Lead Scoring
Lead scoring is a method of assigning numeric points to prospects based on their attributes and behavior to rank how sales-ready each one is.
In depth
Lead scoring works by attaching point values to signals that correlate with buying, such as job title, company size, page visits, or quiz answers, then summing them into a single score and a tier like hot, warm, or cold. Models can be rules-based, where a marketer sets the weights, or predictive, where a model learns patterns from past conversions. The score then drives routing and prioritization so the highest-value contacts reach a rep first.
A frequent pitfall is letting a stale model drift: weights that made sense a year ago start mislabeling leads, and trust in the score erodes. In a quiz-funnel workflow, lead scoring is built directly into the scorecard, where each answer carries points and the final tally instantly places the lead into a tier, so prioritization happens at the moment of capture rather than days later in a CRM batch.
Example in practice
Frequently asked questions
What signals are used in lead scoring?
Scoring combines demographic and firmographic fit, such as role and company size, with behavioral intent like content downloads, email engagement, and quiz answers. Negative signals, such as a personal email domain, can subtract points to filter out poor fits.
What is the difference between rules-based and predictive lead scoring?
Rules-based scoring uses weights a marketer defines manually, making it transparent but maintenance-heavy. Predictive scoring uses a model trained on historical conversions to assign weights automatically, which scales better but needs enough data to be reliable.
How do quiz funnels handle lead scoring?
In a scorecard quiz, each possible answer is assigned points, and the system totals them as the respondent progresses. The final score instantly maps the lead to a tier, so prioritization and routing happen the moment the lead is captured.