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I read 180 reviews of a $116M CRM before we built ours. Here's what small teams actually complain about.

We needed a CRM at our company. We're a software shop, we've shipped plenty of things for other people, and we couldn't find one we'd actually open every day.

I assumed that was a us problem. Bad discipline, wrong process, something like that. So before committing to build anything, I went and checked whether it really was just us.

I read about 180 reviews and threads about Attio. $116M raised, one of the best designed products in the category, the kind of tool people post screenshots of. Sources were G2, Capterra, Trustpilot, TrustRadius, the mobile app stores, Product Hunt, and around 11 Reddit threads including an AMA their team ran themselves. Roughly 85% of it was from this year.

Here's what came out of it.

Nobody complained about the features

The line that stuck with me came from a small team who'd churned:

"Three months and we still hadn't built our lists."

That's the shape of most of the negative reviews. Barely any of them are about missing functionality or bugs. They're about paying for something and still not being set up months later.

Modern CRMs are flexible by design. You get objects, attributes, a data model, and the freedom to build exactly what your business needs. That's a real strength if you have someone whose job is to build it.

If you're 2 to 10 people, nobody has that job. You have Tuesday.

The part that surprised me

I went back and recounted, because I thought I'd made an error.

The most common positive theme across every source was ease of use. People called the interface clean, fast, a pleasure to work in.

The most common negative theme was setup burden.

Both are true at the same time, and they aren't contradicting each other. The product is lovely to use once you've decided what it should contain. The hard part sits before that, and it lands at the exact moment a new customer has the least patience and the least context.

So the gap isn't usability. It's that time to value gets measured in weeks while the trial gets measured in days.

That reframed the whole project for us. We'd been thinking about features. The real opening is the first 30 minutes.

The rest of it, roughly ranked

After setup burden, the themes that kept repeating:

Per seat pricing changes how teams behave. Several reviewers described sitting just under a plan boundary and deciding not to add someone. A pricing model that makes hiring feel expensive is working against its own customer.

Support latency on lower tiers. Less frequent than I expected. Much angrier when it showed up.

Email sync that assumes Gmail or Microsoft. If you're on neither, you're logging correspondence by hand, and nobody sustains that.

Reporting inherits the setup problem. Data that was never modelled properly produces reports nobody trusts, and untrusted reports get ignored.

AI pricing nobody can forecast. This one is growing across the whole category. People aren't objecting to paying for AI. They're objecting to not knowing what a month costs until the month is over.

Mobile trailing the web product badly.

Three things I'd tell myself if I could go back

Read the 3 star reviews. Skip the 1 stars. One star reviews are usually a billing dispute or somebody who bought the wrong tool. Three star reviews are written by people who want the product to work and are telling you precisely where it doesn't.

Count the praise, not just the complaints. I almost built a marketing angle out of "the leader is hard to use." That would have been a lie, and anyone who'd actually used it would have spotted the lie inside one sentence. It's a joy to use. That's why it's the leader. The honest angle turned out to be much narrower and much more useful.

Go read your competitor's own AMA. Attio's team ran one. 42 top level comments showed up asking for things. That's a free, public, ranked feature request list written by exactly the customers you're chasing, and almost nobody bothers to read it.

What we're doing with it

We're building SaleQue. A CRM for teams of 2 to 10, and the entire bet is that first 30 minutes. Leads, deals, tasks and your inbox already set up the first time you log in, so there's nothing to model before the thing is useful. Flat price for the whole team instead of per seat. And when our AI features land, you'll see what an action costs before you run it.

It isn't live. Waitlist opens in a few days. I'm deliberately not dropping a link here because that's not why I wrote this.

What I'd actually like: if you've abandoned a CRM in the last year, tell me which one and where it lost you. I've got 180 strangers' opinions and I'd trade a chunk of them for 10 from people who'll tell me I'm wrong.

on September 14, 2026
  1. 1

    180 reviews is real diligence, most competitive research stops at a handful of the loudest complaints. What surprised you most, was it a complaint that showed up far more often than you expected, or one you assumed would be common that barely came up at all?

  2. 2

    You draw an important line here: a clean interface doesn't mean people can start quickly. The first 30 minutes should help them complete one useful task with real data. Show them the model behind it later. We see the same problem with DictaFlow setup. Custom vocabulary only matters after someone has dictated successfully in the app they need. If customers have to configure the system before their first win, the trial clock is already working against you.

    1. 1

      Vocabulary is the awkward one though. A lawyer dictating without it gets back garbage, decides the transcription is bad, and leaves. So "win first, configure later" quietly becomes "your defaults have to be good enough that nobody needs the setting."

      Same trap for me. A pipeline with the wrong stages is worse than an empty one, because now they're deleting before they're adding.

      How do you handle a first run for someone with heavy jargon?

    2. 1

      The AI pricing one is where I'd push back a bit, or at least add a third option.

      Flat rate and caps both fix forecasting by cutting the link between usage and cost. That works right up until your heaviest users are the ones you most want to keep, and then you're rationing the thing they came for.

      What I think actually solves it is showing the price at the moment of the action. The button says what it costs before you press it, so forecasting stops being the user's problem. There's never a moment where they spent money without deciding to.

      That's the bet anyway. Could be wrong, and it's a lot harder to build than a flat rate.

      On the 1-stars, one thing that surprised me: a decent chunk weren't about the product at all. Sales process, annual contracts, a renewal nobody saw coming. Useful, but for a different team than mine.

      And yeah, the AMA. 42 top level comments, most from people who'd clearly been in the product for years. I almost skipped it because I assumed it'd be marketing.

      Curious what you're building, and whether your category has an equivalent. The AMA thing might be CRM specific and I can't tell yet.

  3. 2

    "Three months and we still hadn't built our lists" is the most honest user complaint I've read about the modern CRM category. The whole flexible-by-design premise assumes someone has the time and context to design it. Small teams don't have that person.

    The 3-star review framing is the one I wish I'd had earlier. Those reviewers had skin in the game - they wanted the product to work. The 1-stars are usually a decision regret or a billing issue. The 3-stars are actual field notes from people who tried.

    On the AI pricing concern: this is going to bite a lot of B2B SaaS this year. Users aren't refusing to pay for AI. They're refusing to plan budgets around costs they can't forecast. The products that flat-rate or cap AI usage - even at lower margins - will win trust faster than those optimising for per-token revenue.

    The competitor AMA insight is underrated for CRM specifically. That's as close as you'll get to a live, public roadmap of what the category's best customers have already decided they need and haven't got.

    Good luck with SaleQue. The first 30 minutes bet is the right bet.

    1. 1

      The AI pricing one is where I'd push back a bit, or at least add a third option.

      Flat rate and caps both fix forecasting by cutting the link between usage and cost. That works right up until your heaviest users are the ones you most want to keep, and then you're rationing the thing they came for.

      What I think actually solves it is showing the price at the moment of the action. The button says what it costs before you press it, so forecasting stops being the user's problem. There's never a moment where they spent money without deciding to.

      That's the bet anyway. Could be wrong, and it's a lot harder to build than a flat rate.

      On the 1-stars, one thing that surprised me: a decent chunk weren't about the product at all. Sales process, annual contracts, a renewal nobody saw coming. Useful, but for a different team than mine.

      And yeah, the AMA. 42 top level comments, most from people who'd clearly been in the product for years. I almost skipped it because I assumed it'd be marketing.

      Curious what you're building, and whether your category has an equivalent. The AMA thing might be CRM specific and I can't tell yet.

  4. 2

    Your “first 30 minutes” thesis is testable before more product build: give five 2–10-person teams a seeded workspace that mirrors their real pipeline, then watch which decision they can make in the first 15 minutes. The most useful initial promise may be “know who needs a follow-up today” rather than generic CRM setup. If that works, flat pricing becomes proof rather than the headline.

    1. 1

      "Know who needs a follow-up today" is a better promise than anything I'd written. Mine was a state, yours is an outcome. Taking it.

      I can run the 5 team test next week, I've got the client list for it.

      The catch is I'd be seeding those workspaces by hand. If that works, the hard part just moves to making the product seed itself, which is the actual build.

      Will report back.

      1. 1

        Exactly—manual seeding is fine for a discovery test if you treat it as concierge onboarding, not evidence of a scalable onboarding flow. For each team, record the smallest inputs you needed, every default they corrected, and whether the same inputs can produce a trustworthy “follow-ups today” list without bespoke interpretation. Those corrections are the spec for the self-seeding product. Alongside time-to-first-outcome, log time-to-first-correction; that will show whether the promise survives a real pipeline.

        1. 1

          Turns the test from a yes/no into a list of things the product has to learn.

          One thing I'd watch: a wrong default nobody corrects looks the same in the logs as a right one. Some people won't fix it, they'll work around it or quietly stop.

          So I'll sit on the calls rather than instrument it. 5 teams is small enough.

          Time-to-first-correction is going in the sheet either way.

  5. 2

    Setup is the failure people can articulate. The one that actually killed CRM rollouts across the 20 years I spent scaling a services company was week six, when reps stopped updating records because updating a record did nothing for the rep, it only fed someone else's dashboard. If your bet is the first 30 minutes, pair it with a rule that every field a person fills in returns something to that person the same day, or you'll win the trial and lose the quarter.

    1. 1

      This is the better critique and I don't have a defence for it.

      It also exposes a hole in my method. Review sites collect people who churned loudly or were still evaluating. The week 6 death is silent. Nobody writes a review because they quietly stopped opening something, so 180 reviews were never going to show me that failure at all.

      The rule is a good one. Applied to a CRM I think it means logging a call has to hand back the next move, not just store the call.

      What did your reps actually want back? I'd guess wrong, and it sounds like you watched this fail more than once.

  6. 2

    The 180 reviews make the setup-burden thesis much stronger than a typical competitor teardown. The real test now seems to be whether “useful in 30 minutes” actually changes adoption—have you validated that small teams can reach a meaningful first outcome that quickly, or is that still the core assumption behind SaleQue?

    1. 1

      Still an assumption. No users, nothing shipped, so I can't claim otherwise.

      What the reviews validate is the problem, that slow setup kills adoption. They say nothing about whether my fix works. Worth keeping those two apart.

      The number I'm holding myself to: 40% of new teams logging 10 or more leads in week 1, and 35% moving a deal. If it misses, the thesis was wrong and 180 reviews won't save it.

      I'll post the real figures either way once there are some.

      1. 1

        The 10-lead and moved-deal thresholds make the test much more concrete. If you’re open to it, what’s the best email to reach you on?

        1. 1

          tamal at eleganttechbd dot com

          I'll send you what comes out of it. And if you've got a real pipeline sitting somewhere, want totest and give feedback? I seed the workspace by hand, you spend 15 minutes in it, I shut up and watch.
          What are you working on?

          1. 1

            Thanks! I’ve just sent it over.

            Looking forward to hearing your thoughts whenever you have a chance.

  7. 1

    The distinction between validating the problem and validating your solution really stood out to me.

    I'm at a similar stage with something I'm building. It's very easy to find evidence that a problem might exist and then convince yourself that means people want your solution.

    I'm trying to get better at separating those two before building more. The 3-star review idea is a good one — I hadn't thought about deliberately focusing there.

    1. 1

      I only learned that one in this thread, two comments up. Someone asked whether I'd validated the 30 minutes or just assumed it, and I had to admit I'd assumed it.

      On the 3-stars, the thing that worked was reading only the "what do you dislike" field and ignoring the rest. Strips out the enthusiasm and the grudges in one move.

      What are you building?

      1. 1

        I'm building a small tool that checks whether ChatGPT recommends your product when people ask for tools in your category, or whether it recommends competitors instead.
        I'm actually trying to validate the assumption behind it right now — whether founders even care about tracking that 😅

        1. 1

          Great to know, good luck to you.

          Interestingly that's my next product, team is working behind it...

          1. 1

            That's interesting — what made you decide this was worth building? Did users actually ask for it, or was it something your team noticed?

            1. 1

              This is one of the hyped product now, and alternative SEO service, it's a growing industry. Let's see .....Good luck to yours.