Recently, Netflix announced that the Asian sleeper hit ‘Squid Games’ is the most popular show in 90 countries, and then it was replaced by a Hollywood big-budget ‘Red Notice’, hailed as Netflix’s most-watched movie ever. These claims did a lot to pique viewer interest along with ending up as news media headlines globally. Netflix’s marketing department was able to turn these data-backed declarations into effective global PR campaigns.
In a similar fashion, the global music behemoth- Spotify has been releasing its own Spotify Wrapped announcement every year since 2016. This curated experience acts as a massive advertising activity while also boosting the app’s ranking on the iOS and Google App store.
These companies have been able to stay ahead of the competition by investing heavily in data. Their innovation or let’s say differentiation emerges from the way they use data. The methodologies and results are very difficult to replicate unless it’s organic; investments in the manner of finance, talent, and time are the ingredients. Data-driven strategies make them capable of taking precise decisions, optimizing marketing strategies, and shunning low success-rate projects. Netflix is currently valued at $271 billion, while the 100-year-old entertainment giant Disney, is ahead by just another $2 billion with a market value of about $273 billion.
The new-age of data driven organizations is here. You can join this party too.
A data-driven engine essential to boost organizational growth –depends on a few foundational guidelines.
Setting Clear Goals
It all starts with the mission-defining step. Laying out well-defined goals and expectations within and without the organization is vital for data scientists to adopt the right methodology. For example, Netflix’s success depends on customer satisfaction. Netflix best effective predictive analytics to present options that the subscribers find enticing. This approach is heavily dependent on how well they managed historical viewership data and user experience. When the options struck a chord with a viewer, credibility increased. Netflix currently has over 213 million users worldwide.
Organizing & Analyzing Available Data
Organizations are often overwhelmed by data. Therefore, it helps to establish the right format to achieve the organization’s goals from the beginning. Let’s have a look at Spotify this time. The company’s goal is to provide top-notch personalized services. For this purpose, a great deal of attention is paid by the platform to both the tracks which the listeners stream as well as how they typically interact with every individual track.
Say a listener skips a track after listening to the initial 30 seconds. Here, Spotify takes into account that the user is not enthusiastic about the song and most likely the song’s information isn’t included while computing playlists. On the other hand, when a listener completes the song, it is considered by the platform as a positive reaction, an affirmation of the user’s taste in music. This aids the algorithm in further developing the overall user profile. This is again a simple criterion for deciding helpful data, Spotify has developed a series of formats to highlight and organize key datasets.
Adopting the Right Methodology and Being Data-Driven
Once the goals have been set and format has been established, it’s time to pay attention to methodologies. The right tool can make or break a data driven strategy.
Netflix uses algorithms for predicting the user’s choice based on their previous ratings. Netflix’s recommendation system influences over 80% of the content the subscribers watch on the platform. A personalized video ranker orders the entire Netflix collection for each member profile in a personalized way, keeping in mind their interests, habits, and choices. At the same time, their content procurement side uses clustering analysis methods to study attributes that connect their users with a movie or a series. A similar approach backed their decision to buy the US version of ‘House of Cards’ for over $ 100 million back in 2011. Back then, Netflix relied on attributes such as the values assigned by users to David Fincher, his movie ‘The Social Network’, the British version of ‘House of Cards’, Kevin Spacey and other interconnected appeals.
Spotify has built its own formidable recommendation engine dependent on machine learning (ML) algorithms, natural language processing (NLP) and convolutional neural networks (CNN). The end result, they can turn past listening data into personalized playlists and music recommendations.
Heraclitus’s maxim- “The only thing constant is change.” Every data-driven model should internalize this truism. Demographics and tastes change continuously and gradually. The algorithms used by Netflix uses are constantly being revised in order to achieve maximum optimization. A/B testing is the name to remember. In 2014, Spotify considered redesigning its UI (User Interface). The dilemma was between pursuing a light UI or a dark UI, and at the same time figuring out if the users will be okay with the makeover. To handle this decision, the UI team came up with two hypotheses for testing.
“By having a dark Spotify UI, the music and content will be more focus for the subscribers, and therefore Spotify will be more accessible and attractive compared to the existing.”
“By using a light Spotify UI, the users will have fresher content, and therefore, Spotify will be more accessible and attractive compared to the dark UI.”
Spotify then conducted an A/B test with 1,600 users from various countries, and evaluated the data around the different designs, i.e., dark and lighter UIs. The learning was that the dark UI performed much better than the light UIs and that decided the verdict. Testing is an opportunity to constantly check for biases and innovate, indispensable for data-driven organizations.
Intelligent use of data, as we speak, is revolutionizing how businesses are conducted and profits are made. The opposite is bringing a knife to a gunfight. As an organization if you are intrigued by data and its application, do reach out to us.