The authors study the effect of word-of-mouth (WOM) marketing on member growth at an Internet social networking site and compare it with traditional marketing vehicles. Because social network sites record the electronic invitations sent out by existing members, outbound WOM may be precisely tracked. WOM, along with traditional marketing, can then be linked to the number of new members subsequently joining the site (signups). Due to the endogeneity among WOM, new signups, and traditional marketing activity, the authors employ a Vector Autoregression (VAR) modeling approach. Estimates from the VAR model show that word-ofmouth referrals have substantially longer carryover effects than traditional marketing actions. The long-run elasticity of signups with respect to WOM is estimated to be 0.53 (substantially larger than the average advertising elasticities reported in the literature) and the WOM elasticity is about 20 times higher than the elasticity for marketing events, and 30 times that of media appearances. Based on revenue from advertising impressions served to a new member, the monetary value of a WOM referral can be calculated; this yields an upper bound estimate for the financial incentives the firm might offer to stimulate word-of-mouth.

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Using the clickstream data recorded in Web server log files, the authors develop and estimate a model of the browsing behavior of visitors to a Web site. Two basic aspects of browsing behavior are examined: (1) the visitor's decisions to continue browsing (by submitting an additional page request) or to exit the site and (2) the length of time spent viewing each page. The authors propose a type II tobit model that captures both aspects of browsing behavior and handles the limitations of server log-file data. The authors fit the model to the individual-level browsing decisions of a random sample of 5000 visitors to the Web site of an Internet automotive company. Empirical results show that visitors' propensity to continue browsing changes dynamically as a function of the depth of a given site visit and the number of repeat visits to the site. The dynamics are consistent both with "within-site lock-in" or site "stickiness" and with learning that carries over repeat visits. In particular, repeat visits lead to reduced page-view propensities but not to reduced page-view durations. The results also reveal browsing patterns that may reflect visitors' timesaving strategies. Finally, the authors report that simple site metrics computed at the aggregate level diverge substantially from individual-level modeling results, which indicates the need for Web site analyses to control for cross-sectional heterogeneity.Since the commercial inception of the Internet, the ability of Web sites to track the behavior of their visitors has been considered one of the most promising facets of the new medium. The detailed records of Web usage behavior (clickstream data) provide researchers and practitioners with the opportunity to study how users browse or navigate Web sites and to assess site performance in various ways. Therefore, the use of clickstream data to model visitors' usage of a specific site and how that usage may change with experience could produce important dividends for researchers and practitioners interested in Web site design, Web site customization, and ongoing monitoring of a site's performance.Despite the importance of analyzing how users browse a given site, to the best of our knowledge, no disaggregate clickstream model of detailed within-site browsing behavior and its potential dynamics has yet appeared in the marketing literature. In this article, we develop a modeling approach for understanding some of the basic aspects of within-site browsing behavior at the individual level. We conceptualize user navigation through an Internet Web site as a series of the following decisions: (1) whether to request an additional page (thereby remaining on the site) or to exit the site and (2) how long to view a given page on the site. We estimate a type II tobit model on the clickstream data collected by the operator of a major commercial Web site in the automotive industry. Our modeling results provide several findings about browsing behavior and a series of implications for Web managers.The existing literature on Web site mode...

When consumers are exposed to pricing and promotional activity by frequently purchased packaged goods, they may develop expectations that are used as points of reference in evaluating future activity. The authors build a model to test for the presence of these reference effects on brand choice behavior. The approach differs from previous research in two ways: (1) the model includes reference effects of promotion in addition to reference effects of price and (2) a threshold model is introduced to capture the formation of the consumer's promotional reference point. The authors calibrate a model of brand choice using IRI scanner panel data on ground coffee. The findings suggest that promotional activity has significant reference effects on consumer response.

The authors develop and estimate a model of online buying using clickstream data from a Web site that sells cars. The model predicts online buying by linking the purchase decision to what visitors do and to what they are exposed to while at the site. To overcome the challenges of predicting Internet buying, the authors decompose the purchase process into the completion of sequential nominal user tasks and account for heterogeneity across visitors at the county level. Using a sequence of binary probits, the authors model the visitor's decision of whether to complete each task for the first time, given that the visitor has completed the previous tasks at least once. The results indicate that visitors' browsing experiences and navigational behavior predict task completion for all decision levels. The results also indicate that the number of repeat visits per se is not diagnostic of buying propensity and that a site's offering of sophisticated decision aids does not guarantee increased conversion rates. The authors also compare the predictive performance of the task-completion approach with single-stage benchmark models in a holdout sample. The proposed approach provides superior prediction and better identifies likely buyers, especially early in the task sequence. The authors also discuss implications for Web site managers.

The authors develop and test a probabilistic model of purchase incidence and brand choice for frequently purchased consumer products. The model incorporates two ways of shopping in a category. Shoppers who have planned their purchasing (made a decision before entering the store) do not process in-store information and show no response to point-of-purchase promotions. Consumers who have not planned their purchasing in a category (deciding at the point of purchase) may process in-store information and may be strongly influenced by promotions. The two modes of information processing are called decision states and are labelled , respectively. The two-state model is calibrated on IRI scanner purchase records for saltine crackers. The model yields a significantly better fit than a one-state nested logit model and provides new insights into the relationship between shopping behavior and consumer purchase response.brand choice, purchase incidence, nested logit, promotion, shopping behavior

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