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

The Sales Effect of Word of Mouth: A Model for Creative Goods and Estimates for Novels

Weekly sales of creative goods – like music records, movies or books – usually peak shortly after release and then decline quickly. In many cases, however, they follow a hump-shaped pattern where sales increase for some time. A popular explanation for this phenomenon is word of mouth among a population of heterogeneous buyers, but previous studies typically assume buyer homogeneity or neglect word of mouth altogether. In this paper, I study a model of new-product diffusion with heterogeneous buyers that allows for a quantification of the sales effect of word of mouth. The model includes Christmas sales as a special case. All parameters have an intuitive interpretation. Simulation results suggest that the parameters are estimable for data that are not too volatile and that cover a sufficiently large part of a title’s life cycle. I estimate the model for four exemplary novels using scanner data on weekly sales.Meistens erreichen die wöchentlichen Verkäufe von kreativen Produkten wie Musikalben, Kinofilmen oder Büchern kurz nach Veröffentlichung ihren Höhepunkt und nehmen dann schnell ab. In einigen Fällen jedoch zeigen sie einen buckelartigen Verlauf mit zunächst ansteigenden Verkäufen. Eine populäre Erklärung für dieses Phänomen beruht auf der Existenz von Mundpropaganda unter heterogenen Käufern, doch bisherige Studien gehen typischerweise von der Annahme homogener Käufer aus oder vernachlässigen Mundpropaganda gänzlich. Dieses Papier betrachtet ein Modell der Verbreitung neuer Produkte unter heterogenen Käufern, welches eine Quantifizierung der Verkaufswirkung von Mundpropaganda ermöglicht. Das Modell beinhaltet Weihnachtsverkäufe als Spezialfall. Alle Modellparameter haben eine intuitive Bedeutung. Ergebnisse einer Simulation zeigen, dass die Parameter empirisch geschätzt werden können, wenn die Daten einen hinreichend großen Teil des Verkaufszyklus eines Titels abdecken und nicht zu volatil sind. Das Modell wird auf Scannerdaten für vier exemplarische Romane angewendet

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

Duration of androgen deprivation therapy with postoperative radiotherapy for prostate cancer: a comparison of long-course versus short-course androgen deprivation therapy in the RADICALS-HD randomised trial

Background Previous evidence supports androgen deprivation therapy (ADT) with primary radiotherapy as initial treatment for intermediate-risk and high-risk localised prostate cancer. However, the use and optimal duration of ADT with postoperative radiotherapy after radical prostatectomy remains uncertain. Methods RADICALS-HD was a randomised controlled trial of ADT duration within the RADICALS protocol. Here, we report on the comparison of short-course versus long-course ADT. Key eligibility criteria were indication for radiotherapy after previous radical prostatectomy for prostate cancer, prostate-specific antigen less than 5 ng/mL, absence of metastatic disease, and written consent. Participants were randomly assigned (1:1) to add 6 months of ADT (short-course ADT) or 24 months of ADT (long-course ADT) to radiotherapy, using subcutaneous gonadotrophin-releasing hormone analogue (monthly in the short-course ADT group and 3-monthly in the long-course ADT group), daily oral bicalutamide monotherapy 150 mg, or monthly subcutaneous degarelix. Randomisation was done centrally through minimisation with a random element, stratified by Gleason score, positive margins, radiotherapy timing, planned radiotherapy schedule, and planned type of ADT, in a computerised system. The allocated treatment was not masked. The primary outcome measure was metastasis-free survival, defined as metastasis arising from prostate cancer or death from any cause. The comparison had more than 80% power with two-sided α of 5% to detect an absolute increase in 10-year metastasis-free survival from 75% to 81% (hazard ratio [HR] 0·72). Standard time-to-event analyses were used. Analyses followed intention-to-treat principle. The trial is registered with the ISRCTN registry, ISRCTN40814031, and ClinicalTrials.gov , NCT00541047 . Findings Between Jan 30, 2008, and July 7, 2015, 1523 patients (median age 65 years, IQR 60–69) were randomly assigned to receive short-course ADT (n=761) or long-course ADT (n=762) in addition to postoperative radiotherapy at 138 centres in Canada, Denmark, Ireland, and the UK. With a median follow-up of 8·9 years (7·0–10·0), 313 metastasis-free survival events were reported overall (174 in the short-course ADT group and 139 in the long-course ADT group; HR 0·773 [95% CI 0·612–0·975]; p=0·029). 10-year metastasis-free survival was 71·9% (95% CI 67·6–75·7) in the short-course ADT group and 78·1% (74·2–81·5) in the long-course ADT group. Toxicity of grade 3 or higher was reported for 105 (14%) of 753 participants in the short-course ADT group and 142 (19%) of 757 participants in the long-course ADT group (p=0·025), with no treatment-related deaths. Interpretation Compared with adding 6 months of ADT, adding 24 months of ADT improved metastasis-free survival in people receiving postoperative radiotherapy. For individuals who can accept the additional duration of adverse effects, long-course ADT should be offered with postoperative radiotherapy. Funding Cancer Research UK, UK Research and Innovation (formerly Medical Research Council), and Canadian Cancer Society

Adding 6 months of androgen deprivation therapy to postoperative radiotherapy for prostate cancer: a comparison of short-course versus no androgen deprivation therapy in the RADICALS-HD randomised controlled trial

Background Previous evidence indicates that adjuvant, short-course androgen deprivation therapy (ADT) improves metastasis-free survival when given with primary radiotherapy for intermediate-risk and high-risk localised prostate cancer. However, the value of ADT with postoperative radiotherapy after radical prostatectomy is unclear. Methods RADICALS-HD was an international randomised controlled trial to test the efficacy of ADT used in combination with postoperative radiotherapy for prostate cancer. Key eligibility criteria were indication for radiotherapy after radical prostatectomy for prostate cancer, prostate-specific antigen less than 5 ng/mL, absence of metastatic disease, and written consent. Participants were randomly assigned (1:1) to radiotherapy alone (no ADT) or radiotherapy with 6 months of ADT (short-course ADT), using monthly subcutaneous gonadotropin-releasing hormone analogue injections, daily oral bicalutamide monotherapy 150 mg, or monthly subcutaneous degarelix. Randomisation was done centrally through minimisation with a random element, stratified by Gleason score, positive margins, radiotherapy timing, planned radiotherapy schedule, and planned type of ADT, in a computerised system. The allocated treatment was not masked. The primary outcome measure was metastasis-free survival, defined as distant metastasis arising from prostate cancer or death from any cause. Standard survival analysis methods were used, accounting for randomisation stratification factors. The trial had 80% power with two-sided α of 5% to detect an absolute increase in 10-year metastasis-free survival from 80% to 86% (hazard ratio [HR] 0·67). Analyses followed the intention-to-treat principle. The trial is registered with the ISRCTN registry, ISRCTN40814031, and ClinicalTrials.gov, NCT00541047. Findings Between Nov 22, 2007, and June 29, 2015, 1480 patients (median age 66 years [IQR 61–69]) were randomly assigned to receive no ADT (n=737) or short-course ADT (n=743) in addition to postoperative radiotherapy at 121 centres in Canada, Denmark, Ireland, and the UK. With a median follow-up of 9·0 years (IQR 7·1–10·1), metastasis-free survival events were reported for 268 participants (142 in the no ADT group and 126 in the short-course ADT group; HR 0·886 [95% CI 0·688–1·140], p=0·35). 10-year metastasis-free survival was 79·2% (95% CI 75·4–82·5) in the no ADT group and 80·4% (76·6–83·6) in the short-course ADT group. Toxicity of grade 3 or higher was reported for 121 (17%) of 737 participants in the no ADT group and 100 (14%) of 743 in the short-course ADT group (p=0·15), with no treatment-related deaths. Interpretation Metastatic disease is uncommon following postoperative bed radiotherapy after radical prostatectomy. Adding 6 months of ADT to this radiotherapy did not improve metastasis-free survival compared with no ADT. These findings do not support the use of short-course ADT with postoperative radiotherapy in this patient population

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