How Comcast Advertising Accelerates Campaign Forecasting with Databricks Apps

Comcast Advertising operates at a scale that makes data science both indispensable and a bottleneck. Reaching 125 million U.S. households across all 210 designated market areas generates massive volumes of viewership, engagement, and campaign performance data. The organization’s data scientists built sophisticated predictive models to optimize ad strategies — but those models lived in technical environments that business users could not access or interpret.

The gap between model output and business decision was a costly one. Traditional BI tools lacked the flexibility to let end users interact with model outputs dynamically. Building custom applications for business teams required separate hosting infrastructure, unfamiliar front-end frameworks, and significant engineering overhead that had nothing to do with improving campaign performance. Data scientists spent time on application plumbing rather than model improvement.

When Databricks Apps became available, the Comcast Advertising data science team recognized an opportunity to close the loop. Already using Databricks for Lakeflow Spark Declarative Pipelines, ML experimentation, Unity Catalog governance, and MLflow model tracking, the team could now build Python-based interactive applications directly within their existing data platform. No separate hosting, no context switching, no new tech stacks to maintain.

The first application was a forecasting dashboard that lets business users adjust input levers — budget allocations, audience targets, campaign parameters — and see immediate revenue predictions. Sales, strategy, and customer experience teams can run unlimited what-if scenarios without queuing requests to data scientists. The feedback loop tightened dramatically, with model outputs reaching decision-makers faster and with more contextual interaction than any static report could provide.

Development time for new data products dropped 10–30%, a consequence of keeping everything inside a unified platform. The forecasting dashboard is currently in user acceptance testing, with plans to expand to hundreds of users. The team is also exploring generative AI capabilities within Databricks, including AI assistant chatbots and the Lakebase architecture to further unify operational and analytical data.