Accurate Tracking Of Home Appliance Sales Migration From Closed Stores

Case Study

Accurate Tracking of Home Appliance Sales Migration
from Closed Stores

Business Objective

Our client is a worldwide market leader in home appliances, headquartered in the United States.

One of the major retailers of our client had closed 600+ stores since 2010 and was also seeing a decline in sales in 100+ of their stores. Our client had two objectives:
1) Impact analysis – what was their sales loss/gain due to the decline/closure of the retail stores?
2) Develop re-allocation strategy – how should the planned shipments to the retailer, in the event of a decline/closure, be reallocated to other retailers?


  • Diverse market spectrum with 13 products across 7 categories. There also exist 30 unique brands in the portfolio
  • Some of the brands are also sold as retailer private labels. Since those products will not be available in the market when retail stores get closed, how do we retain the buyers of those private labels?
  • Sales data was available from only 60% of the retail stores under analysis.

Solution Methodology

  • Performed geospatial analysis of store closure locations – identified neighbourhood stores impacted due to each closed store using a neighborhood store detection algorithm
  • Projected the number of units that would have been sold had a retail store not closed. Controlled for sales trend, promotion effects, and other factors
  • Compared the client’s actual sales data from neighborhood stores after a store has been closed (vs) the projected sales data had the store not been closed
  • Developed a redistribution strategy for our client’s different brands and categories in case a store closed – e.g. how many units needed to be sent to which store in the neighborhood
  • Advocated a customized strategy for different store closure scenarios, e.g. rural vs. competitive markets, the presence of competition, etc.

Business Impact

  • Using the solution, the client was able to track the redistribution of more than two-thirds of sales from closed stores of the retailer to stores of other retailers at a category/brand level. This helped in recommending a customized redistribution strategy for different decline/closure scenarios.
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