Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classification algorithms, our heuristic-based approach achieves reasonable and comparable performance using information from the first session, day, and week of player activity.
Unlike previous work, the focus here is on the problem of rapid prediction of players by considering heuristic approaches owing to their ease of implementation and interpretation. Heuristics are strategies derived from experience with similar problems, using readily accessible information to control problem solving. They can be likened to rules of thumbs. They are often associated with the concept of satisfacing from economic decision-making @cite_11 . When finding an optimal solution is impossible or impractical, heuristic methods can be used for a satisfactory solution. They are used in a similar fashion in computer science, when the computational burden of complex methods is excessive. @cite_0 present a comprehensive review of their use in forecasting and prediction. @cite_4 ) empirically investigate their viability for use in database marketing. @cite_15 detail their application in management more broadly. The work presented here can be viewed as a special case and extension of the previous authors' contributions.
@cite_0 @cite_15 @cite_4 @cite_11
2099549054 2144108520 1990627282 2157581667
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. Summary In the management literature, heuristics are often conceived of as a source of systematic error, whereas logic and statistics are regarded as the sine qua non of good decision making. Yet, this view can be incorrect for decisions made under uncertainty, as opposed to risk. Research on fast and frugal heuristics shows that simple heuristics can be successful in complex, uncertain environments and also when and why this is the case. This article describes the conceptual framework of heuristics as adaptive decision strategies and connects it with the managerial literature. We review five classes of heuristics, analyze their common building blocks, and show how these are applied in managerial decision making. We conclude by highlighting some prominent opportunities for future research in the field. In the uncertain world of management, simple heuristics can lead to better and faster decisions than complex statistical procedures. Copyright © 2014 John Wiley & Sons, Ltd. Abstract Recently, academics have shown interest and enthusiasm in the development and implementation of stochastic customer base analysis models, such as the Pareto NBD model and the BG NBD model. Using the information these models provide, customer managers should be able to (1) distinguish active customers from inactive customers, (2) generate transaction forecasts for individual customers and determine future best customers, and (3) predict the purchase volume of the entire customer base. However, there is also a growing frustration among academics insofar as these models have not found their way into wide managerial application. To present arguments in favor of or against the use of these models in practice, the authors compare the quality of these models when applied to managerial decision making with the simple heuristics that firms typically use. The authors find that the simple heuristics perform at least as well as the stochastic models with regard to all managerially relevant areas, except for ... Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: (a) the discovery of less-is-more effects; (b) the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; (c) an advancement from vague labels to computational models of heuristics; (d) the development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an ‘‘adaptive toolbox;’’ and (e) the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people’s adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies.