This report measures:
This report will help you:
It turns out there’s only one event that can predict high intent with 100% accuracy: a purchase.
If someone buys, we know they had high intent- because they bought.
But if they don’t, all we can do is make a prediction.
Enter: lead scoring.
For decades, marketers have attempted to predict intent using lead scoring points systems.
For example, an ebook download gets 5 points whereas a website visit gets 1, or a direct response ad click gets 6 points whereas a demo request gets 10. Once a buyer accumulates enough points, they get moved to a “high intent” bucket for outreach.
One problem: assigning arbitrary points to different touchpoints is inherently biased and error-prone.
For example, who decides which touchpoints or pieces of content get 5 points vs. 1 point? Why should an ebook download mean more than a pricing page visit? And what if low intent buyers behave like high intent buyers?
The truth: while there’s no way to predict high intent with 100% accuracy, traditional lead scoring systems fail so much more than they succeed that most organizations have discontinued them entirely. And we don’t blame them.
So at HockeyStack, we’re taking a different approach to lead scoring.
Since we know that the one true “high intent” marker is an actual purchase, HockeyStack analyzes all of your historical closed/won journeys and their touchpoints. Then we model intent based on correlations with those past journeys, and use reverse-IP lookup to identify anonymous accounts.
Whereas traditional lead scoring models misinterpret intent by diluting their data with low intent buyers who act like high intent buyers, we only analyze journeys that result in a purchase.
The result?
A list of high-intent accounts that’s a thousand times more accurate than traditional lead scoring models.
Columns are customizable. We score intent using our predictive modeling, not your column touchpoints.
Will this give you a massive list with nothing but high-intent accounts?
Not even close.
Like we mentioned above, no model can predict intent with 100% accuracy- too many variables will create false positives.
However, you will get a list that’s far more accurate than anything you’ve used before, making outbound prospecting more effective, efficient, and predictable, and making marketing and sales alignment a breeze.
Bonus: In addition to intent scores, you can segment high-intent accounts into their own journey report, then drill down into each account one by one to explore individual journeys of everyone in the buying committee (see below).
Learn more about how HockeyStack helps marketing, revenue, and sales teams surface and action insights like the ones in this template by exploring the interactive demo or booking a virtual demo.
What exactly is HockeyStack? How do we help marketers drive revenue more efficiently?
Jump in to uncover the ins and outs of Marketing Mix Modeling – from its mechanics to its ideal applications – with Drew.
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