How Peoplelogic Turned 250 Leads a Month Into About 100 Meetings

Foundation

Peoplelogic is a fifteen person company with around a hundred clients, growing every month. They build performance management software, and they are working through the question everyone in HR tech is working through: how to use AI responsibly, improving the output rather than replacing the people.

Austin Evans runs sales. When he met ColdIQ in October 2023, his problem was not lead volume. It was that he could not tell who Peoplelogic actually sold to.

“We had a guess, but we had not found one that had been very, very repeatable. We could get some conversations going, but we weren’t really finding a common thread.”

That is a harder problem than an empty calendar, because you cannot solve it by working harder. You solve it with volume and testing, and Peoplelogic was not set up for either.

The challenge

• An ICP that was still a guess. Conversations happened, patterns did not.
• No technical setup for volume. Going wide was not something the team could do.
• No way to test quickly. Without rapid A/B testing, each attempt taught them almost nothing.
• A clear bar to clear. Austin’s standard for any agency engagement is 4x the money invested, and he says so before the results, not after.

The engagement started in early November 2023, paused around the holidays, and came back at the end of the month with waterfall leads.

The process

1. Volume as a research method

The point of going wide was not to send more email. It was to generate enough responses that segments could be compared at all.

2. Testing rapidly, and reporting back

The team ran through segments and Austin fed back which were working and which were not, which is how the common thread eventually surfaced: job titles, company size, and the combinations that repeat.

3. Measuring the right conversion

Austin was not counting responses. He was counting the rate at which responses became booked meetings, and then the rate at which those became opportunities. That is the number that tells you whether the segment is real.

4. Shared Slack, real responsiveness

Both teams in one channel, questions answered as they came. Austin’s description of the time zones is the detail that makes it concrete: he never expected twenty minute replies from a team on the other side of the world, and got them most of the time anyway.

The outcome

• Around 250 leads a month on average across the campaigns.
• About 100 meetings a month, at roughly 40% of that volume.
• A 30 to 40% conversion from response to booked meeting, which Austin calls very strong.
• Leads landing quickly, through November, December and January, rather than after a long ramp.
• An ICP that narrowed with every campaign, because the meetings themselves were teaching them who these buyers were.

His phrase for that period is “we were just hammering meetings”, and the meetings were the research.

Lessons learned

1. Volume is how you find an ICP. You cannot pattern match on a handful of conversations.
2. Track the conversion, not the replies. Response to booking is the number that separates a segment that works from one that is merely polite.
3. Set the ROI bar out loud. Austin named 4x before the engagement started, which made every decision easier to judge.
4. Meetings teach you things emails cannot. The feedback loop from real conversations is what sharpened the targeting.

What’s next

Peoplelogic keeps narrowing. The segments that repeat get more volume, the ones that do not get dropped, and the team now has the data to tell them apart.

Final thoughts

Austin’s closing argument is not about the numbers, it is about the approach. Hundreds of teams run demand gen email at volume and, in his words, they all do it the same way. What made ColdIQ worth betting on was a stack and an orchestration he had not seen anywhere else.

If you are still guessing at your ICP, and each guess costs you a quarter, the plays behind this story are in ColdIQ’s GTM Playbook.