What Information Do Investors Want From Feasibility Studies?

A feasibility study is not a business plan dressed up in softer language. Investors read them differently, ask different questions, and notice different gaps. What they are looking for is evidence that the people behind a product have tested their assumptions against reality, not just against their own enthusiasm. The difference matters, because enthusiasm is easy and evidence is not.

The best feasibility studies test assumptions against reality, not just against the founder's enthusiasm.

We have watched investor conversations stall at the most unexpected moments, and the pattern is consistent. It is rarely the big strategic question that trips a founder up. It is the detail underneath it: the assumption that was never tested, the number that cannot be traced to a source, the retention figure that was never measured because the product had not launched yet. A feasibility study that handles those gaps well does not just satisfy due diligence. It demonstrates the kind of thinking investors want to see running the business.

What follows is a breakdown of what investors actually look for inside a feasibility study, drawn from the work we do preparing products and founders for those conversations.

Market Validation and Demand Evidence

The first thing an investor wants to know is whether anyone actually wants this. That sounds obvious, but the evidence founders offer in answer to it is often thinner than they realise. A survey where 70% of respondents said they would probably use the product is stated intent, and stated intent is a different thing from demand evidence. Research from AppTweak puts this gap in sharp relief: when users say they would use an app, 60 to 80% typically respond positively, but actual usage sits at 10 to 20%.

Real demand evidence comes from people doing something, not just saying something. Pre-registrations, waitlists with meaningful conversion, pilot usage data, payments taken, partnerships signed. These are the things that narrow the gap between stated intent and actual behaviour.

We worked on a product aimed at African communities living abroad, designed to help people maintain connection with their home communities. Focus groups with the target market revealed insufficient desire for the product. The research did not just raise a concern, it answered it. That kind of disciplined early validation is exactly what investors are looking for, even when the conclusion is uncomfortable.

Show investors what people did, not just what they said. Pre-registrations, pilot sign-ups, and early conversion figures carry far more weight than survey results.

Financial Projections and Revenue Assumptions

Financial projections in a feasibility study are read as a test of the founder's judgment, not as a forecast. An investor who sees a revenue model built on capturing 8% of a total addressable market in year one does not trust the number. They question whether the founder understands how markets actually work.

New products typically capture somewhere between 0.1 and 1% of their addressable market in the first year, according to AppTweak. Founders regularly assume 5 to 10%. That gap is the difference between a projection grounded in comparable behaviour and one that starts from the desired outcome and works backwards.

What investors want to see is how the revenue assumptions were built. What is the unit economics basis? What does a customer cost to acquire, and what do they generate over time? Which assumptions are pinned to real data, and which are acknowledged as estimates? A model that shows its working is far more credible than one that arrives at an attractive number without explaining how.

Walk investors through the logic of your revenue model step by step. A projection that explains its assumptions is more credible than one that just presents a number.

Comparable Benchmarks and Market Sizing

When no direct comparable exists, investors expect founders to find the nearest equivalent and reason from it, adjusting up or down based on specific differences. Saying there is nothing quite like this in the market is a gap in the analysis, and investors will treat it as one.

On a feasibility study we ran for a niche trading platform, we benchmarked against comparable products in adjacent markets because no direct equivalent existed. We identified products performing some aspects of what the platform would do, examined their revenue and subscription models, and used those as a baseline. Where a comparable product already had strong traction, we scaled our projections down significantly to reflect the reality of launching a new product into the same space.

Where no direct comparable exists, find the nearest equivalent and reason from it deliberately.

This kind of benchmarking does two things. It grounds the numbers in observable behaviour rather than aspiration. And it shows an investor that the founder understands their own market position honestly.

Market sizing works the same way. A top-down calculation that starts with a large total market and divides by a percentage is easy to construct and hard to trust. A bottom-up model that counts realistic customer segments, plausible acquisition rates, and defensible price points is harder to build and much more convincing to read.

Technical Feasibility and Delivery Risk

Investors want to know whether the product can actually be built, by this team, with this budget, in a reasonable timeframe. Technical feasibility is about whether the execution path is credible.

We worked on a product aimed at African communities living abroad where technical feasibility was only part of the concern. The capabilities of devices commonly used in African countries at the time meant that delivering half of the intended experience would have been extremely challenging, regardless of how well the product was built. Market viability and technical constraint were linked, and both had to be addressed.

A feasibility study that skips technical risk assessment leaves a visible hole. Investors with any technical background will find it. The more useful approach is to name the risks clearly, explain how they are being managed, and be direct about what remains unresolved. Vague reassurance reads as a warning sign.

The delivery plan matters too. What will be built first, and why? What does the critical path look like? What are the dependencies? A product roadmap that answers these questions is evidence of operational thinking, not just product thinking, and investors are looking for both.

User Retention and Behavioural Assumptions

Acquisition numbers get more attention than they deserve in early-stage feasibility work. Retention is harder to measure before launch, so founders either skip it or guess. Investors know this, and the absence of a credible retention thesis is a flag.

We worked with a pre-investment client whose investor conversations covered technology, marketing, and market size, but never touched on how the product would feel or what user retention targets looked like. The silence was mistaken for approval. What it actually indicated was that investors had not grasped the emotional dimension of the product, and our team had to go back and build that out explicitly rather than accepting the absence of questions as a sign that everything was understood.

Between funding rounds on a separate project, we made the decision to implement much better tracking of user retention and to proactively ask users how things were going. That came directly from the recognition that investor confidence had been used as a proxy for product health, and that the actual retention picture had never been properly understood. The lesson transferred directly into how we now approach feasibility work.

Behavioural assumptions matter here too. A product that assumes users will return daily needs a clear reason why. What creates the habit? What is the emotional hook? These are not soft questions. They connect directly to lifetime value, to churn, and to the unit economics that underpin every revenue projection in the document.

Competitive Landscape and Differentiation

A competitive analysis that lists competitors and then concludes the product is better across every dimension is advocacy, and investors can tell the difference.

A pre-launch founder in the football industry came to us with a colour-coded spreadsheet mapping out competitor products, with the aim of merging multiple products into one. The founder was visibly excited and pleased with the work they had done. When we began asking questions about prospective users, specifically why someone would choose an all-in-one product over specialised apps and whether consolidation risked weakening individual features, the founder became deflated. That deflation was productive. The questions the spreadsheet could not answer were exactly the ones a serious investor would ask.

What investors want from a competitive section is not proof that the product wins. They want evidence that the founder understands the landscape honestly, including where competitors are strong, where the product is genuinely differentiated, and where the differentiation depends on execution rather than design. A competitive moat that relies on being first is only as durable as the lead time before someone else copies it.

According to an analysis of over 8,000 pitch decks by Evalyze, 67% had at least one issue that would surface as a flag in formal due diligence, with missing competitive moat detail being among the most common. The gap is in the willingness to apply that knowledge honestly.

Founder Objectivity and Research Integrity

A feasibility study is only as useful as the intent behind it. If the research process is designed to confirm what the founder already believes, the output is a document that looks like a feasibility study.

We eventually walked away from an engagement with a founder who had deep industry experience and strong pre-existing views about how the product should work. The research process became a box-ticking exercise. When we showed compelling evidence that a large portion of the target user base did not want certain features and preferred alternatives, the founder dismissed it entirely. That product launched about a year later, largely unchanged, and by all accounts did not go anywhere. The same issues the research had flagged were the ones that mattered in the end.

Investors read feasibility studies with this risk in mind. They are looking for signs that the founder engaged with inconvenient findings, not just the convenient ones. A study that presents only supportive evidence is suspicious. A study that documents where the evidence pushed back, and what the team did with that, is a much stronger document.

Research integrity also means being specific about method. How many people were interviewed? How were they recruited? What questions were asked, and how were the findings weighted? A feasibility study that cannot answer those questions has not done the work, and an investor who has seen enough of these documents will notice.

Include a section in your feasibility study that documents where the evidence challenged your assumptions, and what you did about it. Investors trust founders who engage with uncomfortable findings.

Trust Signals and Transparency

Investors are looking for founders who understand their risks and can explain them clearly. A feasibility study that buries or minimises risk does not reassure an investor. It makes them look harder for what is being hidden.

Transparency works when risks are presented alongside the reasons why the project is worth pursuing despite them. Presenting risks alone, without the counterbalancing case, is its own problem. The study becomes so cautious it gives the reader no reason to proceed. The balance is a risk section that is honest about what could go wrong, specific about the likelihood and impact, and clear about what mitigation is already in place or planned.

On a marketplace checkout project, we observed that confusion around platform fees caused measurable hesitation at the point of transaction, even when the amounts involved were small. The issue was not the size of the fee. It was the ambiguity around whether it had been added or was already included in the price shown. That ambiguity was enough to cause drop-off. The same dynamic applies to a feasibility study: what erodes confidence is the sense that something is unclear and the author knows it.

What Investors Notice When They Stop Asking Questions

Investor silence in a due diligence conversation is easy to misread. When questions dry up, it can feel like approval, like the document has answered everything and there is nothing left to probe. That reading is often wrong.

We worked with a pre-investment client whose investor conversations had covered technology, marketing, and market size without a single question about the emotional experience of the product or what the retention targets looked like. The silence was not endorsement. It was a sign that investors had not yet engaged with that part of the product at all, either because the study had not surfaced it clearly enough or because their mental model of the product did not yet include it. We had to go back and build that section out explicitly, rather than treating the absence of questions as confirmation that everything was understood.

What investors notice in the gaps is usually one of three things. The first is an assumption that was not stress-tested, where the number looks clean but the reasoning behind it is thin. The second is a risk that everyone in the room can see but that does not appear in the document, which suggests the founder is avoiding it. The third is a dimension of the product, often the behavioural or emotional one, that has simply not been thought through yet.

• An assumption that looks clean but has no reasoning behind it
• A visible risk that does not appear anywhere in the document
• A whole dimension of the product that has not been addressed

A strong feasibility study does not just answer the questions investors will ask. It answers the ones they might not think to ask, because the team has thought further ahead than the obvious.

Conclusion

Feasibility studies earn investor confidence by doing something most pitch materials do not: they show the thinking, not just the conclusion. A document that names its assumptions, tests them against real evidence, acknowledges where the evidence pushed back, and presents risks alongside the reasons to proceed anyway is a document that reads like it was written by people who understand their product honestly.

The detail matters. Benchmarks need to be traceable. Retention assumptions need a behavioural basis. Competitive analysis needs to account for where competitors are genuinely strong. Financial projections need to show their working. And the research behind all of it needs to have been conducted with real intent to learn, not just to confirm.

We have worked through this process across products in very different categories, from trading platforms to community tools to marketplace experiences. The same gaps come up. The same questions get asked. And the founders who handle those questions well are the ones who engaged with them before the investor conversation, not during it.

A feasibility study done properly is the foundation that makes everything that follows easier to defend. If you are preparing one and want a second set of eyes on whether it is doing the job it needs to do, let's talk about your feasibility study.

This article is part of our guide to App Planning & Strategy.

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