"Sweet Spot" Bundling: Using POS transaction-log data to design in-store promotions

The following article was originally posted on CSPnet and features commentary from W. Capra Executive Consultant, Ed Collupy.
LAS VEGAS —Where “big data” and complex analytics have drawn interest among those in the convenience-store channel, retailers interested in technology on the first day of the annual NACS convention and trade show heard about how to design more successful promotions using data coming from their point-of-sale (POS) registers.
In a session on “kind of big data,” speaker Jeff Campbell, vice president of Applied Predictive Technologies, Washington, D.C., went through a couple of scenarios where transaction-log data can help retailers choose products to pair up to improve the effectiveness of in-store promotions.
“Today, [retailers’] capability to warehouse data is stronger than ever,” Campbell told about 200 attendees in the workshop session. “The investment is lower, the cost of storage and analysis is lower and return on investment is up.”
Campbell talked about finding a “sweet spot” where the promotion creates the highest amount of incremental return for the least amount of expense. Doing the math for his audience, Campbell said transaction-log analysis can determine what items historically pair well, which identifies potential candidates for bundling.
He said, however, caveats existed. For instance, if an identified pairing—say a hot sandwich and chips—was already strong, discounting one or the other may simply be giving money back without any return.
A stronger pairing may involve a driving product, in this case a sandwich, and a secondary product that may have a weaker basket correlation, but higher margin. A sandwich and a fountain soda could work as an example, Campbell said.
Other factors to consider:

  • Avoiding pairings that don’t make sense, such as a breakfast sandwich with a soda.

 

  • Watch out for cannibalization, where retailers have to consider how the promotion of one product may affect the sales of another.

 

  • Assess loyalty. An item with a mediocre performance may do well in baskets of loyal customers. The rationalization of that item could mean the loss of that basket altogether.That said, Collupy noted how retailers are getting better at using data to cut costs or build margin. He noted a 60-store retailer who was able to assess her chain and defer a capital investment that saved thousands of dollars.

 

  • Campbell said retailers can actively use their store data to develop potentially successful projects in any number of areas, from marketing to store benchmarking.

 

  • Former retailer Ed Collupy, W. Capra Consulting Group, Chicago, who moderated the session, said many small-to-midsized operators struggle with assessing data coming from their POS registers. “The challenge is … turning data into action.”

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