Technographic data is the information that shows which software and hardware solutions an organization uses, how they use them, and the maturity of their technology adoption. Technographics provide an overview of a company's technology stack, which can encompass a wide range of technologies including: software applications, hardware infrastructure, cloud providers, security solutions, website technologies, e-commerce platforms, database technologies, and programming languages.
To ensure accuracy and reliability, each technographic data point is underpinned by a set of key elements: the source it originates from, how it was collected, how frequently it is tracked, and a confidence score that reflects the strength of the detection.
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How is technographic data collected?
Technographic data is collected through two broad categories of sources: passive and active.
Passive sources work by observing and interpreting publicly available information such as job postings, people work summaries, and human curated research — to infer what technologies a company uses.
Active sources, on the other hand, involve directly scanning a company's digital presence to detect software tools and platforms powering its operations. This dual-source approach makes the technographic data reliable — passive sources provide contextual intelligence, while active sources deliver real-time, verifiable signals from a company's digital footprint.
Passive sources
Job Descriptions: When a company posts a job opening, they often list the tools and technologies a candidate is expected to know. For example, a listing that requires experience with Salesforce and Marketo signals that the company actively uses those platforms. Analyzing these job postings at scale reveals a company's current technology landscape.
People Work Summaries: Professional profiles and work histories shared by employees on social platforms often mention the tools and technologies they work with day-to-day. These summaries offer ground-level insight into what technology is actually in-use within a company.
Human Curated Data: Researchers manually scan company websites to identify signals such as customer testimonials, newly added customer logos, and published case studies. These signals help verify and enrich technology data, adding an important layer of human verified data.
Active sources
HTML Source: Analyzing the publicly accessible HTML source code of a webpage reveals technology signatures embedded in it, such as the platforms or frameworks the website is built on. This provides the technology stack behind a company’s web presence.
JavaScript Code: Many software tools leave a traceable footprint in the javascript running on a website. For example, a marketing automation tool or a live chat application will often embed its own script on the webpages where it is active. Detecting these footprints reveals the third-party tools a company has integrated into its operations.
Subdomain Hits: Many SaaS platforms host their customers on dedicated subdomains in the format <tenant-name>.platform.com. By resolving a company’s domain against known subdomain patterns of these multi-tenant platforms and observing the HTTP server response, it is possible to infer whether the company is an active tenant of that platform. This reveals the third-party technologies a company is actively using.
NS/MX Lookups: Name Server (NS) records act as a directory for a company’s online infrastructure, and analyzing these records allows us to infer the DNS technologies powering it. Mail Exchange (MX) records go a step further, beyond revealing which email platform a company uses, such as Google Workspace or Microsoft 365, they can also expose additional layers of a company’s email infrastructure including email security servers and other associated technologies.
How frequently is technographic data updated?
Technographic data is reviewed and updated every 2–4 weeks. This ensures that any changes in a company's technology stack, such as the adoption of a new platform or the discontinuation of an existing tool, are captured in a timely manner.
How is confidence score calculated?
The confidence score is a numerical value between 0 and 100 that indicates how reliable a technographic data point is. A score closer to 100 means the detection is highly reliable, while a score closer to 0 suggests the signal is weak or outdated. The confidence score is calculated by evaluating three key factors for each detection.
Recency: How recently a technology was detected plays a significant role in the score. A detection made last week carries more weight than one made several weeks ago. Each data source follows its own decay logic, meaning the score gradually decreases over time at a rate that reflects how frequently that source is updated.
Detection Volume: For passive sources, such as job descriptions and people work summaries, the number of times a technology is mentioned across multiple records strengthens the confidence score. A technology referenced across many job postings or employee profiles is more likely to be actively in use than one mentioned only once.
Source Reliability: Not all sources carry equal weight. Each data source is assigned a reliability weight based on the accuracy and consistency of signals it produces. Active sources are weighted more heavily than passive sources, reflecting the directness and verifiability of their signals.
These three factors are combined and normalised into a final confidence score, giving a clear and consistent measure of how strongly a technology can be attributed to a company.