Browsing by Author "Berrocal, Javier"
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- Hybrid Random Forest Survival Model to Predict Customer Membership DropoutPublication . Sobreiro, Pedro; Garcia-Alonso, José; Martinho, Domingos; Berrocal, JavierDropout prediction is a problem that must be addressed in various organizations, as retaining customers is generally more profitable than attracting them. Existing approaches address the problem considering a dependent variable representing dropout or non-dropout, without considering the dynamic perspetive that the dropout risk changes over time. To solve this problem, we explore the use of random survival forests combined with clusters, in order to evaluate whether the prediction performance improves. The model performance was determined using the concordance probability, Brier Score and the error in the prediction considering 5200 customers of a Health Club. Our results show that the prediction performance in the survival models increased substantially in the models using clusters rather than that without clusters, with a statistically significant difference between the models. The model using a hybrid approach improved the accuracy of the survival model, providing support to develop countermeasures considering the period in which dropout is likely to occur.
- Predicting High-Value Customers in a Portuguese Wine CompanyPublication . Sobreiro, Pedro; Martinho, Domingos; Pratas, Antonio; Garcia-Alonso, Jose; Berrocal, JavierWine companies operate in a very competitive environment in which they must provide better-customised services and products to survive and gain advantage. The high customer turnover rate is a problem for these companies. This work aims to provide wine companies with new knowledge about customers that help to retain the existing ones. The study applies a collected dataset from a transaction database in a medium-sized ortuguese wine company to determinate: (1) customer lifetime value; (2) cluster customer value as output (customer loyalty). The measurement of the customer lifetime value (CLV) was analysed using the Pareto/NBD model and gamma-gamma model. Clustering techniques are employed to segment customers according to Recency, Frequency, and Monetary (RFM) values. Study findings show that exists three clusters with different interest to the marketing strategies, identifying the high-value customers, to target using marketing to increase their lifetime value effectively. The implications for the marketing strategy decisions is that using techniques based on the RFM model can make the most from data of customers and transactions databases and thus create sustainable advantages.
- A SLR on Customer Dropout PredictionPublication . Sobreiro, Pedro; Martinho, Domingos; Alonso, Jose G.; Berrocal, JavierDropout prediction is a problem that is being addressed with machine learning algorithms; thus, appropriate approaches to address the dropout rate are needed. The selection of an algorithm to predict the dropout rate is only one problem to be addressed. Other aspects should also be considered, such as which features should be selected and how to measure accuracy while considering whether the features are appropriate according to the business context in which they are employed. To solve these questions, the goal of this paper is to develop a systematic literature review to evaluate the development of existing studies and to predict the dropout rate in contractual settings using machine learning to identify current trends and research opportunities. The results of this study identify trends in the use of machine learning algorithms in different business areas and in the adoption of machine learning algorithms, including which metrics are being adopted and what features are being applied. Finally, some research opportunities and gaps that could be explored in future research are presented.