When we launched our software year we set the price at nine dollars a month because we were afraid that nobody would pay more for an early stage tool. Signups came in fast. Our support costs soon grew faster, than our revenue and working late nights became unsustainable.

Of raising the price on the entire public landing page we ran a quiet experiment with new visitors. We added a forty nine dollar tier that included priority setup support. We expected the conversion rate to fall to zero. Surprisingly twenty percent of new users chose the higher tier.

The higher price attracted serious users who needed less hand holding and who valued the software s core utility. Our monthly recurring revenue tripled, even though the customer volume was cut in half proving that underpricing was our bottleneck.

How did you find the courage to raise your price. How did your early users react?

Everyone says to test demand before you build. We didn t. We built the product first, like a lot of people do, and only afterward found a fast way to test the part we d skipped.

What we re testing now isn t really the idea - it s the positioning: Does someone landing on our page understand what the product is? Does the offer make sense? Does the pricing feel right?
We use a paid research panel (Prolific, though there are others like Wynter, PickFu and Lyssna) to put the page in front of people who match our audience. They read it and answer a short questionnaire: What do you think this does? Do you understand it? Does the price seem fair? You pick the audience, they review, you pay per response. A few hours to prepare, results back the same day.

It's been most useful for catching where people misread the offer - something our existing users can t really tell us, because they already understood it well enough to sign up. We ll use this approach for landing page changes from now on, and we ll run these tests before building the next thing rather than after.

How do you test whether people understand your product or your idea before you commit to it?

Being an engineer, I tend to think features first.

Still, the time my business grew fastest wasn't after a feature launch. It was after I took advice from someone outside the company and redid the whole site design. Same product underneath.

What has done the most for your product so far?

I've been building a Telegram Mini App recently, and one thing I've spent time on is making sure the backend doesn't blindly trust requests coming from the frontend. It's tempting to assume that because someone opened your app through Telegram, the request is legitimate. But the frontend is still just a client, and requests can be modified. For my project, I started testing what happens when authentication data is missing or modified, and whether protected endpoints reject those requests properly. It reminded me that a working login flow and a secure login flow aren't necessarily the same thing. For developers who've built Telegram Mini Apps or similar embedded apps, what authentication mistakes have you encountered, and how did you catch them during testing?

Each strategic initiative has its own goals and KPIs, but the data is spread across different teams and tools.

Before every leadership review, someone has to chase updates by email, wait for responses, reconcile numbers, and piece everything together.

The leaders I interviewed described reporting cycles that consumed 20 60 hours of manual effort. And by the time the information reached leadership, some of it was already outdated.

But what interested me most wasn't just the time being lost.

When a user is flagged by my safety layer, the app goes quiet for a while. I kept the whole rule in one pure function: it takes the user s recent flags and the current time, and returns whether they re paused, until when, and why. No database, no clock reads inside, and the lengths live in named constants (pause length, repeat window, minimum gap between two flags that count as a repeat).

The rule itself is small, but time rules go wrong at the edges: exactly at a boundary, two flags a minute apart, a timestamp with microseconds and a +00:00 offset. So I wrote 61 tests, then did something I now do for every rule like this: I broke the code on purpose, seven different ways, and checked that a test failed each time. I removed the minimum gap, counted flags I shouldn t count, measured a repeat from the wrong flag, made the pause read fail closed, and so on. All seven were caught. A test that has never failed proves nothing.

Two things the design review found before users did:

1. I first started the soft period (gentler messaging after a pause) at the flag time. That meant it ended the moment the longest pause ended, which is the worst time to switch back to normal. I moved the start to the pause end. That quietly broke something else: the database query only looked back as far as the old rule needed, so a repeat flag would have fallen outside it while the soft period was still running. The look-back had to grow to cover pause plus soft period plus repeat window. A test now fails if I shrink it.

2. Contact-form flags must not count as repeats. The table holds only a user ID, a level, and a time, and I didn t want a text column, so I added a single yes/no column instead.

For a long time we looked at total daily page views and the number of registered accounts as our main signs of growth. Those easy to see numbers made us feel good each morning. Did not tell us if people were getting real value from the software.

We stopped looking at of-funnel traffic completely and started focusing on one retention measure: whether a new user finished our main setup process within their first ten minutes. If they completed that step their thirty-day retention rate went above sixty percent.

That single measure became our guide for every product change and every engineering project. If a new design caused fewer people to finish the setup process we changed it back away even if it looked nice.

What one user action do you follow to check involvement, with the product instead of just simple activity?

I m trying to understand what makes someone try a SaaS product and then stop using it after a few days.

Analytics can show where people drop off, but it doesn t always explain why. I think a short conversation with those users could help, though I m not sure what to ask without leading them toward an answer.

I've read a lot of conflicting views on this ... I'd love some advice.

We're about to relaunch our news platform ... which has been locked behind an "invite code" for a few months in ~beta state ... and we'd like to create some buzz!!..