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Making Recommendations More Effective through Framings: Impacts of User- versus Item-Based Framings on Recommendation Click-Throughs

Making Recommendations More Effective through Framings: Impacts of User- versus Item-Based Framings on Recommendation Click-Throughs

Phyliss Jia Gai and Anne-Kathrin Klesse

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User-based framing of recommendations provides additional information on taste matching to customers compared to item-based framing, which could motivate customers to click on recommendations. Field studies on WeChat show that user-based framing attracts higher recommendation click-through rates (CTR) compared to item-based framing. We identify three boundary conditions in which user-based framing is no longer advantageous and even becomes disadvantageous relative to item-based framing in terms of recommendation CTR.

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Related Marketing Courses: ​
Consumer Behavior; Digital Marketing; Innovation/New Product Development

Full Citation: ​
Gai, Phyliss Jia and Anne-Kathrin Klesse (2019), “Making Recommendations More Effective through Framing: Impacts of User- versus Item-Based Framings on Recommendation Click-Troughs,” Journal of Marketing, 83 (6), 61-75.

Article Abstract
Companies frequently offer product recommendations to customers, according to various algorithms. This research explores how companies should frame the methods they use to derive their recommendations, in an attempt to maximize click-through rates. Two common framings—user-based and item-based—might describe the same recommendation. User-based framing emphasizes the similarity between customers (e.g., “People who like this also like…”); item-based framing instead emphasizes similarities between products (e.g., “Similar to this item”). Six experiments, including two field experiments within a mobile app, show that framing the same recommendation as user-based (cf. item-based) can increase recommendation click-through rates. The findings suggest that user-based framing (cf. item-based framing) informs customers that the recommendation is based on not just product matching but also taste matching with other customers. Three theoretically derived and practically relevant boundary conditions related to the recommendation recipient, the products, and other users also offer practical guidance for managers, regarding how to leverage recommendation framings to increase recommendation click-throughs.

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Special thanks to Kelley Gullo and Holly Howe, Ph.D. candidates at Duke University, for their support in working with authors on submissions to this program.

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Phyliss Jia Gai is a doctoral candidate in Marketing Management, Rotterdam School of Management, Erasmus University.

Anne-Kathrin Klesse is Assistant Professor of Marketing Management, Rotterdam School of Management, Erasmus University.