Revisiting the Use of Customer Information for CRM

For the past decade, customer relationship management (CRM) has been one of the priorities in marketing research and practice. However, many of the CRM systems did not perform as the companies expected. As such shortcoming could be due to inappropriate data input, this study provides a comprehensive overview of the empirical CRM literature. Along the phases of the CRM process, the authors show which kind of data has successfully proven to achieve the CRM objectives. The study provides researchers with a review of the empirical research on CRM and allows practitioners insights on the usability of customer data for CRM. --Customer Relationship Management (CRM),Customer Data

Predicting Customer Lifetime Value in Multi-Service Industries

Customer lifetime value (CLV) is a key-metric within CRM. Although, a large number of marketing scientists and practitioners argue in favor of this metric, there are only a few studies that consider the predictive modeling of CLV. In this study we focus on the prediction of CLV in multi-service industries. In these industries customer behavior is rather complex, because customers can purchase more than one service, and these purchases are often not independent from each other. We compare the predictive performance of different models, which vary in complexity and realism. Our results show that for our application simple models assuming constant profits over time have the best predictive performance at the individual customer level. At the customer base level more complicated models have the best performance. At the aggregate level, forecasting errors are rather small, which emphasizes the usability of CLV predictions for customer base valuation purposes. This might especially be interesting for accountants and financial analysts.forecasting;value;customer relationship management;customer lifetime value;customer segmentation;database marketing;interactive marketing

Modeling churn using customer lifetime value.

The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution is to redefine the notion of customer loyalty by considering it from a customer-centric viewpoint instead of a productcentric one. We hereby use the customer lifetime value (CLV) defined as the discounted value of future marginal earnings, based on the customer's activity. Hence, a churner is defined as someone whose CLV, thus the related marginal profit, is decreasing. As a second contribution, the loss incurred by the CLV decrease is used to appraise the cost to misclassify a customer by introducing a new loss function. In the empirical study, we compare the accuracy of various classification techniques commonly used in the domain of churn prediction, including two cost-sensitive classifiers. Our final conclusion is that since profit is what really matters in a commercial environment, standard statistical accuracy measures for prediction need to be revised and a more profit oriented focus may be desirable.Data mining; Decision support systems; Marketing; Churn prediction;

The Role of Peer Influence in Churn in Wireless Networks

Subscriber churn remains a top challenge for wireless carriers. These carriers need to understand the determinants of churn to confidently apply effective retention strategies to ensure their profitability and growth. In this paper, we look at the effect of peer influence on churn and we try to disentangle it from other effects that drive simultaneous churn across friends but that do not relate to peer influence. We analyze a random sample of roughly 10 thousand subscribers from large dataset from a major wireless carrier over a period of 10 months. We apply survival models and generalized propensity score to identify the role of peer influence. We show that the propensity to churn increases when friends do and that it increases more when many strong friends churn. Therefore, our results suggest that churn managers should consider strategies aimed at preventing group churn. We also show that survival models fail to disentangle homophily from peer influence over-estimating the effect of peer influence.Comment: Accepted in Seventh ASE International Conference on Social Computing (Socialcom 2014), Best Paper Award Winne

Should they stay or should they go? Reactivation and Termination of Low-Tier Customers: Effects on Satisfaction, Word-of-Mouth, and Purchases

Many companies face the problem of having a substantial number of low-tier customers ? clients at the bottom of the customer pyramid. For this segment, it is necessary to either reactivate or terminate the customer relationships to increase profitability. Managers seek to learn more about marketing actions targeted towards low-tier customers and their response towards these actions. Therefore, we conducted a large field experiment in which we implemented a ?last call? marketing action for a large sample of low-tier customers of a catalogue retailer (N = 12,000). The action aims at sales reactivation, but in case a customer should not react, the relationship will be terminated. We measure customer response in terms of satisfaction, (positive and negative) word-of-mouth, and purchase behavior. We find no harmful effects from relationship termination, such as dissatisfaction or negative word-of-mouth. The results indicate that the ?last call? marketing action reactivates a small fraction of the low-tier customers. These customers remain active in the months following the action period. We discuss managerial implications of our findings and future research on low-tier customer segments.

A logistic regression approach to estimating customer profit loss due to lapses in insurance

This article focuses on business risk management in the insurance industry. A methodology for estimating the profit loss caused by each customer in the portfolio due to policy cancellation is proposed. Using data from a European insurance company, customer behaviour over time is analyzed in order to estimate the probability of policy cancelation and the resulting potential profit loss due to cancellation. Customers may have up to two different lines of business contracts: motor insurance and other diverse insurance (such as, home contents, life or accident insurance). Implications for understanding customer cancellation behaviour as the core of business risk management are outlined.Policy cancellation, customer loyalty, profit loss, customer behavior.

The impact of customer-specific marketing expenses on customer retention and customer profitability

We study the effects of customer-specific marketing expenses on customer retention and customer profitability in a business-to-business setting. Using data from a company providing hygiene services, we look at the impact of a hitherto unstudied type of expense targeted at individual customer relationships: the offering of free equipment to customers. The data allow tracking the activities performed in more than 4,500 customer relationships over a period of 4 years. Retention rates are higher for customers targeted with free equipment, but this effect results from an interaction with customer size. First-order dynamic panel data analyses show that the impact of targeted marketing expenses on customer dollar profit is positive for large customers, but there is no effect for smaller customers. Thus, targeted marketing expenses seem to be a tool for relationship maintenance rather than customer development: they help in retaining large customers that generate more profit, but they do not seem to work in developing new customers into larger, more profitable ones

Portfolio Construction: The Efficient Diversification of Marketing Investments

Efforts in the marketing sciences can be distinguished between the analysis of individual customers and the examination of portfolios of customers, giving scarce theoretical guidance concerning the strategic allocation of promotional investments. Yet, strategic asset allocation is considered in financial economics theory to be the most important set of investment decisions. The problem addressed in this study was the application of strategic asset allocation theory from financial economics to marketing science with the aim of improving the financial results of investment in direct marketing promotions. This research investigated the components of efficient marketing portfolio construction which include multiattribute numerical optimization, stochastic Brownian motion, peer index tracking schemes, and data mining methods to formulate unique investable asset classes. Three outcomes resulted from this study on optimal diversification: (a) reduced saturative promotional activities balancing inefficient advertising cost and enterprise revenue objectives to achieve an investment equilibrium state; (b) the use of utility theory to assist in the lexicographic ordering of goal priorities; and (c) the solution approach to a multiperiod linear goal program with stochastic extensions. A performance test using a large archival set of customer data illustrated the benefits of efficient portfolio construction. The test asset allocation resulted in significantly more reward than that of the benchmark case. The results of this grounded theory study may be of interest to marketing researchers, operations research practitioners, and functional marketing executives. The social change implication is increased efficiency in allocation of large advertising budgets resulting in improved corporate performance