Meta Measures are the AI "ingredients" that tell Wrench.ai what matters to your audience. They're short text descriptions — a sentence or phrase — that capture a signal you want to measure: a value, a behavior, a motivation, a product benefit, or a cultural cue.
Once you add them to your workspace, Wrench scores every contact in your database against each Meta Measure and tells you how strongly that person aligns with it. The result: you know not just who is in your audience, but what they care about — so your messaging, targeting, and outreach can reflect that.
Say you sell project management software. You might add Meta Measures like:
Wrench scores your entire contact database against each of those. A contact who scores 80+ on the first two and 70+ on the third is a high-priority prospect — and you now know exactly how to message them.
Wrench compares each Meta Measure against a contact's enriched profile — their public professional presence, behavioral signals, content they engage with, and job context. The output is an affinity score from 0–100:
| Score range | What it means |
|---|---|
| 70–100 | Strong alignment — this signal is a meaningful fit |
| 40–69 | Moderate alignment — relevant but not defining |
| Below 40 | Weak alignment — this signal doesn't apply |
A score isn't a guarantee of purchase intent. It's a measure of resonance — how much this person's context, language, and behavior overlap with the concept you described. Higher resonance means your message is more likely to land.
Meta Measures aren't standalone scores. They connect to everything else in Wrench:
Lead Scoring — Your lead score model uses Meta Measures as inputs. Contacts who score high on the measures most correlated with conversion rank higher in your pipeline.
Personas — Wrench clusters contacts into personas based on their Meta Measure score patterns. People who score high on similar measures get grouped together — giving you actionable audience segments, not arbitrary demographic buckets.