Churn studies have been used for many years to increase profitability as well as to make customer-company relations sustainable. Ordinary artificial neural network (ANN) and convolution neural network (CNN) are widely used in churn analysis due to their ability to process large amounts of customer data. In this study, an ANN and a CNN model are proposed to predict whether customers in the retail industry will churn in the future. The models we propose were compared with many machine learning methods that are frequently used in churn prediction studies. The results of the models were compared via accuracy classification tools, which are precision, recall, and AUC. The results showed that the CNN model produced a 97.62% of accuracy rate which resulted in a better classification and prediction success than other compared models
Customer lifetime value has been of significant importance to marketing researchers and practitioners in specifying the importance level of each customer. By means of segmentation which could be carried out using value-based characteristics it is indeed possible to develop tailored strategies for customers. In fact, approaches like data mining can facilitate extraction of critical customer knowledge for enhanced decision making. Although the literature has several analytical lifetime value models, comparative assessment of the existing models especially within the context of data mining seems a missing component. The aim of this paper is to compare two different customer lifetime value models within data mining. The evaluation was carried out within the context of customer segmentation using a database of a company operating in retail sector. The results indicated that two models yield the same segmentation structure and no statistical differences detected on the select control variables. However, the remaining model produced rather different segmentation results than their peers and it was possible to identify the most lucrative model according to the statistical analyses that were carried out on the select control variables.
The vast quantity of customer data and its ubiquity, as well as the inabilities of conventional segmentation tools, have diverted researchers in search of powerful segmentation techniques for generating managerially meaningful information. Due to its noteworthy practical use, soft computing-based techniques, especially fuzzy clustering, can be considered one of those contemporary approaches. Although there have been various fuzzy-based clustering applications in segmentation, intuitionistic fuzzy sets that have the complimentary feature have appeared in limited studies, especially in a comparative context. Therefore, this study extends the current body of the pertaining literature by providing a comparative assessment of intuitionistic fuzzy clustering. The comparison was carried out with two other well-known segmentation techniques, [Formula: see text]-means and fuzzy [Formula: see text]-means, based on transaction data that belong to Turkey’s two major cities. Over 10,000 records of customers’ data were processed for segmentation purposes, and the comparative approaches were presented. According to the results, the intuitionistic fuzzy clustering approach outperformed the other methods in terms of the clustering efficiency index being utilized. The validity of the segmentation structure obtained by the superior approach was ensured via nonsegmentation variables. The comparative assessment and the potential managerial implications could be considered as a contribution to the corresponding literature. This study also compares the effects of the different parameter values used in the proposed model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.