Case: Early Warning System B2B

Semi-structured Interviews + Data Fusion

Challenge: Is it possible to find mathematically verifiable indicators for the risk of termination? (Beyond verbally expressed “termination thoughts”) 

Industry: B2B, textile service

Approach: CATI surveys (a total of around 2,500 interviews) and subsequent statistical data processing

Procedure: First of all, the results of various cancellations and customer satisfaction surveys were merged and summarized. In a second step, factor analyses and regressions were performed.

Results: Illustration of the influence of central factors on customer loyalty; regression result: factors with positive as well as factors with negative effects on customer loyalty. The regression coefficient indicates for each factor how strong or weak the respective influence is, and how sales-promoting (positive) or sales-impeding (negative); from this: derivation of “rules” for an early warning system.