u/Creative_Show_8852

We created events for everything. Page views, button clicks, session lengths, feature usage. Thousands of rows per user per day. Our Postgres instance was enormous and we felt incredibly smart about it. Asked a basic question six months in: which features do users touch before converting to paid? Three engineers. Two days. Zero confident answer. That's not a data problem. That's a queryability problem. The data existed. Our ability to actually interrogate it fast enough to make decisions did not. Guesswork is unacceptable when you're sitting on the answer. You just can't reach it but most datadriven startups are actually just data-adjacent. They collect. They don't operate. The gap between those two things is where revenue leaks live, and nobody talks about it.

Eight months. Hundreds of small decisions... Countless strategic pivots I told myself were intentional. One cold DM I almost didn't send drove 40% of my MRR. Not the funnel. Not the SEO play. Not the Product Hunt launch that flopped harder than I want to admit... Guesswork dressed up as strategy, and somehow it worked. That's the truth nobody posts.

So, I told myself manual onboarding was intentional. Relationship building. White glove experience... It was 10 hours a week of me ctrl+C, ctrl+V-ing the same welcome sequences like a sleep-deprived intern. Used skene.ai to map the actual flow, spotted three redundant steps immediately, automated the whole thing in an afternoon. Users got faster responses. I got my nights back. The human touch was a cope.

There’s a lot of confusion around these categories, so I tried to lay them out based on what I’ve seen in research and in recruiter communities.

ATS automation (things built into systems like Greenhouse, Lever, iCIMS) This is usually for teams that already have a defined process but want to cut down on manual work. Moving candidates through stages faster, triggering emails automatically, reducing the amount of admin recruiters have to do. The main limitation is that you’re still working within whatever your ATS can support.

Standalone AI recruiting assistants (tools like Paradox or Findem) These typically sit at certain points in the funnel. They can engage candidates, handle scheduling, and surface information before a recruiter reviews someone. They’re usually faster to roll out, but they tend to solve more specific problems rather than changing the whole process.

Agent-based recruiting platforms (Carv, HireEZ) This is a newer category that gets mixed in with the others a lot. Instead of sitting inside the ATS or helping with one step, the focus is more on where the hiring process breaks down across teams and stages, and trying to automate parts of that coordination. They usually take more work to implement, but they’re trying to tackle a different kind of problem.

End of the day, it just comes down to what the bottleneck is.

Teams slowed down by internal coordination often get a lot of mileage just improving their ATS automation. AI assistants are great but when dealing with sheer volume, the agentic approach of handing off a task to an agent and being able to focus on other process areas is a huge time saver in the long run, though from what Ive seen, these can take a bit to setup.

Where does your team usually lose the most time in the hiring process?

Everyone's in the retention metrics thread talking about NPS and DAU like it's 2015... The metric that actually saved my product: contraction MRR by feature usage segment. Sounds boring. Absolutely is not. When I cross-referenced which features users touched before downgrading, I found out 60% of contractions came from users who never once touched my collaboration tools. Never once.

Didn't need a survey. Didn't need a customer success call. The data just screamed at me. So, I killed three features nobody used, doubled down on the one that correlated with expansion MRR, survived on ramen for another season, and watched net revenue retention go from 94% to 118% in four months. PLG or GTFO, but you actually have to look at the right numbers... Usually people track retention like it's a report card. It's actually a treasure map.