The Front End of Lead Scoring: Fit, Engagement, Intent, Predictive.
Four scores get collapsed into one number because they all sound like the same thing, and the hiring parallel makes the difference obvious: a resume that fits, a candidate who follows you, one who says they want the job, and a model that says they will take it. Same four questions, same order, applied to leads.
Transcript
Today I'm going to talk you through lead scoring — specifically the front end of lead scoring. And the reason I'm doing that is because it's really tempting and easy for folks to group all the scores together, just as we do sometimes with reporting and dashboards. All of these sound the same.
Because really — and I'll do a separate video on reporting — but for scoring, really, all we're doing is continuing to score the lead to know who to talk to and when. So it all sounds like all these scorings sound the same.
So I'm going to break apart a few scoring metrics that you can appreciate, and then you can expand it from there. And the four I'm going to talk about today are fit, engagement, intent, and then predictive lead scoring.
And if you think about it — a real-world example — you're hiring for any type of role in the organization. The first thing you're going to do is see if this person's even a reasonable fit. So you're looking at their resume: where do they live, is this even within the realm of what we're hiring for?
Then you're going to try to look at engagement and say: how engaged are they with our website, our company? Some folks like to follow on LinkedIn. They look at whether they're following them on LinkedIn. And obviously this doesn't feel as heavy as fit.
Then there's intent. Do they actually want to work for the company? Can they express that? Is that expressed in their messaging that they've given to us?
And finally, there's this predictive scoring, utilizing some of the elements. Now that we know that they're a fit, they're engaged, and they have intent, we feed the resume to an AI engine — and the AI engine is going to say, here's what I predict: given your prior hires, and given what I know about this person, they're likely to convert.
And it's the same idea with leads. You have a similar concept. You want to determine first fit — ideal customer profile. Whether they're engaged with all our assets on the internet. Whether they're actually showing intent by researching websites, et cetera. And finally, using something like Einstein lead scoring, predictive scoring, to determine whether it is a high likelihood for them to convert.
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