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Recommended products, on platforms like Amazon and Netflix, are most likely to be clicked on when the suggestion is based on what other people like, according to new research from Rotterdam School of Management, Erasmus University (RSM).

Companies that encourage customers to explore new products through recommendations can be more successful when they optimise these suggestions by using ‘user-based’ explanations, such as “People who like this also like…” or “Customers who viewed this item also viewed…” says Phyliss Jia Gai and Dr Anne-Kathrin Klesse.
However, companies that use item-based recommendations, such as “Similar to [what you have listened to]’, in which products are suggested by customer attributes, may be less successful.

Using data from WeChat, China’s top social media app, in which many users subscribe to news articles offered by media accounts on the platform, the researchers found that user-based explanations over item-based can make recommendations much more effective, by drastically increasing customers’ tendencies to click on future recommendations.

According to Dr Anne-Kathrin Klesse,

“This research highlights that how companies explains their recommendations to their customers clearly matters. If these findings are used, companies can boost the success of their recommendations, which will significantly increase the number of products their customers choose to explore.”

The researchers also highlight situations in which user-based framing becomes disadvantageous. This occurs when consumers perceive the suggested taste match as inaccurate, for instance, because customers perceive themselves as different to other users in categories like age and gender. Similarly, customers with more experience tend to develop more refined tastes, and see their own tastes as distinctive.

By leveraging these findings, managers can tailor the framing of their recommendations to different customers and products and boost click-through rates.
This research was published in the Journal of Marketing.

For more information, a copy of the study, or to speak to Phyliss Jia Gai, contact Kate Mowbray at BlueSky PR on or call +44 (0) 1582 790701

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