Introduction to the special issue: decision support issues in customer relationship management and interactive marketing for e-commerce

Customer relationship management (CRM) plays a critical role in e-business. In this study we seek to quantify the value of Internet-based CRM applications, and the purpose of our research is to identify the mechanisms underlying CRM value creation. Using a unique dataset collected from vendor archives of CRM implementations, we find an increase in firm value associated with CRM applications. This value implication, however, is attributable to integration of CRM with existing enterprise resource planning (ERP) applications, instead of installing CRM functional modules per se. Further, we find that the integration contributes more value in markets featuring higher product differentiation or lower entry costs. Together these findings shed light on the mechanisms through which CRM applications increase firm value in specific competitive environments.

Viral marketing refers to marketing techniques that use social networks to produce increases in brand awareness through self-replicating viral diffusion of messages, analogous to the spread of pathological and computer viruses. The idea has successfully been used by marketers to reach a large number of customers rapidly. If data about the customer network is available, centrality measures provide a structural measure that can be used in decision support systems to select influencers and spread viral marketing campaigns in a customer network. Usage stimulation and churn management are examples of DSS applications, where centrality of customers does play a role. The literature on network theory describes a large number of such centrality measures. A critical question is which of these measures is best to select an initial set of customers for a marketing campaign, in order to achieve a maximum dissemination of messages. In this paper, we present the results of computational experiments based on call data from a telecom company to compare different centrality measures for the diffusion of marketing messages. We found a significant lift when using central customers in message diffusion, but also found differences in the various centrality measures depending on the underlying network topology and diffusion process.

This paper reports the results of a study designed in close collaboration with Merck-Medco to identify key barriers to the success of their customer relationship management. To identify the key factors, we first used focus groups of principle users of the system to brainstorm and generate a list of scenarios and issues. A team of managers, supervisors and customer service representatives then consolidated this list. A 54-item survey was derived from the list and used to collect 1460 responses from the user groups within the company. Data were equally divided into two sets. Exploratory factor analysis was used with the first data set to identify principal factors that explained the majority of problem areas. Structural equation modeling was used with the second data set to further examine and confirm the initial list of factors. The study results suggest a seven-factor 21-item model describing barriers to the success of customer relationship management in terms of ‘standard operating procedure compliance,’ ‘accountability and ownership,’ ‘callback information content,’ ‘customer contact process,’ ‘ billing issues,’ ‘dispensing and replacement process,’ and ‘queuing procedure’. These factors explained the majority of customer relationship problems in the company. These measures can be used by the company to plan and monitor remedial response. Evidence of reliability and construct validity is presented for the measurement models and decision-making implications are discussed.

This paper investigates how to estimate the likelihood of a customer accepting a loan offer as a function of the offer parameters and how to choose the optimal set of parameters for the offer to the applicant in real time. There is no publicly available data set on whether customers accept the offer of a financial product, whose features are changing from offer to offer. Thus, we develop our own data set using a fantasy student current account. In this paper, we suggest three approaches to determine the probability that an applicant with characteristics will accept offer characteristics using the fantasy student current account data. Firstly, a logistic regression model is applied to obtain the acceptance probability. Secondly, linear programming is adapted to obtain the acceptance probability model in the case where there is a dominant offer characteristic, whose attractiveness increases (or decreases) monotonically as the characteristic's value increases. Finally, an accelerated life model is applied to obtain the probability of acceptance in the case where there is a dominant offer characteristic.

Although the application of segmentation and predictive modeling is an important topic in the database marketing (DBM) literature, no study has yet investigated the extent of adoption of these techniques. We present the results of a Dutch survey involving 228 database marketing companies. We find that managers tend to rely on intuition and on the long-standing methods RFM and cross-tabulation. Our results indicate that the application of segmentation and response modeling is positively related to company and database size, frequency of customer contact, and the use of a direct channel of distribution. The respondents indicate that future research should focus on models applicable for Internet marketing, long-term effects of direct marketing, irritation from direct marketing offers, and segmentation and predictive modeling techniques.