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Global Ad Tech file
Built from our global contact database, email exhaust exchange, and company crawler network, the Global Ad Tech file nearly doubles the reach of the contact database alone. While personally identifiable information has been removed, the file retains key B2B targeting attributes such as job title, department, industry, company size, and revenue.
183,700,918 records across 261 countries, keyed on a hashed personal email address rather than a name — built for audience onboarding and programmatic targeting, not for prospecting a named individual.
Attributes on the file
44 fields. The count is the number of records carrying that attribute.
Download the schema| Attribute | Type | Description | US | Non-US | All records |
|---|---|---|---|---|---|
| email_md5_hash | string | MD5 hash of the lowercased email address | 75,729,420 | 107,971,498 | 183,700,918 |
| email_sha256_hash | string | SHA-256 hash (hex) of the lowercased email address | 75,729,420 | 107,971,498 | 183,700,918 |
| email_sha256_to_base64_hash | string | SHA-256 hash of the lowercased email address, base64 encoded | 75,729,420 | 107,971,498 | 183,700,918 |
| email_type | string | Type of email the hash was generated from (personal or free_mail) | 75,729,420 | 107,971,498 | 183,700,918 |
| web_source_url | string | Web page the person was found on, when sourced from a public web page | 47,859 | 30,751,125 | 30,798,984 |
| is_decision_maker | boolean | True when the person holds a decision-making job level | 19,128,377 | 47,248,147 | 66,376,524 |
| job_title | string | Person's job title | 54,505,864 | 44,705,487 | 99,211,351 |
| job_title_level | string | Person's seniority level derived from the job title (e.g. CXO, VP, Director, Manager) | 28,248,699 | 21,098,659 | 49,347,358 |
| job_function | string | Person's functional area derived from the job title (e.g. IT, Finance, Sales) | 46,440,059 | 38,738,022 | 85,178,081 |
| education_level picklist | string | Person's highest education level (high school, undergraduate, post-graduate or doctorate) | 44,274,905 | 33,304,301 | 77,579,206 |
| interests | array(string) | List of the person's interests | 12,110,975 | 6,843,090 | 18,954,065 |
| skills | array(string) | List of the person's skills | 33,407,662 | 25,386,368 | 58,794,030 |
| education | array(object) | List of the person's education history (school, dates and degrees) | 46,434,046 | 39,512,646 | 85,946,692 |
| education.school | string | School name | — | — | — |
| education.end_date | string | Education end date | — | — | — |
| education.start_date | string | Education start date | — | — | — |
| education.degrees | array(string) | Degrees earned | — | — | — |
| experience | array(object) | List of the person's work history (title, company and dates) | 52,220,652 | 45,891,586 | 98,112,238 |
| experience.title | string | Job title | — | — | — |
| experience.start_date | string | Experience start date | — | — | — |
| experience.end_date | string | Experience end date | — | — | — |
| experience.company | string | Company name | — | — | — |
| experience.company_linkedin_url | string | Company LinkedIn URL | — | — | — |
| experience.company_linkedin_url_decoded | string | Company LinkedIn URL (decoded) | — | — | — |
| experience.company_ru_id | string | RampedUp company ID | — | — | — |
| gender picklist | string | Person's gender (assumed male / female) | 58,368,074 | 50,068,068 | 108,436,142 |
| personal_global_region | string | Global region of the person's country (NA, EMEA, APAC or LATAM) | 75,729,420 | 60,158,156 | 135,887,576 |
| personal_country | string | Person's country | 75,729,420 | 60,187,229 | 135,916,649 |
| personal_region | string | Person's state / province / region | 75,107,122 | 37,837,718 | 112,944,840 |
| personal_city | string | Person's city | 75,571,957 | 48,627,268 | 124,199,225 |
| metro_area | string | Metropolitan area of the person's location (US only) | 8,876,713 | 1 | 8,876,714 |
| company_name | string | Name of the person's employer | 61,372,323 | 60,890,984 | 122,263,307 |
| company_domain | string | Website domain of the person's employer | 42,036,390 | 42,948,186 | 84,984,576 |
| company_sector picklist | string | Sector of the person's employer | 42,004,770 | 37,542,016 | 79,546,786 |
| company_industry | string | Industry of the person's employer | 42,006,054 | 38,774,883 | 80,780,937 |
| company_naics_code_6_2022 | string | 2022 NAICS code of the person's employer | 42,031,598 | 37,793,929 | 79,825,527 |
| company_city | string | City of the person's employer | 41,979,130 | 37,209,881 | 79,189,011 |
| company_region | string | State / province / region of the person's employer | 39,550,905 | 27,227,327 | 66,778,232 |
| company_region_code | string | State / province / region code of the person's employer | 39,230,067 | 26,155,983 | 65,386,050 |
| company_global_region | string | Global region of the employer's country (NA, EMEA, APAC or LATAM) | 39,716,231 | 33,658,104 | 73,374,335 |
| company_country | string | Country of the person's employer | 39,716,927 | 33,675,976 | 73,392,903 |
| company_employee_size_range | string | Employee size range of the person's employer | 43,423,054 | 40,102,647 | 83,525,701 |
| company_employees | integer | Number of employees at the person's employer | 43,422,375 | 40,146,650 | 83,569,025 |
| company_revenue_range | string | Revenue range of the person's employer | 43,372,629 | 39,996,975 | 83,369,604 |
The key is a hashed personal email address. No name, no plain-text email, no phone number. That is what makes the file usable for audience onboarding while keeping personally identifiable information out of it.
Where the records are
Top 20 of 261 countries. “Personal” locates the person; “company” locates their employer.
Download all countries| Country | Personal records | Company records |
|---|---|---|
| United States | 75,729,420 | 43,162,367 |
| India | 10,381,642 | 3,443,470 |
| Brazil | 6,401,217 | 2,509,375 |
| United Kingdom | 4,970,390 | 3,966,771 |
| Canada | 3,854,485 | 2,034,781 |
| France | 3,104,732 | 2,172,406 |
| Italy | 2,592,831 | 1,140,428 |
| Australia | 2,168,709 | 1,183,390 |
| Netherlands | 2,133,516 | 1,445,947 |
| Spain | 2,013,239 | 932,617 |
| Germany | 1,803,976 | 1,297,840 |
| Mexico | 1,534,348 | 366,142 |
| Russian Federation | 916,000 | 86,290 |
| Indonesia | 907,483 | 341,107 |
| China | 900,489 | 307,569 |
| Belgium | 807,379 | 492,782 |
| South Africa | 772,741 | 326,390 |
| Poland | 658,593 | 205,627 |
| Switzerland | 636,968 | 703,017 |
| Argentina | 617,406 | 338,693 |
Controlled vocabularies
9 picklists keep a segment meaning the same thing every time it is built. Each number is the count of distinct values.
Download every picklist value10
Job levels
20
Job functions
4
Education levels
2
Gender
20
Sectors
434
Industries
734
2022 NAICS codes
8
Employee size ranges
8
Revenue ranges
Industries, sectors and NAICS align with the taxonomies used across the rest of the platform — see the 2022 NAICS lookup and the industry counts.
How it is built
Three sources feed the file: the global contact database, the email exhaust exchange, and the company crawler network. Combining them reaches records that the contact database alone does not hold.
Personally identifiable information is stripped before delivery. What remains is the B2B targeting frame — seniority, function, employer, firmographics and location — against a hashed key you can match to your own audience.
Delivered as a flat file on an annual subscription with monthly updates. See pricing, or ask for a count against the segment you actually target.
Start with the number.
Tell us the segment you actually sell to and we will send the exact count, the fill rates for that slice, and a sample — before anyone asks you for a budget.