Design an account score your team will actually trust.
“How should I weight these signals into one account score my team will actually trust?”
Picks the signals that separate wins from losses, weights them fairly across channels, adds gates and escalators, and never scores a blank field as zero.
What this skill does
Designs the scoring model your team stack-ranks on, and makes it defensible. It helps you pick the signals that actually separate a good account from a bad one, weight them fairly so an inbound account and an outbound account can each earn their score, add the gates and escalators that stop junk from ranking high, and set the tier bands. The output is a scorer spec with the logic written down, so the score has a reason on every row instead of a black box nobody trusts. This designs the model; a scoring skill then runs it.
Inputs
- Works today with: a description of your ideal customer, the signals you can see (firmographic, product, intent, engagement), and what a win looks like. Paste it and go.
- More powerful connected to a CRM: it reads closed-won and closed-lost so the weights reflect what actually converts, not a guess.
- Sharper with a data warehouse: it backtests the model against a real cohort before you trust it.
- Sharper with enrichment or product data: it scores on real signal coverage instead of assuming every field is filled.
How it works
- 1Pick signals that separate, not signals that exist
A signal earns a place only if it actually differs between accounts that win and accounts that lose. Start from a handful of real wins and losses and ask what was true of the wins. Signals that look the same on both sides are noise, no matter how easy they are to pull.
- 2Weight fairly across channels
An outbound account and a product-led account show up on different signals. If the model only rewards product usage, outbound accounts can never score. Weight so every channel can reach every tier on its own strongest signals. Uncap the channels, then let the gates do the filtering.
- 3Add gates and escalators
Gates are hard rules: a wrong-motion or wrong-region account gets capped or disqualified no matter how good it looks. Escalators are the opposite: one dominant, high-intent signal can lift an account to the top tier on its own. Both keep the middle of the score honest.
- 4Handle missing data honestly
A blank field is not a zero. An account scored on two of six signals must not sit next to a fully-enriched account at the same number. Cap the score by coverage so a thin account cannot hide as a strong one. This is the single most common way scoring models lie.
- 5Set the tier bands and the reason string
Define the bands you route on and, for every account, require a short reason: the signals that drove the score. A score with no reason is a black box reps will ignore. A score with a reason is a score they will work.
Output format
SCORING MODEL SPEC · [your org] · built [date]
Signal Weight Reason it separates
Fit (firmographic) 30 wins cluster in this size + segment
Product usage 25 active trial predicts close
Intent 20 repeat high-intent visits precede wins
Engagement 15 multi-thread predicts a real deal
Recency 10 fresh signal beats stale
GATES: not-our-motion -> DQ · competitor -> cap at Nurture
ESCALATORS: >=3 high-intent sessions in 7d -> min Warm
COVERAGE: score capped at 60 if <3 of 5 signals present
TIERS: Hot >=85 · Warm 65-84 · Nurture 40-64 · DQ gated
Reason string (per account):
"Hot 91 - strong fit + active trial + 4 intent sessions this week"
Tips
- Every signal in the model has a stated reason it separates wins from losses. No signal included just because it is available.
- Every channel can reach every tier on its own signals. No channel is structurally capped.
- Blank fields are never scored as zero; coverage caps the score.
- Every score carries a reason string naming its top drivers.
- Gates and escalators are explicit rules, not vibes buried in a weight.
Turn a category into a real ICP definition.
Who exactly are we for?
Name the moments that signal value in your product.
What should my product analyst actually measure?