Case Studies: applications of data science in marketing

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Here are examples of companies using data science in marketing.

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Airbnb is the perfect example of how data science can be applied to marketing to astonishing effect. It’s important to note that the key to their resounding success is that they hired a data scientist from the very beginning, when there were only seven people on the team.

As the business has grown, their approach to data science has become multi-faceted, with scientists interpreting data for every aspect of the company.

Airbnb data science

Source: Airbnb

Data science has been a priority since the founder first recognized its potential to rapidly aid company growth, meaning that problems and opportunities alike at every level have always been properly explored.

In-house development programs, such as Airflow and Airpal, have put the power of data at every employee’s fingertips and even allowed hosts and customers to learn from one another.


One of the main priorities of a content subscription service such as Netflix is keeping its viewers coming back for more.

Netflix’s recommendation engine serves exactly this purpose and recommends new films and series based on the viewing history of users with similar interests. Though the first-hand effect for the user is enriching, helpful, and personal, the ultimate goal is to keep the user subscribed month after month.

Netflix recommendation system


Similar to Netflix, Spotify aims to sustain its subscribers by providing new, interesting ways for them to discover music. The big difference between these two platforms is the sheer amount of content Spotify offers in comparison to Netflix – a necessary difference given the two content types.

As a result, searching for new music that you will enjoy on Spotify manually is considerably harder than finding a new film or series on Netflix.

Spotify recommendations data science

One initiative that aims to solve this problem is the Discover Weekly playlist. Every Monday, a playlist will be tailor-made for each user, based on the listening habits of similar users.

Equally, the Release Radar lets users stay up-to-date with new releases from the artists that Spotify knows they enjoy, and the Daily Mix blends songs the user already listens to with new music that will encourage them to explore further. All of these ideas keep each user’s account fresh, up-to-date, and interesting, without any action required from them.

Find out more about how Spotify makes its recommendations here.


Data science at Facebook takes a multi-layered form. Not only do they have their own insights to manage and action, but they also provide marketing tools and insights to the thousands of business owners who market through their platform. The need for effective strategies that work for their customers is vital.

Facebook data science

Source: Facebook for Business

The team at Facebook has set up machine learning models that accurately measure the effectiveness of their customers’ marketing campaigns, distribute the campaigns effectively, and allow Facebook’s own marketing team to develop tools and insights to better serve the business owners.


Like Facebook, Google aims to give its business-owning clients a great return on their investment. Most small businesses using Google for their marketing needs will not have an in-house data scientist, so will rely on Google to provide those services behind the scenes. The aim is to make the data and analytics as simple and easy to read as possible.

Source: Google Support

Google provides business owners with only the most valuable tools to create the bones of their marketing campaigns and measure the results, while its own marketing team works on delivering their advertisements to customers that are most likely to purchase.

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