An Empirical Investigation of Customer Retention: Addressing Unique Challenges in Customer-Firm Relationships

Effective customer retention is vital to the survival and prosperity of any customer-centric organization. Systematic examination of different aspects of the customer’s relationship with the firm has the potential to provide valuable insights to support retention efforts. However, the nature of the purchasing options and relationship patterns inherent in each industry require managers to shift their focus on varied aspects of the relationship, thus posing unique challenges. One such challenge is examined in the first essay of this dissertation, in a setting where customer-firm relationships are intermittent, with customers being lost to and won back again by the firm. A unifying model for joint estimation of the customers’ second lifetime duration, multiple repeat churn reasons, and heterogeneity in exhibiting a related churn reason is developed to study this relationship. The findings support the existence of a cured group of returning customers, defined as those who are not susceptible to churn due to a repeated reason. Another challenge is examined in the second essay, which involves a setting where the structure of the purchasing options is a combination of contractual and noncontractual services. The complexities and dynamics of the customer-firm relationship and customers’ underlying commitment to it are modeled through a hidden Markov model, incorporating the dependency between the two purchase processes. The findings suggest that contractual and noncontractual purchase behaviors are distinct but interrelated

New Perspectives on Customer “Death” Using a Generalization of the Pareto/NBD Model

Several researchers have proposed models of buyer behavior in noncontractual settings that assume that customers are “alive” for some period of time and then become permanently inactive. The best-known such model is the Pareto/NBD, which assumes that customer attrition (dropout or “death”) can occur at any point in calendar time. A recent alternative model, the BG/NBD, assumes that customer attrition follows a Bernoulli “coin-flipping” process that occurs in “transaction time” (i.e., after every purchase occasion). Although the modification results in a model that is much easier to implement, it means that heavy buyers have more opportunities to “die.” In this paper, we develop a model with a discrete-time dropout process tied to calendar time. Specifically, we assume that every customer periodically “flips a coin” to determine whether she “drops out” or continues as a customer. For the component of purchasing while alive, we maintain the assumptions of the Pareto/NBD and BG/NBD models. This periodic death opportunity (PDO) model allows us to take a closer look at how assumptions about customer death influence model fit and various metrics typically used by managers to characterize a cohort of customers. When the time period after which each customer makes her dropout decision (which we call period length) is very small, we show analytically that the PDO model reduces to the Pareto/NBD. When the period length is longer than the calibration period, the dropout process is “shut off,” and the PDO model collapses to the negative binomial distribution (NBD) model. By systematically varying the period length between these limits, we can explore the full spectrum of models between the “continuous-time-death” Pareto/NBD and the naïve “no-death” NBD. In covering this spectrum, the PDO model performs at least as well as either of these models; our empirical analysis demonstrates the superior performance of the PDO model on two data sets. We also show that the different models provide significantly different estimates of both purchasing-related and death-related metrics for both data sets, and these differences can be quite dramatic for the death-related metrics. As more researchers and managers make managerial judgments that directly relate to the death process, we assert that the model employed to generate these metrics should be chosen carefully

Profitable Retail Customer Identification Based on a Combined Prediction Strategy of Customer Lifetime Value

As a fundamental concept of customer relationship management, customer lifetime value (CLV) serves as a crucial metric to identify profitable retail customers. Various methods are available to predict CLV in different contexts. With the development of consumer big data, modern statistics and machine learning algorithms have been gradually adopted in CLV modeling. We introduce two machine learning algorithms—the gradient boosting decision tree (GBDT) and the random forest (RF)—in retail customer CLV modeling and compare their predictive performance with two classical models—the Pareto/NBD (HB) and the Pareto/GGG. To ensure CLV prediction and customer identification robustness, we combined the predictions of the four models to determine which customers are the most—or least—profitable. Using 43 weeks of customer transaction data from a large retailer in China, we predicted customer value in the future 20 weeks. The results show that the predictive performance of GBDT and RF is generally better than that of the Pareto/NBD (HB) and Pareto/GGG models. Because the predictions are not entirely consistent, we combine them to identify profitable and unprofitable customers

"Counting Your Customers": When will they buy next? An empirical validation of probabilistic customer base analysis models based on purchase timing

This research provides a new way to validate and compare buy-till-you-defect [BTYD] models. These models specify a customer’s transaction and defection processes in a non-contractual setting. They are typically used to identify active customers in a com- pany’s customer base and to predict the number of purchases. Surprisingly, the literature shows that models with quite different assumptions tend to have a similar predictive performance. We show that BTYD models can also be used to predict the timing of the next purchase. Such predictions are managerially relevant as they enable managers to choose appropriate promotion strategies to improve revenues. Moreover, the predictive performance on the purchase timing can be more informative on the relative quality of BTYD models. For each of the established models, we discuss the prediction of the purchase timing. Next, we compare these models across three datasets on the predictive performance on the purchase timing as well as purchase frequency. We show that while the Pareto/NBD and its Hierarchical Bayes extension [HB] models perform the best in predicting transaction frequency, the PDO and HB models predict transaction timing more accurately. Furthermore, we find that differences in a model’s predictive performance across datasets can be explained by the correlation between behavioral parameters and the proportion of customers without repeat purchases

Accuracy of noncomplex customer lifetime value models in the medical service context

Objectives: The accurate valuation of a customer relationship remains a challenge that researchers and companies alike are struggling to solve. The objective of this study was to assess the accuracy of noncomplex and deterministic customer lifetime value (CLV) assessment models in a semi-contractual service business context. Methodology: The data set encompassed four years of longitudinal behavioral data from 150 customers of the case company. Six noncomplex CLV models were selected and used to (1) predict the CLV's of individual customers, (2) to sort the customers into four equally sized segments based on the rank order of their predicted CLV, and (3) to predict the combined CLV of the customer base. The predictive performance of the models was evaluated by comparing the predicted CLV's with the actual values calculated from a holdout sample. Findings: Four models were found to be quite inaccurate and the remaining two models very inaccurate at predicting the CLV of individual customers. Four models were found to be somewhat accurate in sorting the customer base into four segments and more accurate in predicting the top 25% of customers. One model was also especially accurate in predicting the combined value of the customers and can thus be utilized in the business context of the case company for customer base valuation purposes

Modelling partial customer churn in the Portuguese fixed telecommunications industry by using survival models

Considering that profits from customer relationships are the lifeblood of firms (Grant and Schlesinger, 1995), an improvement on the customer management is essential to ensure the competitivity and success of firms. For the last decade, Portuguese customers of fixed telecommunications industry have easily switched the service provider, which has been very damaging for the business performance and, therefore, for the economy. The main objective of this study is to analyse the partial churn of residential customers in the fixed-telecommunications industry (fixed-telephone and ADSL), by using survival models. Additionally, we intend to test the assumption of constant customer retention rate over time and across customers. Lastly, the effect of satisfaction on partial customer churn is analysed. The models are developed by using large-scale data from an internal database of a Portuguese fixed telecommunications company. The models are estimated with a large number of covariates, which includes customer’s basic information, demographics, churn flag, customer historical information about usage, billing, subscription, credit, and other. Our results show that the variables that influence the partial customer churn are the service usage, mean overall revenues, current debts, the number of overdue bills, payment method, equipment renting, the existence of flat plans and the province of the customer. Portability also affects the probability of churn in fixed-telephone contracts. The results also suggest that the customer retention rate is neither constant over time nor across customers, for both types of contracts. Lastly, it seems that satisfaction does not influence the cancellation of both types of contracts.Considerando que os lucros gerados pelos clientes são vitais para as empresas (Grant e Schlesinger, 1995), uma melhoria na gestão do cliente é fundamental para assegurar a competitividade e o sucesso das empresas. Na última década, os clientes portugueses das empresas de telecomunicações fixas têm mudado de operador com demasiada facilidade, o que tem prejudicado o desempenho das empresas e, consequentemente, a economia. O principal objectivo deste estudo é analisar o cancelamento de contratos de telefone fixo e ADSL por clientes residenciais, através do uso de modelos de sobrevivência. Para além disso, pretende-se testar o pressuposto de que a taxa de retenção de clientes é constante ao longo do tempo e entre clientes. Por último, pretende-se analisar o efeito da satisfação do cliente no cancelamento destes tipos de contratos. Os modelos são construídos com base numa base de dados de larga escala fornecida por uma empresa portuguesa deste sector. Os modelos são estimados com base num vasto número de variáveis, incluindo informação básica sobre o cliente, dados demográficos, indicação sobre o cancelamento do contrato, dados históricos sobre o uso dos serviços, facturação, contracto, crédito, etc.. Os resultados mostram que as variáveis que influenciam o cancelamento de ambos os tipos de contratos são o uso do serviço, a facturação média, o valor em dívida, o número de facturas em dívida, o método de pagamento, o método de pagamento do equipamento, a existência de tarifas planas e o distrito do cliente. A portabilidade de número parece influenciar o cancelamento de contratos de telefone fixo. Os resultados também mostram que a taxa de retenção de clientes não é constante ao longo do tempo nem entre clientes em ambos os tipos de contratos. Por último, parece que a satisfação não influencia o cancelamento de ambos os tipos de contratos

Ranking customers for marketing actions with a two-stage Bayesian cluster and Pareto/NBD models

Modelling customer behaviour to predict their future purchase frequency and value is crucial when selecting customers for marketing activities. The profitability of a customer and their risk of inactivity are two important factors in this selection process. These indicators can be obtained using the well-known Pareto/NBD model. Here we cluster customers based on their purchase frequency and value over a given period before applying the Pareto/NBD model to each cluster. This initial cluster model provides the customer purchase value and improves the predictive accuracy of the Pareto/NBD parameters by using similar individuals when fitting the data. Finally, taking the outputs from both models, the initial cluster and Pareto/NBD, we present some recommendations to classify customers into interpretable groups and facilitate their prioritisation for marketing activities. To illustrate the methodology, this paper uses a database with sales from a beauty products wholesaler.Peer ReviewedPostprint (published version

Estimating a customer churn model in the ADSL industry in Portugal: The use of a Semi-Markov model

Customer churn has been stated as one of the main reasons of profitability losses in the telecommunications industry. As such, it seems critical to have an a priori knowledge about the risk of a given customer to churn at any moment, in order to take preventive measures to avoid the defection of potentially profitable customers. This study intends to develop a duration model of the residential customer churn in this industry in Portugal. We found empirical evidence that the variables that influence customer churn are the total number of overdue bills since ever, average monthly spending, average value of additional internet traffic, payment method, equipment renting, and the subscription of a flat plan. We also found that the probability of a customer to churn is neither constant over time nor across customers.info:eu-repo/semantics/publishedVersio

Detecting customer defections: an application of continuous duration models

The considerable increase of business competition in the Portuguese fixed telecommunications industry for the last decades has given rise to a phenomenon of customer defection, which has serious consequences for the business financial performance and, therefore, for the economy. As such, researchers have recognised the importance of an in-depth study of customer defection in different industries and geographic locations. This study aims to understand and predict customer lifetime in a contractual setting in order to improve the practice of customer portfolio management. A duration model is developed to understand and predict the residential customer defection in the fixed telecommunications industry in Portugal. The models are developed by using large-scale data from an internal database of a Portuguese company which presents bundled offers of ADSL, fixed line telephone, pay-TV and home-video. The model is estimated with a large number of covariates, which includes customer’s basic information, demographics, churn flag, customer historical information about usage, billing, subscription, credit, and other. The results of this study are very useful to the computation of the customer lifetime value

Customer-Base Analysis using Repeated Cross-Sectional Summary (RCSS) Data

We address a critical question that many firms are facing today: Can customer data be stored and analyzed in an easy-to-manage and scalable manner without significantly compromising the inferences that can be made about the customers’ transaction activity? We address this question in the context of customer-base analysis. A number of researchers have developed customer-base analysis models that perform very well given detailed individual-level data. We explore the possibility of estimating these models using aggregated data summaries alone, namely repeated cross-sectional summaries (RCSS) of the transaction data. Such summaries are easy to create, visualize, and distribute, irrespective of the size of the customer base. An added advantage of the RCSS data structure is that individual customers cannot be identified, which makes it desirable from a data privacy and security viewpoint as well. We focus on the widely used Pareto/NBD model and carry out a comprehensive simulation study covering a vast spectrum of market scenarios. We find that the RCSS format of four quarterly histograms serves as a suitable substitute for individual-level data. We confirm the results of the simulations on a real dataset of purchasing from an online fashion retailer