Case Study · YouTube Advertisers

Proving the effectiveness of YouTube campaigns

We worked on a machine learning approach to estimate the true lift created by YouTube campaigns. By separating signal from background noise, teams could better understand whether video activity was driving measurable website impact.

The Challenge

Standard reporting can show correlation between media spend and traffic, but not always true causality. Teams needed stronger evidence that YouTube campaigns were generating incremental site visits.

Our Approach

We built a machine learning model to estimate baseline traffic and isolate campaign-driven lift. The workflow compared expected versus observed outcomes and accounted for temporal and channel-level variation.

The Outcome

Stakeholders gained a clearer view of YouTube effectiveness and could make better budget decisions with more confidence. The model provided a repeatable framework for evaluating incremental impact over time.