Your team reviews 47 support tickets, partnership inquiries, and internal requests every day. Each one looks urgent. Each one demands attention.
Six hours later, you realize half of them were never going to work out. The partnership was with a company too small. The support ticket was from a trial user who never paid. The internal request came from someone who just needed to read the documentation.
Without scoring, every request gets equal treatment. Which means your best people spend time on things that were never qualified to begin with.
Most teams process everything manually. The ones that scale learn to score first.
Before your team touches anything, qualification scoring evaluates it against criteria. Does this partnership inquiry come from a company in your target revenue range? Does this support ticket come from a paying customer? Does this project request have executive sponsorship?
The scoring does not replace human judgment. It replaces the tedious first-pass evaluation that wastes hours. You define the criteria. The system applies them consistently, 24/7, without fatigue or bias.
Score first, then decide. Without scoring, everything seems urgent and nothing gets filtered.
Qualification scoring is not just about filtering requests. It is a pattern that appears whenever you need to decide if something deserves your limited attention.
Every system has limited resources. Qualification scoring protects that capacity by testing items against criteria before they consume resources. The criteria become your defense against overwhelm.
Toggle each criterion to see which requests qualify (score 40+) and how much time you save.
Requests need 40+ points to qualify. Each enabled criterion adds points when matched.
Enterprise integration proposal
Login issues from trial user
New project request from sales
Freelancer wants to resell
Billing question from enterprise
Someone asking a question from docs
Mid-market agency partnership
Feedback from free trial user
Define explicit criteria and assign point values
You create rules like "company revenue > $5M = 20 points" and "has budget confirmed = 15 points." The system adds up scores based on which criteria are met. Simple, transparent, and easy to adjust.
Train a model on historical success patterns
You feed the system your past data: which requests succeeded and which failed. It learns the patterns and predicts scores for new items. Can catch subtle signals humans miss.
Combine rules with learned patterns
Hard rules handle the obvious disqualifications (wrong industry, too small). ML handles the subtle predictions (likelihood to close, fit quality). Best of both worlds.
This flow ensures that incoming requests get evaluated against criteria before consuming team resources. Qualification scoring sits at the decision point, determining whether items proceed to action or get filtered out, saving hours of wasted effort on things that never should have reached your team.
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You build a score from one data point like "has budget" or "replied quickly." But that single signal fails in edge cases. Someone replies fast because they are confused, not because they are qualified. Someone has budget but zero decision-making authority.
Instead: Use 3 to 5 independent signals. Weight them based on historical correlation with successful outcomes. A single strong signal should flag for review, not auto-qualify.
You pick a cutoff of "75 points to qualify" because it sounds reasonable. But you have no idea if 75 is too strict or too lenient. Six months later, you have either rejected good opportunities or wasted time on bad ones.
Instead: Start by scoring everything without filtering. After 30 to 60 days, analyze which scores correlated with success. Set thresholds based on actual data, then adjust quarterly.
Your scoring model worked great last quarter. But your business changed. New services, new team capacity, new customer profiles. The old criteria no longer match reality. Qualified items start failing. Rejected items would have succeeded.
Instead: Review scoring criteria monthly. Compare scored predictions to actual outcomes. When correlation drops below 70%, rebuild the model.
You have learned how to evaluate incoming items before they consume resources. The natural next step is understanding how to rank qualified items so the most important ones get attention first.