From information to operations

Balaji Padmanabhan · Alan R. Hevner · Michael Cuenco · Crystal Shi

42 citations0/0 files storedACM Transactions on Management Information Systems

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Customer churn and segmentation Customer Service Quality and Loyalty Big Data and Business Intelligence

Abstract

In business, information is abundant. Yet, effective use of that information to inform and drive business operations is a challenge. Our industry-university collaborative project draws from a rich dataset of commercial demographics, transaction history, product features, and Service Quality Index (SQI) factors on shipping transactions at FedEx. We apply inductive methods to understand and predict customer churn in a noncontractual setting. Results identify several SQI variables as important determinants of churn across a variety of analytic approaches. Building on this we propose the design of a Business Intelligence (BI) dashboard as an innovative approach for increasing customer retention by identifying potential churners based on combinations of predictor variables such as demographics and SQI factors. This empirical study contributes to BI research and practice by demonstrating the application of data analytics to the fundamental business operations problem of customer churn.

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References

1996The Behavioral Consequences of Service Quality 2004Design science in information systems research 1995Customer Switching Behavior in Service Industries: An Exploratory Study 2001Satisfaction, Repurchase Intent, and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics 1998A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction 1995Return on Quality (ROQ): Making Service Quality Financially Accountable 2005The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention 2008Application of data mining techniques in customer relationship management: A literature review and classification

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2021Customer Churn Prediction in Telecom Sector with Machine Learning and Information Gain Filter Feature Selection Algorithms 2017Applying behavioral economics in predictive analytics for B2B churn: Findings from service quality data 2018An enhanced ensemble classifier for telecom churn prediction using cost based uplift modelling 2002Revolution in Information Affairs

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