--- title: "Intent Data Playbook: Hiring, Tech and Timing Signals | ONEGTMLAB" description: "How we use intent data in B2B: job postings, technographics, funding and news signals, fit scored apart from timing, stacked signals and adversarial review." canonical: https://www.onegtmlab.dev/growth-playbooks/intent-data --- # Intent Data Playbook: Hiring, Tech and Timing Signals | ONEGTMLAB [![](/_astro/69a56255e2154ea94c75641b_onegtm-nav-logo.CkLAn8xX.svg)](/) [ Book a call ](/book) Growth Playbook # Intent Data Playbook A good account list tells you who could buy. Intent data is the part that tells you who to contact this week. We keep the two apart, and we only act when signals stack. ![](/_astro/69a8569f8db69df76ba584e1_jainendra-ojha.CeNJqXRl_Z11dhei.webp) By [Jainendra Ojha](https://www.linkedin.com/in/jaizendra/), Founder, ONEGTMLABLast updated 25 Oct 2026 [ Book a Strategy Call ](/book)[ All playbooks ](/growth-playbooks) What it is ## What it is and when to use it Intent data is any evidence that an account is moving toward a purchase: research behavior, hiring for a function, adopting or dropping a technology, raising money, a leadership change or a public incident. It can be first party (your own site and product), second party (engagement on a platform you run campaigns on) or third party (data collected elsewhere). This playbook covers the intent we have run for clients: job postings, technology data, funding and news, and engagement. It does not cover bought third-party topic intent, which we have not delivered. Two kinds of tools are involved: data sources that return raw rows, and signal engines that watch accounts continuously. Most teams need both. ### Use this playbook when - You have a market list but no timing, and need to know which accounts to work now. - The client's product only matters to companies running a certain technology, which no firmographic field captures. - A team is about to buy an intent tool and wants to know which signals actually separate buyers. Tools ## Tools we use Only tools we have run this playbook with, and what each one does in it. - Blitz: Job-posting search, the main source of hiring and technology evidence - TheirStack: Technographics and jobs; counts are free before revealing records - Apify: Daily LinkedIn job scrapes and Google searches for news - OpenFunnel: Signal agents for hiring and technology on a target account list - Parallel and Crustdata: Funding rounds and product launches - Claude Code: Reading each posting and article by meaning, and the adversarial review - Supabase: One table of accounts and signals, plus replies fed back for re-weighting How it works ## The steps The order we work in. Each step feeds the next, and every result is written to the database as it happens. 1. 01 ### Score fit apart from timing Fit (firmographics, technology, buying power) decides the tier. Intent is a separate timing layer on top. Blending them into one number hides both the quiet perfect buyer and the loud account that will never buy. 2. 02 ### Weight what the product depends on For a product that only matters on certain technology, the technology score carries most of the weight. In one model it was 105 of 150 points. 3. 03 ### Load the blocklist first Customers, open deals, partners and competitors are uploaded before any signal runs, so they are never surfaced as new targets. 4. 04 ### Act on stacked signals Every signal lands on one shared account view. Three or more signals earn a manual touch, two earn a sequence and one is held. 5. 05 ### Read job postings by meaning We sweep postings for the relevant tools and roles, then read each posting for what it says about the hiring company, and remove staffing firms in an adversarial pass. 6. 06 ### Pilot, review, re-weight A scoring model runs on 100 companies first. The output is reviewed by hand, weights are adjusted, and only then does it run on everything. 7. 07 ### Feed outcomes back Replies and meetings go back into the database so the weights can be checked against what actually happened. Good output ## What good output looks like What this has produced: - For a security startup, about 3,000 job postings over 90 days became 913 companies, 150 first-pass qualified and 102 on the send list after adversarial review, with no data cost. - In that review, 21% were dropped from the evidence-rich tier and 47% from the thin tail. Review hardest where the evidence is thinnest. - For a data provider, a scoring pilot on 100 companies flagged 15 for outreach, using $40 of research credit. Common mistakes ## Common mistakes, and the fix Each of these happened on a real run. The fix is now part of how we work. 1. 35% of a job-signal universe were staffing firms and consultancies. Cause They post their clients' requirements as their own jobs. Fix Read the posting text for the tells, such as "our client", and remove them in an adversarial pass. 2. Breach keyword alerts were mostly false positives. Cause Matches on the wrong company or on generic articles. Fix Have the model classify each article per company and require a company-specific source. 3. Technology searches returned zero. Cause Guessed technology identifiers. Fix Test the identifiers first, using the free counts. 4. Some searches returned no companies even though jobs existed. Cause Throttling returned an empty result instead of an error. Fix Space calls about a second apart, retry with backoff and assert that jobs found means companies found. 5. A sailing club and an elevator company appeared in a technology list. Cause A product name that is also an ordinary word. Fix Treat single-word product names as ambiguous and confirm them in the posting text. 6. Website technology crawls missed infrastructure tools. Cause Infrastructure does not show up on a website. Fix Use job postings for infrastructure technology. 7. Late-stage funding outranked real buyers. Cause Funding carried a large multiplier. Fix Remove the multiplier and weight recency instead. 8. A high-confidence verdict had no evidence behind it. Cause A single model pass was trusted as is. Fix Run an adversarial pass and a live web check before the list is final. FAQs ## Questions people ask about intent data What is intent data? Intent data is evidence that a company or person is moving toward a purchase: research behavior, hiring for a function, adopting a technology, raising money or a leadership change. We use it as a timing layer on top of a fit score, never in place of one. Can you give an example of intent data? A company posting jobs that name the tool your product works with. For a security startup we swept about 3,000 such job postings, read each one by meaning, removed staffing firms that post other companies' jobs, and ended with 102 companies to contact. What is the difference between first-, second- and third-party intent data? First-party intent comes from your own properties, such as your website and product. Second-party intent is another platform's first-party data shared with you, such as engagement on a platform you advertise or post on. Third-party intent is collected across many outside sources and aggregated or sold. What are buyer intent signals? The individual events behind intent data: a relevant job posting, a technology adopted or dropped, a funding round, a new executive, a public incident or engagement with your or a competitor's content. We act when several stack on one account: three or more earn a manual touch, two a sequence, one is held. ## Related service - [Account-Based Marketing Tier your named accounts, act on their signals and reach the whole buying committee.](/services/account-based-marketing) - [GTM Engineering Signal-led outbound systems that enrich, score, sequence and sync to your CRM.](/ai-gtm-engineering) ## Related playbooks - [Social Listening Playbook Turn LinkedIn posts, event chatter and competitor engagement into a qualified list, not a noisy feed.](/growth-playbooks/social-listening) - [Outbound Sales Playbook From a signal or event list to the right buyers, one channel each, with every row explained.](/growth-playbooks/outbound-sales) - [Data Enrichment Playbook Reuse what you hold, enrich the company first, judge from the description, and keep every raw payload.](/growth-playbooks/data-enrichment) ## Further reading - [25 B2B Buying Signals Sales Teams Can Turn Into Pipeline→](/blog/25-b2b-buying-signals-sales-teams-can-turn-into-pipeline) - [The ABM Workflow Every Team Needs In 2026→](/blog/the-abm-workflow-every-team-needs-in-2026) - [Case study: Vorlon Security defined a new security category and built the full GTM engine to own it→](/case-studies/vorlon-security-defined-a-new-security-category-and-built-the-full-gtm-engine-to-own-it) - [GTM Glossary: the terms used in these playbooks→](/glossary) GET STARTED ## Want this playbook running on your market? 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