Seminario Interno FEN: David Díaz
Fecha de inicio: 22 de Noviembre, 2019, 13:00 hrs.
Fecha de término: 22 de Noviembre, 2019, 14:00 hrs.
Estimados Académicos FEN,
Los invitamos al Seminario Interno FEN que dictará el académico David Díaz.
Título: “I Love You and I Won’t Leave You”: Unpacking Customer Loyalty using Machine Learning Model
Autores: Mohamed Zaki (*), Janet R. McColl-Kennedy (**), David Díaz (+), Dalia Kandil (*), Andy Neely (*)*: Cambridge Service Alliance, University of Cambridge, UK.
**: Business School, University of Queensland, Australia
+: FEN, Universidad de Chile.
Resumen: Customer loyalty in business-to-business (B2B) is a strategic priority. Accordingly, customer loyalty is considered a critical indicator of a company’s performance. It is surprising then that so many firms rely on simple single-metric to measure customer loyalty and are unaware of what their customers really think of them. While such measures are easy to administer and provide a set of numbers that can be presented to the board, they fail to take into account multi-faceted dimensions to customer loyalty which cannot be measured by a single data point. The authors contribute to customer loyalty management theory and practice in three important ways. First, by conceptualizing customer loyalty as a multi-faceted construct and offer a novel conceptual framework that integrates prior research to unpack loyalty—comprised of attitudinal (superior performance and desirable dimensions), emotional (adoration dimension) and behavioral (purchase and communication dimensions). Second, by employing a machine learning model using multi-data sources (attitudinal and behavioral) to empirically test our conceptual framework that combines quantitative and qualitative measures. Our prediction model (using ensembled C5.0 algorithm) correctly predicts 93% of customers likely to be loyal, based on the results of the testing dataset. Third, by providing a step-by-step guide for implementing the machine learning approach in practice, assisting managers to develop a much richer view of customer loyalty. The authors demonstrate the strengths of this new approach relative to traditional measures and concludes with directions for future research.