AdPreference Domain Name Data

Datarade seller/AdPreference Domain Name Data is a Technographics & Tech Stacks data product listed on Datarade and indexed by The Alternative Data Encyclopedia.

Datarade seller publishes AdPreference Domain Name Data as a Datarade offering in the Technographics & Tech Stacks signal family.

The data

A domain-name dataset of made-for-advertising (MFA) sites that lets advertisers detect and exclude low-quality websites built primarily to earn ad revenue, so campaigns run on premium, brand-safe inventory reaching real audiences.

It is one of 71 listings in the Technographics & Tech Stacks family; between them the practical differences come down to coverage, history depth and how the raw signal is cleaned and delivered.

Structure, access and licensing

Pricing: Commercial product (via Datarade).

Access is through a dataset that is accessed via Datarade, the marketplace that connects buyers with the seller. The listing does not state an explicit refresh schedule, which is worth confirming before backtesting.

The signal

Detection of technology stacks per company: which technologies each business uses, with evidence and date of observation, plus adoption-change events.

Stack changes flag investment, migrations and vendor churn before they surface in revenue or surveys. Rows typically map a company to a technology, a detection method (DNS, certificate, pixel, job ad) and a first/last-seen date. The derived layer adds adoption and abandonment events.

Vendors combine several collection techniques: crawling public web assets and partner badges, scanning DNS records and TLS certificates, and reading hiring signals from job postings. Each detection carries a confidence score and a timestamp, and change-detection pipelines compare successive snapshots to emit adoption and abandonment events rather than raw states.

For a first-party benchmark in this category, see TheirStack and its technographics dataset, indexed on this hub with delivery, history and refresh details.

Caveats and compliance

Detection coverage is uneven across stacks; web-only signals miss back-office software. History depth matters because change events, not stock levels, drive most of the alpha.

Signals are derived from public web artifacts and job ads, so personal-data exposure is low. Buyers should still check scraping terms of service and ensure job-posting text is stripped of applicant names before use.

Who uses this signal

Software equity analysts, GTM teams and VC read stack changes as churn, migration and investment signals.

Complementary signals

This kind of signal pairs naturally with adjacent categories of the encyclopedia:

Further reading