Digital news outlets rely on subscriptions, but pricing, trial paths, and paywalls vary widely by country. Traditional benchmarks are fragmented, often anecdotal, and miss the link between model, subscriber base, and revenue.
This segment standardizes outlet data across geographies: paywall type, trial availability, pricing tiers, conversion friction, and estimated subscriber revenue. Each field is aligned to a common taxonomy, scored for confidence, and ready for strategic or operational use.
Normalize outlets and domains with standardized taxonomy.
Enrich with paywall type, trial, pricing tiers, and subscriber base.
Add friction score and comments to explain conversion barriers.
Model revenues with transparent rules, FX-normalized.
Provide confidence scores per field, source, and method.
This segment is activated with a blend of trusted sources and your own inputs
AI reasoning
Curated APIs
Web intelligence
Comparable paywall and subscription data across markets.
Transparent pricing tiers in local and normalized USD.
Subscriber and revenue estimates with confidence scoring.
Friction scores to compare onboarding experiences.
Your benchmarks, structured and decision-ready.
Your questions on this segment, answered
Your questions on this segment, answered
What about DIY scraping or internal tracking?
DIY scraping gives control but is hard to maintain, inconsistent, and not standardized across markets.
How does this compare to analyst reports?
Analyst reports give useful trends, but are often high-level, slower, and not outlet-level with confidence scoring.
Can I request additional outlets or geographies?
Yes, custom extracts can be generated on demand with the same taxonomy and scoring.
How do you model subscriber revenue?
It’s estimated as median subscription price × active subscribers, adjusted if discounts are known.
Do you include free or ad-funded outlets?
Yes, they are included with “none” as the paywall type.
How is the conversion friction score calculated?
It combines onboarding steps, trial design, and payment options, explained with a short note.
Can I use this for market sizing and benchmarking?
Yes, it provides standardized pricing, subscriber, and revenue data across markets.
What makes this dataset reliable?
Each field carries a confidence score based on source availability, freshness, and methods.
How often is the paywall dataset refreshed?
Updates run monthly or on-demand, depending on outlet changes.
{ "_meta": { "dictionaryColumns": ["Variable", "Data_Type", "Sample_Value", "Description"] }, "data": [ { "Variable": "outlet_name", "Description": "News outlet name", "Business_Rules": "Standardized, no duplicates", "Source_System": "Curated Listings", "Data_Type": "VARCHAR", "Sample_Value": "The New York Times" }, { "Variable": "primary_domain", "Description": "Primary domain for the outlet", "Business_Rules": "Lowercase, no trailing slash", "Source_System": "Web Intelligence", "Data_Type": "VARCHAR", "Sample_Value": "nytimes.com" }, { "Variable": "geo_country", "Description": "Country of operation", "Business_Rules": "ISO 3166 alpha-2 or name", "Source_System": "Web Intelligence", "Data_Type": "VARCHAR", "Sample_Value": "US" }, { "Variable": "paywall_type", "Description": "Paywall classification", "Business_Rules": "ENUM: hard|metered|freemium|none", "Source_System": "Web Intelligence + AI Reasoning", "Data_Type": "ENUM", "Sample_Value": "metered" }, { "Variable": "trial_available", "Description": "Whether free trial is offered", "Business_Rules": "BOOLEAN true/false", "Source_System": "Web Intelligence", "Data_Type": "BOOLEAN", "Sample_Value": "true" }, { "Variable": "subscription_currency", "Description": "Currency of pricing", "Business_Rules": "ISO 4217", "Source_System": "Web Intelligence", "Data_Type": "VARCHAR", "Sample_Value": "USD" }, { "Variable": "subscription_price_min_local", "Description": "Minimum monthly price tier (local currency)", "Business_Rules": "Decimal >=0; exclude temporary promos if unclear", "Source_System": "Web Intelligence", "Data_Type": "DECIMAL", "Sample_Value": "9.99" }, { "Variable": "subscription_price_max_local", "Description": "Maximum monthly price tier (local currency)", "Business_Rules": "Decimal >= min; highest regularly available tier", "Source_System": "Web Intelligence", "Data_Type": "DECIMAL", "Sample_Value": "29.99" }, { "Variable": "subscription_price_median_local", "Description": "Median monthly price across tiers (local currency)", "Business_Rules": "Median of active tiers; exclude enterprise/bundles", "Source_System": "AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "15.99" }, { "Variable": "subscription_price_median_usd", "Description": "Median price normalized to USD", "Business_Rules": "FX-normalized to analysis date", "Source_System": "AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "15.99" }, { "Variable": "conversion_friction_score", "Description": "Composite score of onboarding friction", "Business_Rules": "INTEGER 0–100 (higher = more friction)", "Source_System": "AI Reasoning", "Data_Type": "INTEGER", "Sample_Value": "40" }, { "Variable": "conversion_friction_comment", "Description": "Short note explaining friction drivers", "Business_Rules": "One line; cites main blockers (e.g., forced app, card-first)", "Source_System": "AI Reasoning", "Data_Type": "VARCHAR", "Sample_Value": "Card required before trial; multi-step email verification." }, { "Variable": "active_paid_subscribers", "Description": "Estimated number of active paid subscribers", "Business_Rules": "INTEGER >=0; modeled where undisclosed", "Source_System": "Web Intelligence + AI Reasoning", "Data_Type": "INTEGER", "Sample_Value": "9500000" }, { "Variable": "estimated_subscription_revenue_monthly", "Description": "Estimated monthly subscription revenue (local currency)", "Business_Rules": "active_paid_subscribers × subscription_price_median_local; adjusted for discounts if known", "Source_System": "AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "151800000.0" }, { "Variable": "regulatory_context", "Description": "Summary of regulation affecting paywalls for this geography", "Business_Rules": "Short text; neutral phrasing; optional if none", "Source_System": "AI Reasoning", "Data_Type": "TEXT", "Sample_Value": "No constraints on metered paywalls; crisis info exceptions." } ] }