Store Clustering for Smarter Decision-Making


There are two extreme ends of the retail business model, each with its pros and cons. At one end all stores are virtually identical (think chains like Starbucks, Gap, Subway, etc.), which makes for simpler operations and a consistent, predictable customer experience. On the flip side are chains where the owner operator has a great deal of discretion on assortment, pricing and promotions. For example, retailers like Aeon Group in Japan, ICA in Sweden and Metro in Canada give significant control to store operators.

Wherever a retailer is located on the spectrum, grouping stores together into a manageable number of homogenous groups — a machine learning technique called store clustering — will help them find a profitable middle ground between a cookie-cutter approach and a franchisee free-for-all.

The concept of store clusters isn’t new, but few retailers use a scientific approach to create them, and fewer still operationalize the process. This is unfortunate, because store clustering can provide foundational insights for making strategic supply chain, marketing and merchandising decisions.

Clustering involves classifying stores together based on one or more metrics. Essentially, this means creating several key measures, such as % sales on deal or category sales mix, and grouping stores based on how closely they align on these metrics. Next, you use additional reference points like competitor presence, geo-location and demographics to clearly identify and differentiate the clusters.


Through clustering, retailers can target the demands of similar catchments and drive new operational efficiencies. Take, for example, a convenience store chain that wants to adapt its daypart breakfast, lunch and dinner basket merchandising approach to multiple locations and customer shopping goals. New coffee, bakery and chilled fixtures are expensive; two similar-sized stores that are geographically close might appear similar, but could be very different prospects for take-out versus eat-in foods. Clustering can help management make the right merchandising and purchasing decisions.

Where regulations allow, store clusters can also help with price zoning decisions. Sites with a high proportion of one-off customers are likely to be more price elastic than those with a high proportion of loyal, price-sensitive shoppers. (There is a reason why you pay more for candy at the airport or train station than your neighborhood store.) Store clustering helps identify stores with characteristics like the travel-adjacent store, for example, and bring more science into the price zoning or merchandising process.

If your business is planning a promotional flyer and you want to know the optimal number to print and where to distribute them, your Test and Learn program will benefit from clustering. When you are looking at your enterprise demand signal across the supply chain, understanding local demand will help improve forecast accuracy and ultimately boost sales and. Store clusters also aid category space allocation decisions since they create structure around allocating space between departments to meet local demand.

Amazon will only get better and better at e-commerce. How will you differentiate your store portfolio to stay relevant?