Lead Scoring
Lead Scoring
What is lead scoring?
- Scoring leads on behavior and attributes
- Route ready leads to sales first
- The shared standard for marketing and sales alignment
Adding lead scoring changes a salesperson’s day first. Before, reps called leads in the order they came in, or in the order of company names they recognized. With scores, who to call first in the morning is already sorted, and the conversation where marketing had to persuade sales that "this lead looks good" becomes a shared language of numbers: "82 points, 3 pricing page visits, 500+ employees." Start from the principle that a score is a tool for deciding contact order, not a measure of a lead’s worth, and the rules will hold up.
What teams most often forget is negative scoring and time decay. Visits to the careers page, personal email domains, and competitor IPs should earn negative points. Without a decay rule that halves behavior scores after 30 days, a lead who only opened newsletters all year will eventually cross the threshold. Instead of looking at a single combined score, you can also split fit scores and behavior scores into a grid. High fit with low behavior means nurture; the reverse is usually a student or a peer from the same industry who came to benchmark.
The threshold does not need to be precise at first. Plot the score distribution of 30–50 leads won in the last 6–12 months, start at the line that captures the top 20–30%, and adjust every quarter. Using score to separate MQL from SQL is another common mistake. Scoring is accountable only up to MQL; SQL is a label applied after sales confirms budget, timing, and approval chain on a call.
The most dangerous misconception is that a high score means a lead is ready to buy. A score is only a trace of interest; it proves neither budget nor timing. And when MQL volume is set as a marketing KPI, the threshold gets quietly lowered at the end of every quarter. The numbers fill up, but within two or three months sales says, "It’s been worse since we started scoring," and stops opening the lists it receives altogether. Building the rules once and leaving them untouched for six months has the same result.
John KimInstructor John Kim, author of "B2B Marketing Design" and CEO of Nanum Consulting
Jongsung EunExpert in AI, marketing, business models, and management strategyA standard practice that spread with marketing automation and inbound marketing, it grew out of the need to turn lead qualification (MQL/SQL) into an objective score.
Consider a hypothetical case: Company A, a B2B industrial-safety SaaS firm with 140 employees and KRW 32 billion (≈ US$23,941,300) in annual revenue. Monthly inbound leads had grown to 600, and marketing was passing 220 of them to sales as MQLs, yet only 8% turned into meetings. What prompted the company to adopt scoring was a remark by the head of sales in a weekly meeting: "Our six reps can call all day, and half the people they reach are students or competitors."
- The marketing lead traced the entry records of 47 deals won in the last 12 months and found that leads who viewed the pricing page 2+ times accounted for 68% of wins.
- We spent two hours with the head of sales calibrating the attribute scorecard. We agreed on +20 for 300+ employees, +15 for safety or environment department roles, and +10 for manufacturing and construction.
- We added negative scoring rules: -50 for competitor domains, -10 for personal email, and -5 per week after 30 days with no activity.
- We ran a four-week pilot with the threshold set at 70 points. Monthly MQLs fell from 220 to 91, and the time the six reps spent per lead more than doubled.
- Six months later, the MQL-to-meeting conversion rate had risen from 8% to 24%, and the average time from lead intake to first contact had dropped from 3.1 days to 0.5 days. Quarterly pipeline value grew 41%.