Unraveling the Information Role of Online Reviews: Distinguishing between the Competing Effect, Local and Global Peer Effects on Consumer Choice

This study examines the information role of online reviews on competing products and from local and global peers on consumers’ choice of products. By empirically analyzing a data set from a restaurant review website, we find that a one unit increase in average valence (volume) of spatially adjacent and feature similar alternatives reduces the odds of choosing a focal product by 92.0% (66.6%) and by 72.2% (45.8%) respectively. Competition faced by restaurants, in terms of review valence and volume, is thus stronger along geographical space, rather than characteristics space. The valence, volume and dispersion of valences of reviews posted by local peers are found to have influences on purchase decisions. Importantly, local peer effects exert more significant influences on consumer choices as compared to global peer effects. These new findings on the dimensions of competition, local and global peer effects of online reviews provide implications for academic research and practice

Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce

Electronic commerce is revolutionizing the way we think about data modeling, by making it possible to integrate the processes of (costly) data acquisition and model induction. The opportunity for improving modeling through costly data acquisition presents itself for a diverse set of electronic commerce modeling tasks, from personalization to customer lifetime value modeling; we illustrate with the running example of choosing offers to display to web-site visitors, which captures important aspects in a familiar setting. Considering data acquisition costs explicitly can allow the building of predictive models at significantly lower costs, and a modeler may be able to improve performance via new sources of information that previously were too expensive to consider. However, existing techniques for integrating modeling and data acquisition cannot deal with the rich environment that electronic commerce presents. We discuss several possible data acquisition settings, the challenges involved in the integration with modeling, and various research areas that may supply parts of an ultimate solution. We also present and demonstrate briefly a unified framework within which one can integrate acquisitions of different types, with any cost structure and any predictive modeling objectiveNYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Time-weighted multi-touch attribution and channel relevance in the customer journey to online purchase

We address statistical issues in attributing revenue to marketing channels and inferring the importance of individual channels in customer journeys towards an online purchase. We describe the relevant data structures and introduce an example. We suggest an asymmetric bathtub shape as appropriate for time-weighted revenue attribution to the customer journey, provide an algorithm, and illustrate the method. We suggest a modification to this method when there is independent information available on the relative values of the channels. To infer channel importance, we employ sequential data analysis ideas and restrict to data which ends in a purchase. We propose metrics for source, intermediary, and destination channels based on twoand three-step transitions in fragments of the customer journey. We comment on the practicalities of formal hypothesis testing. We illustrate the ideas and computations using data from a major UK online retailer. Finally, we compare the revenue attributions suggested by the methods in this paper with several common attribution methods

The Future of A/B Testing in Social Network Advertising

This research addresses the future of A/B testing in social network advertising. A/B test is a well- studied comparison problem with two different samples with the goal of testing the treatment effect of old and new variations. In recent years, through the rise of the internet, A/B testing in social networks has gained sharpened focus and is commonly used in social network advertising. Due to the market-driven strategy the companies should today aim for, the development of A/B testing in social network advertising can help in gathering useful insights of consumer preferences and attitudes. A/B testing has been perceived as cheap, simple and reliable way of optimizing advertisement and mining data from site users. However, as currently performed A/B testing has criticized as manual and time-consuming activity that requires complex set of statistical and engineering skills. This study focuses on overcoming these problems through automation and machine learning algorithms. Besides, the importance of shifting organizational focus on optimal usage of data-driven decision making through A/B testing, and user attitudes towards social network advertising and their ad-clicking behaviour are addressed

Computer Crime and Identity theft

The problem at hand is the increased amount of vulnerabilities and security hazards for individuals engaging in e-commerce, business transactions over the World Wide Web. Since the majority of people aren\u27t paying their bills by mailing in their payment to the vendor, they pay for the items they purchase online, which makes them open to hackers and social engineering attacks. They place their credit card/debit card numbers, their phone number and home address, and even their birth date information on company websites. All these security vulnerabilities make the risk of identity theft increasingly high. Identity theft is when an individual\u27s personal (confidential) information, such as social security or account numbers, is stolen and used against them

Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce

Electronic commerce is revolutionizing the way we think about data modeling, by making it possible to integrate the processes of (costly) data acquisition and model induction. The opportunity for improving modeling through costly data acquisition presents itself for a diverse set of electronic commerce modeling tasks, from personalization to customer lifetime value modeling; we illustrate with the running example of choosing offers to display to web-site visitors, which captures important aspects in a familiar setting. Considering data acquisition costs explicitly can allow the building of predictive models at significantly lower costs, and a modeler may be able to improve performance via new sources of information that previously were too expensive to consider. However, existing techniques for integrating modeling and data acquisition cannot deal with the rich environment that electronic commerce presents. We discuss several possible data acquisition settings, the challenges involved in the integration with modeling, and various research areas that may supply parts of an ultimate solution. We also present and demonstrate briefly a unified framework within which one can integrate acquisitions of different types, with any cost structure and any predictive modeling objectiveNYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Recommending Best Products from E-commerce Purchase History and User Click Behavior Data

E-commerce collaborative filtering recommendation systems, the main input data of user-item rating matrix is a binary purchase data showing only what items a user has purchased recently. This matrix is usually sparse and does not provide a lot of information about customer purchases or product clickstream behavior (eg., clicks, basket placement, and purchase) history, which possibly can improve product recommendations accuracy. Existing recommendation systems in E-commerce with clickstream data include those referred in this thesis as Kim05Rec, Kim11Rec, and Chen13Rec. Kim05Rec forms a decision tree on click behavior attributes such as search type and visit times, discovers the possibility of a user putting products into the basket and uses the information to enrich the user-item rating matrix. If a user clicked a product, Kim11Rec then finds the associated products for it in three stages such as click, basket and purchase, uses the lift value from these stages and calculates a score, it then uses the score to make recommendations. Chen13Rec measures the similarity of users on their category click patterns such as click sequences, click times and visit duration; it then can use the similarity to enhance the collaborative filtering algorithm. However, the similarity between click sequences in sessions can apply to the purchases to some extent, especially for sessions without purchases, this will be able to predict purchases for those session users. But the existing systems have not integrated it, or the historical purchases which shows more than whether or not a user has purchased a product before. In this thesis, we propose HPCRec (Historical Purchase with Clickstream based Recommendation System) to enrich the ratings matrix from both quantity and quality aspects. HPCRec firstly forms a normalized rating-matrix with higher quality ratings from historical purchases, then mines consequential bond between clicks and purchases with weighted frequencies where the weights are similarities between sessions, but rating quantity is better by integrating this information. The experimental results show that our approach HPCRec is more accurate than these existing methods, HPCRec is also capable of handling infrequent cases whereas the existing methods can not