When you watch a movie on Netflix, when you listen to music on Spotify, when you watch clips on Youtube, when you search for connections on LinkedIn or when you are shopping online on Bol.com, it appears often that you come across a sentence like: “you might also like”, “people you may know”, “customers who bought this item, also bought…”. Sometimes this suggestion might interest you and you click on it, but that is not always the case. Why do these recommendations appear and how do companies find the correct ones to recommend? What do these products have in common and how do they influence each other?
This paper of Oestreicher-Singer and Sundararajan (2012) explains these questions by focusing on online product or copurchase networks. In these networks, related products that each have their own network position, are linked to each other. The associations among products, and thus the product’s virtual shelf positions, are visible to the customers through recommendation hyperlinks. The main result states that this visibility of networks can cause a threefold average increase in the influence that complementary products, and thus not only recommended products, have on each other’s demand levels and that it amplifies the shared purchasing of complementary products.
To come to this result, data about the copurchase network of 250,000 books sold on Amazon.com is collected, which is used to test how demand levels are related. The data are tested on three types of possible correlations in demand. Firstly the visible network neighbours with explicit visible hyperlinks. Secondly the complementary products without visible hyperlinks, but with related demand which is controlled for unobserved sources of complementarity that might exist regardless of a visible hyperlink by constructing three alternative sets of complementary products and finally the similar environmental conditions with similar individual or environmental characteristics of the products. Besides these types of correlations, the study found that demand is also affected by the product’s individual characteristics of price, secondary market offers, vintage, in-degree and assortative mixing.
Strength of this study is that a real life setting is used to test the interdependencies, which increases the validity. Downside however is that the study is only about books. For the generalizability, it would be better to also look at other products and services such as movies, cameras or clothing.
The most remarkable outcome, besides the main result, is probably that this visibility has a stronger influence on newer and more popular products, because they ‘use’ the attention of their network position more efficiently. Recently published products are more influenced by neighbouring products, because the effect of observational learning on sales will be smaller when a consumer already has a strong prior idea of a product. Additionally, the conversion rate of recommendations that originate from more popular products is higher and sequentially the same level of total incoming traffic from fewer, more popular sources is associated with higher demand.
This study is important, because as the importance of electronic commerce continues to grow, the ability to control cross-product effects in electronic markets has become a key strategic marketing lever for firms, especially with new and popular products, and that is exactly why you see recommendation sentences when you shop online.
Oestreicher-Singer, G., Sundararajan, A. (2012) The Visible Hand? Demand Effects of Recommendation Networks in Electronic Markets. Management Science 58(11):1963-1981.
Tsekouras, D. (2017) ‘Session 2: Personalization & Product Recommendations, Rotterdam: Rotterdam School of Management (9th of February 2017).