Banner Advertising as a Customer Retention Tool in Customer Relationship Management
The Stephen M. Ross School of Business at the University of Michigan
Jean-Pierre H. Dube
University of Chicago - Booth School of Business
Khim Yong Goh
National University of Singapore
Pradeep K. Chintagunta
University of Chicago
One of the major advances of the digital economy is the facilitation of building and managing individual customer relationships - a process usually referred to as "customer relationship management" or CRM. For a typical web site selling frequently-purchased consumer items, the most important stage of CRM is customer retention. This is because the long-term viability of a website is based on its ability to retain a significant customer base. In this study, we focus on a hitherto unexplored question - does banner advertising have a role to play in the customer retention phase of CRM. Using a rich behavioral database consisting of individual customer purchases at a web site along with individual advertising exposure, we measure the impact of banner advertising on customer retention (via purchase acceleration).
We formulate a model of individual purchase timing behavior as a function of advertising exposure. We model the probability of a current customer making a purchase in any given week (since last purchase) via a survival model. The duration dependence in the customers' purchase behavior is captured through a flexible, piecewise exponential hazard function. The advertising covariates enter via a proportional hazards specification. These covariates, richer than have typically been used in past research, consist of strictly advertising variables such as weight and quality as well as advertising/individual browsing variables represented by where and how many pages on which customers are exposed to advertising. Our model also controls for unobserved individual differences by specifying a distribution over the individual customer advertising response parameters. We do this by formulating our model in a hierarchical Bayesian framework. This also allows us to provide some insights into where the returns from targeted banner advertising are the highest and the extent to which the returns are higher compared to no targeting.
Our results show that the number of exposures, number of websites and number of pages on which a customer is exposed to advertising all have a positive effect on customer retention. Interestingly, increasing the number of unique creatives to which a customer is exposed lowers the customer retention probability. We also find evidence of considerable heterogeneity across consumers in response to various aspects of banner advertising. The extent of heterogeneity shows that the returns from targeting individual customers are likely to be the highest for the weight of advertising (the number of advertisements that they were exposed to in a given week) followed by the number of sites that they are exposed to advertising on. To demonstrate the value of the obtained individual response parameters, we carry out a simple experiment in which we compare sales response with and without targeting. We show that, relative to no targeting, targeting results in significant increases in the effectiveness of banner advertising on customer retention and hence, on profitability. Finally, in terms of the broader area of research on the effects of (any type of) advertising, we provide somewhat unique evidence that advertising does affect the purchase behavior of current, in contrast to new, customers.
Number of Pages in PDF File: 38
Keywords: Advertising response, banner advertising, e-commerce, internet retailing, targeting, micromarketing, survival models, hierarchical Bayesian models, Markov Chain Monte Carlo methodsworking papers series
Date posted: December 17, 2003
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