Smart building and energy firms need to know where certified renovation capacity is strong or weak to prioritize markets and allocate Sales resources. Raw registries differ by country and scheme, making comparisons unreliable.
This segment aggregates certifications bottom-up, normalizes schemes across regions, and delivers transparent confidence scores so your GTM activation is based on trusted insights.
Collect certification data across regional registries and public sources
Normalize multiple schemes into one taxonomy
Aggregate certified firm counts into capacity metrics
Enrich with density, coverage, and supply-demand gap indicators
Add confidence scores and freshness dates for full transparency
This segment is activated with a blend of trusted sources and your own inputs
AI reasoning
User Input
Web intelligence
Regional snapshot of certified renovation capacity
Comparable metrics across regions and countries
Transparent confidence and freshness for decision support
Scorecard outputs ready for Sales & Marketing activation
Clear, bottom-up view of where to focus your GTM.
Your questions on this segment, answered
Your questions on this segment, answered
What alternatives exist to get similar data?
Some teams scrape registries or use consultancy reports, but these lack standardized normalization and confidence scoring.
Can I use this data directly in my GTM decks?
Yes. Outputs are snapshot-ready, designed for Sales & Marketing scorecards and presentations.
How do you ensure comparability across different certification schemes?
All schemes are normalized into a single taxonomy, so outputs are consistent across countries and regions.
What problems does regional certified capacity solve for Sales & Marketing?
It helps prioritize regions, focus commercial resources, and build business cases with trusted numbers.
How is this different from the Certified Energy Contractors segment?
That segment tags individual contacts in a CRM. This one aggregates bottom-up regional capacity, showing where certified firms are concentrated.
{ "_meta": { "dictionaryColumns": ["Variable", "Data_Type", "Sample_Value", "Description"] }, "data": [ { "Variable": "country_code", "Description": "Two-letter ISO country code", "Business_Rules": "Valid ISO 3166-1 alpha-2", "Source_System": "Web Intelligence", "Data_Type": "CHAR", "Sample_Value": "FR" }, { "Variable": "region_name", "Description": "Region or subnational unit provided by user", "Business_Rules": "Standardized to user-provided region list", "Source_System": "Web Intelligence", "Data_Type": "VARCHAR", "Sample_Value": "Île-de-France" }, { "Variable": "certification_scheme", "Description": "Energy renovation certification scheme relevant to the region", "Business_Rules": "ENUM; provided by user per country (e.g., RGE, MCS, DGNB, CasaClima, BREEAM)", "Source_System": "Web Intelligence", "Data_Type": "ENUM", "Sample_Value": "RGE" }, { "Variable": "certified_firm_count", "Description": "Number of certified firms in the region", "Business_Rules": "Integer >= 0", "Source_System": "Web+AI Reasoning", "Data_Type": "INTEGER", "Sample_Value": "4280" }, { "Variable": "inhabitants", "Description": "Number of inhabitants in the region", "Business_Rules": "Integer >= 0", "Source_System": "Web Intelligence", "Data_Type": "INTEGER", "Sample_Value": "12278210" }, { "Variable": "housing_stock", "Description": "Estimated housing stock in the region", "Business_Rules": "Integer >= 0", "Source_System": "Web Intelligence", "Data_Type": "INTEGER", "Sample_Value": "5200000" }, { "Variable": "contractor_density_per_100k", "Description": "Certified firms per 100,000 inhabitants", "Business_Rules": "Decimal >= 0; 2–3 decimals", "Source_System": "Web+AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "52.7" }, { "Variable": "coverage_ratio_vs_housing_stock", "Description": "Coverage ratio vs. housing stock (0–1)", "Business_Rules": "Decimal between 0 and 1", "Source_System": "Web+AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "0.62" }, { "Variable": "supply_demand_gap_index", "Description": "Index of supply vs. estimated demand (negative = deficit)", "Business_Rules": "Decimal centered on 0; <0 deficit, >0 surplus", "Source_System": "Web+AI Reasoning", "Data_Type": "DECIMAL", "Sample_Value": "-0.18" }, { "Variable": "last_verified_date", "Description": "Date of last web intelligence verification", "Business_Rules": "YYYY-MM-DD", "Source_System": "Web Intelligence", "Data_Type": "DATE", "Sample_Value": "2025-07-31" }, { "Variable": "confidence_score", "Description": "Overall confidence score of the regional estimates", "Business_Rules": "Range 0–100", "Source_System": "Web+AI Reasoning", "Data_Type": "INTEGER", "Sample_Value": "91" } ] }
