To overcome these hurdles, the authors decompose the user’s purchase process into the completion,of sequential,Nominal,User Tasks (nuts) and account,for heterogeneity,across visitors at the county,level.
These include: (1) online buying,probabilities are usually low which can lead to a lack of predictive and explanatory power from models, (2) it is difficult to effectively account for what Web users do, and to what they are exposed, while browsing a site, and (3) because online stores reach a diverse user population across many competitive environments, models of online buying must account for the corresponding user heterogeneity. Predicting Internet buying poses several modeling,challenges. The model,predicts online buying,by linking the purchase,decision to what,visitors do and to what,they are exposed,while at the site.
We find that the proposed model offers excellent statistical properties, including its performance in a holdout validation sample, and also provides useful managerial diagnostics about the patterns underlying online buyer behavior.Ībstract The authors develop and estimate a model,of online buying using clickstream data from a Web site that sells cars. We test different versions of the model that vary in the complexity of these two key components and also compare our general framework with popular alternatives such as logistic regression.
The purchasing threshold captures the psychological resistance to online purchasing that may grow or shrink as a customer gains more experience with the purchasing process at a given website. For example, some visits are motivated by planned purchases, while others are associated with hedonic browsing (akin to window shopping) our model is able to accommodate these (and several other) types of visit-purchase relationships in a logical, parsimonious manner. Visit effects capture the notion that store visits can play different roles in the purchasing process. Each component is allowed to vary across households as well as over time. Specifically, we decompose an individual's conversion behavior into two components: one for accumulating visit effects and another for purchasing threshold effects. We offer an individual-level probability model that allows for different forms of customer heterogeneity in a very flexible manner. This paper develops a model of conversion behavior (i.e., converting store visits into purchases) that predicts each customer's probability of purchasing based on an observed history of visits and purchases. This analysis of the buying process allows us to more carefully examine the relationship between store visits and purchasing behavior. Using commonly available clickstream data, this article discusses the benefits of separating an individual customer's buying behavior into simple patterns of visits and purchasing conversion. As a result, e-commerce managers have been focusing on aggregate level summary statistics rather than fully leveraging their data. However, the volume of this data has overwhelmed many e-commerce managers. Thus, the new struggle has been to manage this information and to use it accurately and efficiently to measure customers, trends, and performance. Regardless of the answer to that debate, one thing is certain: the Internet provides managers with an enormous amount of customer information that was previously unavailable. The analysis suggests that the content-targeting approach can potentially increase the expected number of click-throughs by 62%.Īcademics and practitioners alike have been arguing about whether the Internet brings a revolutionary change in the fundamental way we do business or if it simply offers a new distribution channel and communication medium. The authors apply the model to the context of permission-based e-mail marketing, in which the objective is to customize the design and content of the e-mail to increase Web site traffic. The authors use clickstream data from users at one of the top ten most trafficked Web sites to estimate the model and optimize the design and content of such communications for each user. The authors develop a statistical and optimization approach for customization of information on the Internet. In spite of such potential benefits, few models exist in the marketing literature to exploit the Internet's unique ability to design communications or marketing programs at the individual level.
Customized communications have the potential to reduce information overload and aid customer decisions, and the highly relevant products that result from customization can form the cornerstone of enduring customer relationships.
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