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From information to operations: Service quality and customer retention

From information to operations: Service quality and customer retention From Information to Operations: Service Quality and Customer Retention BALAJI PADMANABHAN and ALAN HEVNER, University of South Florida MICHAEL CUENCO and CRYSTAL SHI, FedEx Services Corporation 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. Categories and Subject Descriptors: H.2.8 [Database Applications]: Data Mining General Terms: Algorithms, Design, Human Factors Additional Key Words and Phrases: Business intelligence, customer churn, service quality index, data mining, data analytics, pattern discovery http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Management Information Systems (TMIS) Association for Computing Machinery

From information to operations: Service quality and customer retention

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISSN
2158-656X
DOI
10.1145/2070710.2070712
Publisher site
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Abstract

From Information to Operations: Service Quality and Customer Retention BALAJI PADMANABHAN and ALAN HEVNER, University of South Florida MICHAEL CUENCO and CRYSTAL SHI, FedEx Services Corporation 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. Categories and Subject Descriptors: H.2.8 [Database Applications]: Data Mining General Terms: Algorithms, Design, Human Factors Additional Key Words and Phrases: Business intelligence, customer churn, service quality index, data mining, data analytics, pattern discovery

Journal

ACM Transactions on Management Information Systems (TMIS) – Association for Computing Machinery

Published: Dec 1, 2011

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