How to Find In-Market Leads: A 5-Step Guide for Lean B2B Teams

Key takeaways

• In-market leads are companies showing a buying signal right now, not just companies that match your firmographics.
• The process is five steps: define your ICP, pick your signals, score fit times intent, act in the window, measure.
• The single biggest mistake is acting too slowly. Fast signals like a pricing-page visit are worth hours; act on a weekly batch and the freshest leads have gone cold.
• You can do this by hand with free tools (LinkedIn, job boards, funding news, your own analytics). It works; it just costs time.
• Pyng automates the loop, watching signals, scoring fit and intent, and surfacing who to contact and why, with a person approving the send.

To find in-market leads, stop starting from a static list and start from signals of active demand. Define your ICP precisely: industry, size, role, and the trigger that tends to make this kind of company buy. Then watch for buying signals: companies hiring for roles your product serves, raising funding, changing their tech stack, engaging competitors, or visiting your pricing page. Score each prospect on fit (do they match your ICP?) and intent (are they showing a live signal?), multiply the two, and prioritise the high-fit, high-intent accounts. Then reach them inside the buying window, often a matter of days, with a message tied to the trigger. You can run this by hand with free tools, or a tool like Pyng can automate the loop: infer the ICP, monitor the signals, score, and surface who is in-market and why, with you approving the outreach.

What “in-market” actually means

An in-market lead is not the same as a good-fit lead, and confusing the two is why a lot of well-targeted outreach still gets ignored.

A good-fit lead matches your ICP: the right industry, size, and role. There might be thousands of them, and most are not buying anything right now. An in-market lead is a good-fit company that is also showing a live buying signal, evidence that something just changed or that they are actively researching the problem you solve. That second group is much smaller and far more valuable.

The reason the distinction matters is a number worth repeating: only about 5% of B2B buyers are in-market in a given quarter, according to Ehrenberg-Bass Institute research published with the LinkedIn B2B Institute. We unpack that fully in signal-based selling; the short version is that finding in-market leads means finding that 5%, and a firmographic list alone cannot tell them apart from the 95% who are not buying. Signals can.

Step 1: Define your ICP precisely

You cannot recognise an in-market lead until you know who you are looking for. Start by building the ideal customer profile from your best existing customers, not from who you wish would buy.

Look at the accounts that closed fast, stayed, and expanded. Find what they share: industry, company size, geography, the buyer’s role, the pain you solved best, and crucially the trigger that made them start looking. A good ICP is specific enough to disqualify; it tells you who to ignore as clearly as who to chase. (For the full method, see how to identify your ICP.)

The trigger part is what links your ICP to your signals. If your best customers tend to buy right after they hire their first SDR, then “hiring an SDR” is a signal you should be watching. The ICP and the signal list are two halves of the same definition.

Step 2: Pick the signals that reveal in-market intent

Now choose the buying signals that map to your ICP’s triggers. You do not need all of them. You need the handful that, for your product, reliably precede a purchase.

Signals fall into three groups: intent (the account is researching your category), fit changes (their situation changed, funding, hiring, a tech-stack change), and engagement (they interacted with you directly). The complete taxonomy, with how fast each one decays, is in B2B buying signals. For most lean teams, a strong starter set is:

• Hiring for a role your product supports (job boards, LinkedIn).
• Job changes, especially a past champion moving to a new company (LinkedIn, contact-data tools).
• Funding rounds in your ICP segment (Crunchbase, press).
• Competitor engagement, people liking or commenting on a competitor’s posts (LinkedIn).
• Pricing-page and repeat website visits (your own analytics, de-anonymisation).

These five are mostly detectable without an expensive enterprise intent platform, which makes them the right place for a small team to start.

Step 3: Score fit times intent

With your ICP defined and your signals chosen, rank prospects by scoring two axes and multiplying them.

Fit is how well the company matches your ICP. Score it 0 to 1. Intent is how strong and fresh the live signal is. A repeat pricing-page visit today scores high; a single topic surge from three weeks ago scores low. Score it 0 to 1. Multiply the two.

Prospect	Fit	Intent	Score (fit x intent)	What to do
Perfect ICP, just raised a round, visited pricing twice	0.9	0.9	0.81	Contact today
Perfect ICP, no live signal	0.9	0.1	0.09	Nurture, do not pitch yet
Weak fit, strong signal	0.2	0.9	0.18	Mostly skip
Decent fit, fresh hiring signal	0.7	0.7	0.49	Contact this week

The multiplication is the discipline. A high score on one axis cannot rescue a low score on the other, because a perfectly-timed message to the wrong company is still spam, and a perfect-fit message with no live trigger lands in a closed window. Only the accounts that clear both bars are genuinely in-market.

Step 4: Act inside the buying window

This is the step teams get wrong, and it is the one that decides whether the whole effort pays off. Signals are perishable, and acting late wastes them.

The windows are short. Directionally, across 2026 sales-trigger reporting, pricing-page visits are best acted on within hours, job changes within 7 to 14 days, funding rounds within 30 to 60 days, and most intent signals decay within about 30 days. Reaching a prospect within 24 to 48 hours of a fast trigger tends to book materially more meetings than acting on a weekly batch, though treat the exact lift as directional.

So the rule is: match your response speed to the signal, and tie the message to the trigger. “Saw you just opened a London office, here is what teams usually need when they expand into the UK” is a real reason to reach out, and it writes itself from the signal. Inside the window, a fast and slightly rough message beats a polished one that lands two weeks late.

The uncomfortable implication for a busy founder doing this by hand: if your process takes longer to act on a signal than the signal takes to decay, you are paying to collect data you cannot use in time. That is the real argument for automating the watching, even if a person still approves the sending.

Step 5: Measure, then quantify your own waste

Most teams cannot tell you their cost per reply or how many hours a week they lose to prospecting. They describe the pain generically, “I waste hours,” “my replies are scattered”, but they do not have the number. Getting the number is what turns a vague sense of inefficiency into a decision.

Track two things. First, performance by signal type: which signals produce replies and meetings, and which are noise for your product. Drop the ones that do not convert and double down on the ones that do. Second, your own cost. A rough calculation:

• Hours per week spent building lists, enriching, and writing first messages.
• Replies per week, and meetings per week, from that effort.
• Cost per reply = (your hourly value x hours) / replies.

Run that honestly and most lean teams find the cost per reply on volume outbound is far higher than it feels, because so much effort lands on the 95% who are not in-market. The point of finding in-market leads is to move that number: fewer, better-timed messages, more replies, less time. If you cannot measure it, you cannot tell whether the switch to signals is working, so measure it from the start.

Manual versus automated

You can do all five steps by hand, and for a very small operation that is a reasonable place to begin. The honest trade-off is time, not capability.

	Manual (free tools)	Automated (an AI GTM agent)
ICP definition	You build it from won deals	Inferred from your website, you refine it
Signal watching	You check LinkedIn, job boards, funding news, analytics by hand	Monitored continuously across sources
Scoring	A spreadsheet you maintain	Fit and intent scored automatically, with the reasoning shown
Speed in the window	As fast as you can check and write	Fast enough to act on fresh signals at scale
Cost	Free, but it eats hours	A subscription, but it returns the hours
Best for	A founder with a few hours a week and a narrow ICP	A team that needs the window honoured at scale

The manual route fails not because it does not work, but because the freshest signals decay faster than a part-time human can keep up. Pricing-page visits worth acting on within hours do not survive a process that checks once a week. Automation earns its place by closing that gap, not by sending more.

How Pyng finds in-market leads

Pyng is an EU-native AI GTM agent built to run exactly this loop. Stated honestly, because Pyng is early and pre-launch: this describes how the product is built, not customer outcomes we do not yet have.

• Infers your ICP from your website, so step 1 starts from who you actually sell to, and you refine it.
• Watches the signals continuously, hiring, funding, job changes, tech-stack changes, competitor engagement, lookalikes, and profile and post activity, instead of waiting for you to check by hand.
• Scores fit and intent and shows why, so you see the specific signal that surfaced each account and can judge it yourself.
• Keeps you in the window and in control. Pyng drafts from the signal and a person approves what gets sent, the human-in-the-loop model. The machine provides the speed the method needs; you keep the judgment.

Finding in-market leads is not a volume problem, it is a timing problem. The teams that do it well are not the ones sending the most. They are the ones reaching the right 5% while the window is still open.

FAQ

How do I find in-market leads? Stop starting from a static list. Define your ICP, watch for buying signals (hiring, funding, job changes, competitor engagement, pricing-page visits), score each prospect on fit times intent, and reach the high-scoring accounts inside the signal’s window with a message tied to the trigger.

What signals show a company is in-market? Hiring for a role your product supports, a funding round, a champion changing jobs, a tech-stack change, competitor engagement, and repeat pricing-page visits are among the strongest. Combine a fit change with active intent and you have a genuinely in-market account.

Can you find in-market leads for free? Partly. LinkedIn, job boards, funding news, and your own website analytics are free signal sources you can monitor by hand. The cost is time. Because fresh signals decay in hours to days, the limit on the manual approach is how fast one person can check and respond, not the tools.

How long does the buying window last? It varies by signal. Pricing-page visits are worth acting on within hours, job changes within 7 to 14 days, funding rounds within 30 to 60 days. Directionally, many mid-market buying windows appear to run about two to four weeks from the trigger, and most intent signals decay within about 30 days.

How does Pyng find in-market leads? Pyng is built to infer your ICP from your website, watch buying signals continuously, score fit and intent (and show why each lead surfaced), then draft outreach tied to the signal, with a person approving what gets sent. It automates the watching and scoring so the buying window gets honoured at scale.

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Pyng is an EU-native AI outbound platform, currently pre-launch. We build in the open and we will tell you exactly what is live and what is still being built. See how signal-based selling works →