How Feedback Can Improve Managerial Evaluations of Model-Based Marketing Decision Support Systems

42 Pages Posted: 16 Aug 2006

See all articles by U. Kayande

U. Kayande

Pennsylvania State University - Institute for the Study of Business Markets

Arnaud De Bruyn

ESSEC Business School

Gary L. Lilien

Pennsylvania State University - Institute for the Study of Business Markets

Arvind Rangaswamy

Pennsylvania State University - Department of Marketing

G.H. van Bruggen

Erasmus University Rotterdam (EUR) - Rotterdam School of Management (RSM); Erasmus Research Institute of Management (ERIM)

Multiple version iconThere are 3 versions of this paper

Date Written: August 14, 2006

Abstract

Marketing managers often provide much poorer evaluations of model-based marketing decision support systems (MDSSs) than are warranted by the objective performance of those systems. We show that a reason for this discrepant evaluation may be that MDSSs are often not designed to help users understand and internalize the underlying factors driving the MDSS results and related recommendations. Thus, there is likely to be a gap between a marketing manager’s mental model and the decision model embedded in the MDSS. We suggest that this gap is an important reason for the poor subjective evaluations of MDSSs, even when the MDSSs are of high objective quality, ultimately resulting in unreasonably low levels of MDSS adoption and use. We propose that to have impact, an MDSS should not only be of high objective quality, but should also help reduce any mental model-MDSS model gap. We evaluate two design characteristics that together lead model-users to update their mental models and reduce the mental model-MDSS gap, resulting in better MDSS evaluations: providing feedback on the upside potential for performance improvement and providing specific suggestions for corrective actions to better align the user's mental model with the MDSS. We hypothesize that, in tandem, these two types of MDSS feedback induce marketing managers to update their mental models, a process we call deep learning, whereas individually, these two types of feedback will have much smaller effects on deep learning. We validate our framework in an experimental setting, using a realistic MDSS in the context of a direct marketing decision problem. We then discuss how our findings can lead to design improvements and better returns on investments in MDSSs such as CRM systems, Revenue Management systems, pricing decision support systems, and the like.

Keywords: Learning, Feedback, Marketing Decision Models, Marketing Decision Support Systems, Marketing Information Systems

Suggested Citation

Kayande, Ujwal and De Bruyn, Arnaud and Lilien, Gary L. and Rangaswamy, Arvind and van Bruggen, Gerrit H., How Feedback Can Improve Managerial Evaluations of Model-Based Marketing Decision Support Systems (August 14, 2006). ERIM Report Series Reference No. ERS-2006-039-MKT, Available at SSRN: https://ssrn.com/abstract=924506

Ujwal Kayande (Contact Author)

Pennsylvania State University - Institute for the Study of Business Markets ( email )

University Park, PA 16802-3306
United States

Arnaud De Bruyn

ESSEC Business School ( email )

France

Gary L. Lilien

Pennsylvania State University - Institute for the Study of Business Markets ( email )

University Park, PA 16802-3306
United States
814-863-2782 (Phone)
814-863-0413 (Fax)

HOME PAGE: http://www.smeal.psu.edu/isbm/about/people/LILIEN.

Arvind Rangaswamy

Pennsylvania State University - Department of Marketing ( email )

University Park, PA 16802-3306
United States

Gerrit H. Van Bruggen

Erasmus University Rotterdam (EUR) - Rotterdam School of Management (RSM) ( email )

P.O. Box 1738
Room T08-21
3000 DR Rotterdam, 3000 DR
Netherlands
+31 10 408 2258 (Phone)
+31 10 408 9011 (Fax)

Erasmus Research Institute of Management (ERIM)

P.O. Box 1738
3000 DR Rotterdam
Netherlands

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