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

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.Learning;Feedback;Marketing Decision Models;Marketing Decision Support Systems;Marketing Information Systems

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

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

From academic research to marketing practice: Exploring the marketing science value chain

We aim to investigate the impact of marketing science articles and tools on the practice of marketing. This impact may be direct (e.g., an academic article may be adapted to solve a practical problem) or indirect (e.g., its contents may be incorporated into practitioners' tools, which then influence marketing decision making). We use the term "marketing science value chain" to describe these diffusion steps, and survey marketing managers, marketing science intermediaries (practicing marketing analysts), and marketing academics to calibrate the value chain.In our sample, we find that (1) the impact of marketing science is perceived to be largest on decisions such as the management of brands, pricing, new products, product portfolios, and customer/market selection, and (2) tools such as segmentation, survey-based choice models, marketing mix models, and pre-test market models have the largest impact on marketing decisions. Exemplary papers from 1982 to 2003 that achieved dual - academic and practice - impact are Guadagni and Little (1983) and Green and Srinivasan (1990). Overall, our results are encouraging. First, we find that the impact of marketing science has been largest on marketing decision areas that are important to practice. Second, we find moderate alignment between academic impact and practice impact. Third, we identify antecedents of practice impact among dual impact marketing science papers. Fourth, we discover more recent trends and initiatives in the period 2004-2012, such as the increased importance of big data and the rise of digital and mobile communication, using the marketing science value chain as an organizing framework

Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules

Exploiting dependencies between labels is considered to be crucial for multi-label classification. Rules are able to expose label dependencies such as implications, subsumptions or exclusions in a human-comprehensible and interpretable manner. However, the induction of rules with multiple labels in the head is particularly challenging, as the number of label combinations which must be taken into account for each rule grows exponentially with the number of available labels. To overcome this limitation, algorithms for exhaustive rule mining typically use properties such as anti-monotonicity or decomposability in order to prune the search space. In the present paper, we examine whether commonly used multi-label evaluation metrics satisfy these properties and therefore are suited to prune the search space for multi-label heads.Comment: Preprint version. To appear in: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2018. See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3074 for further information. arXiv admin note: text overlap with arXiv:1812.0005

Openness and Innovation Performance Revisited

Firms increasingly source new ideas and knowledge from alliances with external partners. Laursen and Salter's (2006) seminal research shows that while such openness in innovation benefits firms, too much openness can have a negative effect on innovation performance. We provide a conceptual replication of this finding, relying on a unique longitudinal panel data set comprising three different innovation performance metrics: product and service innovations, process innovations, and marketing innovations

From academic research to marketing practice: Exploring the marketing science value chain

We aim to investigate the impact of marketing science articles and tools on the practice of marketing. This impact may be direct (e.g., an academic article may be adapted to solve a practical problem) or indirect (e.g., its contents may be incorporated into practitioners' tools, which then influence marketing decision making). We use the term “marketing science value chain” to describe these diffusion steps, and survey marketing managers, marketing science intermediaries (practicing marketing analysts), and marketing academics to calibrate the value chain. In our sample, we find that (1) the impact of marketing science is perceived to be largest on decisions such as the management of brands, pricing, new products, product portfolios, and customer/market selection, and (2) tools such as segmentation, survey-based choice models, marketing mix models, and pre-test market models have the largest impact on marketing decisions. Exemplary papers from 1982 to 2003 that achieved dual – academic and practice – impact are Guadagni and Little (1983) and Green and Srinivasan (1990). Overall, our results are encouraging. First, we find that the impact of marketing science has been largest on marketing decision areas that are important to practice. Second, we find moderate alignment between academic impact and practice impact. Third, we identify antecedents of practice impact among dual impact marketing science papers. Fourth, we discover more recent trends and initiatives in the period 2004–2012, such as the increased importance of big data and the rise of digital and mobile communication, using the marketing science value chain as an organizing framework

Formulating appropriate utility functions and personal financial plans

Without inflation protected pensions, people need decision making tools (financial calculators) to make informed decisions about savings and investment for retirement. For investment, they need a framework to trade off risk and return. This paper examines the assumptions underlying some of the common utility functions in the financial literature and suggests ways of making them more consistent with the behavioural and happiness literature. In particular, frictional costs are introduced to explain loss aversion. The results are illustrated in a way that could perhaps be presented to users of financial calculators to elicit their preferences and assist in making more coherent decisions

From academic research to marketing practice: Some further thoughts

In this rejoinder, we share some further thoughts that were triggered by the insightful comments of Lehmann and Winer, and address some concerns expressed by them. We argue that our work can be interpreted using two different reference points, leading to an optimistic view or a more pessimistic one. We also advance a number of strategies for those in our field who aspire to influence the decisions that managers actually make

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Without inflation protected pensions, people need decision making tools (financial calculators) to make informed decisions about savings and investment for retirement. For investment, they need a framework to trade off risk and return. This paper examines the assumptions underlying some of the common utility functions in the financial literature and suggests ways of making them more consistent with the behavioural and happiness literature. In particular, frictional costs are introduced to explain loss aversion. The results are illustrated in a way that could perhaps be presented to users of financial calculators to elicit their preferences and assist in making more coherent decisions