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Media verified Verified Outcome TRL 9

Dynamic Paywall & Subscription Propensity

domain Client: Top Tier Newspaper handshake Provider: Internal (Dynamic Meter) schedule Deploy: 2018-Present
97 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: Very High

Executive Summary

ANALYST: COI RESEARCH

Deployment of a machine learning model that adjusts the number of free articles offered to a non-subscriber based on their likelihood to subscribe, optimizing conversion rates.

rate_review Analyst Verdict

"The engine behind the subscription success. A static paywall (10 articles/month) is inefficient. By making the meter dynamic (some get 0, some get 20), they maximize revenue without killing reach. It treats the paywall as a personalized product feature."

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

warning The Challenge

A rigid paywall acts as a blunt instrument. It blocks casual readers who generate ad revenue but will never subscribe, while failing to pressure high-intent readers quickly enough. The NYT needed to balance ad revenue (reach) with subscription growth (conversion).

psychology The Solution

Built the 'Dynamic Meter'. The model analyzes user behavior (reading history, device, time of day, newsletter clicks) to calculate a 'propensity to subscribe' score. High-propensity users hit the paywall sooner (or immediately). Low-propensity users get more free articles to monetize via ads and build habit.

settings_suggest Technical & Deployment Specs

Integrations
Paywall / CMS
Deployment Model
Proprietary
Data Classification
User Behavior
Estimated TCO / ROI
R&D
POC Summary ( to )

"N/A"

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Model Bias Low Continuous A/B testing.
User Frustration Medium Registration walls as soft step.

trending_up Impact Trajectory

Audited value realization curve

Significant increase in conversion rate Verified Outcome
Primary KPIOptimization of Ad vs Sub revenue mix
Audit CycleGranular segmentation of audience

policy Compliance & Gov

  • Standards: CCPA
  • 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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verified_user
Technical Audit
PDF • Audited
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The "Blind Verification" Protocol

How we verified these outcomes for Top Tier Newspaper without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-729
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-729

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