Saying what is important

One of the central tasks of market research is to say what is important. The second part of the series on preference measurement methods deals with the group of methods for indirect relevance analysis. In particular, we would like to introduce the Sequential Driver Analysis developed by us.

Compared to the specialists in the field of indirect relevance analyses, such as penalty and reward, decision trees or Shapley value regressions, sequential driver analysis is a typical generalist. This is because it overcomes several restrictions at once: The method does not require the replacement of missing values, determines valid results even for small subgroups, and can detect linear as well as non-linear relations. Thus, the method proves to be a versatile solution approach for the differentiated understanding of driver structures, which can be optimally transferred into the prioritization logic of an action-relevance matrix.

Download publication (published in Planung & Analyse, 3, 2022)

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