Predicting replacement of smartphones with mobile app usage

© Springer International Publishing AG 2016. To identify right customers who intend to replace the smart phone can help to perform precision marketing and thus bring significant financial gains to cell phone retailers. In this paper,we provide a study of exploiting mobile app usage for predicting users who will change the phone in the future. We first analyze the characteristics of mobile log data and develop the temporal bag-of-apps model,which can transform the raw data to the app usage vectors. We then formularize the prediction problem,present the hazard based prediction model,and derive the inference procedure. Finally,we evaluate both data model and prediction model on real-world data. The experimental results show that the temporal usage data model can effectively capture the unique characteristics of mobile log data,and the hazard based prediction model is thus much more effective than traditional classification methods. Furthermore,the hazard model is explainable,that is,it can easily show how the replacement of smart phones relate to mobile app usage over time

The dangers of using intention as a surrogate for Retention in brand positioning decision support systems

The purpose of this paper is to explore the dangers of using intention as a surrogate for retention in a decision support system (DSS) for brand positioning. An empirical study is conducted, using structural equations modeling and both data from the internal transactional database and a survey. The study is aimed at evaluating whether the DSS recommends different product benefits for brand positioning when intention is used as opposed to retention as a criterion variable. The results show that different product benefits are recommended contingent upon the criterion variable (intention vs. retention). The findings also indicate that the strength of the structural relationships is inflated when intention is used. This study is limited in that it investigates only one industry; the newspaper industry. This study provides guidance for brand managers in selecting the most appropriate benefit for brand positioning and advices against the use of intention as opposed to retention in DSS. To the best of our knowledge this study is the first to challenge and refute the commonly held belief that intention is a valid surrogate for retention in a DSS for brand positioning

Customer Base Analysis: Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting

Customer Relationship Management (CRM) enjoys increasing attention as a countermeasure to switching behaviour of customers. Because foregone profits of (partially) defected customers are significant, an increase of the retention rate can be very profitable. In this paper, we focus on the treatment of a company’s most promising customers in a non-contractual setting. We build a model in order to predict partial defection by behaviorally-loyal clients using three classification techniques: Logistic regression, ARD Neural Networks and Random Forests. Classification accuracy (PCC) and area under the receiver operating characteristic curve (AUC) are used to evaluate classifier performance. Using real-life data from an FMCG retailer we show that future partial defection can be successfully predicted. Similar to direct-marketing applications, we find that past behavioral variables, more specifically RFM variables (recency, frequency, monetary value) are the best predictors of partial customer defection.Marketing; Forecasting; Churn analysis; Retailing; Classification.

Customer-Adapted Coupon Targeting Using Feature Selection

The management of coupon promotions is an important issue for marketing managers since it still is the major promotion medium. However, the distribution of coupons does not go without problems. Although manufacturers and retailers are investing heavily in the attempt to convince as many customers as possible, overall coupon redemption rate is low. This study improves the strategy of retailers and manufacturers concerning their target selection since both parties often end up in a battle for customers. Two separate models are built: one model makes predictions concerning redemption behavior of coupons that are distributed by the retailer while another model does the same for coupons handed out by manufacturers. By means of the feature-selection technique ‘Relief-F’ the dimensionality of the models is reduced, since it searches for the variables that are relevant for predicting the outcome. In this way, redundant variables are not used in the model-building process. The model is evaluated on real-life data provided by a retailer in FMCG. The contributions of this study for retailers as well as manufacturers are threefold. First, the possibility to classify customers concerning their coupon usage is shown. In addition, it is demonstrated that retailers and manufacturers can stay clear of each other in their marketing campaigns. Finally, the feature-selection technique ‘Relief-F’ proves to facilitate and optimize the performance of the models.Data mining; Classification; Feature selection; Retailing

Using machine learning techniques to predict defection of top clients

Fierce competition in many industries causes switching behavior of customers. Because foregone profits of defected customers are significant, an increase of the retention rate can be very profitable. In this paper, we focus on the treatment of companies' most promising current customers in a non-contractual setting. We build a model in order to predict chum behavior of top clients who will (partially) defect in the near future. We applied the following classification techniques: logistic regression, linear discriminant analysis, quadratic discriminant analysis, C4.5, neural networks and Naive Bayes. Their performance is quantified by the classification accuracy and the area under the receiver operating characteristic curve (AUROC). The experiments were carried out on a real life data set obtained by a Belgian retailer. The article contributes in many ways. The results show that past customer behavior has predictive power to indicate future partial defection. This finding is from a companies' point of view even more important than being able to define total defectors, which was until now the traditional goal in attrition research. It was found that neural networks performed better than the other classification techniques in terms of both classification accuracy and AUROC. Although the performance benefits are sometimes small in absolute terms, they are statistically significant and relevant from a marketing perspective. Finally it was found that the number of past shop visits and the time between past shop incidences are amongst the most predictive inputs for the problem at hand

Finding the Hidden Pattern of Credit Card Holder's Churn: A Case of China.

In this paper, we propose a framework of the whole process of churn prediction of credit card holder. In order to make the knowledge extracted from data mining more executable, we take the execution of the model into account during the whole process from variable designing to model understanding. Using the Logistic regression, we build a model based on the data of more than 5000 credit card holders. The tests of model perform very well