This paper analyzes customer product-choice behavior based on the recency and frequency of each customer's page views on e-commerce sites. Recently, we devised an optimization model for estimating product-choice probabilities that satisfy monotonicity, convexity, and concavity constraints with respect to recency and frequency. This shape-restricted model delivered high predictive performance even when there were few training samples. However, typical e-commerce sites deal in many different varieties of products, so the predictive performance of the model can be further improved by integration of such product heterogeneity. For this purpose, we develop a novel latent-class shape-restricted model for estimating product-choice probabilities for each latent class of products. We also give a tailored expectation-maximization algorithm for parameter estimation. Computational results demonstrate that higher predictive performance is achieved with our latent-class model than with the previous shape-restricted model and common latent-class logistic regression.
One of the most active areas of clickstream research has been the analysis of online purchasing behavior of customers on e-commerce sites @cite_15 . Moe and Fader @cite_8 proposed a stochastic model for predicting online purchasing conversion rates based on an observed history of visits and purchases. Many other studies used logit and or probit modeling based on various types of explanatory variables to predict online purchasing behavior @cite_21 @cite_17 @cite_27 @cite_40 @cite_10 , whereas @cite_38 applied several classification algorithms to predict customers' buying intentions. However, these studies focused on predicting customer visits that culminated in purchases, and so they did not assign a purchase probability to each product. Although various studies have analyzed online product-choice behavior, most have emphasized more detailed data (e.g., multiplex data @cite_32 , social media profiles @cite_42 , and product reviews and ratings @cite_11 ) rather than clickstream data. In contrast to these studies, the present paper focuses on investigating the relationship between customer PVs and product-choice probabilities based on clickstream data. Our research will be of significant value to most e-commerce sites, for which the analysis of clickstream data is a major challenge.
@cite_38 @cite_11 @cite_8 @cite_21 @cite_42 @cite_32 @cite_27 @cite_40 @cite_15 @cite_10 @cite_17
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Nowadays customers would rather buy their needs online than visiting a retail store because of many reasons such as saving time. Therefore, in order to increase efficiency of online shopping websites, many companies have invested in researches toward prediction of users purchases and recommendation systems that may help and motivate a user to buy products that he may be interested in. However, most efforts in this area has been around classification and predictions based on users interests in specific types of products. In this paper, we have studied efficiency of numerous algorithms toward building a classification model to predict the probability of a complete purchase by users only based on their behavior models in the system and regardless of their interest. Therefore, we experimented accuracy of different algorithms and proposed a novel classification model that is able to predict whether a user will be interested in buying a certain set of products that are placed in the online shopping cart or not. Predicting customer purchase behaviour is an interesting and challenging task. In e-commerce context, to tackle the challenge will confront a lot of new problems different from those in traditional business. This study investigates three factors that affect purchasing decision-making of customers in online shopping: the needs of customers, the popularity of products and the preference of the customers. Furthermore, exploiting purchase data and ratings of products in the e-commerce website, we propose methods to quantify the strength of these factors: (1) using associations between products to predict the needs of customers; (2) combining collaborative filtering and a hierarchical Bayesian discrete choice model to learn preference of customers; (3) building a support vector regression based model, called Heat model, to calculate the popularity of products; (4) developing a crowdsourcing approach based experimental platform to generate train set for learning Heat model. Combining these factors, a model, called COREL, is proposed to make purchase behaviour prediction for customers. Submitted a purchased product of a customer, the model can return top n the most possible purchased products of the customer in future. Experiments show that these factors play key roles in predictive model and COREL can greatly outperform the baseline methods. 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. 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's 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. Thepurchasing 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. Clickstream data provide information about the sequence of pages or the path viewed by users as they navigate a website. We show how path information can be categorized and modeled using a dynamic multinomial probit model of Web browsing. We estimate this model using data from a major online bookseller. Our results show that the memory component of the model is crucial in accurately predicting a path. In comparison, traditional multinomial probit and first-order Markov models predict paths poorly. These results suggest that paths may reflect a user's goals, which could be helpful in predicting future movements at a website. One potential application of our model is to predict purchase conversion. We find that after only six viewings purchasers can be predicted with more than 40 accuracy, which is much better than the benchmark 7 purchase conversion prediction rate made without path information. This technique could be used to personalize Web designs and product offerings based upon a user's path. In the era of social commerce, users often connect from e-commerce websites to social networking venues such as Facebook and Twitter. However, there have been few efforts on understanding the correlations between users' social media profiles and their e-commerce behaviors. This paper presents a system for predicting a user's purchase behaviors on e-commerce websites from the user's social media profile. We specifically aim at understanding if the user's profile information in a social network (for example Facebook) can be leveraged to predict what categories of products the user will buy from (for example eBay Electronics). The paper provides an extensive analysis on how users' Facebook profile information correlates to purchases on eBay, and analyzes the performance of different feature sets and learning algorithms on the task of purchase behavior prediction. In the customer-centered marketplace, the understanding of customer behavior is a critical success factor. The big databases in an organization usually involve multiplex data such as static, time series, symbolic sequential and textual data which are separately stored in different databases of different sections. It poses a challenge to traditional centralized customer behavior prediction. In this study, a novel approach called collaborative multiple kernel support vector machine (C-MK-SVM) is developed for distributed customer behavior prediction using multiplex data. The alternating direction method of multipliers (ADMM) is used for the global optimization of the distributed sub-models in C-MK-SVM. Computational experiments on a practical retail dataset are reported. Computational results show that C-MK-SVM exhibits better customer behavior prediction performance and higher computational speed than support vector machine and multiple kernel support vector machine. Abstract 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 rat... Clickstream data are defined as the electronic record of Internet usage collected by Web servers or third-party services. The authors discuss the nature of clickstream data, noting key strengths and limitations of these data for research in marketing. The paper reviews major developments from the analysis of these data, covering advances in understanding (1) browsing and site usage behavior on the Internet, (2) the Internet's role and efficacy as a new medium for advertising and persuasion, and (3) shopping behavior on the Internet (i.e., electronic commerce). The authors outline opportunities for new research and highlight several emerging areas likely to grow in future importance. Inherent limitations of clickstream data for understanding and predicting the behavior of Internet users or researching marketing phenomena are also discussed. This empirical study investigates the contribution of different types of predictors to the purchasing behaviour at an online store. We use logit modelling to predict whether or not a purchase is made during the next visit to the website using both forward and backward variable-selection techniques, as well as Furnival and Wilson’s global score search algorithm to find the best subset of predictors. We contribute to the literature by using variables from four different categories in predicting online-purchasing behaviour: (1) general clickstream behaviour at the level of the visit, (2) more detailed clickstream information, (3) customer demographics, and (4) historical purchase behaviour. The results show that predictors from all four categories are retained in the final (best subset) solution indicating that clickstream behaviour is important when determining the tendency to buy. We clearly indicate the contribution in predictive power of variables that were never used before in online purchasing studies. Detailed clickstream variables are the most important ones in classifying customers according to their online purchase behaviour. In doing so, we are able to highlight the advantage of e-commerce retailers of being able to capture an elaborate list of customer information. Social shopping communities (SSCs) evolve from a linkage of social networking and online shopping. Apart from direct shopping features in shopbots (e.g., search fields), SSCs additionally offer user-generated social shopping features. These include recommendation lists, ratings, styles (i.e., assortments arranged by users), tags, and user profiles. Purchases can be made by following a link to a participating online shop ("click-out"). SSCs are experiencing high growth rates in consumer popularity (e.g., Polyvore attracts more than 6 million unique visitors per month). Thus, this business model has received considerable venture capital in recent years. By analyzing clickstream data, we investigate which factors, especially social shopping features, are significant for predicting purchasing behavior within SSCs. Our logit model includes about 2.73 million visiting sessions and shows that social shopping features exert a significant impact, both positive and negative. Tags and high ratings have a positive impact on a click-out. In contrast, the more lists and styles used, the less likely the user is to make a click-out. Yet, lists and styles seem to enhance site stickiness and browsing. Moreover, the more direct shopping features that are used, the less likely the user is to conduct a click-out. Increasing transaction costs and information overload could be potential reasons. We also found that community members are more likely to make a click-out than ordinary users. This implies that community members are more profitable.