The invisible cost of prioritizing badly
False positives show up right away: meetings that go nowhere and a demoralized team. False negatives never show up, because they're accounts nobody called that appeared six months later on a competitor's website.
Any prioritization criteria has to account for both errors, not just the one that's easy to count.
Three layers of information, not one
A score built on a single type of data ages badly. The ones that hold up combine three layers that answer different questions.
- Fit: does this company look like the ones that already buy from us?
- Intent: have they done something that suggests they're looking right now?
- Relationship: have we talked before, with whom, and how did it end?
“Fit tells you who to call. Intent tells you when. Without both, all you have is a list sorted by something.”
The score is not a verdict
A low number should mean "later," not "never." The practical difference is having a return loop: accounts that drop into nurture with a review date, instead of disappearing from the funnel.
And the rep has to be able to override the score when they know something the system doesn't. If they can't, they'll stop looking at it.
Review the criteria every quarter
The ideal customer profile changes with the product, the price, and the market. Criteria that never get reviewed end up describing the customers of two years ago. Half an hour a quarter, looking at what closed and what was lost, is enough to keep it honest.
Conclusion
Prioritizing well isn't about always being right; it's about being wrong in a reviewable way: with the reason written down, with a date to come back, and with the criteria open to debate. That way the opportunities that don't fit today are still opportunities tomorrow.
