Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/14042
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dc.contributor.authorS.M., Adinarayana M.
dc.date.accessioned2015-07-08T09:16:56Z
dc.date.available2015-07-08T09:16:56Z
dc.date.issued2015
dc.identifier.citationS.M., A. M.. (2015). Measuring Relative Impact of KPIs – A Game Theory Approach. 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. Indian Institute of Management, Ahmedabaden_US
dc.identifier.urihttp://hdl.handle.net/11718/14042
dc.description.abstractCustomer satisfaction (CSAT) and Net Promoters Score (NPS) are related to key measures of future financial performance for firms. The ability to find key drivers for CSAT & NPS is an important step in any organizations’ strategy that leads to high quality of service and long-term relationship with customers. One of the traditional techniques to assess the relative importance of attributes is using standardized regression coefficients. But the presence of high degree of multicollinearity among the attributes leads to imprecise and unstable coefficients and thus assessing relative importance among predictors is a challenging task. To handle this type of problem, Shapley value regression (which was developed by Nobel Prize winner Lloyd S. Shapley), which is a cooperative game theory concept using R-square decomposition is applied. In this method, the R-squared is decomposed into contributions from the different predictors in the model and these contributions are referred as importance measures. Shapley’s solution allocates the importance of each predictor fairly even in the presence of high multicollinearity in the data.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management, Ahmedabaden_US
dc.relation.ispartofseriesIC 15;053
dc.subjectMulticollinearityen
dc.subjectStandardized Regression Coefficientsen
dc.subjectShapley Value Regressionen
dc.subjectR-square decompositionen
dc.titleMeasuring Relative Impact of KPIs – A Game Theory Approachen_US
dc.typeArticleen_US
Appears in Collections:4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

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