Regional preferences: reading local data without stereotyping

Regional data is where analysts most often talk themselves into stories. A flavor sells unusually well in one metro area, and within minutes someone has an explanation about the people who live there. Reading regional preferences in retail data well means doing the opposite: compare against a relevant baseline, check the store mix, and describe what sold before speculating about why.

The method is simple. The discipline is the hard part.

What baseline should a regional comparison use?

Start by asking what "unusually well" means. If a flavor accounts for a larger slice of category sales in one region than across all regions, that's a starting point. But the comparison is only meaningful if the regions are being measured the same way, over the same period, with the same product definitions.

An index is a common tool. Take the item's share of category sales in the region, divide by its share across all regions, and multiply by 100. A value of 100 means the region matches the overall pattern. A value of 130 means the item over-indexes, and 70 means it under-indexes.

A hypothetical: a mango drink makes up 6 of every 100 dollars in the beverage subcategory across all regions, and 9 of every 100 in one metro. The index is 150. That's a notable difference worth investigating, and it isn't yet an explanation.

Is it the region, or the store mix?

Often it's the stores. Regions differ in the kinds of stores they contain. One area might have more small stores near transit and fewer larger grocers. Another might have the reverse. If an item sells well in small transit-area stores everywhere, a region with many of those stores will look like it "prefers" the item.

The fix is to compare like with like. Group stores by format, size and location type, then compare regions within each group. If the difference disappears within groups, the regional effect was really a store-mix effect.

Distribution is another common cause. If the item simply isn't carried in many stores in one region, it will look unpopular there. Velocity per store carrying separates availability from demand. Store clustering like this is standard practice in category management.

Why avoid demographic explanations?

Because the data doesn't support them. Store-level scan data shows what sold in each store, by item and day. It doesn't show who bought it. Attributing a regional pattern to an ethnic, income or other group assumes information the data doesn't contain.

Those assumptions also tend to be wrong in practice. A neighborhood includes many kinds of people, and a store serves commuters, workers and visitors along with residents. A pattern that seems to fit a group story often turns out to reflect distribution, store format, pricing or a local promotion.

And they're a poor basis for decisions. A plan built on a stereotype can lead to narrow assortments that miss what shoppers actually buy. Describing the pattern in product terms, such as "this flavor over-indexes in small stores in this metro," is both more accurate and more useful.

How should you act on a regional difference?

Check it first. Confirm the pattern holds over several periods and survives the store-mix and distribution checks. A one-month spike may be a promotion or a single large store.

Then act on the product facts. If an item over-indexes in a set of stores, make sure it's reliably stocked there. If an item under-indexes where it's carried, look at price, placement and shelf position before concluding anything about demand.

Finally, test. A regional assortment change tried in a few stores and compared against similar stores gives you evidence instead of a theory. Category context from NRS Insights and its news page can help you judge whether the whole category is moving in the region.