Fit beats intent every time
A perfect-fit account that is lukewarm is worth more than an excited prospect who can never succeed with you, so weight fit first and let intent decide timing.
The most common qualification mistake is to chase the loudest lead. Someone downloads three guides, replies fast, books a call within the hour, and it feels like a hot prospect. But enthusiasm is not the same as being a good customer. If that excited lead is too small, in the wrong industry, or solving a problem your product does not really fix, all that intent leads to a deal that either never closes or closes and churns. Fit is the floor; intent only tells you when to act on a good fit.
Fit and intent are two different signals
Fit is how well a lead matches the customers you already win with; intent is how actively they are looking to buy right now.
Fit is firmographic and structural: company size, industry, the role of the person, the use case, the budget reality. It answers "can this account succeed with us and is it worth our time?" Intent is behavioural: pages visited, content downloaded, pricing-page dwell, emails opened, demo requested. It answers "are they in the market this month?" Both matter, but they answer different questions, and the order is fixed. Fit decides whether; intent decides when.
Why fit has to come first
If you sort on intent first, you fill your calendar with eager poor-fit leads and let quiet perfect-fit accounts go cold. That is backwards for a lean founder. A poor-fit lead with high intent costs you the most time, because they engage, they ask questions, they take the call, and then the deal dies at the part you could have predicted from the start. A high-fit lead with low intent costs you almost nothing to keep warm, and converts later at a far better rate.
So the rule is: a lead must clear a fit bar before intent earns it any of your live time. Below the fit bar, no amount of enthusiasm books a call. Above it, intent decides the queue order.
How to define and score fit
- List your best ten customers. Real ones, the deals you would clone. Write down what they have in common: size band, industry, the job title who signed, the trigger that made them buy.
- Turn those patterns into fit criteria. "20 to 200 employees", "regulated industry", "head of ops or founder", "already using a spreadsheet they have outgrown". Three to five criteria is plenty.
- Layer intent on top, not underneath. Once a lead clears the fit criteria, rank them by behaviour: a fit lead who visited pricing twice outranks a fit lead who opened one email.
- Get the firmographic data you do not have. Most inbound leads give you an email and a name, not a company size or industry. Enrich them so the fit score is based on facts, not guesses.
A worked example
A 30-person B2B SaaS selling to logistics firms was booking demos off raw form fills and closing about 8 per cent of them. Their leads looked busy but converted badly because half were freelancers and tiny shops that could never afford the product. They wired up intent and firmographic enrichment with Dealfront so that every inbound lead arrived already tagged with company size, sector and on-site buying signals. Leads below the fit bar were routed to a self-serve nurture track and never reached the founder; leads above it were ranked by intent. Demo-to-close rose from roughly 8 per cent to 19 per cent in two quarters, on the same traffic, because the founder's calls were now spent only on accounts that could actually buy. Want to see which companies on your site are real-fit accounts before you ever pick up the phone? Start with the firmographic and intent layer, then score.
Pitfalls
- Mistaking responsiveness for fit. Fast replies feel like buying signals; they are intent at best, and intent on a poor fit is a trap.
- Scoring fit you cannot verify. If "enterprise budget" is a guess, it is not a criterion. Enrich or drop it.
- Letting one strong signal override the floor. A single pricing-page visit does not rescue a wrong-industry lead. Fit is a gate on time, not a tiebreaker.
With fit defined and intent layered on top, you have the raw signals. Next we turn them into numbers, scoring each signal with points and routing leads with thresholds.