How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?

We investigate the moderating effect of product attributes and review ratings on {views, conversion|views (conversion conditional on views), final conversion} of a purchase-based collaborative filtering recommender system on an e-commerce site. We run a randomized field experiment on a top retailer with 184,375 users split into a recommender-treated group and a control group. We tag theory-driven attributes of 37,125 unique products via Amazon Mechanical Turk to augment the usual product data (e.g., review ratings, descriptions). By examining the recommender’s impact through different stages awareness (views), salience (conversion|views), and final conversion and across product types we provide nuanced insights.The study confirms that the recommender increases {views, conversion|views, final conversion} rates by {15.3%, 21.6%, 7.5%} respectively, but this lift is moderated by product attributes and review ratings. We find that the lift on product views is greater for utilitarian products compared to hedonic products and as well as for experience products compared to search products. In contrast, the lift on conversion|views rate is greater for hedonic product compared to utilitarian product. Furthermore, lift on views rate is greater for products with higher average review ratings, which suggests that a recommender acts as a complements to review ratings, while the opposite is true for conversion|views recommender and review ratings are substitutes. Additionally, a recommender’s awareness lift is greater than its saliency impact. We discuss the potential mechanisms behind our results as well as their managerial implications