Moving recommender systems from on-line commerce to retail stores

The increasing diversity of consumers' demand, as documented by the debate on the long tail of the distribution of sales volume across products, represents a challenge for retail stores. Recommender systems offer a tool to cope with this challenge. The recent developments in information technology and ubiquitous computing makes it feasible to move recommender systems from the on-line commerce, where they are widely used, to retail stores. In this paper, we aim to bridge the management literature and the computer science literature by analysing a number of issues that arise when applying recommender systems to retail stores: these range from the format of the stores that would benefit most from recommender systems to the impact of coverage and control of recommender systems on customer loyalty and competition among retail store

Optimal Pricing with Recommender Systems

We study optimal pricing in the presence of recommender systems. A recommender system affects the market in two ways: (i) it creates value by reducing product uncertainty for the customers and hence (ii) its recommendations can be offered as add-ons which generate informational externalities. The quality of the recommendation add-on is endogenously determined by sales. We investigate the impact of these factors on the optimal pricing by a seller with a recommender system against a competitive fringe without such a system. If the recommender system is sufficiently effective in reducing uncertainty, then the seller prices otherwise symmetric products differently to have some products experienced more aggressively. Moreover, the seller segments the market so that customers with more inflexible tastes pay higher prices to get better recommendations.Recommender system, Collaborative filtering, Add-ons, Pricing, Information externality

Three Essays on Big Data Consumer Analytics in E-Commerce

Consumers are increasingly spending more time and money online. Business to consumer e-commerce is growing on average of 20 percent each year and has reached 1.5 trillion dollars globally in 2014. Given the scale and growth of consumer online purchase and usage data, firms\u27 ability to understand and utilize this data is becoming an essential competitive strategy. But, large-scale data analytics in e-commerce is still at its nascent stage and there is much to be learned in all aspects of e-commerce. Successful analytics on big data often require a combination of both data mining and econometrics: data mining to reduce or structure (from unstructured data such as text, photo, and video) large-scale data and econometric analyses to truly understand and assign causality to interesting patterns. In my dissertation, I study how firms can better utilize big data analytics and specific applications of machine learning techniques for improved e-commerce using theory-driven econometrical and experimental studies. I show that e-commerce managers can now formulate data-driven strategies for many aspect of business including cross-selling via recommenders on sales sites to increasing brand awareness and leads via social media content-engineered-marketing. These results are readily actionable with far-reaching economical consequences

The Differences between Recommender Technologies in their Impact on Sales Diversity

Recommender systems are frequently used as part of online shops to help consumers browse through large product offerings by recommending those products which are the most relevant for them. Although consumers’ interactions with recommender systems have been subject to substantial research, it is still unclear what the effect on aggregated sales diversity is, i.e. whether this leads to predominance of fast-selling or niche products. It is also unclear, whether any potential effects would differ between specific recommender technologies. We created a realistic web-experiment to monitor consumer behavior while purchasing digital music tracks when different recommender technologies are present. To analyze potential changes in sales diversity we used the Gini coefficient as well as additional measures. We found that sales diversity increases for all recommender technologies, except for bestseller lists. Furthermore, the differences across recommender technologies are rather small. Our findings have significant implications for online retailers and for producers

E-commerce Product Networks, Word-of-mouth Convergence, and Product Sales

Driven by the network theory on status, we propose an interesting argument that network connection between two products affects their word-of-mouth (WOM) rating convergence and that WOM rating convergence affects their sales. To empirically validate this argument, we analyze data from China\u27s largest business-to-consumer platform, Tmall.com. After addressing potential endogeneity issues and performing various robustness checks to ensure the consistency of our findings in various ways, we found that network connection between two products via recommender systems was related to the convergence of WOM rating between the two products. Moreover, WOM rating convergence between two products was associated with a decrease in the sales quantity of the product with higher WOM rating, whereas it was associated with an increase in the sales quantity of the product with lower WOM rating. Overall, WOM rating convergence was associated with an increase in the total sales quantity of the two products. Our findings provide important theoretical contributions and notable implications for e-commerce product marketing and platform design

How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment

We investigate the impact of collaborative filtering recommender algorithms (e.g., Amazon\u27s “Customers who bought this item also bought”) commonly used in e-commerce on sales diversity. We use data from a randomized field experiment run on a top retailer in North America across 82,290 SKUs and 1,138,238 users. We report four main findings. First, we demonstrate across a wide range of product categories that the use of traditional collaborative filters (or CFs) is associated with a decrease in sales diversity relative to a world without product recommendations. Further, the design of the CF matters. CFs based on purchase data are associated with a greater effect size than those based on product views. Second, the decrease in aggregate sales diversity may not always be accompanied by a corresponding decrease in individual-level consumption diversity. In fact, it is even possible for individual consumption diversity to increase while aggregate sales diversity decreases. Third, co-purchase network analysis shows that recommenders can help individuals explore new products but similar users end up exploring the same kinds of products resulting in the concentration bias at the aggregate level. Fourth and finally, there is a difference between absolute and relative impact on niche items. Specifically, absolute sales and views for niche items in fact increase, but their gains are smaller compared to the gains in views and sales for popular items. Thus, while niche items gain in absolute terms, they lose out in terms of market shares

Interactive product catalogue with user preference tracking

In the context of m-commerce, small screen size poses serious difficulty for users to browse effectively through a product catalogue, given the limited number of products that may be presented on-screen. Despite the availability of search engines, filters and recommender systems to aid users, these techniques focus on a narrow segment of product offering. The users are thus denied the opportunity to do a more expansive exploration of the products available. This paper describes a novel approach to overcome the constraints of small screen size. Through integration of a product catalogue with a recommender system, an adaptive system has been created that guides users through the process of product browsing. An original technique has been developed to cluster similar positive examples together to identify areas of interest of a user. The performance of this technique has been evaluated and the results proved to be promising