Online retailers are increasingly using information technologies to provide value-added services to customers. Prominent examples of these services are online recommender systems and consumer feedback mechanisms, both of which serve to reduce consumer search costs and uncertainty associated with the purchase of unfamiliar products. The central question we address is how recommender systems affect sales. We take into consideration the interaction among recommendations, sales, and price. We then develop a robust empirical model that incorporates the indirect effect of recommendations on sales through retailer pricing, potential simultaneity between sales and recommendations, and a comprehensive measure of the strength of recommendations. Applying the model to a panel data set collected from two online retailers, we found that the strength of recommendations has a positive effect on sales. Moreover, this effect is moderated by the recency effect, where more recently released recommended items positively affect the cross-selling efforts of sellers. We also show that recommender systems help to reinforce the long-tail phenomenon of electronic commerce, and obscure recommendations positively affect cross-selling. We also found a positive effect of recommendations on prices. These results suggest that recommendations not only improve sales but they also provide added flexibility to retailers to adjust their prices. A comparative analysis reveals that recommendations have a higher effect on sales than does consumer feedback. Our empirical results show that providing value-added services, such as digital word of mouth and recommendations, allows retailers to charge higher prices while at the same time increasing demand by providing more information regarding the quality and match of products.

Empirical Analysis of the Impact of Recommender Systems on Sales

This article studies how a recommender system may incentivize users to learn about a product collaboratively. To improve the incentives for early exploration, the optimal design trades off fully transparent disclosure by selectively overrecommending the product (or “spamming”) to a fraction of users. Under the optimal scheme, the designer spams very little on a product immediately after its release but gradually increases its frequency; she stops it altogether when she becomes sufficiently pessimistic about the product. The recommender’s product research and intrinsic/naive users “seed” incentives for user exploration and determine the speed and trajectory of social learning. Potential applications for various Internet recommendation platforms and implications for review/ratings inflation are discussed.

Recommender Systems as Mechanisms for Social Learning

In the future peer-to-peer service oriented computing systems, maintaining a cooperative equilibrium is a non-trivial task. In the absence of Trusted Third Parties (TTP's) or verification authorities, rational service providers minimize their costs by providing ever degrading service quality levels. Anticipating this, rational clients are willing to pay only the minimum amounts (often zero) which leads to the collapse of the market.In this paper, we show how a simple reputation mechanism can be used to overcome this moral hazard problem. The mechanism does not act by social exclusion (i.e. exclude providers that cheat) but rather by allowing flexible service level agreements in which quality can be traded for the price. We show that such a mechanism can drive service providers of different types to exert the social efficient effort levels.

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Reputation-based pricing of P2P services

Rapid improvements in the precision of mobile technologies make it possible for advertisers to go beyond using the real-time static location and contextual information about consumers. In this study, we propose a novel “trajectory-based” targeting strategy for mobile recommendation that leverages full information on consumers’ physical movement trajectories using granular behavioral information from different mobility dimensions. To analyze the effectiveness of this new strategy, we design a large-scale randomized field experiment in a large shopping mall that involved 83,370 unique user responses for a 14-day period in June 2014. We find that trajectory-based mobile targeting can lead to higher redemption probability, faster redemption behavior, and higher transaction amount from customers compared to other baselines. It also facilitates higher revenues for the focal store as well as the overall shopping mall. Moreover, the effect of trajectory-based targeting comes not only from improvements in the efficiency of customers’ current shopping process, but also from its ability to nudge customers towards changing their future shopping patterns and generate additional revenues. Finally, we find significant heterogeneity in the impact of trajectory-based targeting. It is especially effective in influencing high-income consumers. Interestingly, it becomes less effective in boosting the revenues of the shopping mall during the weekends and for those shoppers who like to explore across products categories. Our finding suggests that highly targeted mobile promotions can have the inadvertent impact of reducing impulse purchase behavior by customers who are in an exploratory shopping stage. On a broader note, our work can be viewed as a first step towards studying the large-scale, fine-grained digital trace of individual physical behavior, and how it can be used to predict and market to individual anticipated future behavior.

Mobile Targeting Using Customer Trajectory Patterns

Recommender systems that inform consumers about their likely ideal products have become the cornerstone of e-commerce platforms that sell products from competing manufacturers. Using a model of an ...

Recommender System Rethink: Implications for an Electronic Marketplace with Competing Manufacturers

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 in.exible tastes pay higher prices to get better recommendation.

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Optimal pricing with recommender systems

We look at one of the informational novelties introduced by the existence of internet market, namely "the recommender systems". A recommender system is a system employed by some internet sellers, which collects data from all previous customers about their experiences and makes inferences from this data to recommend a product to an active customer. The recommender system can also be interpreted as a peerto-peer system where the seller provides a platform for the buyers to share their experiences. We interpret the role of a recommender system as reducing uncertainty for the customers, which creates some additonal surplus to be distributed between the customers and sellers employing such systems. We differentiate the customers with respect to the extremity of their preferences, which also implies di®erent valuations for decreased uncertainty. We show that an internet seller employing such as system can extract a non-negligable share of this surplus from the customers through higher prices in the presence of a competitive fringe without recommender systems. However optimal pricing by the seller with the system leads to a less than full market share, since the seller nds it optimal to leave out the buyers with moderate taste to the fringe. Thus the optimal pricing mechanism does not employ the recommender system at the e±cient level, in other words there is under utilization. We also nd that the overall under-utilization might entail over-utilization of the system for some products and under-utilization for others.

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Optimal Pricing Policy 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.

Optimal Pricing with Recommender Systems

We study the efficient allocation of buyers 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 generates informational externalities. We investigate the impact of these factors on the efficient allocation of buyers across different products. We find that the efficient allocation requires that the seller with the recommender system has full market share. If the recommender system is sufficiently effective in reducing uncertainty, it is optimal to have some products to be purchased by a larger group of people than others. The large group consists of customers with flexible tastes.

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Deran Ozmen

Papers

Optimal pricing with recommender systems

Optimal Pricing Policy with Recommender Systems

Optimal Pricing with Recommender Systems

Efficient Recommender Systems

Efficient Recommender Systems