Customer lifetime value modeling and its use for customer retention planning

Neumann, Einat; Eick, Uri; Vatnik, Nurit; Idan, Yizhak

doi:10.1145/775094.775097

Cited by 21 publications

(27 citation statements)

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“…Statistical Market Analysis. Most of the classical market analysis approaches such as life time value modeling [12] focus on sales history. Recent studies [7] show that the share-of-wallet is in fact a better indicator of the customer growth potential.…”

Section: Related Workmentioning

confidence: 99%

A new multi-view regression approach with an application to customer wallet estimation

Merugu

Perlich

2006

Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Motivated by the problem of customer wallet estimation, we propose a new setting for multi-view regression, where we learn a completely unobserved target (in our case, customer wallet) by modeling it as a "central link" in a directed graphical model, connecting multiple sets of observed variables. The resulting conditional independence allows us to reduce the maximum discriminative likelihood estimation problem to a convex optimization problem for exponential linear models. We show that under certain modeling assumptions, in particular, when there exist two conditionally independent views and the noise is Gaussian, this problem can be reduced to a single least squares regression. Thus, for this specific, but widely applicable setting, the "unsupervised" multi-view problem can be solved via a simple supervised learning approach. This reduction also allows us to test the statistical independence assumptions underlying the graphical model and perform variable selection. We demonstrate the effectiveness of our approach on our motivating problem of customer wallet estimation and on simulation data.

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Section: Related Workmentioning

confidence: 99%

A new multi-view regression approach with an application to customer wallet estimation

Merugu

Perlich

2006

Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…Retaining the most profitable customers is a serious concern when determining targeted marketing campaigns. To undertake effective customer relationship management, a number of researches have provided customer value based on customer lifetime value (Berger & Nasr, 1998;Pfeifer & Carraway, 2000;Rosset et al, 2002;Hwang et al, 2004). In our proposed procedure, customer lifetime value analysis can be used to decide whether potential defectors are valuable to retain in terms of their expected contribution.…”

Section: Marketing Strategies For Defection Preventionmentioning

confidence: 99%

Strategies for preventing defection based on the mean time to defection and their implementations on a self-organizing map

2005

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Customer retention is a critical issue for the survival of any business in today's competitive marketplace. In this paper, we propose a dynamic procedure utilizing self-organizing maps and a Markov process for detecting and preventing customer defection that uses data of past and current customer behavior. The basic concept originates from empirical observations that identified that a customer has a tendency to change behavior (i.e. trim-out usage volumes) before eventual withdrawal and defection. Our explanatory model predicts when potential defectors are likely to withdraw. Two strategies are suggested to respond to the question of where to lead potential defectors for the next stage, based on anticipating when the potential defector will leave. Our model predicts potential defectors with little deterioration of prediction accuracy compared with that of the multilayer perceptron neural network and decision trees. Moreover, it performs reasonably well in a controlled experiment using an online game.

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“…Recommender systems have been developing over the last decade and have performed well in several applications, including those for recommending books, CDs, and news articles (Marlin 2003;Rosset et al 2002), and some of these methods are used in the ''industrial-strength'' recommender systems, such as those deployed at Amazon (Linden, Smith, and York 2003) and MovieLens (Miller et al 2003). Traditional recommender systems can be extended in a wide variety of ways.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Profiling for Hybrid Multicriteria Recommendation Based on Implicit Multicriteria Information

Premchaiswadi

Poompuang

2013

Applied Artificial Intelligence

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& Multicriteria recommender systems typically gather the user preferences by asking a user to rate different aspects of an item on a sliding scale explicitly. However, this approach could possibly cause intrusiveness and conflict on user preferences. For example, an individual's preference on each aspect of an item may conflict with an overall preference. To overcome such limitations, we proposed the hybrid profiling framework to generate a set of useful implicit dataset to support multicriteria recommender systems. We also proposed two hybrid multicriteria recommendation approaches, namely the user-attribute-based (UAB) and the user-item matching (UIM) to improve recommendation accuracy. Finally, we conducted experiments to confirm the efficiency of the proposed approaches. The experiments show that the profiling framework and two hybrid recommendation approaches can alleviate the problem in an intrusive manner and decrease the degree of preference conflict without decreasing the accuracy of the recommendation. They also show that our proposed hybrid multicriteria recommendation approaches can significantly outperform both the traditional collaborative filtering and the simple multicriteria filtering approaches.

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Customer lifetime value modeling and its use for customer retention planning

Cited by 21 publications

References 0 publications

A new multi-view regression approach with an application to customer wallet estimation

A new multi-view regression approach with an application to customer wallet estimation

Strategies for preventing defection based on the mean time to defection and their implementations on a self-organizing map

Hybrid Profiling for Hybrid Multicriteria Recommendation Based on Implicit Multicriteria Information

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