For a long time, we had one lead score. It mixed engagement with fit, which sounded reasonable until we actually looked at the leads it was ranking.

Someone from a company that would never buy could score highly because they were reading everything we published. At the same time, someone from an ideal account could visit once, book a call, and end up with a surprisingly low score.

The problem wasn't the formula. We were trying to make one number answer two different questions.

So we split it.

One score tracks engagement in HubSpot. The other looks at fit: firmographics, plus whether the company is actively hiring for the function we sell to. We use Coresignal for that part, mainly because the hiring data needs to be fresh enough to use as a signal.

A low-engagement, high-fit lead now goes into a different workflow from a high-engagement, low-fit one. Before, both could land in the same middle of the score range.

The annoying part was getting everyone to accept two scores. Sales wanted one number they could sort by. Fair enough, but the second score explained why some of the leads at the top weren't actually worth pursuing.

How are others handling this? One combined score, or separate scores for fit and engagement?

The sales team keeps flagging that records are out of date. Contacts who've changed roles, companies that have restructured, accounts that no longer fit the ICP but are still in active sequences.

Tested a few options. Apollo is solid with contact coverage but the company record freshness has been inconsistent in testing. PDL has better raw volume but the dataset refresh cadence on company records hasn't been tight enough. Coresignal has been a more reliable option for data freshness so far.

Has anyone built something more automated here? Interested in what the trigger logic looks like when it actually works.