Find out exactly where your SaaS pipeline is leaking — and which single fix matters most.
This report benchmarks your win rate, deal cycle, CAC payback, pricing model and retention against 10,000+ B2B SaaS companies. You’ll see where you sit by ARR band. Your score is also your AI readiness score — the number the board will ask about next. Score yourself across six dimensions in ten minutes. Know which one fix will move the needle most — before you spend another quarter guessing, or another dollar on AI.
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What you’ll know after 20 minutes
This report gives you six things. Each one answers a question your board is already asking — or will ask next quarter. Including the AI question.
Where you rank by ARR band
Growth rate, Rule of 40, Magic Number and burn multiple — split by $5M–$10M, $10M–$25M and $25M–$50M. You’ll see if you’re top quartile, median or falling behind at your scale.
Whether AI-native retention will hurt you
AI-native companies below $250K/month lose customers 12 points faster than traditional SaaS. Above $1M, it normalises. The report shows why — and what the survivors changed.
How much revenue your pricing model is leaving behind
Hybrid pricing delivers 110% NRR. Per-seat delivers 101%. That gap is real revenue from customers you already have. You’ll see which model wins — and why 62% of SaaS companies haven’t switched yet.
Your score — and your AI readiness
A self-assessment you can finish in ten minutes. Score each dimension 1 to 5. Your total out of 30 is also your AI readiness score. Below 20 means your CRM has a structural problem — and AI on top will make it worse. The report tells you which fix comes first.
A real company that fixed a 44% quota collapse
A $18M ARR company traced their problem to signal architecture — not sales training, and not AI. Pipeline contamination dropped from 38% to 11%. Quota attainment rose from 44% to 61%. No new hires. No AI tools.
Where the market is investing right now
7,200 SaaS companies tracked. Product launches outpace sales hiring 3:1. Engineering plus product hiring now exceeds sales hiring — the AI-native shift is real. The report shows what this means for your GTM.
Five things this report will change about how you think
Preview of what’s inside. Each finding points to a fix you can act on — not just a number to stare at.
Your Magic Number tells you more than your growth rate
SaaS growth is back to 22%. That’s the headline. But the median company spends $1.56 to earn $1 of new ARR. Top-quartile companies spend $1.05. The gap is not headcount. It’s process discipline. The report shows where the top quartile is different — across six specific dimensions.
The companies compressing CAC payback all fixed one thing first
Median CAC payback dropped from 29 months to 24 months in one year. The companies driving that drop have one thing in common: they fixed signal architecture before they fixed anything else. PLG pipeline converts at 34%. Outbound converts at 12%. Adding AI scoring on top of a broken signal mix just flags the same bad pipeline faster. The report shows how to shift the mix.
AI-native retention is a pricing problem — not a product problem
Below $250K/month, AI-native companies retain just 74% of revenue. That’s 12 points below traditional SaaS. Above $1M, it normalises to 88%. The product works fine at scale. The pricing architecture doesn’t work at small scale. The report shows what to change before the 2026 AI pricing wave locks your model in.
Growth alone no longer protects your valuation
Public SaaS multiples fell 32% from peak. Private M&A hit a record 2,698 deals. Buyers now split SaaS into two buckets: AI-native companies at 9–12x revenue and “legacy” SaaS at 5–7x. They’re paying for revenue process discipline that proves AI can work on top of the CRM. The report shows the six dimensions acquirers and boards now look at.
Your L2O score is also your AI readiness score
87% of SaaS companies missed forecast in 2025 despite record AI spend. 48% say their own revenue data isn’t AI-ready. 67% don’t trust their own numbers. The single number that tells you whether AI will work on top of your CRM is your Lead-to-Order score. Below 20 out of 30, AI amplifies the chaos. Above 22, it multiplies what’s already working. That’s the answer your board is looking for.
Normally £495. Free for a limited time. No sales call required. AI readiness scored at the same time.
This is why benchmarks matter
A real SaaS company. A real problem everyone misdiagnosed. The report would have shown them the answer in ten minutes — before they wasted money on more tools or AI.
Quota attainment collapsed to 44%. Everyone blamed the reps.
Quota attainment fell to 44%. The board approved $500K for a new sales enablement platform, more SDR headcount, and an AI lead scoring tool to fix the pipeline.
Hired more reps. Bought new tools. Bolted AI on top of the CRM. $500K spent treating a symptom that wasn’t the cause. The AI scoring would have flagged the same bad pipeline faster — not less of it.
Signal architecture couldn’t tell the difference between trial users exploring the product and trial users ready to buy. 38% of “qualified” pipeline was junk before it reached a rep.
Rewrote their PQL definitions to separate exploring from evaluating. No new hires. No new tools. No AI. Just better signal rules underneath the CRM they already had.
Result: Pipeline contamination dropped from 38% to 11% in one quarter. Quota attainment rose from 44% to 61%. Same team. Same tools. Same market. And now — when they do add AI — it will multiply something that actually works.
Score yourself in 10 minutes
The report includes a full scoring framework. These are the six questions. If you can’t answer them clearly, that’s the gap. Most SaaS companies between $5M and $50M score 14–21 out of 30. Your total is also your AI readiness score — below 20, your AI tools don’t have the foundation they need.
D1 Signal Architecture
Which signal source has your highest close rate? What share of pipeline does it produce? If you don’t know, you’re flying blind — and AI scoring won’t save you.
D2 Pipeline Structure
What share of your pipeline has sat open longer than your average deal cycle? Can you tell stale deals from ones that just take longer? AI forecasting can’t tell either if the stages were never defined.
D3 Conversion Mechanics
Do you track win rate by deal size, number of stakeholders and source? Or is it one blended number that hides everything?
D4 Pricing Realisation
Is your pricing built around how your buyer measures value? Or are you pricing per seat because that’s what everyone else does — right as AI pricing resets the whole category?
D5 Retention & Expansion
How much of your net revenue retention happens on its own — through usage or contracts — versus needing a sales motion to capture?
D6 Process Discipline
What’s your forecast variance over the last four quarters? Do you know how much revenue came from new logos versus expansion? Can you answer the board’s AI question with a number?
What to do after you read the report
“The report shows you where SaaS companies like yours score. The assessment shows you what it’s costing yours — and whether your AI spend has a foundation to deliver.”— Michael Williamson · Lead-to-Order Architect · Platform-Independent · 25 years at O2, Vodafone, Symantec and Equifax
Six dimensions. Your own data. Every gap costed. AI readiness included. Delivered in five working days.
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