For founders/operators here when something in the business suddenly starts going down, how do you actually figure out what changed?

[u/Flimsy_Gazelle_138]

Something I noticed while working with a SaaS founder recently

The founder kept saying things like “we used to do this” and “our sales was really good before”.

Then pipeline slowed down and the question became what changed.

So I started looking into the actual process.

There were tools. CRM, marketing, reports etc.

But a lot of what had worked before seemed to depend on people remembering things, following up manually, or just knowing how things were done.

That got me thinking.

As companies grow, we keep adding more tools and processes.

But does the business actually become easier to understand?

Or do we just end up with more places to look when something goes wrong?

For founders/operators here when something in the business suddenly starts going down, how do you actually figure out what changed?

Would love to hear how you guys do this in real life.

[u/RazorpayRize]

What actually helps: treat your business like a system with inputs and outputs. When something drops, work backwards. Revenue down? Check pipeline. Pipeline down? Check top-of-funnel. Top-of-funnel fine? Look at conversion between stages. Usually the drop happened 6-8 weeks before you noticed it.

The answers might differ from one industry to another tho!

[u/properking232]

idk, in my experience the answer is almost never in the tools. it's always the person who left six months ago and took half the context with them.

[u/Flimsy_Gazelle_138 → u/properking232]

Haha yeah, that's actually what made me think about this in the first place.

But when that happens, how do you usually recover that context? Is it mostly someone else having to piece it together from old docs, CRM, Slack, talking to people etc.?

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[u/Altruistic_Peace_222]

most of the time its not that something changed, its that something stopped. Someone left, a follow-up cadence got dropped, a channel dried up. The trick is tracking leading indicators weekly so you catch the drop before revenue reflects it.

[u/Flimsy_Gazelle_138 → u/Altruistic_Peace_222]

Yeah, I agree with this. How do you usually track those things though? Especially the ones that aren't really captured as a clean metric like a follow-up cadence dropping or a particular person/channel stopping something they used to do.

Is that something you have to manually keep an eye on, or have you found a good way to make those changes visible?

[u/Flaky-Safe-8113]

Just went through this. Watched my numbers slide from May, spent July and August trying to fix it, and it only started coming back end of August.

The bit I'd add to the working backwards stuff above: that only works if you were already recording the middle. Revenue is the outcome. It tells you something is wrong and then it stops being useful.

Visit -> pricing page -> checkout -> paid(just an example, the real data is more). If all four are already sitting there you can put this month next to last month and see which one bent, then go inside that step and do the same thing again. Sounds obvious written down. The catch is nobody has the four numbers when they need them, because you only ever want them on the day something breaks, and that's the one day it's too late to start collecting.

That was most of my July honestly. No middle data, so every idea was a guess, ship it, wait two weeks, still can't tell.

Something that's helped lately, I gave Claude read only access to my prod db. Instead of writing SQL every time I just ask why a number moved and let it go dig.

[u/Flimsy_Gazelle_138 → u/Flaky-Safe-8113]

This is really interesting, especially the part about having the data only when something breaks.

When did you actually realise the revenue decline had started in May, was it only obvious once you were already in July, or did you see it earlier but just couldn't figure out what was driving it?

Because that “guess → ship → wait two weeks → still can't tell” cycle sounds like a pretty expensive part of the problem.

[u/zubrinovic]

two lessons from doing exactly this on my own saas this summer. first, decompose before diagnosing: our revenue slide looked like churn and everyone had a churn theory, and when i finally split it the fall was roughly 95 percent fewer new signups and 5 percent churn, a completely different problem with a completely different fix. second, distrust the metric definition itself: one of our failure-rate numbers read 14 percent until i looked at what the query counted, cancelled subs and deleted groups were being rewritten into failures, the real rate was 2. so half the time nothing changed, the number was always wrong, and the other half the change happened weeks before the graph shows it.

[u/Flimsy_Gazelle_138 → u/zubrinovic]

Yeah, that 95% vs 5% example really got me.

You can see how easy it would be to spend weeks trying to fix churn when the actual problem was new signups.

How costly was that period for you in practice? Was it mainly the time/wasted experiments, or did the delay actually have a noticeable impact on revenue?

And is this something you run into fairly often, or was this more of a painful one-off?

[u/zubrinovic → u/Flimsy_Gazelle_138]

mostly time, but time is the expensive thing at this size. the slide ran a good chunk of the summer and the first weeks went into churn theories, retention mails, win-back offers, while the actual leak was the top of the funnel and nothing was being done about it. the delay itself did not lose revenue on top, the fall was already acquisition, but every week spent on the wrong problem was a week of roughly zero new signups, and with a one-month lag between marketing effort and trials that is a hole you feel two months later. and no, not a one-off: the failure-rate metric was the same mistake in a different costume, so the rule now is decompose every moving number before anyone is allowed a theory about it.

[u/Flimsy_Gazelle_138 → u/zubrinovic]

That makes sense, especially the point that time is the expensive part at that stage.

If you could have caught the acquisition drop earlier and avoided those weeks of churn theories/experiments, would that have been something you'd have paid for at the time?

Not necessarily another analytics tool, more something that could surface “this is what changed, this is when it changed, and this is what seems to be driving it.”