Product qualified lead scoring

Funnelsight scores accounts on how they actually use your product, not just on form fills or page visits. Usage frequency, feature adoption, and account activity get combined into a single score your team can act on.

What it does

PQL scoring ranks accounts by how likely they are to convert or expand, based on real product usage. For teams running a product-led sales motion, the same mechanism produces SQL scores for the accounts a sales rep should prioritize. Either way, the score sits in the same funnel view as your CRM data, so a self-serve account and a sales-sourced deal get the same visibility.

How it works

The score combines three signal types: how often an account uses the product, which features it has adopted, and what's happening on the account level in your CRM. You set the weighting for each signal. That means the score reflects what actually matters for your product, not a generic formula someone else decided on.

Funnelsight calls this AI-assisted, not AI-powered or predictive, because the logic is rule-based: you set the signals and the weighting, and you can see and change that logic at any time. Update what counts as meaningful usage, and the score moves with it.

You don't need a data team to set this up. A marketing or growth lead can define the initial rules directly inside Funnelsight and have a working score the same day.

Why it matters

Sales teams waste time on leads that look active but aren't. Marketing teams get blamed for lead quality when the real issue is that nobody agreed on what "qualified" means. A shared, editable score fixes both problems at once: everyone works from the same definition, and that definition can evolve as your product and your ideal customer profile change.

Start simple if you need to. You don't have to model every usage pattern on day one. Many teams start with two or three signals (say, login frequency and one core feature) and expand the model once it's proven useful. That's a scope decision, not a shortcut on data quality: whatever signals you do include should still come from real, verified usage.