Launching my SaaS-only buyer database today on Product Hunt. Here's the bet I made on pricing.

Thank you I appreciate the support and kind words. Hopefully you can try it out sometime as well!

Good to find someone in the same boat, credit models make this problem so much sharper than seat-based ones do. Here's where I've landed so far, though I'm still figuring it out too.

What's helped most is measuring dips against each account's own baseline instead of an absolute number. A team that buys every week going three weeks silent is a real signal; the same gap from a monthly buyer is nothing. So I track days-since-last-purchase relative to that account's median interval and flag when someone breaks their own rhythm, rather than watching a global average.

The other piece is leading indicators before the buying stops. Logins and searches usually decay before purchases do, so someone still showing up and running filters but not spending is a different problem (friction, or they stopped finding value) than someone who's gone fully dark. Catching that first group is where the save actually happens, because by the time purchases stop you're often too late.

Still doing a lot of it by hand while I'm small enough to. Would genuinely like to compare notes as you go, sounds like we're solving the identical thing from two directions.

This is a great call, and it's the axis a pure "who" database is missing. Being a software buyer tells you someone could buy, timing tells you they might right now, and funding is one of the cleanest budget signals there is. Form D is a smart source for it, public and fast.

A "raised in the last 90 days" filter is exactly the kind of intent layer I want sitting on top of the static list, so this is squarely the direction. The thing I'd want to get right: funding is one timing signal, not the only one. New exec hires in the buying function and fast headcount growth often move budget as much as a raise does, and Form D misses the bootstrapped and revenue-funded teams entirely. So I'd build it as one signal in a stack rather than the whole intent story. But you've named the highest-leverage one to start with. Appreciate the specific pointer, that's a useful one.

Thanks, appreciate that. Questioning the default was the whole starting point, so it's good to hear it reads as a reason to try the product rather than a risk.

On the EA and social side, I'm keeping things lean and running the social myself for now, since it's founder-brand-led and tough to hand off, but I appreciate you putting it out there. I'll keep you in mind if that changes as we grow. Best of luck with the search.

Thanks. The smaller TAM was the deliberate part, I'd rather own the software niche completely than be one more general database competing on row count.

On spend, the honest answer is usually both, for a specific reason. The thing nobody says about seat pricing is how much of the allocation goes unused. People buy 10,000 credits, pull 800, and pay again the next year for the 9,000+ they never touched. Pay-per-contact just stops billing them for the waste, so "cheaper" for most buyers isn't a discount, it's not paying for what they didn't use. Clearer comes on top, since you always know exactly what a given list cost, no annual true-up.

Where I'll be straight: a genuine high-volume user who burns their whole seat every month can come out cheaper on a flat plan. Those aren't really the buyers this is built for, and I'm fine with that. Too early for clean 30-day cohort numbers, but the teams who've switched so far skew lighter and mid-volume, which is exactly where the savings are real.

Thanks, appreciate that. The niche-first call was the one I was most nervous about, since every instinct in this category says a bigger database wins, so it's good to hear it reads as more valuable rather than thinner.

Retention is the metric I'm watching hardest too, and it looks different without a subscription. There's no renewal date, so nobody formally churns, they just stop topping up. That makes the signal quieter and slower to read, so I'm tracking top-up cadence instead of renewals and reaching out to anyone who goes quiet while I'm still small enough to ask. Too early to call it, but that's the number that'll tell me whether the model actually holds up long term.

Thank you I didn't go higher than safe for me the price per customer isn't the biggest driver, I want users to get value.

You're right, and I'd push it further: raising the stakes on every row is the point. A subscriber pays up front and forgives a few bad contacts because the money's already spent. Mine hasn't paid yet, so every credit has to earn it. Uncomfortable, and also the thing that keeps us honest. Revenue only moves when the data's genuinely worth paying for, so quality stops being a roadmap line and becomes the business.

The preview does more work here than people expect. You see the match before you spend the credit, so you're never buying blind.

The pricing-as-moat framing is sharper than how I'd been putting it. A SaaS-only dataset is copyable. "We only get paid when you find someone worth paying for" isn't, not without a competitor blowing up their own ARR to match it. Taking that line.

Quiet churn is the one that actually worries me, for the reason you gave: no renewal date means no moment where anyone tells you they're done, they just go quiet. So I'm treating days-since-last-purchase as the renewal signal I don't otherwise get, and reaching out to anyone who was active and went dark, by hand, while I'm still small enough to. Already teaching me more than the dashboards. Appreciate you thinking past the pitch.

Thanks the the goal is to have an outcome pricing model in the future as well, instead of just the typical subscription model

Hi thanks for introducing yourself and nice to meet you as well