Instant Customer Base Analysis: Managerial Heuristics Often “Get It Right”

Wübben, Markus; Wangenheim, Florian von

doi:10.1093/acprof:oso/9780199744282.003.0036

Cited by 85 publications

(126 citation statements)

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“…For the apparel retailer, the hiatus heuristic correctly classified 83% of customers, whereas the Pareto/NBD model classified only 75% correctly. For the airline, the score was 77% versus 74%, and for the online CD business, the two methods tied at 77% (Wübben & Wangenheim 2008). Similar results were found for forecasting future best customers and for a second complex statistical model.…”

Section: Definitionsupporting

confidence: 65%

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Heuristic Decision Making

Martignon

Gaissmaier

2011

Annu. Rev. Psychol.

2,883

1,740

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As reflected in the amount of controversy, few areas in psychology have undergone such dramatic conceptual changes in the past decade as the emerging science of heuristics. Heuristics are efficient cognitive processes, conscious or unconscious, that ignore part of the information. Because using heuristics saves effort, the classical view has been that heuristic decisions imply greater errors than do "rational" decisions as defined by logic or statistical models. However, for many decisions, the assumptions of rational models are not met, and it is an empirical rather than an a priori issue how well cognitive heuristics function in an uncertain world. To answer both the descriptive question ("Which heuristics do people use in which situations?") and the prescriptive question ("When should people rely on a given heuristic rather than a complex strategy to make better judgments?"), formal models are indispensable. We review research that tests formal models of heuristic inference, including in business organizations, health care, and legal institutions. This research indicates that (a) individuals and organizations often rely on simple heuristics in an adaptive way, and (b) ignoring part of the information can lead to more accurate judgments than weighting and adding all information, for instance for low predictability and small samples. The big future challenge is to develop a systematic theory of the building blocks of heuristics as well as the core capacities and environmental structures these exploit.

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Section: Definitionsupporting

confidence: 65%

“…However, most managers in Europe, North America, Japan, Brazil, and India rely on "intuitive" heuristics rather than on this or similar statistical forecasting methods (Parikh 1994). Wübben & Wangenheim (2008) reported that experienced managers use a simple recency-of-last-purchase rule:…”

Section: Definitionmentioning

confidence: 99%

Heuristic Decision Making

Martignon

Gaissmaier

2011

Annu. Rev. Psychol.

2,883

1,740

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“…The stochastic models proposed by the marketing literature [14] are somewhat ill-equipped to deal with this kind of data as they were conceived for purchase related data. It would certainly be interesting to extend them to highly detailed usage data, but this is beyond the scope of the current study -especially as their performance in applied settings remains uncertain [26]. We wish to emphasize that our methodological thinking deviates substantially from [10] who build on stochastic models and disregard non-purchasing related information.…”

Section: Predicting Ltv In Non-contractual Freemium Settingsmentioning

confidence: 99%

Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE

Sifa¹,

Runge²,

Bauckhage³

et al. 2018

Proceedings of the Annual Hawaii International Conference on System Sciences

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In non-contractual freemium and sharing economy settings, a small share of users often drives the largest part of revenue for firms and co-finances the free provision of the product or service to a large number of users. Successfully retaining and upselling such high-value users can be crucial to firms' survival. Predictions of customers' Lifetime Value (LTV) are a much used tool to identify high-value users and inform marketing initiatives. This paper frames the related prediction problem and applies a number of common machine learning methods for the prediction of individual-level LTV. As only a small subset of users ever makes a purchase, data are highly imbalanced. The study therefore combines said methods with synthetic minority oversampling (SMOTE) in an attempt to achieve better prediction performance. Results indicate that data augmentation with SMOTE improves prediction performance for premium and high-value users, especially when used in combination with deep neural networks.

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“…Cue validities vary little, low redundancy (Hogarth & Karelaia, 2005, 2006 Can predict as accurately as or more accurately than multiple regression Hiatus heuristic (Wübben & Wangenheim, 2008) Assume that customers who have not purchased in a fixed period of time are inactive…”

Section: Heuristicmentioning

confidence: 99%

Fast and frugal forecasting

Goldstein

Gigerenzer

2009

International Journal of Forecasting

153

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Simple statistical forecasting rules, which are usually simplifications of classical models, have been shown to make better predictions than more complex rules, especially when the future values of a criterion are highly uncertain. In this article, we provide evidence that some of the fast and frugal heuristics that people use intuitively are able to make forecasts that are as good as or better than those of knowledge-intensive procedures. We draw from research on the adaptive toolbox and ecological rationality to demonstrate the power of using intuitive heuristics for forecasting in various domains including sport, business, and crime.

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Instant Customer Base Analysis: Managerial Heuristics Often “Get It Right”

Cited by 85 publications

References 0 publications

Heuristic Decision Making

Heuristic Decision Making

Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE

Fast and frugal forecasting

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