From information to operations: Service quality and customer retention

2012 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Padmanabhan, Balaji; Hevner, Alan; Cuenco, Michael; Shi, Crystal

Abstract: In business, information is abundant. Yet, effective use of that information to ... Expand

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. Collapse

Topics: service quality logistics management digital dashboard database system statistical process control

Methods: regression analysis method survey machine learning logistic regression descriptive statistic

Machine learning and genetic algorithms in pharmaceutical development and manufacturing processes

2009 | Decision Support Systems | Citations: 1

Authors: Chi, Hoi-Ming; Moskowitz, Herbert; Ersoy, Okan K.; Altinkemer, Kemal; Gavin, Peter F.; Huff, Bret E.; Olsen, Bernard A.

Abstract: We develop an adaptive Automated Intelligent Manufacturing System (AIMS) for Cas ... Expand

Abstract: We develop an adaptive Automated Intelligent Manufacturing System (AIMS) for Case 1:to a well-understood-pharmaceutical-process to demonstrate our methodology, Case 2:with clustering, to a not-well-controlled or understood-process for seemingly identical experiments yielding disparate results, Case 3:to scale-up a process from development to manufacturing, and Case 4:to deploy AIMS adaptively, to modify the process model and reoptimize the system contemporaneously, when predictive errors are significant. The results showed AIMS had both explanatory and predictive power. We have developed the following methodological extensions: a random probe method for feature selection, a simulation approach to establish tolerances for target inputs, and an adaptive capability integrated with statistical-process-control to modify the model. Collapse

Topics: statistical process control new product development decision making manufacturing information system database system

Methods: computational algorithm support vector machine machine learning experiment cluster analysis

Theories: statistical learning theory

Diagnosing decision quality

2008 | Decision Support Systems | Citations: 7

Authors: Davern, Michael J.; Mantena, Ravi; Stohr, Edward A.

Abstract: Human decision making is error-prone and often subject to biases. Important info ... Expand

Abstract: Human decision making is error-prone and often subject to biases. Important information cues are misweighted and feedback delays hamper learning. Experimentally, task information has been shown to be valuable in improving decision making. However, such information is rarely available. Generalizing from lab-based approaches, we present a new field methodology for the “diagnosis of decision quality” (DDQ) that helps decision makers discover such information. We illustrate our approach in the context of hotel revenue management and demonstrate how it can identify context-specific systematic errors in decision making in a manner that facilitates adaptive changes and improved performance. Collapse

Topics: price management decision making dynamic pricing decision quality statistical process control

Methods: machine learning post-hoc analysis design artifact computational algorithm laboratory experiment

Data Mining: A Practical Perspective

2001 | Americas Conference on Information Systems | Citations: 0

Authors: Jackson, Joyce

Abstract: This tutorial provides an overview of the data mining process. Methodological co ... Expand

Abstract: This tutorial provides an overview of the data mining process. Methodological considerations are discussed and illustrated. The tutorial provides a basic understanding of how to plan, evaluate and successfully refine a data mining project particularly in terms of model building and model evaluation. Collapse

Topics: marketing management customer relationship management research and development statistical process control

Methods: machine learning