Technographic data tells you what a company runs. Intent data tells you that something is changing there. The first answers whether an account fits what you sell and the second answers whether now is the time, and a list built on one alone is either too long to work or full of accounts that cannot use you. This guide explains what each records, names a documented example of each kind of source, shows the point where the two meet, and gives a query that ranks accounts on both.
What each one records
Technographic data is a record of state. A detector reads what a website sends to a browser, matches it against a catalogue of known signatures and lists the tools it finds: analytics, advertising, commerce, CRM, content management. BuiltWith is a documented example of the kind. Its website states that it covers more than 127,000 internet technologies, among them analytics, advertising, hosting and content management systems, and its Lists API returns the websites that use a given technology, each with the times the technology was first and last detected.
Intent data is a record of movement. It collects signals that a company is researching or preparing to buy, and scores them by topic. Bombora is a documented example of the behavioural kind. Its Company Surge page states that the data is derived from content consumption across its Data Co-op, collected through a consent-driven tag on member sites, and scored by comparing an account's most recent three weeks of activity on a topic with its 12-week baseline.
A second kind of intent data, which Fokals produces, is built from what a company does in public: a tool adopted, a role opened, a filing made, an announcement. Fokals intent scores are evidence-backed, and every score carries the dated signals behind it, so the reason for a ranking can be opened and checked. The comparison of company signals and third-party intent data sets the two kinds side by side. The table below holds for both.
| Technographic data | Intent data | |
|---|---|---|
| Question it answers | What does this company run? | Is this company moving towards a purchase? |
| Nature | A state, with the dates it was first and last seen | A change, scored over a recent window |
| What is observed | Technologies detected from page content, scripts, network requests, response headers and DNS records | The content people read, or the public acts of the company |
| Unit | A website and a technology | A company and a topic |
| How fast it moves | Slowly, between occasional changes | Quickly: a score fades within weeks |
| Used for | Fit, segmentation, market sizing, installed base | Timing, prioritisation, triggers |
| How to read it | As presence on the website, with the dates it was observed | As evidence of movement on a topic, strongest when recent |
Fit and timing
Fit is a property of the account that holds for months. If you sell an add-on for a commerce platform, an account that does not run the platform cannot buy it, whatever its staff are reading. Technographic data draws that line, which is why it sits beside firmographic data in an ideal customer profile.
Timing is a property of the moment. Among the accounts that fit, a few are choosing a supplier this quarter and most are not, and intent data is built to find those few. It weights recency by design. A Fokals score is built from the dated signals of the trailing 90 days, weighted by recency, because a signal from four months ago says little about this week.
Each alone is half an answer. A technographic list is correct and inert: thousands of accounts on a competitor's tool, with no reason to call any one of them today. An intent ranking is lively and unfiltered: it puts a surging account at the top whether or not that account can use what you sell.
Where the two meet: a change in the stack
A stack is a state until it changes, and the change is an intent signal. A company that adds a marketing automation tool has shown, in public, that it is investing in that area. A company that removes a tool may be consolidating, or about to replace it.
Fokals records this crossing explicitly. Every adoption, removal and platform migration is a dated event in Technology Changes, with the time it was observed. The same change becomes a signal in Company Signals, with its kind, its topics and its weight, and counts towards the weekly score for each of those topics in Intent Scores.
| Change on the website | Event in Technology Changes | Read as a signal of |
|---|---|---|
| An advertising platform adopted | Adoption | New spend on a channel |
| A tool adopted | Adoption | Investment in the tool's category |
| A tool removed | Removal | Consolidation, or a replacement under way |
| A commerce or content platform replaced by another | Platform migration | A completed re-platforming, and the projects that follow one |
Each kind of signal carries its own weight, and the methodology documents them. Two properties of the record keep the crossing sound. A first observation sets a baseline and is never counted as a change, so a tool already in place is never mistaken for an adoption. A removal is confirmed before it is written, which keeps tags that come and go with consent banners and tests out of the record.
Job postings give a second crossing. A posting that names a tool is technographic evidence of the systems behind the website, such as a data warehouse, and it is also a signal that counts towards the topic's score. Job Postings lists the tools each posting names, on the same company ID as the website detections.
Combining them: a worked query
Suppose you sell a product that works with Salesforce and you want the accounts that run it and show intent on CRM. Fit comes from Technology Stack, timing from Intent Scores, and the join is the company ID.
with fit as (
select distinct company_id
from company_technologies
where technology = 'salesforce'
and missing_since is null
),
timing as (
select company_id, score, surge, signals, evidence
from company_intent_weekly
where topic = 'crm'
and week_start = date '2026-09-28'
)
select f.company_id, t.score, t.surge, t.signals, t.evidence
from fit f
left join timing t on t.company_id = f.company_id
order by t.surge desc nulls last, t.score desc nulls last;Take the technology and topic keys from the data before you write the filters: the technology catalogue spans 68 categories, and the intent taxonomy groups its buying topics under nine headings. The left join keeps every account that fits. An account with no row was quiet on the topic that week, so it sorts to the bottom of the list and stays on it.
Act on the result by its two dimensions.
| A surge or a high score | A low score or none | |
|---|---|---|
| Fits | Work the account now, and open with the evidence | Keep it in the plan and watch for a change |
| Does not fit | Check whether the signal is a move towards fitting, then decide | Leave |
Illustrative: Acme Robotics runs Salesforce and scores 61 on the CRM topic, flagged as a surge. Its evidence lists a new account under a tool already on its website, a job posting whose title names the tool and a role that selects tools. The Technology Stack record says the account fits. The Intent Scores record says its CRM estate is being worked on now, and gives the rep three dated facts to open with.
One reading rule belongs to the fit side. The first-seen date in Technology Stack is the first observation of a tool. When the question is when a tool was adopted, use Technology Changes, where every event is a change observed against a baseline.
How to read each one
- A detection shows presence on the website. Technology Stack describes the customer-facing stack, detected from page content, scripts, network requests, response headers and DNS records. Read presence as a tool in place, and read the tools named in Job Postings for the systems behind the website.
- A score names its evidence. Every Fokals intent score carries the dated signals behind it, so a rep or a model reviewer can open the source and weigh the reason. Read a score together with its evidence, and read a surge as a change against the company's own twelve-week average.
How Fokals delivers both
Both datasets sit on one company index, so the join above needs no matching step. The marketing stack dataset holds Technology Stack, Technology Changes and Website Profile, refreshed daily to weekly. The intent dataset holds Intent Scores, written weekly, and Company Signals, refreshed daily.
They are delivered direct, by REST API and as bulk files in JSON, JSON Lines or CSV, which you load where your scoring runs. The guide to finding accounts by the technology they run takes the fit side further, and competitor displacement from technology changes works through the events.
Frequently asked questions
What is the difference between technographic data and intent data?
Technographic data records the technologies a company runs, detected on its website and in its DNS records. Intent data scores signals that a company is researching or preparing to buy something. The first describes a state and is used to judge fit. The second describes a change and is used to judge timing. A company that has just added a tool appears in both: as an event in its stack, and as a signal that raises an intent score.
Is technographic data a type of intent data?
No, though the two overlap. A list of the tools a company runs says nothing about what it will buy next. A change to that list does: a tool added, removed or replaced is a public act that an intent model can count as a signal, as the Fokals model does. Treat the current stack as fit and treat changes to it as intent.
Which is better for prioritising accounts, technographic data or intent data?
Neither alone. Technographic data defines which accounts can use what you sell and gives a list that barely moves from week to week. Intent data orders that list by what is happening now, but it ranks unsuitable accounts as readily as suitable ones. Filter on fit first, then sort by intent, and keep the accounts that fit and show no intent at the bottom of the list.
How do you combine technographic and intent data?
Join them on a company identifier. Select the accounts that run the technology you depend on, or lack the one you replace, then attach each account's intent score for the topic you sell into and sort by surge and score. In Fokals data the join is the company ID, the fit comes from Technology Stack and the timing from Intent Scores.
Can technographic data show that a company is about to switch tools?
It can show the switch under way. A second tool of the same category appearing beside the first, a removal, or a job posting that names a competing product are public signs of a change in progress. You need a dated feed of events to see them, because a snapshot shows only the result. Fokals delivers stack changes as dated events in Technology Changes and counts them, together with the tools named in job postings, as signals behind the Intent Scores, so every sign arrives with its date and its source.
The queries and code on this page are examples to adapt. Test them in your own environment before you rely on them.
What this page says about the products it names was checked against their public documentation on 4 October 2026. Product and company names are trademarks of their owners. Fokals is not affiliated with them or endorsed by them.