Using cluster analysis to inform store-specific marketing tactics

Most marketers know that not all stores are created equal, and therefore shouldn’t all be treated the same (nor are all products in a brand portfolio, all retailers in the distribution chain, and so on). But often, they don’t understand how they’re different or what to do about it. 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. The Callahan Intelligence Platform is a powerful tool that helps us do this exact analysis and segmentation for our clients.

For example, let’s look at a cluster analysis we created for a 400-unit chain with stores across the U.S. We pull a lot of granular data into the Intelligence Platform for this client, including sales by store, by day of week, by time of day, by product type, by individual item sold, and so forth.

Once we gather the data in the Intelligence Platform, we process and analyze it to understand where the business’ strengths and weaknesses are, 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.” This is very valuable, because it sheds light on the greatest strengths, vulnerabilities and opportunities for every store across the system. As the client’s agency, we’re able to make recommendations for specific marketing activities that address vulnerabilities and to 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.

For example, in one specific Texas DMA our client has 80 locations. They have stores in this market that fall into all seven of the cluster groups. So, not all stores in this market fit into just one or two of the clusters: that would be aggregating too broadly and missing store-level opportunities. Armed with this knowledge we can target advertising within the market to individual store trade areas where, for example, one store needs more weekday business, another store needs more weekend customers. To accomplish this, we use social media promotions targeted to the individual store trade areas based on what their needs are. This lets us address the greatest opportunities for growth on a store-by-store basis.

So, not all stores (or products, or retailers) are created equal. We can use the Callahan Intelligence Platform to develop cluster analyses that inform smarter, tailored marketing, increased sales and improved ROI.

Photo by William Felker on Unsplash