Predicting customer churn based on changes in their behavior patterns

Customer retention is one of the most important tasks of a business, and it is extremely important to allocate retention resources according to the potential profitability of the customer. Most often the problem of predicting customer churn is solved based on the RFM (Recency, Frequency, Monetary) model. This paper proposes a way to extend the RFM model with estimates of the probability of changes in customer behavior. Based on an analysis of data relating to 33 918 clients of a large Russian retailer for 2019-2020, it is shown that there are recurring patterns of change in their behavior over a single year. Information about these patterns is used to calculate the necessary probability estimates. Incorporating these data into a predictive model based on logistic regression increases prediction accuracy by more than 10% on the metrics AUC and geometric mean. It is also shown that this approach has limitations related to the disruption of behavioral patterns by external shocks, such as the lockdown due to the COVID-19 pandemic in April 2020. The paper also proposes a way to identify these shocks, making it possible to forecast degradation in the predictive ability of the model.

A Machine Learning Approach to Predict Customer Churn of a Delivery Platform

The use of delivery platforms has become widespread due to the impact of the Covid-19 and the O2O industry. However, the ELEME delivery platform, a subsidiary of Alibaba Group, which represents China, has recently been losing market share. This means that companies need to constantly look at strategies to attract new customers and maintain existing ones. In general, it costs at least five times more to attract new customers than it does to manage existing customers. This paper attempts to predict customer churn using the ELEME customer dataset to develop strategies to identify and prevent churn in advance. The results of the analysis using machine learning approach found that the most influential feature that can predict churn is the number of clicks made by the user. This paper presents the process and explanation of applying various algorithms for predicting customer churn on a distribution platform. It also proposes strategies for dealing with customer churn. © 2023 IEEE.

Not All Churn Customers Are the Same: Investigating the Effect of Customer Churn Heterogeneity on Customer Value in the Financial Sector

This research suggests a way to sustain a firm’s business by focusing on the economic aspects of relationship marketing by managing the heterogeneity of churn customers. In general, firms have regarded churn customers as a homogeneous segment, for they have not been conscious that churn ego can be various. However, customer churn can be divided into voluntary and involuntary, implying that firms should reform the retention strategy by focusing on egos that seem homogenous but are heterogeneous in terms of churn behavior. Using a multiple regression model, this study analyzed customer data from an insurance company to investigate the heterogeneous impacts of churn customers. It measured the impact based on the period and revenue in the second lifetime, comprehensively representing customer satisfaction. Empirical results show that customer churn heterogeneity significantly affects customers’ second-lifetime behavior. The analysis reveals how the firm effectively performed customer regaining initiatives and successfully maintained persistency. This research also concludes that voluntary and involuntary churn occurred by intrinsic and extrinsic motivation. Finally, this research implicates the retention strategy that differs from the heterogeneity to achieve a firm’s high performance and suggests an empirical method of spurious loyalty avoidance by hedging loyal customer selection risk.

An Ensemble Model for Predicting Retail Banking Churn in the Youth Segment of Customers

(1) This study aims to predict the youth customers’ defection in retail banking. The sample comprised 602 young adult bank customers. (2) The study applied Machine learning techniques, including ensembles, to predict the possibility of churn. (3) The absence of mobile banking, zero-interest personal loans, access to ATMs, and customer care and support were critical driving factors to churn. The ExtraTreeClassifier model resulted in an accuracy rate of 92%, and an AUC of 91.88% validated the findings. (4) Customer retention is one of the critical success factors for organizations so as to enhance the business value. It is imperative for banks to predict the drivers of churn among their young adult customers so as to create and deliver proactive enable quality services.

Low Socio-economic Status and Customer Churn Respect to Home Internet Service in the Intra-city Context

Home Internet is important and even more so since the beginning of the COVID-19 pandemic. Internet enables communication with co-workers, family, basic services providers, etc. Customer churn means that a costumer has left their service provider for some reason. Scientific literature addresses customer churn in various business areas from different perspectives, however, it focuses very little on the socio-economic factor as a possible cause for the customer churn from residential Internet service. The objective is to determine if the socio-economic factor influences the customer churn from the residential Internet service in the intra-city context. The percentage of customer churn associated with the economic factor is important (38%). This case study is focused to the phenomenon of customer churn due to economic reasons affects low-income areas of the city. This study reaffirms that it is necessary to study the barriers for Internet adoption in different contexts and socio-economic groups. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea

In the airline industry, customer satisfaction occurs when passengers’ expectations are met through the airline experience. Considering that airline service quality is the main factor in obtaining new and retaining existing customers, airline companies are applying various approaches to improve the quality of the physical and social servicescapes. It is common to use data analysis techniques for analyzing customer propensity in marketing. However, their application to the airline industry has traditionally focused solely on surveys;hence, there is a lack of attention paid to deep learning techniques based on survey results. This study has two purposes. The first purpose is to find the relationship between various factors influencing customer churn risk and satisfaction by analyzing the airline customer data. For this, we applied deep learning techniques to the survey data collected from the users who have used mostly Korean airplanes. To the best of our knowledge, this is the one of the few attempts at applying deep learning to analyze airline customer propensities. The second purpose is to analyze the influence of the social servicescape, including the viewpoints of the cabin crew and passengers using aircraft, on airline customer propensities. The experimental results demonstrated that the proposed method of considering human services increased the accuracy of predictive models by up to 10% and 9% in predicting customer churn risk and satisfaction, respectively.