Building the Composite Score

The composite lead score combines fit, engagement, and intent into a single number that ranks every lead in your pipeline by likelihood to convert. The architecture decision is how to weight each dimension. Equal weighting (33/33/33) is the simplest but rarely optimal. Most B2B organizations find that engagement is the strongest near-term predictor, fit is the strongest long-term predictor, and intent is the most volatile but most timely signal.

Composite Score Weighting — Three Common Configurations

Configuration     Fit    Engagement   Intent   Best For
────────────────  ─────  ──────────   ──────   ────────────────────────
Balanced          33%    34%          33%      Early-stage companies
                                               with limited historical data

Engagement-Heavy  25%    50%          25%      Inbound-heavy motions where
                                               engagement = buying signal

Fit-Heavy         45%    35%          20%      Enterprise sales where ICP
                                               match is the primary qualifier

Formula: Composite = (Fit × W_fit) + (Engagement × W_eng) + (Intent × W_int)

Example (Engagement-Heavy):
  Fit: 72 × 0.25 = 18.0
  Engagement: 85 × 0.50 = 42.5
  Intent: 60 × 0.25 = 15.0
  Composite Score: 75.5 / 100

Some organizations prefer a matrix approach over a single composite number — displaying fit and engagement as separate axes on a 2x2 grid. High fit / high engagement goes to sales immediately. High fit / low engagement goes to marketing nurture. Low fit / high engagement gets a disqualification review. Low fit / low engagement is deprioritized. The matrix preserves the dimensionality that a single number collapses.