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Dynamic churn prediction framework with more effective use of rare event data: the case of private banking
(Pergamon-Elsevier Science Ltd, 2014) Department of Business Administration; N/A; Ali, Özden Gür; Arıtürk, Umut; Faculty Member; PhD Student; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Business; 57780; N/A
Customer churn prediction literature has been limited to modeling churn in the next (feasible) time period. On the other hand, lead time specific churn predictions can help businesses to allocate retention efforts across time, as well as customers, and identify early triggers and indicators of customer churn. We propose a dynamic churn prediction framework for generating training data from customer records, and leverage it for predicting customer churn within multiple horizons using standard classifiers. Further, we empirically evaluate the proposed approach in a case study about private banking customers in a European bank. The proposed framework includes customer observations from different time periods, and thus addresses the absolute rarity issue that is relevant for the most valuable customer segment of many companies. It also increases the sampling density in the training data and allows the models to generalize across behaviors in different time periods while incorporating the impact of the environmental drivers. As a result, this framework significantly increases the prediction accuracy across prediction horizons compared to the standard approach of one observation per customer; even when the standard approach is modified with oversampling to balance the data, or lags of customer behavior features are added as additional predictors. The proposed approach to dynamic churn prediction involves a set of independently trained horizon-specific binary classifiers that use the proposed dataset generation framework. In the absence of predictive dynamic churn models, we had to benchmark survival analysis which is used predominantly as a descriptive tool. The proposed method outperforms survival analysis in terms of predictive accuracy for all lead times, with a much lower variability. Further, unlike Cox regression, it provides horizon specific ranking of customers in terms of churn probability which allows allocation of retention efforts across customers and time periods. (C) 2014 Elsevier Ltd. All rights reserved.
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The effects of brand equity on price strategies: an agent based model
(Univ De La Laguna, 2009) Delre, Sebastiano A.; Department of Business Administration; Esseghaier, Skander; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; N/A
Consumers are highly sensible to different price structures and price promotions. Many studies have showed how custÖmers differently respond when prices are split into separate parts, e.g. a regular price and a shipping and handling surcharge. This phenomenon has recently received much more attention because online sales have continuously and substantially increased in the last years and because online sales imply a price partitioning: product price and shipping price. This gives opportunities to online retailers. They can decide whether to apply promotional tactics on both regular prices and on shipping and handling prices. The price partitioning decision becomes more complicated than usual. Retailers have to choose between a free shipping offer strategy and a price partitioning strategy. In the former case they have to decide a single price that includes the shipping cost and in the latter cases they have to choose upon two prices, a price for the item and a price for the shipping. This paper investigates how firms decide which of these two strategies to adopt and how their brand equities affect their decisions. An agent based model is built in order to replicate Gumus et al. (2009) results and to depart from it bringing new insights about market partitioning (how many firms adopt which strategy) and the effects of brand equities.
Metadata only
SKU demand forecasting in the presence of promotions
(Elsevier, 2009) van Woensel, Tom; Fransoo, Jan; Department of Business Administration; Department of Business Administration; Ali, Özden Gür; Sayın, Serpil; Faculty Member; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; College of Administrative Sciences and Economics; 57780; 6755
Promotions and shorter life cycles make grocery sales forecasting more difficult, requiring more complicated models. We identify methods of increasing complexity and data preparation cost yielding increasing improvements in forecasting accuracy, by varying the forecasting technique, the input features and model scope on an extensive SKU-store level sales and promotion time series from a European grocery retailer. At the high end of data and technique complexity, we propose using regression trees with explicit features constructed from sales and promotion time series of the focal and related SKU-store combinations. We observe that data pooling almost always improves model performance. The results indicate that simple time series techniques perform very well for periods without promotions. However, for periods with promotions, regression trees with explicit features improve accuracy substantially. More sophisticated input is only beneficial when advanced techniques are used. We believe that our approach and findings shed light into certain questions that arise while building a grocery sales forecasting system. (C) 2009 Elsevier Ltd. All rights reserved.

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