Personalized Key Drivers for Individual Responses in Regression Modeling

Stan Lipovetsky
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Stan Lipovetsky: Independent Researcher, USA

International Journal of Risk and Contingency Management (IJRCM), 2020, vol. 9, issue 3, 15-30

Abstract: Identification of personalized key drivers is useful to managers in finding a special set of tools for each customer for a better contingency to a higher satisfaction and loyalty and for diminishing risk and uncertainty of decision making. Finding the most attractive attributes of a product for a buyer, or the main helpful features of a medicine for a patient, can be considered via identifying the key drivers in regression modeling. The problem of predictor importance is usually considered on the aggregate level for a set of all respondents. This article shows how to identify a specific set of key drivers for each individual respondent. Two techniques are proposed: the orthonormal matrices used for the relative importance by Gibson and R. Johnson, and the cooperative game theory by Shapley value of predictors in regression. Numerical estimations show that a specific set of key drivers can be found for each respondent or customer, that can be valuable for managerial decisions in marketing research and other areas of practical statistical modeling.

Date: 2020
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