The Center for Business Analytics is pleased to recognize Best Buy as an honoree of the 2023 Drexel LeBow Analytics 50 Awards. Read more about how Best Buy used analytics to solve a business challenge.
Submitter
Craig Brabec, Chief Data Analytics Officer
Company
Best Buy
Industry
Retail
Business Challenge
Best Buy puts a strong emphasis on creating positive customer experiences in its stores, online and through its services. The quantity and variety of experiences Best Buy creates can make it challenging to analyze feedback for continual improvement. Prioritization using quantitative data, such as the value of a positive experience, is also needed to focus investments on the highest impact areas first.
Analytics Solution
Best Buy performed a matched-pair analysis — which enabled a comparison of customers with the same profiles, yet different shopping experiences — that controlled for extraneous variables. Two questions were analyzed: What is the difference in spend between someone who had a good experience versus a bad experience? If we don’t answer the phone at the store in a timely fashion, will the customer spend less money? Utilizing analytical techniques was very important, as Best Buy didn’t want to run a field experiment where it purposely gave a customer a bad experience! This method allowed the company to look through very large datasets to find “statistical twins,” where one had a good experience and another a bad experience.
Impact
Understanding that resources are finite, Best Buy was able to prioritize experience improvements appropriately using this solution. The company gained understanding that there is a ceiling to experience improvements, allowing a focus on improvement to a point instead of to “perfect.” Best Buy can now show revenue impact by service type based on improved experiences. While specific numbers aren’t able to be shared, some service improvements can result in twice the number of gains in future revenue from a customer. During the analysis, the company uncovered additional insight on data from store calls (customers calling one of the store locations directly versus a central call center). Customers with a “sales intent” who give up while waiting for a call to be answered spent less over the next year. Increasing the ability to have these calls answered faster can result in a significant sales lift.