Most B2B companies have more revenue potential sitting in their CRM than in their pipeline.
Not because they lack leads or tools.
But because their CRM reflects years of outdated assumptions, inconsistent data, and half-implemented GTM experiments.
We see this constantly at Forabilis.
- Segments marked as “dead” that were never really worked.
- “Small” accounts dismissed because of deal size, even though they close faster.
- ICP rules that made sense three years ago but quietly cap growth today.
The gold is there. It’s just buried under messy CRM data and legacy thinking.
This case study is how we used data, AI-supported analysis, and GTM strategy to surface that opportunity and build a new motion that added $1M in ARR in 2025, from a segment the company had written off.
The Ask: “Build an automated outreach program”
At the beginning of the year, we were asked to do something very specific.
Build a fully automated outreach program for mid-sized accounts.
Clear brief. Clear scope. Easy to execute.
But before we built anything, we did what we always do. We stopped and looked at the data behind the ask. That pause is where this story actually starts.
The moment we noticed something was off
When we pulled the full target list from HubSpot, one number stood out immediately.
About 67% of the companies were labeled as “small accounts.”
And because of that label, they were excluded from every outbound and GTM motion.
No experiments. No scaled outreach. No structured attempts to see what would happen.
On paper, this made sense. Small deals do not move ARR one by one. Sales teams are trained to prioritize what closes biggest.
Still, something felt unfinished.
So we asked a simple question.
What if this segment behaves very differently when you stop treating it deal by deal and start treating it as a motion?
When we raised it with the CEO, the response was immediate and fair.
“Prove it.”
Step 1: Let the data speak before the strategy does
We started with the existing CRM data.
Not a full system overhaul. Not a hygiene crusade.
We validated historical deal data, standardized segmentation logic, and focused only on the fields that mattered for this question: deal velocity, win rates, volume, and ARR contribution over time.
Then we used Claude to help analyze and visualize patterns across segments.
At first glance, nothing surprising showed up. Small accounts meant lower ACV. Individually, they barely moved the ARR needle.
But when we looked at behavior over time, a different pattern emerged.
These deals:
- Closed significantly faster
- Maintained competitive win rates.
- At scale, the compound effect added up
Individually, these deals didn’t move the ARR headline. In volume, they absolutely did.
That is where most RevOps teams get stuck.
They optimize for deal size, not for speed × scale × predictability.
Research on efficient SaaS growth has shown that high-velocity, lower-ACV segments can be some of the healthiest growth engines when executed properly.
And once you see that pattern, you cannot unsee it.
Step 2: Understanding what “small” actually meant
Next, we needed to sanity-check the segment.
Was this a statistical coincidence, or a real opportunity?
We enriched the account list using ZoomInfo to understand revenue ranges, headcount, and industry mix. Then we added context with Clay to uncover things static databases rarely show clearly: team structure, locations, and likely buying roles.
This mix of structured firmographic data and contextual signals let us see what the CRM labels couldn’t:
Many “small” accounts were actually fast-growing teams below arbitrary revenue thresholds
Decision-making structures were leaner, meaning faster sales cycles
The product was a good functional fit for these users, even if their current spend potential was lower
In other words, this wasn’t a junk segment. This was a systematically ignored, high-velocity motion hiding inside their CRM.
Step 3: Turning insights into a plan (and a business case)
Data without translation doesn’t convince execs. So we built a clear business case.
Using Claude, we modeled scenarios for:
- Pipeline volume from this segment
- Expected win rates (based on historical behavior)
- Realistic ARR contribution under different levels of outreach intensity
No hype. No heroic assumptions.
We set clear KPIs; number of accounts touched, SQLs generated, and ARR per quarter from the motion.
This is where GTM strategy and RevOps actually do their job: translate raw data into simple, defensible revenue scenarios.
Under reasonable conditions, the math showed how this “ignored” segment could realistically add around $1M in ARR if worked as a motion.
That was the proof the CEO needed.
Step 4: Building the motion the segment actually needed
With alignment in place, we moved to execution.
The principle was simple.
Automate everything until there is a positive intent signal.
We built a fully automated outreach flow covering the entire segment. ChatGPT helped us develop and iterate messaging variations. Sales only stepped in when intent signals appeared (opens, clicks, replies, web behavior).
Key principles:
- No SDRs wasting time on cold, unqualified first touches
- Messaging tightly aligned with the segment’s actual pains, not generic ICP fluff
- Cadences tuned for speed and clarity, not cute copy
This is where AI tools actually matter in B2B sales and marketing: as a way to scale first-touch coverage so humans spend time where they can move revenue.
Any combination of tools can work if you’re ruthless about who does what and when.
Step 5: Making sure marketing was not running a parallel universe
Outbound alone was not enough.
To support the motion, marketing focused on creating familiarity before sales engagement.
We ran PPC campaigns through Toffu.ai against the same account set, synced social engagement signals into the CRM, and launched a short, focused newsletter with relevant product and use-case updates.
The goal was simple.
When outreach landed, it should not feel like a first introduction.
Step 6: Measuring, adjusting, and staying honest
Every two months, we reviewed what actually happened.
We looked at messaging performance, timing, and the signals that triggered sales involvement. Then we adjusted.
Some things worked immediately.
Others needed to be cut or reshaped.
The motion is still evolving.
But one thing is clear. A segment that previously received zero attention contributed $1M in ARR in 2025, with momentum continuing into 2026.
Your tools won’t save you
It would be easy to frame this as a tooling story.
HubSpot. ZoomInfo. Clay. Claude. ChatGPT. Toffu.
That is not what made the difference.
What changed the outcome was:
- Questioning the original ask
- Challenging old segmentation rules
- Looking at behavior, not labels
- Using AI to amplify thinking, not replace it
Most companies already have enough tech.
What they lack is a disciplined, end-to-end GTM operating system that forces them to see, and act on what’s hiding in plain sight.
If your CRM quietly excludes a large part of your universe, the problem is rarely the size of the accounts.
It is the assumptions you stopped questioning.
And that is usually where the real gold is hiding.
That’s the work we do at Forabilis. No magic, no hype, just data, strategy, and execution that actually talk to each other.