Demographic profiling provides customer insights, telling us who is buying the product.  We all use that info to determine where the product should be (i.e. which metros and markets).  Although demographic profiling doesn't tell us why our customers buy or how they make purchase decisions, it does provide a good profile filter to target populations of high characteristic density.

The map of stores in a metro area on the right highlights the shopper mix at each store, which helps category managers answer the question: where should I be that I'm not already? 

AWhere uses 2009 projected census demographics to build retail trade areas around each store location.  It's a proprietary algorithm which determines distance around each store to aggregate demographics of the shopper base - a quantitative scientific approach rather than a qualitative approach based on local knowledge.

If you know your target demographic -- let's use 'income level' in this case -- then we can simply illuminate those stores that meet the criteria.

Mid-High Income stores using Walmart's SOTC definitions
(disclaimer: this map uses mocked-up data. Real data can only be accessed by approved Walmart vendors)

But usually the question is one of exceptions: which stores am I not in that meet my target criteria?  To answer that question its best to show 'traited' or sales volume by store against stores that meet the criteria.

Sales volume by store, compared to Mid-High Income stores using Walmart's SOTC definitions
(disclaimer: this map uses mocked-up data. Real data can only be accessed by approved Walmart vendors)

A great double check that can be done using Walmart's SOTC (store of the community traits) is to show which stores carry the item compared to SOTC traited stores.

Stores 'traited' for item against Mid-High Income stores using Walmart's SOTC definitions
(disclaimer: this map uses mocked-up data. Real data can only be accessed by approved Walmart vendors)

Are you missing opportunity?  By using AWhere to develop custom maps illuminating your business, you can prove a case to retail buyers and achieve your goal of obtaining more stores.

To get help identifying the 'right stores', or for more information about using location intelligence for the consumer goods industry, please contact Chuck Ray at 1.303.279.9293x202 or chuckray@awhere.com. 

Location Intelligence allows you to dive deep on these vectors to understand and optimize performance based on ‘where’ –what sells where and who are the shoppers there– to allow you to target by store, metro, and marketing area.

• Understand your category vs the competition – illuminate category vs brand by retail chain, marketing areas, metros, and individual stores [CDI/BDI].
• Review your item set and evaluate product mix. – what is the modular performance and how does the product mix compare in different regions.  Is there better item clustering?
• Who buys your product? Are you in the right stores? – use demographics and POS to analyze who buys your product and verify the best stores to be in. example
• Measure, track, and analyze sales performance – understand sales patterns thru geo-visuals; see performance by retail chain, store locations, metro and marketing areas; group by product line, modular, or department; review volume, velocity, pricing and gaps, or margin and profit.
• Manage replenishment by exceptions and optimize metrics – see patterns in OOS and manage low WOH; analyze velocity and inventory turns by store or DC and optimize reorder points to maximize metrics.
• Weather driven demand – use long term weather to forecast seasonal sales; start/end seasonal products at the right; and manage inventory ahead of weather driven demand. example
  
• Improve promotion effectiveness – know where your consumer is and use targeted promotion; measure media spend effectiveness by store sales and by market areas.