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 But because their CRM reflects years of outdated assumptions, inconsistent data, and half-implemented GTM experiments.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.
And AI won’t fix that on its own. If a segment is missing from your target list, automating the outreach won’t bring it back.
That’s why we put so much focus on the Operational ICP. Not just a description of your ideal customer, but clear rules that tell your tools which companies to target, which to skip, and why.
The gold is there. It’s just buried under messy CRM data and legacy thinking.
This case study is how we used data, AI, and GTM strategy to challenge those assumptions, uncover an opportunity everyone had missed, and build a new motion that added $1M in ARR in 2025, from a segment the company had written off.
At the beginning of the year, we were asked to do something very specific.
Build an AI-powered automated outreach plan 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.
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.”
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 analyzed the data and looked for patterns across the different groups of accounts.
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:
That raised a bigger question: what could this segment contribute if we approached more of these companies?
That is where most RevOps teams get stuck.
They optimize for deal size, not for speed, scale, and predictability.
And that’s exactly why an Operational ICP can’t rely on company size alone. It needs to reflect which customers actually buy, how fast they buy, and what they contribute to the business.
And once you see that pattern, you cannot unsee it.
Next, we needed to sanity-check the segment.
Was this a statistical coincidence, or a real opportunity?
We used Clay to bring the research together, connecting ZoomInfo, Apollo and other data sources and web scrapers to look at revenue ranges, headcount, industry, team structures, locations, likely buying roles, and timing signals.
This gave us a clearer picture of these companies than the “small” label in the CRM.
What we found:
In other words, this wasn’t a junk segment. It was one the company had written off, even though the numbers suggested it could make a real difference to ARR.
Data without translation doesn’t convince execs. So we built a clear business case.
We used this segment’s historical conversion rates to calculate what could happen if we contacted many more of these companies.
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 actually does its job: turning raw data into a simple, realistic plan for growth.
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.
With alignment in place, we moved to execution.
The principle was simple.
Automate everything until there is a positive intent signal.
But the important part wasn’t just sending more emails. It was connecting who we target, what we say, and when sales should step in.
That’s how we believe cold outbound should work.
We built a fully automated outreach flow covering the entire segment. Sales only stepped in when prospects showed interest, through replies, email clicks, or visits to the website.
That meant sales could focus on the prospects who were showing interest.
Key principles:
This is where AI tools actually matter in B2B sales and marketing: they help us do the research, personalize the messaging, and reach more of the right people, while sales spends time where it can move revenue.
That leaves sales with more time to talk to people who are actually interested.
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.
The same companies saw our ads, emails, and social posts before sales reached out.
Everything worked toward reaching the same accounts with a consistent message.
The channels weren’t working separately. They were supporting each other.
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.
The opportunity came from challenging an assumption. The revenue came from acting on what we found.
It would be easy to frame this as a tooling story.
HubSpot. ZoomInfo. Clay. Toffu.
That is not what made the difference.
What changed the outcome was:
Most companies already have enough tech.
What they lack is a clear GTM strategy and a marketing engine that connects the data, the decisions, and the execution.
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, AI, and execution that actually talk to each other.
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