Store Personas: Using Demand Data to Match Your Products to Your People.

With more point-of-sale data at hand, retailers are moving beyond target markets and getting wise to buyer personas — a deeper understanding of customers based on characteristics such as gender, occupation, income, marital, parental or employment status. Retailers use these personas to better understand and address the wants and needs of their customers. While the retail chain may identify a handful of buyer personas, they don’t generally seek to address each persona in each store — instead, they assign two or three personas to a particular store or region and customize the retail environment and product offerings to match those personas.

If your retail team isn’t organizing the shelf around relevant store profiles, you’re missing opportunities, losing potential sales and probably retaining inventory you don’t need. Start by looking at the regional, demographic profile of a store. There are thousands of ways to categorize a store — is it near a military base, in a strip mall, a stand alone, or situated near a competitor?

Once you have the stores categorized, you should begin to analyze the POS data around common store attributes. Index the average sales for each store group against the overall chain to determine which attributes deviate significantly from the norm in your category. This metric will show you which stores “over index” (sell more than the chain average) and which “under index” (sell less than the chain average). Do affluent stores trend higher? What are the trends in rural locations? Does your category perform differently in a super versus non-super store format? The key is to then alter your planogram so that the on-shelf assortment of the over- and under-achievers is aligned with consumer preferences.

You may find that there isn’t much difference in sales by obvious geographic groupings such as state or region, that’s why you next have to give serious consideration to the buyer personas your retailer is targeting in a particular store. Remember — not every store attribute of the buyer persona will be relevant to your category. By reviewing patterns in consumer POS data, you can determine which store attributes have the largest effect on sales.

The next step is to utilize relevant store attributes in creating optimized, consumer-centric planograms that cater to their preferences. Within a category, you may find that brand preference varies across income demographics. For example, if you find that sales per store per week for premium brand X are $110 in stores in low-income areas, but are $300 in high-income stores, you may want to reduce the shelf space allotted to the premium brand in the low-income stores. Conversely, the private label brand may index high in the low-income stores — allocating more shelf space to the private label brand can grow volume in these stores. Based on this analysis, you may even consider introducing a super-premium brand in a number of high-income stores to test whether there is an appetite for even higher price point items. By tracking the POS data in these test stores, you can ascertain whether a broader rollout of the super-premium brand is warranted in stores with similar profiles. Ultimately, by using space versus sales analysis you can determine if you’re allocating shelf space within the category in accordance with store-specific customer preferences.