Score Explainability
A per-entity why-is-this-ranked-here drill-down: the drivers, the weight profile, and how your tuned weights differ from the defaults.
Overview
Score Explainability opens up any score in the product. Every ranking, deal risk, awareness, customer health, technical risk, ecosystem fit, comes from one scoring substrate, and this audits it: it shows the drivers behind a number, the weight profile that produced it, and exactly how your learned weights differ from the shipped defaults. It re-scores nothing, it re-runs the same engine every surface uses, so what you see is what actually ranked the entity.
Drivers
Enter the signals for an entity and the profile it is scored on, and the drill-down shows each signal's contribution to the score, side by side under the default profile and, when you have a learned override, the tuned profile. This is the "why" a rep or a manager reads first: the score is never a black box, it is a sum of named, weighted signals.
Default vs tuned weights
The scoring substrate has a learning loop: a weekly retune nudges each tenant's weights toward the signals that actually predicted outcomes. This surface makes that change auditable. It lists every signal's default weight, the tuned weight, and the delta, with the changed weights first, so you can see exactly what the learning loop moved and defend the score in a review. When no override has been learned, the tuned view is absent and every delta is zero, honestly.
Deterministic
The explanation is deterministic: the same signals, profile, and override always produce the same drivers and the same weight comparison. Nothing is invented, it reports only real contributions and real weight deltas from the same scoring code that ranks your book.