In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-03

AUTHORS

Eva Ascarza, Scott A. Neslin, Oded Netzer, Zachery Anderson, Peter S. Fader, Sunil Gupta, Bruce G. S. Hardie, Aurélie Lemmens, Barak Libai, David Neal, Foster Provost, Rom Schrift

ABSTRACT

In today’s turbulent business environment, customer retention presents a significant challenge for many service companies. Academics have generated a large body of research that addresses part of that challenge—with a particular focus on predicting customer churn. However, several other equally important aspects of managing retention have not received similar level of attention, leaving many managerial problems not completely solved, and a program of academic research not completely aligned with managerial needs. Therefore, our goal is to draw on previous research and current practice to provide insights on managing retention and identify areas for future research. This examination leads us to advocate a broad perspective on customer retention. We propose a definition that extends the concept beyond the traditional binary retain/not retain view of retention. We discuss a variety of metrics to measure and monitor retention. We present an integrated framework for managing retention that leverages emerging opportunities offered by new data sources and new methodologies such as machine learning. We highlight the importance of distinguishing between which customers are at risk and which should be targeted—as they are not necessarily the same customers. We identify trade-offs between reactive and proactive retention programs, between short- and long-term remedies, and between discrete campaigns and continuous processes for managing retention. We identify several areas of research where further investigation will significantly enhance retention management. More... »

PAGES

65-81

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40547-017-0080-0

DOI

http://dx.doi.org/10.1007/s40547-017-0080-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092592186


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