Using cluster analysis to inform store-specific marketing tactics

Analyzing data in order to cluster stores, products, brands, retailers or customers into segments is critical for brands to maximize the effectiveness of their marketing. In this case study, we used cluster analysis to target our client’s social media advertising within individual store trade areas based on their specific needs.

The problem: With a total of more than 400 stores, our client knew that not all their stores should be treated the same from a marketing perspective. They had a ton of data. What they didn’t know was what to do with that data and how to segment their stores appropriately.

The solution: We pulled granular data on sales by store, by day of week, by time of day, by product type, by individual item sold, and so forth into the CALLAHAN INTELLIGENCE PLATFORM for this client. We processed and analyzed it to understand where the business’ strengths and weaknesses were, as well as its vulnerabilities and opportunities.



With a chain of this size, there’s a lot of variability from store to store. For instance, there’s a group of stores that struggle with product line A, but are strong with product line B. There’s another segment that’s the opposite. Then there are other stores that are strong during the week and struggle on the weekends, and vice versa. And there are stores that are great performers in all areas.

Using granular data for each individual store, we built a segmentation that placed each location into one of seven groups or “clusters.” We discovered something interesting: that the organization of their stores into DMAs didn’t match with the clusters our INTELLIGENCE PLATFORM had created.

The aha:

One specific DMA for this client has 80 locations, but stores in this market fall into all seven of the cluster groups we created. Knowing this, we could avoid aggregating too broadly and missing store-level opportunities in this DMA.


The result:

Armed with this knowledge, we were able to target advertising within this DMA to individual store trade areas where, for example, one store needed more weekday business and another store needed more weekend customers. To accomplish this, we used social media promotions targeted to the individual store trade areas based on their specific needs. This let us address the greatest opportunities for growth on a store-by-store basis.


Services this client used:

Cluster analysis and data segmentation can be valuable for many types of brands, because it sheds light on the greatest strengths, vulnerabilities and opportunities for an individual entity within any retail or distribution system. With our INTELLIGENCE PLATFORM, we’re able to make recommendations for specific marketing activities that address vulnerabilities and leverage strengths. It also puts us in a position to test and learn using a sampling of stores in a cluster and roll those learnings out to similar stores systemwide.

If you’re interested in how our data cluster analysis services could lead to smarter, tailored marketing, increased sales and improved ROI, fill out the form below. We can’t wait to hear from you!

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