2026-06-07 –, Grand Hall 1
Our video streaming service hosts vast catalogue of content, but producing tailored marketing assets is slow, manual, and costly and therefore limited to the most popular shows with the biggest budgets. This talk describes how we’re using python to automate the creation of thousands of marketing assets to promote our full catalogue on and off-platform. The system combines audience data, programme metadata, machine learning, and automated rendering in Adobe After Effects. For editorial safety, we’ve built AI-assisted QA layers, automated Slack messaging, and plotly dash apps to allow controlled human review and intervention. All using python (mostly!)
Large content catalogues create a classic long-tail problem: while a small number of titles receive heavy promotion, a large proportion of overall consumption comes from many programmes with relatively small individual audiences. Producing bespoke marketing assets for this long tail is usually impractical, as traditional workflows rely on manual design and editing.
This talk presents a real-world Python-based system that automates marketing asset production at scale by combining audience data, asset metadata, machine learning models and automated rendering through Adobe After Effects. The pipeline generates thousands of platform-specific video and image assets, including multi-title creatives populated dynamically using recommendation outputs. We’ve even gone a step further by tapping into catalogue ads in paid social marketing and we’re able to deploy direct to audience-facing without any human intervention using python’s Dropbox API.
A key focus of the talk is how we made automation safe for audience-facing outputs without compromising editorial standards. We will cover the design of automated QA layers that utilise python’s OpenAI API, rule-based validation, and alerting mechanisms using python’s Slack API that trigger human intervention when necessary. Plotly dash apps allow review and controlled interventions such as blacklisting problematic shows.
While the domain is media, the architectural challenges can be applied to other data-driven workflows: orchestration, quality assurance, risk management and human-in-the-loop design. The session is aimed at data scientists, ML engineers and data engineers interested in automation and production pipelines. The talk will aim to be accessible to all and focus on the application and output interspersed with relevant python code snippets.
Rough timings:
0–5 min — The long-tail problem in large content catalogues
5-10 min — Examples of marketing creative
10 - 15 min — Demo of running Adobe After Effects through python
15 - 25 min — System overview: data sources, models, and orchestration
25–30 min — Making automation safe: QA layers, rules, tooling, and alerting
30–35 min — Multi-title assets and recommendation-driven content selection
35–40 min — Key lessons, design principles, and audience Q&A
Matt Crooks is a Principal Data Scientist at the BBC, where he works in the audiences data science team applying statistical and machine learning models to understand and improve marketing effectiveness and audience engagement. His current work focuses on using data and AI to automate the production of personalised creative assets at scale. Previous work has involved building an ML-powered adaptive learning quiz for BBC Bitesize during Covid. He has also had a previous role leading and developing the experimentation tooling and best practices at Typeform. Matt holds a PhD in Mathematics from the University of Manchester and began his career in academic research into weather and climate.