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common situation. For example, the marketing team may announce, “Once a
lead exceeds a lead score of 50, the lead will be passed to sales.” In a vacuum,
this statement sounds harmless. But what does a score of 50 mean? How does
the scoring algorithm work?
In many cases, the scoring algorithm is based on an overly complicated set of
factors. For example, if the lead provides an email, the lead score increases by
two points. If the lead views the pricing page, the lead score increases by seven
points. If the lead requests a demo, the lead score increases by 10 points. The act
of downloading an ebook increases the score by five points. Additional ebook
downloads are two points each. There are so many permutations that could get
the lead score above 50, or keep the lead score below 50. How do you know that
passing the lead to sales precisely when the lead score exceeds 50 is the right
move in all cases? Depending on how the lead score is set up, a start-up intern
who downloads 20 ebooks on a Saturday night might get passed to sales, while
an important individual who visits one page and requests a demo (but takes no
further action) might not.
At HubSpot, we tried the lead scoring approach, but ran into the problems I just
described. We evolved to implement an alternative approach we called the
“Buyer Persona/Buyer Journey” matrix, or buyer matrix for short. Figure 11.2
shows an example of a buyer matrix.

Figure 11.2 Buyer Persona/Buyer Journey Matrix
The vertical axis (y-axis) shows the different buyer personas the company
targets. Buyer personas are defined by primarily static attributes about the buyer
that will not change. Example attributes include the size of the business, the