By Rob Kall, Co-Founder & CEO, Cien.ai

“We were surprised that we had a high propensity segment of the market that had almost completely ignored where the potential ACV was over $1M per account.”

– Public Company CEO

The Problem: Your SAM Isn’t Useful Without This

Quantifying your Serviceable Available Market (SAM) is essential—whether you’re building your GTM strategy or trying to justify headcount or budget expansion. If your current sales motion has unlocked $10M in Annual Contract Value (ACV) from a $1B SAM using a lean team, it’s easy to pitch the ROI of doubling your investment in Sales and Marketing.

But what’s the ACV potential of each account in that SAM?

Too many RevOps teams simply average current ACV across accounts—leading to skewed assumptions. Small customers, incomplete implementations, and early-stage segments muddy the water. Just because a large account is only paying $20K doesn’t mean that’s all they’re worth. It could just mean you haven’t unlocked their full potential yet.

The three biggest drivers of ACV potential:

1. Company Size – Larger companies can buy more, but not in a linear fashion.
2. Industry – Some verticals derive much more value from your solution than others.
3. Geography – Your product might resonate deeply in France but not in Japan.

The Solution: Factor-Based ACV Estimation

Start by looking at the top 20% of your accounts by size segment. These typically represent your best-case monetization so far. From there, instead of relying on simplistic linear regression (which can mislead due to non-linear scaling or sparse data combinations), use a factor-based model:

– Derive normalized factors for size, industry, and geography.
– Avoid creating a separate model for every permutation—that introduces dimensionality problems.
– Use your factors to estimate ACV for any account, regardless of current spend.

This method has two powerful benefits:

1. It scales. You can apply it across thousands of accounts quickly and consistently.
2. It’s explainable. No black-box model—each factor is derived from real-world outcomes.

Example Table: Size-Based ACV Estimation

These factors are based on actual high-performing accounts and are stored so that anyone can replicate or validate the logic.

What Does Success Look Like?

You have a model that:

• Flags high-value accounts that are under-monetized.
• Enables territory planning and quota setting based on real potential.
• Lets you speak confidently with investors about how much upside remains in your SAM.
• Powers data-driven account prioritization for your sales team.
  

With Cien.ai, we’ve refined this process into a repeatable service that builds explainable ACV models by integrating CRM, firmographics, and win-rate trends. It’s part of our GTM Suite and can be calibrated and deployed in days.

About the Practical RevOps Analytics Series

This article is part of our Practical RevOps Analytics Series, inspired by our work with B2B business leaders, growth consultants, and PE operating partners. These articles focus on the technical aspects of improving GTM performance. For business-focused strategy content, check out our companion series, Growth Essentials.