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The approach we developed from this research and applied in subsequent years uses just two survey questions to predict how many repeat purchases or referrals from one customer will be generated. One question gives the customer an idea of which of her rental transactions are the most recent (a source of variability) and the other question gives the customer a sense of whether that customer has experience with the company for the first time (a source of consistency).

The survey can be integrated into an existing customer satisfaction process, either on an experimental basis, or as part of a full repurchase analysis. One vendor uses the Loyalty Acid Test as an initial guide to build a survey. Then its team of six or so statisticians does a full repurchase analysis using in-house scoring rules. That information, in turn, is used by a team of staffers to conduct secondary interviews with customers. An e-mail or phone call from an employee lets customers know about the company and why they were surveyed. Then the Loyalty Acid Test is administered as a followup to that initial survey and used to cross-validate the initial analysis. The researchers can calculate a customer satisfaction score from the results of both surveys. That score is used by the company to determine the motivation of its customers to repurchase.

Folks in the business of selling surveys often ask that question: What happens if the math-based models used to draw inferences from survey data are wrong? A researcher at Bain, Tobias Taeschke, and I decided to investigate how changing the weighting algorithms might affect the predicted score for the Satisfaction Acid Test. A couple of the algorithms weighed each customer's answers to each of the questions equally. 84d34552a1