Netflix: recurring revenue model shakes but holds up

by bold-lichterman

When a company like Netflix grows 26% and its stock price drops by 10%, it is a sign that its economic model is raising very high expectations. Netflix was one of the first to ride this new economy of recurring revenue. By moving from mail order DVD rentals to the subscription model, Netflix has built a subscription-based empire that is now at the heart of a war with the proliferation of video streaming platforms. Inventory of the model.

Data at the heart of the subscription model

Like Amazon Prime, Netflix represents one of the leading examples of the success of the subscription model. It guarantees both recurring income for the company and improves its ability to anticipate financially. But the strength of this model is above all to collect data on users and to exploit it through a multitude of applications.

First, data is extremely useful for personalizing the user experience and thus improving customer retention. When you use Netflix, the company collects for example the type of content watched, when to stop and resume watching, the time of watching etc.). This data is used to personalize the recommended films and series, but also the visuals of each content thumbnail. And that’s only the beginning.

Netflix is ​​looking to employ even more machine learning to optimize – beyond the content of recommendations – the way to recommend, for example: customization of synopsis, titles, displayed metadata, order of recommendations and design of the mobile interface based on users’ viewing behavior. The company’s strategy also includes a reflection on taking into account the behavioral biases induced by the recommendation.

Second key operation for the millions of data collected: optimizing the quality of video streaming, managed by servers all over the world. Providing a quality streaming experience to 152 million members (2/3 of whom are outside the United States) is indeed a huge technical challenge. Thanks to machine learning, Netflix creates adaptive algorithms to allow all audiences to view their content on networks with varying speeds and on devices with very different capacities. The data has therefore not finished fueling the ambitions of the streaming giant.

An iteration of the model in real time

With the first drop in US subscribers this quarter, Netflix appears to have reached its peak user base in the United States, comprising 60.1 million subscribers. A variation punished by investors this quarter since Netflix shares fell 10%. This fall in price may seem relatively exaggerated since the potential of the company is not called into question. Internationally, the number of subscribers continues to grow significantly, with 2.9 million additional subscribers compared to the previous quarter.

Beyond the subscription model which improves user retention, Netflix cares for a very strong ecosystem, in particular thanks to its original productions, like Stranger Things or Black Mirror. Thus, 13% of ex-Netflix users have re-registered to watch season 3 of Stranger Things. And half of the 41 million subscribers who follow Eleven’s adventures are already sure to watch the next season according to Wall Street firm Cowen & Co.

These loyal users are the perfect beta testers of the platform’s innovations. For example, last March, Netflix tested in Europe the display of higher prices to people wishing to subscribe to the service. The objective is to refine the knowledge of the “value that users place on Netflix” according to the company, and thus to be able to envisage a future price increase.

The challenge of “digital sobriety”

Netflix still has a bright future ahead of it, but nevertheless faces new challenges. Revealed a few days ago by the think tank The Shift Project, the ecological cost of video streaming raises real questions of impact.

Digital pollution constitutes 4% of greenhouse emissions in 2019, and video streaming is responsible for a fifth of this pollution. In the era of “flygskam” (the shame of flying), binge-watching is also likely to take a slight hit – it remains to be seen how Netflix and industry players will react to avoid subscribers leaking out.

The contributor:

Arielle joined Fabernovel in April 2018 as Project Analyst. She is involved in strategic analysis studies or the design of new products and services. Before joining Fabernovel, Arielle worked in marketing studies at PUIG, a multi-brand luxury group, then as an M&A Analyst at Compagnie Financière du Lion on financial advisory projects for CAC40 companies and French institutions. She also carried out a consulting assignment at Capgemini Consulting in Singapore, and worked in New York in the American cleantech investment fund Capricorn Investment Group. Arielle is a graduate of the Master of Science in Management from ESSEC Business School.