'Bricks and Clicks': The Impact of Product Returns on the Strategies of Multichannel Retailers

Elie  Ofek; Zsolt   Katona; Miklos  Sarvary

Article | Marketing Science | January – February 2011

'Bricks and Clicks': The Impact of Product Returns on the Strategies of Multichannel Retailers

by Elie Ofek, Zsolt Katona and Miklos Sarvary

Abstract

The Internet has increased the flexibility of retailers, allowing them to operate an online arm in addition to their physical stores. The online channel offers potential benefits in selling to customer segments that value the convenience of online shopping, but it also raises new challenges. These include the higher likelihood of costly product returns when customers' ability to "touch and feel" products is important in determining fit. We study competing retailers that can operate dual channels ("bricks and clicks") and examine how pricing strategies and physical store assistance levels change as a result of the additional Internet outlet. A central result we obtain is that when differentiation among competing retailers is not too high, having an online channel can actually increase investment in store assistance levels (e.g., greater shelf display, more-qualified sales staff, floor samples) and decrease profits. Consequently, when the decision to open an Internet channel is endogenized, there can exist an asymmetric equilibrium where only one retailer elects to operate an online arm but earns lower profits than its bricks-only rival. We also characterize equilibria where firms open an online channel, even though consumers only use it for research and learning purposes but buy in stores. A number of extensions are discussed, including retail settings where firms carry multiple product categories, shipping and handling costs, and the role of store assistance in impacting consumer perceived benefits.

Keywords: Price; Profit; Marketing Channels; Consumer Behavior; Online Technology; Retail Industry;

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Citation:

Ofek, Elie, Zsolt Katona, and Miklos Sarvary. "'Bricks and Clicks': The Impact of Product Returns on the Strategies of Multichannel Retailers." Marketing Science 30, no. 1 (January–February 2011).

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Article | Management Science | August 2019

When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation

Yicheng Song, Nachiketa Sahoo and Elie Ofek

Sometimes we desire change, a break from the same or an opportunity to fulfill different aspects of our needs. Noting that consumers seek variety, several approaches have been developed to diversify items recommended by personalized recommender systems. However, current diversification strategies operate under a one-shot paradigm without considering the evolution of preferences resulting from recent consumption. Therefore, such methods often sacrifice accuracy. In the context of online media, we show that by recognizing that consumption in a session is the result of a sequence of utility-maximizing selections from various categories, one can increase recommendation accuracy by dynamically tailoring the diversity of suggested items to the diversity sought by the consumer. Our approach is based on a multicategory utility model that captures a consumer’s preference for different categories of content, how quickly the consumer satiates with one category and wishes to substitute it with another, and how the consumer trades off costly search efforts by selecting from a recommended list to discover new content. Taken together, these three elements allow us to characterize how an individual selects a diverse set of items to consume over the course of a session and how likely the individual is to click on recommended content. We estimate the model using a clickstream data set from a large media outlet and apply it to determine the most relevant content to recommend at different stages of an online session. We find that our approach generates recommendations that are on average about 10% more accurate than optimized alternatives and about 25% more accurate than those diversified using existing diversification strategies. Moreover, the proposed method recommends content with diversity that more closely matches the diversity sought by readers, exhibiting lower concentration–diversification bias than other personalized recommender systems. Using a policy simulation, we estimate that recommending content using the proposed approach would result in visitors reading 23% more articles at the studied website and deriving 35% higher utility. This could lead to immediate gains in revenue for the publisher and longer-term improvements in customer satisfaction and retention at the site.

Keywords: recommender systems; personalization; recommendation diversity; variety seeking; collaborative filtering; consumer utility models; digital media; clickstream analysis; learning-to-rank; Consumer Behavior; Media; Customization and Personalization; Strategy; Mathematical Methods;

Citation:

Song, Yicheng, Nachiketa Sahoo, and Elie Ofek. "When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation." Management Science 65, no. 8 (August 2019): 3737–3757. View Details

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'Bricks and Clicks': The Impact of Product Returns on the Strategies of Multichannel Retailers

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