Your Lead Score Is Four Different Scores. Stop Collapsing Them.
Fit, engagement, intent, and predictive are four separate reads on the same lead — most teams mash them into one number and lose the signal. Using hiring as the analogy, this breaks down what each score actually measures, why small companies blend them, and why you have to split them apart as you scale.
Transcript
I want to explain to you how you can think about separating different types of numbers. So it could be scoring, it could be reporting — and when and why those look different.
So let me give you a real-world use case. You're recruiting, you're hiring folks for the company, and the first thing you're going to do is look at the resume and say: hey, does this person even match a need that we're even hiring for? Are they in the right place? Do they have basic skills?
And there's other things you're going to look at, potentially. How engaged are they with your organization? Do you see them follow you on LinkedIn? LinkedIn even offers that. Are they following you? Is that important to them? Are you and your mission important to them, generally, within their activities online?
The next question is: what is their intent? Do they communicate clearly — in a cover letter, or to you, or through a referral — that they really want to work for the organization?
And finally, with new technology, you take that information and you could say: give me a lookalike profile, and show me a predictive score, whether this person is likely to convert. There's obviously other elements when you're hiring, but you want to know how likely is this person to become an employee at the organization, given conditions — lookalike conditions on this profile to others we've recently hired.
And if you have those elements in place, you're doing really, really well. Most companies — or many smaller companies — will have one person doing all those roles. Larger companies split those up. You know, you have one system or person just taking a pulse check: is this resume even a fit? Are they engaged with us? Do they express intent? And then you're going to run through a model, even in your own brain many times, and say: given the folks we've hired, does this person match that profile?
When we think about scoring or metrics — I'm going to talk about lead scoring, because that's something I've worked with now on a day-to-day and it triggered this conversation. When we think about lead scoring, we think to ourselves, it all sounds the same, right? We're just trying to move the lead and determine who to prioritize and when. All these scores sound similar. But really — not really.
The first thing we do is try to identify an ideal customer profile. Is this person even within our range? Many companies will then say, how engaged is this person with our material, whatever that material is. But really, you're looking at the intent. Are they downloading our white papers? Are they ready to buy — because you're getting information from, say, G2 Crowd, or you're trying to get real intent. Then you can use additional tooling to do predictive scoring to say: given profile, lookalike profiles, is this person predicted to convert?
Those are separate scores, and the bigger your company gets, the more you separate those out.
Same is true with metrics. We'd like to say, oh, those numbers are all the same. Maybe I'll do a separate video on how you think about separating those numbers and saying, well — no, no, dashboards aren't meant for everybody.
If you like this video, share it with someone who wants to learn more about thinking differently about the problem. Like it, share it — and if you want some information about other scoring and numbers of the business and you want to break those open, let me know and I'll create something for that. Have a great day.
