# Peer Benchmarking

*AI Data & Analytics Team*

How you stack up against the cohort that matters

Your AI analytics team builds peer cohorts on your own definition — same size, same vertical, same stage, same geography — and benchmarks every KPI you care about against the group.,Sources blend public data, industry surveys, and authorized peer-network exchanges so the numbers reflect reality, not vendor marketing.,Output is a quarterly benchmark book: where you lead, where you lag, what the median looks like, what changed.

## Benefits

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## How It Works

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## At a Glance

- **Custom** Peer cohort definition
- **Quarterly** Refresh cadence default
- **20+** KPIs benchmarked per book
- **3+** Source types triangulated
- **Rule of 40** Standard SaaS composite benchmark included

## Beat The Vendor-Marketing Median

## FAQ

### How are private-company KPIs sourced?

Industry surveys, peer-network exchanges (anonymized), authorized estimates, and triangulation from public proxies.

### Can we contribute our data to the network?

Optional. Contributors get access to higher-resolution peer data in exchange.

### What confidence do we have in the numbers?

Every KPI carries a confidence band. Thin-data benchmarks shown with explicit caveats.

### Can it integrate with our board reporting?

Yes — benchmark slides drop straight into your board deck template.

### Which SaaS benchmarks matter most for a board deck?

Growth rate, net dollar retention, gross margin, CAC payback, burn multiple, and the Rule of 40 (growth rate plus profit margin) are the comparisons that come up in almost every board conversation. Your AI analytics team benchmarks whichever of these, or any custom KPI, matters most for your specific meeting.