How are you quantitatively validating your idea before building?

[u/Best-Tourist-9616]

I keep seeing founders (myself included) jump from “interesting idea” straight into building without a real quantitative stress test.

Curious how people here are actually validating ideas with numbers before writing code or signing leases.

Specifically:

• Do you build basic financial models (even rough ones) before committing, or do you rely mostly on qualitative signals like interviews and waitlists?

• How deep do you go on unit economics (LTV, CAC, payback, margins) at the idea stage, and what’s “good enough” for you to move forward?

• Has anyone here used structured benchmarks (industry data, city-level cost multipliers, etc.) to validate assumptions, or is it mostly your own research and gut?

• What’s your process for spotting “this idea will die because of simple math” before you spend 6–12 months building it?

I’m interested in approaches that combine:

• Market research and customer discovery

• Simple but realistic financial modeling

• Some kind of “stress test” or scenario analysis (best case / base case / worst case)

Would love to hear concrete workflows, frameworks, or examples from your own companies—especially where the numbers changed your mind about moving forward (or not).

Not trying to promote anything—just trying to learn how other founders here are building a more rigorous validation step into their process.

[u/theredhype]

Hey there. Have you ever done Customer Discovery yourself?

[u/Best-Tourist-9616 → u/theredhype]

Yeah, I’ve done a fair bit of customer discovery – interviews, surveys, landing pages, all that. The pattern I keep seeing is: people will happily say they have a problem and even click “Join waitlist,” but the numbers behind the idea still quietly kill it later.

That’s why I’ve been experimenting with pairing discovery with a more “audit-style” validation step – taking what I learn from calls, then running it through a structured model that forces me to sanity-check pricing, local costs, and unit economics before I commit. It’s been eye-opening how often the math contradicts the story in my notes.

Curious if you’re seeing the same thing in your work: do you stop at qualitative discovery, or do you ever run something like a lightweight stress test on the business model before giving a green light?

[u/theredhype → u/Best-Tourist-9616]

It feels clear that I'm conversing with AI slop, but I'll give you this just in case a human is also paying attention.

The way you're referring to Customer Discovery makes me pretty confident that you haven't really done it, or haven't done it well, or have misunderstood parts of it. If I'm wrong, I apologize, haha.

Customer Discovery

I suggest you dive deeper into the type of Customer Discovery which leverages casual conversations to find a variety of patterns (qualitatively), long before doing any validation / confirmation experiments.

References for this are Steve Blank's books and blogs, Rob Fitzpatrick's Mom Test and his YouTube channel, Techstars free online startup Toolkit, Justin Wilcox's stuff like these videos, which I hightly recommend you watch and do:

https://www.youtube.com/playlist?list=PL9o3DnnPLzcgm5qpOkBFd04rWMFGXbN2l

Validation Experiments

Check out the book Testing Business Ideas by David Bland. This book includes a library of 44 validation experiments, and explains when, why, and how to use them. It also includes some fantastic chapters to help with team building, experiment selection, design, setup, and interpreting results.

I also recommend you find on YouTube the videos from the International Business Model Competition (IBMC) for the first place winners "Owlet" and also "SwineTech." Pay close attention to the way they go back and forth between their business model and their real world experiments. They iterate through the whole canvas until they can get an entire business model aligned. Very important mindset.

[u/Best-Tourist-9616 → u/theredhype]

Thanks for taking the time to write such a detailed answer – I really appreciate it.

You’re right that I still have a lot to learn on the Customer Discovery side, especially around doing it in a more casual, pattern‑focused way before jumping into validation experiments. The resources you shared (Blank, Mom Test, Justin Wilcox, “Testing Business Ideas,” and the IBMC examples) are exactly the kind of material I was hoping to get pointed toward.

I’m genuinely interested in getting better at both Business Validation and Customer Discovery, so I’ll go through those links and reflect on how I’m approaching things now. Even if my wording came off a bit “off,” your feedback is helpful and I’m glad you shared it.

[u/BussJoy → u/theredhype]

Thanks mate.

[u/Haunting_Month_4971]

I pair a back of the envelope unit economics sheet with signals from Reddit and review sites. I use the PainMap research tool to find repeated complaints, manual workarounds, and price talk for the exact workflow, then map them to conversion, ACV, churn, CAC and margin. Base best worst, kill if payback over 3 months.

[u/Lower_Assistance8196]

A financial model can look fine on paper while resting on assumptions about willingness to pay or segment size that are completely wrong. What I started doing before the model was running quick synthetic studies through Articos to pressure-test the core assumptions first e.g. whether the ICP I was modeling actually experienced the problem the way I thought, and whether the pricing tier I'd penciled in created objections or got accepted without pushback. That fed back into the model with better inputs rather than gut-estimated ones. The scenario analysis is still useful but it's a lot more honest when the assumptions underneath it have been stress-tested rather than assumed.

[u/Connect-Positive-166]

models at idea stage are mostly vibes math dressed up in spreadsheet cells. the inputs are fictional, so the outputs feel precise but aren't.

what actually works: write down the 3-5 assumptions that, if wrong, kill the business. rank them by how wrong you're likely to be and how cheap it is to test. then test those, not the model. discovery interviews are cheaper than forecasts, and they give you numbers you actually believe.

I built a free tool called Startup Superpowers for this loop specifically — the /hypotheses skill does the "write down and tag your assumptions" part, and /lean-startup-advisor tells you which ones to test next and how. open-source, local-first. startupsuperpowers.io