Academic Research for Cross Selling

• Cross-selling through database marketing: A mixed data factor analyzer for data augmentation and prediction, Kamakura, W. A., Wedel, M., De Rosa, F., & Mazzon, J. A. (2003). International Journal of Research in marketing, 20(1), 45-65. This paper illustrates how cross-selling can be done sing database marketing. According to the authors, a critical aspect of customer relationship marketing orientation is the use of customer transaction databases. These databases help in cross-selling new products and services. In the study, the authors propose the use of a mixed data factor analyze which integrates survey information with customer base data on service usage and transaction volume, to predict the ownership of services with service providers and competitors. The data augmentation tool was found to be more flexible in the case of transaction database data. The authors tested the proposed model using databases of large commercial banks and considered 4 types of data distribution including Bernoully, rank-order binomial, poisson, and transaction volumes normal presentation.
• Cross-selling sequentially ordered products: An application to consumer banking services, Li, S., Sun, B., & Wilcox, R. T. (2005). Journal of Marketing Research, 42(2), 233-239. This article provides an overview of how consumer banking services are promoted through the use of a cross-selling technique. Customers have predictable purchasing journey, and as such firms selling multiple products and services are able to identify such life cycles. They are able to identify the products that are more likely to be purchased before others thereby providing opportunities to cross-sell additional products to existing customers. This article, thus, presents the model of structural multivariate probit that investigates how consumer demand evolves over time in the context of multiple products and the implications this has on acquisition patterns of naturally ordered products and services. Customer purchase patterns for products have been investigated in regard to a large Midwestern bank. One of the findings is that women and older customers care more about their overall satisfaction with the bank compared to young and male customers when determining whether to consider additional services from their financial institutions. In addition, it was found that households that have a head with greater level of education are more likely to move quickly along financial maturity continuum compared to households whose heads have less education or are females.
• Nextproducttobuy models for crossselling applications, Knott, A., Hayes, A., & Neslin, S. A. (2002). Journal of interactive Marketing, 16(3), 59-75. This paper discusses the next-product-to-buy models (NPTB) that are used to promote cross-selling. The authors evaluate NPTB models to improve the performance and effectiveness of cross-selling technique. NPTB models have been found to reduce wastage of poorly targeted cross-selling techniques through proper prediction of products that every customer may be interested in. The authors describe model-building processes as well as practical and theoretical issues that arise when developing NPTB models. The effectiveness of the models is then tested using a field test. The findings/tests reveal that compared to heuristic approach, NPTB models increase profit and that profits increase over and above sales that would have occurred using other marketing channels.
• Identifying innovators for the cross-selling of new products, Kamakura, W. A., Kossar, B. S., & Wedel, M. (2004). Management Science, 50(8), 1120-1133. This paper aims towards identifying innovators to cross-sell new products. According to the authors, recent information technology advances has enabled companies to amass extensive customer databases. The information in the databases is used to identify customers that are most likely to purchase new products as well as in predicting when the adoption should be done. This information, according to the authors, can assist marketers in identifying potential consumers that should be targeted for product promotion as well as increase manufacturing and distribution efficiency. The information may also assure faster investment return. Because of this purpose, the authors propose a model which considers timing of past purchases of different product categories and estimate the propensity of each customer who has ever purchased a particular product. The model has been proposed to help managers identify the best prospects in one of the multiple product categories based on the generalization obtained from information of past offers. The model also offers projections of new brands aggregate penetration within the database.
• Research note: customer intimacy and cross-selling strategy, Akura, M. T., & Srinivasan, K. (2005). Management Science, 51(6), 1007-1012. This article assesses how cross-setting strategy impacts customer intimacy. The authors assert that using better targeting opportunities and increase the purpose of information-intensive environments have been proven to create new challenges for firms that are attempting to achieve consumer satisfaction. This information can help firms increase their sales through cross-selling techniques. However, the article notes that revealing contact information and personal preferences and contact information can cause consumer risks when dealing with a firm. As a result, some customers may trade off the benefit and risks of revealing their personal data.
• Customer retention in the insurance industry: using survival analysis to predict cross-selling opportunities, Harrison, T., & Ansell, J. (2002). Journal of Financial Services Marketing, 6(3), 229-239. This article uses a survival analysis technique to predict cross-selling opportunities. The authors explore how customer retention offers several benefits to companies in saturated markets. However, these benefits have not fully been embraced in practice. Thus, the paper focuses on predicting cross-selling opportunities as well as attempt to answer questions of who are more likely to be buyers of additional products from similar companies. In addition, the study examines the next products that are likely to be purchased during the next sales. A sample of 9,000 customers from random data warehouses of international financial institutions has been used. The authors also discuss and illustrate the use of survival analysis techniques and suggest further research.
• Cross-selling in the financial sector: customer profitability is key, Jarrar, Y. F., & Neely, A. (2002). Journal of Targeting, Measurement and Analysis for Marketing, 10(3), 282-296. This article assesses customer compatibility as the critical aspect of cross-selling in the financial sector. The authors describe customer relationship management (CRM) as the growing trends in banks and billions of dollars are already spent on the management approach. However, different financial service providers (FSPs) are identifying many challenges when implementing CRM. This paper, therefore, provides an overview of research work taken to assess how valid the concept of sales through service is. The authors aimed at identifying what is required to have a successful cross-selling system which provides an external input to the existing initiative already ongoing in a bank. The paper summarizes the work done to present what is considered a considerable gap between information published in CRM literature and the actual real life implementation. The conclusion driven is focused on the appropriate cross-selling initiatives at the bank.
• Not all repeat customers are the same: Designing effective cross-selling promotion on the basis of attitudinal loyalty and habit, Liu-Thompkins, Y., & Tam, L. (2013). This article attempts to design the effective cross-selling that can enhance attitudinal loyalty and habit among customers. The authors elaborate that not all repeat customers are the same due to the fact that they consider different driving factors. Some may be driven by both positive reaction toward a brand and non-brand related contextual cues such as habits. Using both loyalty literature and recent habit research, the author suggest the methods of distinguishing the two driving factors and examine how they affect the response of consumers towards cross-selling promotions.
• Revenue management through dynamic cross selling in e-commerce retailing, Netessine, S., Savin, S., & Xiao, W. (2006). Operations Research, 54(5), 893-913. This paper provides an overview of dynamic cross selling in the case of an e-commerce retailing. The authors consider the existing problem of dynamically cross-selling products and services in an e-commerce setting such as books and travel reservations. In particular, the authors consider a company that faces stochastic customer arrivals and decide to offer every customer a choice between the requested products and a package that contains the requested product and another product, which is referred to as a packaging complement. As such, two issues are analyzed in the paper: how to carry out selection of packaging complements, and how to price the product packages in order to maximize profit. Further, the authors formulate cross-selling problem as a stochastic dynamic program blended with combinatorial optimization.
• Cross-selling the right product to the right customer at the right time, Li, S., Sun, B., & Montgomery, A. L. (2011). Journal of Marketing Research, 48(4), 683-700. This article discusses how companies can channel the right products to the customers at the right time. According to Li, Sun, and Montgomery, a major challenge that faces firms is the way to improve cross-selling campaigns. As such, the authors propose the need to incorporate a customer-response model that considers customer demand involvement for various products; the multifaceted role of cross-selling strategies for advertising, promotion, and education; and customer heterogeneous preference for different channels of communication. Based on this assumption, the authors develop cross-selling campaigns which help address a stochastic dynamic programming problem in which the goal of a firm is to maximize the profit of its existing customers which considering the development of customer demand with time and the multistage role of cross-selling strategies.
• Cross-selling: Offering the right product to the right customer at the right time, Kamakura, W. A. (2008). Journal of Relationship Marketing, 6(3-4), 41-58. This article also discusses how companies can offer the right product to the right customers at the right time using cross-selling technique. Kamakura asserts that cross-selling is an old valuable technique used by companies to increase their sales volume and transform a single-product buyer into a multi-product one. The technique has evolved in the customer relationship management (CRM). The article starts off by discussing the benefits and challenges of cross-selling technique and how this impacts customer development in CRM. This discussion is followed by an analysis of analytical tools used to identify prospects for cross-selling as well as technological and organizational requirements for a successful implementation of a cross-selling approach.