Advertisers and agencies negotiate CPMs blind. Industry averages (Statista, WARC) only cover broad regions or social giants, leaving newsletters, podcasts, and independent outlets invisible. Adtech dashboards track only their own networks.
The result: advertisers overpay, agencies lack leverage, and niche publishers are ignored. This segment delivers outlet-level CPM benchmarks across display, video, newsletters, and podcasts — traceable, confidence-scored, and Excel-ready in 72h.
Collects and validates CPM ranges across multiple ad formats.
Enriches with outlet attributes, geography, and audience signals.
Packages benchmarks in structured, explainable tables with confidence scores.
This segment is activated with a blend of trusted sources and your own inputs
AI reasoning
Web intelligence
User Input
Cross-format CPM/CPV tables, including niche outlets ignored by dashboards.
Traceable and confidence-scored benchmarks to power negotiations.
Excel-ready dataset for agencies, advertisers, and adtech platforms.
Delivered in 72h, globally scoped, niche-inclusive.
Your questions on this segment, answered
Your questions on this segment, answered
How fast is it delivered and in what format?
The benchmark is delivered within 72 hours, as a filterable Excel or CSV file including for each outlet and format the CPM ranges, inventory volumes, viewability, and explanatory comments. These files are designed to integrate directly into existing media planning workflows.
Who is this segment for?
This segment is designed for media agencies seeking stronger negotiation leverage, advertisers looking to control spend and avoid overpaying, and niche publishers who need credible comparative data to showcase their inventory against the large platforms.
How reliable is the data?
The so-called “hard signals” such as display, video, and audio CPM ranges are estimated at 100% confidence based on consolidated data. The “enriched signals” such as newsletters or podcasts rely on modeled estimates and carry a 70–90% confidence level. Each observation includes a traceable range (min, max, median), contextual attributes like editorial category or viewability, and an AI-generated comment explaining the pricing drivers.
How is this different from Statista, WARC or adtech dashboards?
Statista and WARC provide global averages, useful for presentations but not actionable for outlet-level negotiations. Adtech dashboards from Google, Meta, or DSPs only show their own inventory and never cover niche channels. Starzdata delivers outlet-level, multi-format, multi-country benchmarks within 72 hours, including newsletters, podcasts, and independent publishers, with source transparency and confidence scoring.
What data do I need to provide?
Les seuls éléments nécessaires sont vos zones géographiques ou segments d’intérêt. Starzdata se charge d’identifier les outlets, de collecter les signaux d’audience et d’estimer les plages de CPM min, max et médian pour chaque format. Vous n’avez donc pas besoin de fournir de données propriétaires, même si l’intégration de vos inputs peut affiner le benchmark.
Which advertising formats are covered?
The benchmark covers all major formats used in media planning. This includes display, video, newsletters, and podcasts. Each format is enriched with contextual attributes such as country, editorial category, and estimated monthly inventory volume. The result is reliable cross-format benchmarks that can be directly applied to negotiations.
Why do I need a CPM benchmark?
Advertisers and agencies negotiate CPMs with little visibility. Published averages from Statista or WARC focus only on the large social platforms, while adtech dashboards report solely on their own inventory. This leaves newsletters, podcasts, and regional or independent publishers invisible. With Starzdata, you get outlet-level CPM benchmarks across formats, including niche channels, with traceable ranges and a confidence score for each estimate.
{ "_meta": { "dictionaryColumns": ["Variable", "Data_Type", "Sample_Value", "Description"] }, "data": [ { "Variable": "outlet_name", "Description": "Publisher or media outlet name", "Business_Rules": "Standardized; unique per domain", "Source_System": "Outlet Listing", "Data_Type": "VARCHAR", "Sample_Value": "El País" }, { "Variable": "outlet_domain", "Description": "Primary domain name of the outlet", "Business_Rules": "Lowercase, no trailing slash", "Source_System": "Web Intelligence", "Data_Type": "VARCHAR", "Sample_Value": "elpais.com" }, { "Variable": "geo_country", "Description": "Country where inventory is measured", "Business_Rules": "ISO 3166 alpha-2 or country name", "Source_System": "Web Intelligence", "Data_Type": "VARCHAR", "Sample_Value": "Spain" }, { "Variable": "ad_format", "Description": "Advertising format", "Business_Rules": "ENUM: display|video|newsletter|podcast", "Source_System": "Web Intelligence + AI Reasoning", "Data_Type": "ENUM", "Sample_Value": "podcast" }, { "Variable": "cpm_min_usd", "Description": "Estimated minimum CPM in USD", "Business_Rules": "DECIMAL >= 0", "Source_System": "AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "4.5" }, { "Variable": "cpm_max_usd", "Description": "Estimated maximum CPM in USD", "Business_Rules": "DECIMAL >= cpm_min_usd", "Source_System": "AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "18.0" }, { "Variable": "cpm_median_usd", "Description": "Estimated median CPM in USD", "Business_Rules": "FX-normalized to reference date", "Source_System": "AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "9.2" }, { "Variable": "inventory_volume_monthly", "Description": "Estimated monthly available impressions", "Business_Rules": "INTEGER >= 0", "Source_System": "Web Intelligence + AI Reasoning", "Data_Type": "INTEGER", "Sample_Value": "12500000" }, { "Variable": "fill_rate_percent", "Description": "Estimated ad inventory fill rate (%)", "Business_Rules": "DECIMAL 0–100", "Source_System": "AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "72.5" }, { "Variable": "viewability_index", "Description": "Proxy for ad viewability (0–100)", "Business_Rules": "Derived from speed, engagement, format", "Source_System": "AI Reasoning", "Data_Type": "INTEGER", "Sample_Value": "68" }, { "Variable": "audience_income_multiplier", "Description": "Income-level adjustment factor applied to CPM", "Business_Rules": "DECIMAL multiplier, baseline=1.0", "Source_System": "AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "1.2" }, { "Variable": "editorial_category", "Description": "Main editorial content category", "Business_Rules": "ENUM: finance|B2B|lifestyle|sports|general|other", "Source_System": "Web Intelligence", "Data_Type": "ENUM", "Sample_Value": "finance" }, { "Variable": "ai_reasoning_comment", "Description": "Explanation of CPM estimation drivers", "Business_Rules": "VARCHAR <= 200 chars", "Source_System": "AI Reasoning", "Data_Type": "VARCHAR", "Sample_Value": "Finance outlet with affluent audience → CPM uplift." } ] }
