Adtech

Media Recommendations Engine for a content media agency
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The Situation

One of the UK’s leading Software Development Agencies (SDA) contacted us for cooperation on behalf of one of its Clients (NDA) operating in the Content Media Market. The Client 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 him engaged for a prolonged time period. The Client was seeking ways to extend technological man- and brain-power.

Both SDA and the Client treated this project as crucial and mutually-beneficial. From SDA’s point of view, it was challenging because of the scarcity of Big Data experts in the local UK market and the level of complexity in finding a solution without attracting external experts.

From the Client’s perspective, the issue consisted in the emergence of huge amounts of the messy user data. They were looking to develop a strategy-related decision facilitated by well-grounded analytical results in order to increase the platform growth and market expansion.

The Solution

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

SDA 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 experts 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 in researching and developing two different Recommendation Engines, each using a different approach (content-based and user-based).

The Result

As a result, SDA met all the deadlines for the Client, having brought the highest level of delivery and technical expertise.

The Client, in its turn, 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 10%.

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

Team Solution

Dedicated Data Science Team

Team Solution

Big Data Processing and Analysis, Machine Learning, Predictive Analytics, Recommendation Engine

Team Solution

Media, Entertainment

Team Solution

Python, Scala, Apache Spark

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