all that matters.
Demographics, psychographics, attitudinal data, brand engagement, sentiment analysis, and all the other “fluffy” metrics that are the currency of so much of today’s marketing double-speak — they are all worthless unless they can be used to predict actual consumer buying behavior. You can’t deposit “brand engagement” in the bank. Social media buzz doesn’t help make payroll.
You are what you buy. If a shopper says they despise your store or brand or product, but they buy from you anyway, then who cares? Their money is worth the same as the person who says they love you.
Now, *if* you can demonstrate that those other metrics really do correlate–on an *individual shopper level* — to a shopper’s buying behavior, then they may have some predictive value, and should be used to augment your behaviorally-based analysis. But unless those measures map back to real purchases made with real dollars by real customers (read: “real behavior”), then it’s all an academic exercise.