Dynamic Conversion Behavior at E-Commerce Sites

This paper develops a model of conversion behavior (i.e., converting store visits into purchases) that predicts each customer\u27s probability of purchasing based on an observed history of visits and purchases. We offer an individual-level probability model that allows for different forms of customer heterogeneity in a very flexible manner. Specifically, we decompose an individual\u27s conversion behavior into two components: one for accumulating visit effects and another for purchasing threshold effects. Each component is allowed to vary across households as well as over time. Visit effects capture the notion that store visits can play different roles in the purchasing process. 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. 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. 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. 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

Capturing Evolving Visit Behavior in Clickstream Data

Many online sites, both retailers and content providers, routinely monitor visitor traffic as a useful measure of their overall success. However, simple summaries such as the total number of visits per month provide little insight about individual-level site-visit patterns, especially in a changing environment such as the Internet. This article develops an individual-level model for evolving visiting behavior based on Internet clickstream data. We capture cross-sectional variation in site-visit behavior as well as changes over time as visitors gain experience with the site. In addition, we examine the relationship between visiting frequency and purchasing propensity at an e-commerce site. We find evidence supporting the notion that people who visit a retail site more frequently have a greater propensity to buy. We also show that changes (i.e., evolution) in an individual\u27s visit frequency over time provides further information regarding which customer segments are likely to have higher purchasing conversion rates

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Which visits lead to purchases? Decomposing the buying process into visiting and conversion behavior

When studying consumer buying behavior, most marketing models have focused on purchasing events only and ignored much of the information provided by visiting patterns. One reason for this is that purchases are easily observable whereas visits, especially those not associated with a purchase, are not. However, with the Internet and the emergence of e-commerce, marketers are able to observe more than just what and when consumers purchased, but they can also observe the individual store visiting patterns that affect purchasing. The richness of this information has the potential to provide marketers with an in-depth understanding of consumer shopping processes and the ability to more effectively segment and target consumers. Using commonly available clickstream data, this dissertation examines both visiting and purchasing patterns at the individual level. By decomposing the buying process into a pattern of visits and purchase conversion at each visit, we can better understand the relationship between consumer visiting and purchasing patterns. This allows us to more accurately forecast a shopper\u27s future behavior (both visits and purchases) at the site and hence determine the value of individual customers to the site. In this dissertation, I will show that individual patterns of visits provide valuable information about consumers\u27 purchasing tendencies and their value as customers. Additionally, I will show that forecasts of purchasing behavior are more accurate when visiting patterns are incorporated into the model than when purchases are modeled in the absence of any visiting information. This dissertation will develop two separate models, one of individual visiting behavior and one of conversion probabilities. Both models will allow for non-stationarity. The evolving visit (EV) model examines the timing of each individual\u27s sequence of visits. It allows individual households to change their rate of visiting over time, as well as drop out and stop visiting all together, as they gain experience with the site. The second model developed in this dissertation is one of conversion behavior, or purchasing probability at each visit. Given a sequence of visits, this dissertation will model individual conversion probabilities at each of the visits and examine how they change from visit to visit. The final piece of the dissertation will combine the two models in an effort to better forecast purchasing

Should We Wait to Promote?: The Effect of Timing on Response to Pop-Up Promotions

This paper highlights a large scale field experiment conducted at an informational website where the timing of pop-up promotions being offered were varied. Specifically, I examine the effect of these promotions during the course of a web user's online experience. Often, these promotions are viewed by the web user as a nuisance that interrupts his or her online experience. Other times, these offers are perceived as providing useful information, thereby enriching their website experience. This paper proposes that the internet user's response to the pop-up promotion will vary depending not only on his or her own information seeking objectives at a particular online site but also on the timing of the promotion itself, in terms of when during the online session it is offered. Models of the web user's reaction to the promotion in terms of (1) a direct response to the promotion (i.e., click-through on the pop-up) and (2) any indirect response in terms of changes in the user's probability of exiting the site (i.e., exiting either earlier or later than expected) are developed and estimated. 1

Online Display Advertising: Modeling the Effects of Multiple Creatives and Individual Impression Histories

Online advertising campaigns often consist of multiple ads, each with different creative content. We consider how various creatives in a campaign differentially affect behavior given the targeted individual\u27s ad impression history, as characterized by the timing and mix of previously seen ad creatives. Specifically, we examine the impact that each ad impression has on visiting and conversion behavior at the advertised brand\u27s website. We accommodate both observed and unobserved individual heterogeneity and take into account correlations among the rates of ad impressions, website visits, and conversions. We also allow for the accumulation and decay of advertising effects, as well as ad wearout and restoration effects. Our results highlight the importance of accommodating both the existence of multiple ad creatives in an ad campaign and the impact of an individual\u27s ad impression history. Simulation results suggest that online advertisers can increase the number of website visits and conversions by varying the creative content shown to an individual according to that person\u27s history of previous ad impressions. For our data, we show a 12.7% increase in the expected number of visits and a 13.8% increase in the expected number of conversions

Online Advertising Response Models: Incorporating Multiple Creatives and Impression Histories

Online advertising campaigns often consist of multiple ads, each with different creative content. We propose a model that evaluates the effectiveness of each creative in a campaign given the targeted individual’s ad impression history, as characterized by the timing and mix of previously seen ad creatives. We examine the impact that each ad impression has on both visitation and conversion behavior at the advertised brand’s website. Our model is constructed at the individual level and takes into account correlations among the rates of ad impressions, website visits and conversions. We also allow for the accumulation and decay of advertising effects, as well as ad wear-out and restoration effects. Our results highlight the importance of accommodating both the existence of multiple ad creatives in an ad campaign as well as the impact of an individual’s ad impression history. We demonstrate with a simulation how this modeling approach can be used for online ad targeting. Specifically, our results suggest that, using our model, online advertisers can increase the number of website visits and conversions by varying the creative content shown to an individual according to that individual’s history of previous ad impressions. For our data, we show a 12.7% increase in the expected number of visits and a 13.8% increase in the expected number of conversions