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Measuring the impact of data mining on churn management

Internet Research · 2001 · 147 citations · 25 references

TL;DR

Churn management is increasingly critical in the digital economy, prompting customer‑oriented strategies and the adoption of advanced data‑mining techniques to better understand and retain customers. The paper proposes a customer‑relationship‑management framework that integrates electronic channels and uses sensitivity analysis to forecast how emerging data‑mining advances will affect churn management and decision making. The framework comprises four tools for data collection, processing, and analysis, and employs sensitivity analysis to evaluate the influence of data‑mining enhancements on churn outcomes.

Abstract

Churn management is a fundamental concern for businesses and the emergence of the digital economy has made the problem even more acute. Companies’ initiatives to handle churn and customers’ profitability issues have been directed to more customer‐oriented strategies. In this paper, we present a customer relationship management framework based on the integration of the electronic channel. This framework is constituted of four tools that should provide an appropriate collection, treatment and analysis of data. From this perspective, we pay special attention to some of the latest data mining developments which, we believe, are destined to play a central role in churn management. Relying on sensitivity analysis, we propose an analysis framework able to prefigure the possible impact induced by the ongoing data mining enhancements on churn management and on the decision‐making process.