When Wawa, the $6 billion convenience-store chain, came up with a new flatbread breakfast product in 2008, the company’s marketing department was flat-out excited. The product had performed exceptionally well in spot testing and seemed ready for a systemwide rollout.
At the same time, though, Wawa was rethinking its traditional method of customer testing. Specifically, it was experimenting with a software-based approach to designing and evaluating tests. A second round of testing based on the new approach got far different results; in fact, the breakfast item was killed. “We found it was cannibalizing other, more-profitable products,” says Wawa CFO Chris Gheysens.
The new method hinges on a more scientific way of selecting stores for test and control groups, and uses regression analyses to weed out irrelevant “noise” from test results. The software at the heart of the effort is called Test & Learn, from Applied Predictive Technologies (APT).
Wawa’s previous approach was what Gheysens calls a “good old manual” testing process in which financial analysts used spreadsheets to select stores and evaluate flux and trending data.
That process was problematic, says Gheysens, “because with that kind of manual analysis and all the noise in the data, you really rely on influence and emotion more than facts.”
In addition to testing marketing activities and new products, Wawa has also used the software to test labor assumptions, disproving some long-held assumptions about when to add extra cashiers, but discovering other situations in which deploying more staff does help drive sales.
Test & Learn is designed to provide visibility into the impact of any kind of program, investment, or activity that may influence customer behavior. It provides three levels of understanding, says Scott Setrakian, APT’s managing director: how an action will affect overall sales or profitability; how to tailor the action for maximum effectiveness, such as whether a 5%, 10%, or 15% discount is best; and the impact by market, store, or even customer.
The software is most likely to appeal to large companies: The average annual cost for a typical three-year license is between $700,000 and $1 million, and it takes anywhere from two weeks to a few months to establish a daily data feed from a customer to APT. One way that APT attempts to differentiate itself in the booming market for analytics software, says Gartner analyst Kevin Sterneckert, is by bundling in tools that allow a client to perform the kind of data analysis that companies often pay consultants to provide.