Our Work

Media Recommendations Engine for a content media agency

Solution:Dedicated Data Science Team
Business Domain: Media, Entertainment
Technology and Tools:

Python, Scala, Apache Spark; Big Data Processing and Analysis, Machine Learning, Predictive Analytics, Recommendation Engine

THE SITUATION

Our Client is one of the UK’s leading media content providers that successfully launched a platform similar to 9gag and Reddit alongside a mobile application. Their business goal was to enhance platform monetization by increasing the end-user’s loyalty and willingness to click and go to the partners’ offers. The only way to achieve this was to provide content which would be interesting, relevant and eye-catching for the end user and could keep them engaged for a while. The Client was seeking ways to extend technological man- and brain-power to boost the platform’s growth and achieve global market expansion.

The Client faced a huge challenge in setting up a software development team in-house due to the scarcity of Big Data expertise in the local UK market.

THE SOLUTION

Having collected and reviewed references from our current partners, The Client chose 8allocate  to perform the above task.

The Client stressed explicitly that we were chosen in particular due to our robust Big Data expertise in various domains.

The high-level goal was to build a recommendation engine which would use Big Data analytics and machine learning to analyze individual end user’s preferences and to help discover new content accordingly.

The project was comprised of two substantial parts: 1) setting up a dedicated team of data scientists for continuous collaboration, and 2) helping the Client achieve digital transformation by building added-value solutions for end users.

We have allocated two in-house data science experts, as well as quickly engaged one pre-interviewed expert from the available HR pool. The proposed solution consisted of researching and developing two different Recommendation Engines, each using a different approach (content-based and user-based).

THE RESULT

The Client managed to increase its audience monetization crucially (for the first six months after the deployment, click-and-go for partner offers grew by 20% compared to the previous period), whilst the overall audience increased by approximately 40%.

As reported by the Client, the breakeven point was reached in five months after the investment into the solution development.