How Netflix used big data and analytics to generate billions

Netflix is successful thanks to big data and analytics.

With a company valuation of over $164 billion, Netflix has surpassed Disney as the most valued media company in the world. Their success can be attributed to their impressive customer retention rate, which is 93% compared to Hulu’s 64% and Amazon Prime’s 75%. However, it’s not just their ability to retain most of their 151 million subscribers that have made them successful.

Netflix has flown ahead of its competitors because it also makes more successful TV shows and movies, hits like ‘House of Cards’, ‘Orange Is The New Black’, and ‘Birdbox’ have garnered a lot of attention and high viewership, driving up the rate of subscriptions. Netflix has also been more successful in identifying what their audience wants.

In 2017, 93% of original TV shows were renewed. A contrast to cable television where there is only a 35% chance of a show being renewed after the first season. What is the secret to their success? Big data and analytics.

How Netflix uses big data and analytics

So, how does Netflix use data analytics? By collecting data from their 151 million subscribers, and implementing data analytics models to discover customer behaviour and buying patterns. Then, using that information to recommend movies and TV shows based on their subscribers’ preferences.

According to Netflix, over 75% of viewer activity is based off personalised recommendations. Netflix collects several data points to create a detailed profile on its subscribers. The profile is far more detailed than the personas created through conventional marketing.

Most significantly, Netflix collects customer interaction and response data to a TV show. For example, Netflix knows the time and date a user watched a show, the device used, if the show was paused, does the viewer resume watching after pausing? Do people finish an entire TV show or not, how long does it take for a user to finish a show and so on.

Netflix even has screenshots of scenes people might have viewed repeatedly, the rating content is given, the number of searches and what is searched for. With this data, Netflix can create a detailed profile on its users. To collect all this data and harness it into meaningful information, Netflix requires data analytics. For example, Netflix uses what is known as the recommendation algorithm to suggest TV shows and movies based on user’s preferences.

Netflix’s ability to collect and use the data is the reason behind their success. According to Netflix, they earn over a billion in customer retention because the recommendation system accounts for over 80% of the content streamed on the platform. Netflix also uses its big data and analytics tools to decide if they want to greenlight original content. To an outsider, it might look like Netflix is throwing their cash at whatever they can get, but in reality, they greenlight original content based on several touch points derived from their user base.

For example, Netflix distributed ‘Orange is the New Black’ knowing it would be a big hit on their platform. How? Because ‘Weeds’, Jenji Kohan’s previous hit performed well on Netflix in terms of viewership and engagement.

Netflix even uses big data and analytics to conduct custom marketing, for example, to promote ‘House of Cards’ Netflix cut over ten different versions of a trailer to promote the show. If you watched lots of TV shows centred on women, you get a trailer focused on the female characters. However, if you watched a lot of content directed by David Finch, you would have gotten a trailer that focused the trailer on him. Netflix did not have to spend too much time and resources on marketing the show because they already knew how many people would be interested in it and what would incentivise them to tune in.

In addition to collecting data on subscriber actions, Netflix also encourages feedback from its subscribers. One feedback system is the thumbs up/thumbs down system that replaced their rating system, the system improved audience engagement by a significant margin, which enabled them to customise the user’s homepage further. According to Joris Evers, Director of Global Communications, there are 33 million different versions of Netflix.

Key takeaways

Powerful analytics models can process terabytes of data to churn out meaningful information. Judicious use of data analytics is the main reason for Netflix’s success. In fact, big data and analytics are so vital to Netflix’s success that you may as well call them an analytics company instead of a media company. Netflix’s success highlights the value of data analytics because it presents an incredible insight into user’s preferences allowing them to make smart decisions that deliver maximum ROI on their choices.

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Can we use big data analytics as an anti-corruption tool

Public corruption has cost governments millions of dollars, and severely worsened the plight of those who are poor. The Panama Papers revealed how high-ranking officials, energy companies, foundations, and trusts colluded, especially in tax-free areas. Corruption has proven to be very difficult to solve and fighting it remains challenging. However, experts are turning to a new tool to fight corruption: big data analytics.

How does big data analytics work?

Governments and corporate records are often lengthy, complex documents that the average person cannot read easily. Indeed, it would take someone of immense discipline and technical knowledge to discover the intricate patterns that indicate mishandling of funds – a sign of corruption. However, with big data analytics, it’s possible to comb through the information to find intricate details that humans can miss. For example, the Panama Papers was written because journalists used big data to analyse to review over 11,000 documents.

Furthermore, big data analytics can assess several different information sources to discover trends that are otherwise easy to miss. For example, the European Commission and Transparency International used their own proprietary big data analytics software to compare data from private and public institutions, to find irregularities, conflicts of interest and other signs of corrupt behaviour. The ability to analyse and compare different sources of information is one of the biggest advantages of big data.

Big data analytics can employ data mining techniques to discover tax fraud and evasion. David Frankel, a New York finance commissioner, used data mining techniques to survey the tax records of businesses based in the city. Investigators used big data to find businesses that did not fit the normal tax payment pattern, a possible sign of tax evasion. The new information allowed investigators to identify businesses that were potentially using schemes to evade taxes. In other words, investigators could conduct a smarter, more efficient tax evasion investigation process.

The presence of big data analytics could deter corruption simply because it is so effective in identifying incidents of corruption. Recently, Timothy Persons, who was the Chief Scientist of the United States Government Accountability Office (GAO) in 2016, said that corruption is a cost-benefit analysis, those who commit to corruption do so because the benefits outweigh the costs.

Challenges of big data analytics

Despite the immense potential of big data in fighting corruption, and its use by major organisations like the European Commission, there are still some difficulties to overcome.

The need for open data

Most public institutions do not have a framework for sharing data. Therefore, it is very difficult for most people to find data connected to budgets for development projects, or policy decisions. Having a common framework for open data helps set a common standard. As a result, it will be much easier to read and interpret the information from different organisations. When it is easier to read data, investigators can ask more complex questions about how resources were spent, making it much easier to track down incidences of corruption.

Data and analytics tools must be reliable

There is no point in investing in data analytics platforms if the data is not accurate. The latest analytics platforms are intelligent with AI and machine learning capabilities. However, these platforms are still designed to process data and, if the information is not accurate, the results will not be accurate either. This highlights the importance of national institutions responsible for collecting the data that analytics platforms will use.

The analytics tools also require a strong team of data scientists to get the best out of the tools. However, finding qualified data scientists remains a huge challenge because data analysts are in high demand, and many of them are lured into the private sector via lucrative offers.

Key takeaways

Corruption has always plagued bureaucracy, costing governments millions of dollars. Fighting corruption has proven to be a challenge. However, big data analytics could change the situation. Data analytics can assess terabytes of information to find trends and identify cases deviating from the norm to improve investigations into corruption. However, despite the immense potential of analytics, there are still some challenges to overcome, like the need to standardise open data, make sure data is reliable and building a strong analytics team.

Want to read more about data analytics? Find everything you need here.