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Media verified Verified Outcome TRL 9

Personalized Artwork Optimization

domain Client: Global Streaming Service handshake Provider: Internal (Contextual Bandits) schedule Deploy: 2017-Present
96 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: Very High

Executive Summary

ANALYST: COI RESEARCH

Use of machine learning (Contextual Bandits) to dynamically select which thumbnail image to show a user for a specific title, based on their viewing history (e.g., showing a romance scene vs. an explosion).

rate_review Analyst Verdict

"The gold standard in personalization. It recognizes that the 'packaging' matters as much as the product. By algorithmically A/B testing artwork at the individual user level, they maximize the play-rate of their catalog."

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Full Audit Report Available Includes Risk Register, Technical Specs & Compliance Data.

warning The Challenge

With thousands of titles, users spend seconds scanning. A single static image can't appeal to everyone—a sci-fi fan might click for a robot image, while a drama fan might click for the actor's face. Using one image for everyone left potential views on the table.

psychology The Solution

Developed an algorithm that selects the artwork from a pool of images for each title. If a user watches a lot of Uma Thurman movies, 'Pulp Fiction' will show her face. If they watch John Travolta, it shows him. The system learns in real-time using 'Contextual Bandits' to balance exploring new images vs. exploiting the best-performing ones.

settings_suggest Technical & Deployment Specs

Integrations
UI Rendering
Deployment Model
Proprietary Algorithm
Data Classification
User Behavior
Estimated TCO / ROI
High (R&D)
POC Summary ( to )

"N/A"

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Misleading Artwork Low Editorial guidelines for image pool.
Compute Cost Medium Offline batch inference.

trending_up Impact Trajectory

Audited value realization curve

Significant lift in aggregate play rate Verified Outcome
Primary KPIReduction in browsing time before play
Audit CycleProcess handles 20M+ requests per second

policy Compliance & Gov

  • Standards: GDPR (User profiling)
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: User Behavior

folder_shared Verified Assets

description
Verified Case Study
PDF • Version 1
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Technical Audit
PDF • Audited
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Security Architecture

The "Blind Verification" Protocol

How we verified these outcomes for Global Streaming Service without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-723
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-723

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