2025 UK edition

The Lowry, Salford · 18-19 September 2025

Thirty data and analytics leaders from major UK and European media organisations gathered for two days at The Lowry in Salford. This was the first Entertainment Analytics conference to comprehensively address the impact of language models on media businesses. Sessions ran 30 minutes each, split equally between presentation and Q&A under Chatham House Rules.

Talks

• “Estimating Cross-Media Reach Using Copulas” – A public broadcaster showed how copulas (a multivariate technique from finance) de-duplicate audience reach across TV, radio and online for 42 local areas, feeding an automated weekly pipeline that local newsrooms use in near real-time.
• “Optimising YouTube Distribution Through Genre-Specific Channels” – A public service broadcaster found 72% of YouTube views came from algorithmic recommendations but only one or two shows per channel ever broke through. Restructuring into niche genre channels using demographic clustering drove 77% year-on-year growth.
• “Outcome Measurement for Television Advertising” – A commercial broadcaster modelled 640+ brands to show TV ads produce a large short-term spike in website visits plus a tiny long-term effect lasting six months that roughly equals the short-term impact in total value.
• “Structuring Unstructured Recipe Data with Classical ML” – A major newspaper’s data team extracted structured data from 30,000 free-text recipes using fine-tuned named entity recognition, achieving 96–99% accuracy while deliberately avoiding generative AI so they could diagnose every error precisely.
• “Multi-Agent Systems for Audience Insights” – An assessment of where multi-agent AI systems genuinely work in the audience insights pipeline. The standout finding: persona simulation generated approximately $12 million in net profit across 58 projects in four months, where companies simulate their existing segmentation answering hundreds of additional questions.
• “Z-Score Audience Analysis in Sports Media” – A European media group with 230 million monthly devices used Z-scores to analyse reader behaviour across content categories. The simplicity of the metric was its greatest strength: editorial teams immediately understood and acted on it. A parallel “zero Google” analysis showed 80%+ of sports traffic arrives directly.
• “The AI Transformation Chain” – Interviews with 22 insight leaders revealed 19 are failing at AI transformation. Success requires a six-link chain: executive sponsorship, app approvals, foundational skills, standardised tasks, process orchestration, and sustained reinforcement. One bright spot: a consumer goods firm delivering 36% more insights with 20% fewer people.
• “A Completion-Based Model for Fairer Streaming Royalties” – A former chief economist proposed adding a completion threshold to music royalties. Analysis of 15,000 songs showed completion rates are consistent across song lengths (85–95%), so the model does not punish longer songs. It also eliminates click-farm fraud engineered to last just 31 seconds.
• “Customer Segmentation Across Linear and Streaming” – A pay-TV operator simplified 16-segment clustering into four primary consumption cohorts to enable cross-platform comparison for the first time. The key finding: ~25% of the base relies heavily on linear partner channels, making channel-dropping decisions riskier than assumed.
• “AI-Generated Long-Form Sports Content at Scale” – A sports publisher produces 15,000 AI-generated match previews per year across 49 football leagues. The architectural insight: turn structured data into text strings first using traditional ML, then use the language model purely for fluency. AI content showed ~20% lower engagement on major leagues but near-parity on obscure leagues where no human alternative existed.
• “AI-Powered Product Discovery in Luxury Retail” – A luxury retailer demonstrated text-to-image search, image-to-product matching, and an LLM outfit stylist, all built by a single data scientist. The image search required bespoke training to bridge the domain gap between clean e-commerce shots and distorted real-world photography.
• “Visual Exploration of Content Catalogues Using AI Embeddings” – An interactive tool maps 20,000 streaming titles into navigable 2D space using language model embeddings. Filtering by date reveals the emergence of genre clusters over time. The presenter argued chat interfaces alone are insufficient for exploration when you do not yet know the question.
• “Estimating Impact of LLMs on Traffic” – A major international news publication shared data on millions of weekly visits lost to AI platforms, with scrape-to-referral ratios reaching 60,000:1 for some AI services.
• “AI in Academic Publishing” – A global academic publisher explored how language models are reshaping scholarly research workflows and discovery.
• “Data Democratisation in News Media” – A European media company described building a self-service analytics culture across newsrooms.
• “Using LLMs to Analyse Survey Responses at Scale” – A UK broadcaster applied language models to process a massive volume of open-text survey responses, dramatically reducing the time from collection to insight.
• “Candid Truths about AI in Insights” – A practitioner guide to the specific risks of using language models for data analytics, where compounding errors through iterative analysis can be far more costly than a poorly written email.

What we learned

Language models and AI have moved from experimental to existential for media organisations. Technical capabilities exist but human adoption, political barriers, and fundamental business model questions remain largely unresolved.