Analytics democratised across a fragmented media business.
Real-time insights, personalised content, reduced churn.

Data science lived in silos across a fragmented business.

The business ran streaming and linear side by side, but neither had a shared data science strategy. Impact wasn’t scaling — teams worked channel by channel, with no unified plan connecting the two sides of the business.

Content recommendations weren’t moving the needle on churn, and forecasting accuracy varied depending on which channel you looked at. Product and marketing teams were making calls without a consistent, real-time view of what was actually working.

Three constraints were non-negotiable:

• Data science impact wasn’t scaling across a fragmented business
• Content recommendations weren’t cutting churn
• Forecasting accuracy varied across channels

One team, one strategy, board-level buy-in.

We built a cross-functional data science team and put together an AI strategy the executive board signed off on — the first time streaming and linear had a shared plan to work from.

From there, the team shipped AI-driven tools for content recommendation, search optimisation, and forecasting, built to work across both streaming and ad sales rather than one channel at a time.

Alongside those, we developed computer vision models and toolkits — sharpening content metadata, optimising carousel placement, and improving spend analysis.

Analytics for everyone, not just the data team.

What started as a fragmented, channel-by-channel approach to data science became a single strategy that streaming and linear teams could both work from. Recommendation, search, and forecasting tools now run on the same foundation, and computer vision keeps content metadata and spend analysis sharp — turning analytics from a specialist function into something the whole business runs on.