A consumer perspective on Netflix’s recommender system. A qualitative analysis

Netflix has dominated the subscription video on demand for the past decade. Platforms like Netflix specifically offer an unlimited access to catalogues of movies and series for monthly subscriptions. The market for SVOD services is projected to reach US$ 71,237 million by the end of this year (Statista, 2020). The key to its success revolves around the personalization of the service that was reached through recommender systems which have been growing in cultural industries and lead to user friendly platforms endowed with tailored experiences. The motive behind this study revolves around understanding the consumer perception of SVOD recommender systems and more particularly that of Netflix. There are plenty of studies regarding the way recommender systems work and their accuracy but little to no study understand the effects these personalization’s have on shaping customers preferences and choices. To understand how consumers are affected, a select sample of millennials and generation z having a personal Netflix account was gathered and engaged through to in-depth interviews. The data from these interviews was analyzed through a constructivist grounded theory process. The findings of the study highlighted four different themes: first, viewers were found to be ambivalent regarding their use of the platform and showed different expectations. Second, they overall found accuracy in the way the recommender system operated. Third, the participants described a process of mutual domestication towards the platform as they described an interdependent relationship. Finally, the findings of this research underline the limitations coming from the recommender system regarding an excess of personalization. The findings led to an interesting discussion that led to the main theoretical framework. Profiling, digital nudging, personalization paradox and mutual domestication were not used directly by the interviewees during the interviews but the concepts were described leading to a better understanding of the way Netflix shapes their consumption. This study indicates that these Netflix’s subscribers have a strong attachment to recommender systems and valued its improvements as it helped them get a better experience and showed appreciation for personalized platforms. One of the drawbacks of these recommendations was seen as group experiences, given that SVOD platforms were often used with family or friends, which did not fit to their recommender systems. This created struggles for Netflix to understand group personalization. Further research could look at how recommender systems shape consumer tastes and how Netflix could improve its algorithms to make a better experience out of it.