Will the global village fracture into tribes? Recommender systems and their effects on consumer fragmentation


Netflix, Spotify and Amazon and many other companies recommend personalized content to best suit the customers’ preferences and increase consumption or sales (De et al., 2007). Recommender systems are used for different types of media such as movies, books, music, news and television. Moreover, they strongly affect what customers view and buying behavior. Many positive effects of personalization are known such as reduction of information overload, increasing relevance and loyalty (Tsekouras, lecture 2017).

However, personalization may also have drawbacks. As the internet becomes more and more specific to our interests, this “ hyperspecification” may fragment users, reduce shared experience and narrow media consumption. For example, fully personalizing news sites may mean that we no longer see the same news articles. Therefore one could argue that recommender systems will create fragmentation and will cause users to have less in common.
On the other hand, recommenders may have the opposite effect as they share information among customers who otherwise would not have communicated. The paper presents empirical evidence on whether recommender systems fragment or homogenize customers.

In an observational two-group experiment the behavior of the participants before and after recommendations is compared. Recommendations are provided in iTunes and the user experience is personalized through mostly content-based recommendations and a small part collaborative data.

Interestingly, contrary to the author’s expectations, recommendation systems increase purchase similarity (homogeneity among customers) and therefore do not increase fragmentation. Customers tend to purchase more after being exposed to personalized recommendations due to the volume effect, which in turn increases the chance of customers purchasing the same product.
Furthermore, customers buy a more similar mix of products after recommendations due to the product-mix effect. Consequently, customer networks become denser and smaller. However, it is important to note that the recommender system does not recommend the same items to many users but that the diversity of items consumed increases.

Within business, the findings of the paper confirm that recommendation tools can help marketers to increase sales but also alter the product mix customers buy. Off course, these are some of the the reason why numerous retailers (e.g. Netflix, Amazon, Spotify) use these recommendation systems.
However, what I find more interesting about the paper is the application of recommendation systems in the context of society. As we increase to spend time online and recommendation systems become more advanced, I think this is an interesting application. Similarly, I think it would be interesting to research to what extent the proposed homogenization of customers has implications for society and (business) cultures.
Fragmentation may have negative implications for society as when people share little information filter bubbles and echo chambers may exist. However, in business context, I think there are situations in which fragmentation of customers may provide better results than homogeinity . For example in the context of innovation, crowdsourcing and generating new business ideas, a variety of views and opinions may yield to better and more refreshing ideas from different anchors. In this case, it would not be desirable if every customer listens to the same music and reads the same news articles and books, as different point of views are needed. I think for companies who want to generate new ideas from their customers, the homogenization of their customer base might be something to take into account.

References

De P, Hu YJ, Rahman MS (2010) Technology usage and online sales: An empirical study. Management Sci. 56(11):1930–1945.

Hosanagar, K., Fleder, D., Lee, D., & Buja, A. (2013). Will the global village fracture into tribes? Recommender systems and their effects on consumer fragmentation. Management Science60(4), 805-823.

Tsekouras, D. (10 February 2017), Lecture Customer Centric Digital Commerce, “Personalization & Product Recommendations.

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