TL;DR
Paramount / Global Content Distribution Research team. An AI agent and audience-profiling pipeline for content distribution across 25 territories.
65% time reduction on pre-release research, 150+ sales decks produced, 25 territories analyzed weekly, 20+ global offices supported.
THE PROBLEM
Global Content Distribution Research (GCDR) at Paramount sits at the intersection of data and dealmaking. The team supports international and domestic content licensing across 20+ global sales offices, translating viewership data from linear TV and streaming platforms into insights that inform licensing negotiations, marketing campaigns, and Home Entertainment strategy.
GCDR operates on compressed timelines. When a show gains momentum, sales teams need performance data immediately to capitalize on international buyer interest. But research workflows at GCDR were manual, repetitive, and slow: pre-release performance sheets required 2-3 hours of manual research per title. The team needed infrastructure to surface insights faster without sacrificing quality.
Goals
- Automate repetitive research workflows to reduce time spent on manual data retrieval and formatting.
- Analyze weekly viewership trends across 150+ titles in 25 territories to support licensing negotiations.
- Build data-backed audience profiles for Home Entertainment to inform targeting and marketing strategy.
- Translate complex performance metrics into compelling visual narratives for sales teams in 20+ global offices.
THE SOLUTION
AI Automation of Pre-Release Performance Sheets
Before a title goes to market, the research team creates “pre-release performance sheets” (PRPS): standardized documents with cast details, IP background, comparable titles, and historical performance data. Creating one sheet manually took 2-3 hours of research, formatting, and quality control.
I built a Microsoft CoPilot Agent to automate the most time-consuming parts:
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01
Scoping the Automation
Rather than trying to automate everything, I identified what AI could reliably handle:
- Retrieving actor filmographies from internal databases + IMDb
- Pulling IP history (book series, franchise timelines)
- Formatting data according to PRPC template
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02
Agent Design
- Configured CoPilot Agent to query internal Paramount databases first (higher accuracy)
- Built fallback logic: if internal data missing → search credible external sources (IMDb, Wikipedia)
- Created formatting rules matching existing PRPC standards (avoiding team retraining)
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03
Iteration & Refinement
- Tested on 10 recent titles, compared AI output to manually created sheets
- Identified hallucination patterns (AI inventing award nominations that didn’t exist)
- Added verification layer: Agent flags any claim it’s <80% confident about for human review
Tools: Microsoft CoPilot Agent, internal Paramount API, prompting strategies.
Key Skill: Understanding AI limitations: knowing when to automate vs. when human judgment is essential.
Iteration cycles: 15+ prompt refinements to reduce hallucinations and improve formatting consistency.
Home Entertainment Audience Profiling
Paramount’s Home Entertainment division needed to understand who buys digital content (movies/shows for download or rental). Marketing assumptions existed, but no data-backed audience profile to guide targeting strategy or promotional spend.
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01
Research Design
Using GWI (Global Web Index), I designed queries to compare digital transactors against general population across:
- Demographics (age, income, education, location)
- Media consumption habits (streaming platform usage, genre preferences)
- Psychographics (early adopter tendencies, media spending patterns)
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02
Data Analysis
For each variable, I ran:
- Correlation analysis: Which traits most strongly predicted digital transaction behavior?
- Statistical significance testing: Which patterns were real vs. noise?
- Hypothesis testing: Validated or challenged existing marketing assumptions
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03
Synthesis & Presentation
- Built database organizing all findings by demographic category
- Created presentation deck translating statistical insights into actionable marketing recommendations
- Presented findings to Home Entertainment team (my first stakeholder presentation at Paramount)
Tools: GWI platform, Excel (correlation matrices, significance testing), PowerPoint (data visualization).
Statistical Methods: Correlation analysis, chi-square tests for categorical variables, confidence interval calculations.
THE RESULT
- 65% reduction in time spent on PRPC creation (from 2-3 hours → 45 minutes per sheet)
- Team successfully integrated tool into weekly workflow
- Agent now handles 80% of data retrieval; humans focus on analysis and strategic comparisons
- Successfully presented data-backed audience profile to HE leadership team
- Findings validated some existing assumptions (younger skew) but challenged others (income was less predictive than media consumption patterns)
- Opened funding for deeper audience research: team allocated budget for follow-up studies on specific HE demographics