What lead scoring is for
Lead scoring ranks inbound and marketing-sourced leads so sales spends time on the ones most likely to buy. Done well, it shortens response time on good leads and stops reps from ignoring marketing entirely.
Done badly, it produces a number nobody looks at.
Use two scores: fit and intent
Combining everything into one number hides why a lead scored high. Split it.
- Fit score: how closely the company and person match your ICP. Inputs include industry, employee count, region, tech stack, and job title.
- Intent score: how actively the person is engaging. Inputs include pricing page visits, demo requests, repeat visits, email replies, and product sign-ups.
Step 1: Start from closed-won deals
Pull the last 12 to 24 months of won and lost opportunities. Look for the attributes that appear far more often in wins. Those become your fit criteria. Look at what won deals did in the 30 days before they became opportunities. Those become your intent signals.
Step 2: Assign simple point values
Use a small range, such as 0 to 100 for each score, and only a handful of rules. A model with 40 rules is impossible to debug. Give negative points for clear disqualifiers like students, competitors, or personal email domains.
Step 3: Set thresholds and routing
Create a simple grid. High fit and high intent goes to sales immediately. High fit and low intent goes to nurture or targeted outbound. Low fit and high intent gets a quick qualification check. Low fit and low intent stays in marketing.
Write the service level for each cell, for example "high fit, high intent leads are contacted within one business hour."
Step 4: Build it in your CRM
Most CRMs, including HubSpot and Salesforce, support property-based scoring natively. Keep the logic in one place and document every rule in a shared sheet so anyone can see why a lead scored the way it did.
Step 5: Calibrate every quarter
Compare scores to outcomes. If a large share of high-scoring leads are rejected by sales, the fit rules are too loose. If deals keep closing from low-scoring leads, you are missing a signal. Adjust, then tell sales what changed.
Frequently asked questions
What is a good lead score threshold?
There is no universal number. Set the threshold so the volume of sales-ready leads matches the capacity of the team that has to call them.
Should we use predictive lead scoring?
Predictive models need a lot of historical data to be reliable. Most startups get better results from a transparent rules-based model first.
Who should own lead scoring?
RevOps or marketing operations should own the model, with sales leadership signing off on thresholds.
