How big should the scoring sample be?

For lists up to 5,000 rows, 50 to 100 randomly sampled rows is sufficient. For larger lists (10,000 to 50,000), sample 150 to 200. The goal is statistical confidence, not exhaustive verification.

Can you automate list quality scoring?

Partially. Reachability and freshness automate cleanly. ICP fit and signal density can be semi-automated. Stakeholder coverage and data accuracy still benefit from a human sampling step. Hybrid is the practical configuration.

What is the single most predictive dimension?

ICP fit is usually the single most predictive dimension. A list with 95 percent ICP fit out-performs a list with fresher data but weaker fit. Signal density is a close second in active outbound motions.

Should scoring weights change per campaign?

Yes, modestly. ABM motions weight stakeholder coverage more heavily. Broad outbound weights reachability more. Event-driven campaigns weight signal density more. Shift 5 to 10 points between dimensions based on motion type.

List Quality Scoring: How to Rate a Prospect List Before Sequencing

A list quality score rates a prospect list across six dimensions before sequencing. It predicts whether the list will produce the reply-rate and meeting-rate outcomes the campaign was designed for.

Six dimensions

ICP fit (25%), data accuracy (20%), freshness (15%), stakeholder coverage (10%), reachability (15%), signal density (15%).

Workflow

Sample 50 to 100 rows, verify each dimension, extrapolate, apply weights. Takes 30 minutes per list.

Thresholds

80+ ships, 60 to 79 remediates, below 60 pauses and re-sources.

Why it matters

Only 47 percent of B2B records are considered accurate by default. Scoring is the control that keeps the other 53 percent from consuming touches.