Product Qualified Lead

Filtering prospects ran on paperwork and conversation for a long time. Someone who left contact details to download a guide went onto the list, and someone who opened the pricing page three times or used a company email address got promoted to the promising side. In a structure where selling happens before anyone touches the product, that was all the information available.

Products that let people try first are in a different position, because a far stronger signal is already sitting there. The record showing that one account created fifty documents, invited eight teammates, and uses the product daily is much closer to a purchase than three visits to a pricing page.

And yet that account was routinely absent from the list, because it had never downloaded anything. Sales would call thirty paperwork-derived leads a day, and most of those people had not even opened the product. Meanwhile the accounts that were ready to buy sat against the free-plan ceiling without hearing from anybody. The list and the readiness were pointing at entirely different accounts.

So a separate name appeared for filtering on usage — knowing from what someone already did rather than from what they said. The abbreviation PQL, for product qualified lead, is what teams use for it in practice.

Build the criteria from behavior and judge at the account level. The sequence goes like this.

1. Find the behavior that actually preceded payment. Put the first 30 days of accounts that paid over the last six months next to accounts that didn't, and pick the fields where the gap is largest. Guessing tends to land on the conspicuous number, like volume of use, while the field that really separates them is often something involving several people, like teammates invited.
2. Make the condition a combination of two or three fields. One field alone lets in accounts that are large on that field and nothing else. Requiring three or more teammates invited, use on three or more days a week, and 80 percent of the free ceiling reached together leaves fewer accounts, with a higher share of them paying.
3. Treat hitting the ceiling as the strongest signal. An account that has run into the free limit is standing in front of a decision right now. Miss that moment and the team usually scatters toward some other approach. A team that waits a week to call an account at its ceiling is calling a team that already moved on.
4. Judge accounts, not people. One person met the conditions; a team does the paying. If that person isn't the decision maker, finding the decision maker becomes the next step.
5. Decide in advance what happens to a flagged account: in-product upgrade guidance, a human reaching out, or both. A person attached to a small account doesn't pay for itself, so most teams split by contract size — under about $2,300 a year, guidance inside the product only; above it, a person.
6. Recompute the criteria every six months. When the product changes, the behavior that precedes payment changes with it.

The mechanics match lead scoring, which ranks leads by points; the raw material differs. Instead of putting company size or job title into the rules, this uses the usage record and nothing else.

Four places open this distinction.

1. The order of a salesperson's day. Sort the list by usage instead of by paperwork. The number of calls stays the same while the rate per call changes, and switching the list requires no new hires and no new tooling.
2. In-product guidance. The screen speaks before a person does. An account at 80 percent of its ceiling gets shown the next step, and small customers complete the purchase through that path alone.
3. Customer success. The signal separates which newly onboarded accounts are growing, and effort goes to those first. An account whose teammate count doesn't grow in the first month usually leaves within six.
4. Judging marketing performance. Count accounts that reached this signal rather than signups per channel. That's where a channel that only produces signups separates from one that brings teams who actually work in the product. Signups are the cheapest number to inflate, which is most of why the switch matters.

The distinction pays off most in a product-led growth structure, where using the product drives the purchase. With no salesperson leading, the product's own record has to carry that judgment.

"This account just came up as a PQL. Worth calling today."

The timing matters because the account is at a decision point against the free limit.

"Compare channels on PQLs, not signups."

Judge a channel by whether the people it brought actually use the product.

"Don't just look at document count. What separates the paying accounts is invites."

Set the criteria from past data rather than intuition.

Ravi dug through six months of paying accounts when Pagedeck set these criteria. The separating field wasn't document count but the number of teammates invited. Accounts with a hundred documents and one user paid at 4 percent; accounts with twenty documents and five invited teammates paid at 31 percent.

Setting the bar at three or more teammates and use on three or more days a week produced six to eight accounts flagged a day. When Sofia switched to that list, her call volume stayed flat while closed deals went from four a month to nine.

The most common belief is that heavy use means a purchase is near. Large volume suggests someone likes the product, so the inference comes naturally. But an account that gets everything it needs inside the free ceiling has no reason to pay. Pagedeck's single-user, hundred-document accounts were exactly that. So the criteria have to come from behavior that actually preceded payment, and only past data can establish which behavior that is. Flag heavy use instead and sales spends its day calling the busiest free users in the product.

The other frequent belief is that this signal makes the earlier filters unnecessary. Usage is the stronger evidence, so paperwork criteria look obsolete. But someone who hasn't opened the product produces no such signal at all. A large enterprise with no free signup, or a company just beginning to look around, still has to be handled with the earlier criteria, and the two lists don't substitute for each other. Two lists with two sets of criteria is the normal end state, not a transitional one.