A promising solution is not automatically a viable business. Pressure-testing your business model means examining whether a clearly identified buyer will pay, whether you can reach and convert customers through a realistic route to market, and whether the resulting revenue can eventually support the costs of acquiring, delivering to, and serving them.

A startup can have a real problem, a clever product, and enthusiastic users, and still not have a business.

That is the core of the Business Risk profile.

The product may create genuine value. The market may even care. But “useful” does not pay invoices on its own.

A business model describes how your startup creates, delivers, and captures value. The Business Model Canvas popularised this wider view by encouraging teams to look beyond the product and consider customer segments, channels, revenue streams, key activities, partners, and costs. [1]

For founders, the practical message is simple:

Your product is only one part of the business model.

If the economic buyer is unclear, willingness to pay remains hypothetical, the route to market is unrealistic, or the cost structure cannot support the expected revenue, even an excellent product may not become a sustainable company.

 

Start with the 4 money questions

 

To pressure-test your business model, begin with four questions:

• Who pays?
• Why do they pay?
• How do you reach them?
• Can you deliver profitably or sustainably?

Early-stage teams tend to answer the first question too vaguely: “companies”, “hospitals”, “universities”.

These are not customers. They are categories.

A better answer identifies the economic buyer: the person or unit with budget, urgency, and authority.

For example:

• Not “hospitals”, but “heads of radiology trying to reduce reporting backlog”.
• Not “manufacturers”, but “operations managers responsible for reducing downtime”.
• Not “research institutions”, but “technology transfer offices looking for licensing-ready proof”.

If you know who pays, your sales process becomes much easier.

 

Separate users from buyers

 

In B2B and deep-tech startups, the person who uses the product may not be the person who pays for it. This creates business model risk.

A lab technician may love your tool. The department head may worry about budget. IT may worry about integration. Procurement may worry about vendor risk. Legal may appear late in the process, carrying a very small hammer and a very large checklist.

This is why pressure-testing requires conversations with multiple stakeholders. It is important to interview potential customers, partners, and business stakeholders to assess whether an innovation could form the basis of a sustainable business model. [2]

Your goal should be to understand the buying system.

Ask:

• Who feels the pain?
• Who owns the budget?
• Who approves the purchase?
• Who can block adoption?
• What evidence is needed internally?
• What alternatives are already approved?

Test willingness to pay early

 

It is tempting to delay pricing because there is the fear it will scare customers away.
But avoiding price discussions hides business risk.

You do not need a perfect pricing model at the beginning.
But you do need evidence that the value is strong enough to support payment.

Ask customers:

• What does this problem cost today?
• Is there a budget for solving it?
• What category would this purchase fall into?
• What have you paid for similar solutions?
• What would make this worth paying for?
• Who would need to approve it?

And test behaviour:

• paid pilot,
• letter of intent,
• budget owner meeting,
• procurement introduction,
• pre-order,
• implementation fee,
• design partner agreement.

You can distinguish between stronger evidence, such as actual behaviour, purchases, usage, and retention, and weaker evidence, such as opinions and hypothetical responses. [4] Look for the stronger one.

 

Pressure-test your channel

 

A business model can fail because the product is too hard or expensive to sell.

This is especially relevant for startups selling to enterprises, public institutions, healthcare systems, universities, or regulated industries.

Long sales cycles, tender processes, integration requirements, and trust barriers can turn a theoretically attractive market into a slow and expensive one.

Pressure-test:

• How will customers first hear about you?
• Can you reach them directly?
• Do you need partners?
• How long does the sales cycle take?
• What proof is required before purchase?
• What does customer acquisition cost?
• Can your pricing support that cost?

If your expected contract value is small but the sales process requires six months, three committees, and a ceremonial procurement dance, the business model needs work.

Check the basic economics

 

At the pre-seed stage, startups do not need a perfect financial model,
but they do need credible economic logic.

Founders should estimate key variables such as pricing, gross margin, cost to serve, onboarding and sales effort, support requirements, retention, and expansion potential.

For hardware, biotech, climate tech, and other deep-tech ventures, this should also account for factors such as manufacturing costs, certification, regulatory timelines, maintenance, and partner margins.

The point is not precision, but to identify economically impossible assumptions before significant time and capital are invested.

 

Conclusion

 

Business model risk appears when the solution may be valuable, but the path to a viable company is still unclear.

Pressure-testing your business model means moving from attractive slides to uncomfortable specifics: who pays, why they pay, how you reach them, what adoption requires, and whether the numbers can eventually work.

A good business model gives the product a route into the world.

If you want structured support with business model development, validation, market access, and growth, explore the INiTS SCALEup Incubation Program.

Explore INiTS SCALEup → inits.at/en/scaleup

 

The Big Takeaways

 

Identify the economic buyer, not just the user.
The person with the pain and the person with the budget may not be the same.

Test willingness to pay before scaling activity.
Budget conversations are uncomfortable, which is exactly why they are useful.

Pressure-test the route to market.
A valuable product can still fail if it is too expensive or slow to sell.

 

Sources

 

[1] Alexander Osterwalder / Harvard Business Review — A Better Way to Think About Your Business Model — 2013 — https://hbr.org/2013/05/a-better-way-to-think-about-yo

[2] U.S. National Science Foundation — I-Corps Report — 2019 — https://nsf-gov-resources.nsf.gov/2022-06/I-CorpsReport–6_4_19FINAL_508_0.pdf

[3] Strategyzer — Testing Business Ideas: Book Summary and Key Takeaways — https://www.strategyzer.com/library/testing-business-ideas-book-summary

[4] Strategyzer — Testing Business Ideas — https://www.strategyzer.com/library/testing-business-ideas-book

 

 

At the beginning, every startup is partly fiction.

The customer segment is a hypothesis. The problem is a hypothesis. The product is a hypothesis. The price, channel, adoption process, and market timing are hypotheses too.

Starting with assumptions is the nature of building something new.

The danger begins when assumptions quietly get promoted to facts because the team has repeated them often enough. A sentence on a pitch deck can become company doctrine surprisingly fast.

The Evidence Risk profile means your startup may have promising signals, but not enough proof from the market yet. The next step is to turn assumptions into evidence.

 

Evidence reduces uncertainty

 

The Lean Startup approach frames startup progress as a process of building, measuring, and learning through experiments. [1] A similar logic can be applied to science and engineering teams:

participants conduct customer discovery to assess whether a technological innovation may support a sustainable business model and to develop evidence-based decision-making. [2]

The important word is “decision”.
An experiment is useful only if it helps you decide what to do next.

 

Step 1: List your assumptions

 

Start by writing down what must be true for your startup to work.

Use categories:

 

Customer

• Who has the problem?
• How urgent is it?
• Who pays?

Problem

• How often does it happen?
• What does it cost?
• What happens if it remains unsolved?

Solution

• Does the product solve the problem?
• Can users adopt it?
• Does it fit the workflow?

Business model

• What will customers pay?
• How will you reach them?
• Can you deliver sustainably?

Execution

• Can the team build, sell, and support this?
• What capabilities are missing?

This exercise can be irritating because it reveals how much is unknown.
But that’s a good thing.

A startup that knows where it is guessing is already less dangerous to itself.

 

Step 2: Prioritise the assumptions that could kill you

 

Not all assumptions deserve equal attention.

A button color is an assumption.
Whether hospitals can legally use your product is also an assumption.

These should not receive the same calendar space.

Prioritise assumptions by:

Importance: If false, would this seriously damage the business?

Uncertainty: Do we actually know this?

Cost of learning later: Would it be expensive to discover this in six months?

The best experiments often target assumptions that are both highly important and highly uncertain.

 

Step 3: Choose the right evidence

 

Not all evidence is equally reliable.

Compliments, opinions, likes, and statements such as “I would use this” may indicate interest, but they reveal little about actual customer behaviour.

Repeated problem descriptions, past behaviour, qualified sign-ups, and prototype engagement provide stronger signals.

The most convincing evidence comes from commitment: customers paying, using and returning to a product, signing a pilot, involving budget owners, sharing data, entering procurement, or changing established workflows.

Weak evidence is not useless, it can help founders identify where to investigate next. However, it should not justify decisions that require significant time, capital, or product development.

 

Step 4: Design a small experiment

 

A useful experiment begins with a clear assumption and defines how it will be tested.

A good experiment has 5 parts:

Assumption: What are we testing?

Method: How will we test it?

Audience: Who must participate?

Success criterion: What result would count as evidence?

Decision: What will we do if the result is positive, negative, or unclear?

For example, a startup might test whether operations managers will share anonymised downtime data in exchange for an analysis of recurring bottlenecks. If at least three out of ten target managers provide data and agree to a follow-up, the team has a reason to develop the analysis workflow further. If they do not, the result points to questions about urgency, trust, or data-access barriers.

The experiment does not need to be elegant or scalable. Its purpose is to produce evidence that informs the next decision.

 

Step 5: Record what you learned

 

Capture your learnings!
Sometimes teams rely too much on memory, but memory is an unreliable colleague.

After each experiment, document:

• what you assumed,
• what you tested,
• who participated,
• what happened,
• what evidence was strong or weak,
• what surprised you,
• what decision follows.

This prevents “selective learning”, where teams remember the encouraging parts and forget the awkward ones.

 

Common mistakes

 

The first mistake is testing too late.
If an assumption is critical, test it before building the expensive version.

The second is testing too many things at once.
If a landing page fails, was it the segment, message, offer, channel, or call to action?
Nobody knows. The spreadsheet shrugs.

The third is confusing activity with evidence.
Fifty meetings are not automatically progress.
Ten relevant conversations with clear patterns may be more valuable.

The fourth is ignoring negative evidence.
Bad news early is useful. Bad news late is a budget event.

 

Conclusion

 

Evidence-driven founders do not wait until everything is certain. Startups never get that luxury. But they do create enough evidence to make better decisions under uncertainty.

You can never remove all risk. But you can stop pretending that guesses are facts.

If you want structured support turning assumptions into experiments, evidence, and market progress, explore the INiTS SCALEup Incubation Program.

Explore INiTS SCALEup → www.inits.at/en/scaleup

 

 

The Big Takeaways

 

Write assumptions down explicitly.
Hidden assumptions cannot be tested.

Prioritise the assumptions that could kill the business.
Do not spend weeks testing cosmetic details while ignoring buying risk.

Define the decision before the experiment.
Evidence is useful only if it changes what you do next.

 

Sources

 

[1] Eric Ries / The Lean Startup — Methodology Principles — https://theleanstartup.com/principles

[2] U.S. National Science Foundation — I-Corps Report — 2019 — https://nsf-gov-resources.nsf.gov/2022-06/I-CorpsReport–6_4_19FINAL_508_0.pdf

 

 

If your product risk is high, the problem may be real.
But your solution may not yet be the right one.

 

Before adding features, validate whether users understand, use, and value the core solution enough to change their behaviour.

A common early-stage trap looks productive from the outside: the team keeps shipping.
New features appear. The roadmap gets longer. The demo improves. Everyone is busy.

 

But the uncomfortable question remains:

Are you building something customers actually need or just making the wrong solution more impressive?

This is the essence of Product Risk.

It does not necessarily mean your idea is weak.
It means your solution has not yet earned the right to become more complex.

For early-stage tech startups, product validation is about finding evidence that the solution helps a specific customer make progress on a real problem. The Lean Startup approach describes the minimum viable product (MVP) as a way to begin learning as quickly as possible through the build-measure-learn loop, not as a smaller version of the final product.[1, 2]

 

An MVP is not necessarily something you “build” in code.
It is the fastest way to test critical assumptions with the least effort.

That distinction matters.

Many founders use MVPs to start building.
Better founders use MVPs to start learning.

 

Start with the riskiest product assumption

 

Before building anything else, ask:

What must be true for this solution to work?

Examples:

• Customers understand the value proposition.
• The workflow fits into how they already work.
• The user can reach the desired outcome without heavy support.
• The buyer sees enough value to pay.
• The technical performance is good enough for the use case.

Do not test everything at once.
Choose the assumption that would most seriously damage the business if it were false.

For example, if you are building AI software for clinical documentation, the riskiest assumption may not be whether the model can generate summaries. It may be whether doctors trust the output enough to use it, whether hospitals allow the workflow, or whether the time saved justifies procurement effort.

 

Test the core value, not the full product

 

Founders often postpone validation until the product is “ready”.
However, this is often expensive procrastination wearing a lab coat.

The better question is:

What is the smallest test that can reveal whether customers value the solution?

Possible tests include:

• a clickable prototype,
• a landing page with a clear call to action,
• a concierge test where the team manually delivers the result,
• a pilot with a narrow use case,
• a workflow simulation,
• a paid discovery or implementation project.

Even lightweight coded MVPs require support, bug fixes, and infrastructure. Hence validating the value proposition before committing to code is a good idea.[3]

 

Watch what users do, not what they praise

 

Positive feedback is useful only when it is connected to behaviour.

“Looks great.” “This could be useful.” or “I can imagine using this.” are weak signals. You should not count on them.

Stronger signals:

• The user completes the task in the prototype.
• The user asks for access to try it with real data.
• The buyer introduces the team to procurement or IT.
• The customer agrees to a pilot with clear success criteria.
• The customer pays, even a small amount.

User research should not be reduced to a yes/no validation exercise. When research is used only to confirm an existing decision, teams may miss the nuance that makes the research valuable.[4]

The real goal is to understand what creates value, what creates friction, and what must change before adoption becomes realistic.

 

Define success before the test

 

Before you run a test, define what evidence would make you continue, change direction, or stop.

For example:

• At least 5 out of 8 target users complete the core workflow without help.
• Three qualified customers agree to a pilot.
• One customer pays for a limited implementation.
• Users repeatedly return to the product without reminders.

Avoid vague goals such as “get feedback” or “see if they like it”.
Look for real evidence.

 

Do not mistake feature requests for validation

 

Feature requests feel exciting because they sound like demand.
But they can also be a polite way for customers to postpone commitment.

When someone asks for a feature, ask:

• What would this allow you to do?
• How do you solve this today?
• How often does this matter?
• Who else needs this?
• Would this change your decision to use or buy?
• What would happen if this feature did not exist?

A feature request is only a roadmap item when it is connected to a valuable use case, repeated across relevant customers, and aligned with your strategic focus.

 

Conclusion

 

Product validation is not about building less.
It is about learning faster based on evidence.

If your biggest risk is the product, do not build more yet.
Test whether the core solution creates value, whether users can adopt it, and whether customers are willing to make a real commitment.

A product becomes stronger when every important feature has earned its place.

If you are building a tech-driven startup and want structured support with validation, product-market fit, and growth, explore the INiTS SCALEup Incubation Program.

Explore INiTS SCALEup → https://www.inits.at/en/scaleup/?src=wp

 

 

The Big Takeaways

 

Test the riskiest product assumption first.
Do not validate the easy parts while ignoring the adoption risk.

Use prototypes and pilots to learn before you build.
The goal of an MVP is learning, not feature delivery.

Treat behaviour as stronger evidence than praise.
Usage, payment, data access, and pilots matter more than compliments.

 

Sources

 

[1] Eric Ries / The Lean Startup — Methodology Principles — https://theleanstartup.com/principles

[2] Strategyzer — Don’t Build When You Build-Measure-Learn — https://www.strategyzer.com/library/dont-build-when-you-build-measure-learn

[3] Nielsen Norman Group — Minimum Viable Product: Definition — https://www.nngroup.com/articles/mvp-definition/

[4] Nielsen Norman Group — In User Research, Don’t Stop at “Yes” or “No” — https://www.nngroup.com/articles/research-yes-or-no/

Why connected startup ecosystems matter for Europe’s future

 

“Build together. Grow together. Strengthen Europe together.”, this was the theme of this year’s INiTS Sommerfest, held in the historic Arkadenhof of the University of Vienna.

As our biggest event of the year, the Sommerfest is always an opportunity to celebrate. But this year’s theme also reflected something much bigger: the need to strengthen the connections that allow innovation to grow not only in Vienna or Austria, but across Europe.

 

 

 

 

Europe has innovation worth building on

 

Europe has strong universities, excellent research institutions, highly qualified talent and a growing community of ambitious founders. It is particularly strong in areas where scientific expertise and technological innovation come together (from life sciences and climate technologies to artificial intelligence, quantum computing and advanced manufacturing).

The latest figures underline this potential. European startups attracted about €55,9 Mrd in venture capital in 2025. European deep-tech startups alone raised €17,8 Mrd. This is approximately 32 percent of all European startup investment that year.

These figures are encouraging. They show that investors recognize the potential of technologies emerging from European research, engineering and entrepreneurship.

However, investment figures alone do not tell the whole story.

Europe’s central challenge is not a lack of ideas. It is ensuring that promising ideas can move from research to application, from an initial market to international growth, and from a strong startup to a globally competitive company.

The European Commission therefore identifies a thriving startup and scaleup ecosystem as an important driver of productivity, high-quality jobs, talent and investment. At the same time, its Startup and Scaleup Strategy points to persistent barriers, including fragmented regulatory frameworks, limited access to later-stage finance, difficulties in accessing talent and infrastructure, and uneven conditions for entering markets across the EU.

 

A single market that does not always feel like one

 

For startups, expanding into another European country can still mean entering a substantially different market.

National regulations, administrative requirements, procurement processes, financing structures and business cultures vary. A solution that works in Austria cannot automatically be introduced in France, Sweden or the Netherlands using exactly the same approach.

This is particularly challenging for deep-tech startups. They often require specialized infrastructure, regulatory expertise, patient capital, research partnerships and industrial pilot customers long before they can scale commercially.

Strong startup ecosystems cannot remove all these structural barriers. They cannot replace regulatory reform or solve Europe’s scaleup financing gap on their own. But they can help founders navigate complexity, find the right partners and gain access to knowledge and resources that would otherwise be difficult to reach.

That’s why cross-border networks are so valuable.

 

Networks turn individual strengths into shared opportunities

 

A strong ecosystem is more than a collection of startups, investors and support organizations. Its value lies in the quality of the connections between them.

Trusted networks can help startups:

• understand the realities of a new market before investing significant time and capital;
• connect with local mentors, investors, research institutions and potential customers;
• access specialized infrastructure and expertise;
• identify suitable partners for pilot projects and market entry;
• learn from founders and ecosystem organizations in other countries;
• gain visibility beyond their domestic market.

For incubators and innovation hubs, European cooperation also creates opportunities to exchange methods, share experience and develop joint approaches to recurring challenges. What works well in one ecosystem can inform and strengthen another.

The purpose is not networking for networking’s sake. The purpose is to make Europe’s distributed strengths more accessible and to help good ideas reach the people, markets and resources they need.

 

Let’s contribute to a more connected Europe

 

INiTS is involved in several European and national networks that address different parts of this challenge.

 

Rise Europe: connecting leading startup ecosystems

 

Rise Europe brings together startup ecosystem builders from 14 European countries. The network was formed at its first summit in May 2023 to strengthen cooperation between leading entrepreneurship hubs and help European startups scale across borders.

INiTS has been part of the alliance since that first summit and is currently the only Austrian institution listed among its members.

Through Rise Europe, startup and entrepreneurship hubs combine their experience, networks and local market knowledge. This cooperation creates stronger links between European startup regions and supports the shared ambition of helping more European technology companies grow into sustainable global players.

 

EuroIncNet: supporting international market entry

 

INiTS is also a partner of EuroIncNet, a European soft-landing network established in 2015.

Its focus is practical: helping technology startups explore and enter other European markets. Through trusted local incubators and accelerators, startups can gain access to market knowledge, experienced mentors, industrial partners and potential investors.

This kind of support matters because successful market entry requires local understanding, relevant contacts and a realistic assessment of whether and how a business model can be transferred.

 

AI Factory Austria: linking infrastructure, expertise and application

 

As a partner of AI Factory Austria, INiTS is also involved in a nationwide initiative that forms part of the European High-Performance Computing Joint Undertaking.

AI Factory Austria combines AI-optimized computing infrastructure with expertise, support services and a physical innovation hub. It is designed to support businesses, startups, public institutions and applied research organizations along their AI journey (from identifying relevant use cases to practical implementation).

The initiative illustrates why ecosystems matter: advanced infrastructure alone is not enough. Its value increases when it is connected with scientific expertise, entrepreneurial talent, real-world applications and organizations capable of translating technology into viable solutions.

 

From individual ideas to European impact

 

Europe’s startup ecosystem does not need to become more uniform. Its diversity is one of its strengths. Different regions contribute different research capabilities, industries, talent pools and areas of expertise.

The real opportunity lies in connecting these strengths more effectively.

When knowledge moves more easily between research and industry, when startups can access partners across borders, and when infrastructure, capital and market opportunities become easier to reach, individual ideas have a better chance of growing into meaningful impact.

That is what this year’s INiTS Sommerfest stood for.

Not only celebrating entrepreneurship in Austria, but celebrating the relationships that allow innovation to travel further.

Thank you to all our partners, friends and supporters who joined us in the Arkadenhof of the University of Vienna.

We truly enjoyed celebrating with you.

The Truth Detective: How to Uncover Real Customer Needs (Without Getting Lied To)

 

Building a startup without qualitative customer interviews is like trying to navigate a dense fog with a compass that only points toward what you want to hear. You think you’re heading North, but you’re actually walking off a cliff.

To navigate safely, you don’t need a map of where you want to go.
You need a tool that shows you what your customers’ lives are really like.

If you walk up to a stranger and ask, „Would you use an app that organizes your grocery list?“ they will likely say „Yes.“ Not because they need it, but because humans are social creatures programmed to be polite.

This is the „Mom Test“ trap: your friends, family, and even potential customers will lie to you just to avoid hurting your feelings.

To find the truth, you have to start being a detective.

 

1. The Mom Test: Stop Pitching, Start Probing

 

The core principle of The Mom Test by Rob Fitzpatrick is simple: you aren’t allowed to tell people what your idea is, and in return, they aren’t allowed to tell you what they think of it.

When you pitch your idea, you trigger a psychological response where the other person feels obligated to give you positive reinforcement. You get „compliments,“ which are the most dangerous form of data. Compliments feel like validation, but they are actually empty noise.

To pass the Mom Test, your conversation must stay focused on the customer’s life and not your solution. Instead of asking about your product, ask about their problems.

If you are building a productivity tool, don’t ask „Do you think people need better time management?“. Ask „Walk me through how you planned your tasks yesterday.“

 

2. The Art of the Question: Fact over Fiction

 

The difference between a breakthrough insight and a wasted afternoon lies in the phrasing of your questions. You are looking for behavioral facts, not hypothetical opinions.

 

The „Bad“ Questions (The Hypothetical Trap)

 

• „Would you use a tool that does X?“ (Hypothetical)
• „Is this a good idea?“ (Fishing for compliments)
• „How much would you pay for this?“ (Guesswork)
• „Do you think this is important?“ (Opinion-based)

These questions invite fluff, the mental equivalent of cotton candy.
It’s sweet, but it has zero nutritional value for your business.

 

The „Good“ Questions (The Behavioral Anchor)

 

• „Tell me about the last time you encountered [problem].“ (Specific past behavior)
• „What are you currently doing to solve this?“ (Identifies existing competition/effort)
• „What was the hardest part about that process?“ (Pinpoints specific pain)
• „How much time/money do you currently spend on this?“ (Quantifies the actual cost of the problem)

By anchoring your questions in the past, you force the customer to provide evidence rather than promises.

 

3. Recruiting Your Informants: Finding the Right Voices

 

A detective is nothing without informants.
If you’re talking to the wrong people, you may gather plenty of opinions, but little meaningful evidence.

Finding the right interview partners requires targeted research. Focus on people who have firsthand experience with the problem you want to understand.

• Search strategically:Use LinkedIn, professional directories, industry associations, or company websites to identify people with relevant roles and experience. Don’t pitch your solution. Ask for 15 minutes to learn how they currently handle a specific task or challenge.
• Go where your target group meets:Explore relevant online communities, conferences, trade fairs, meetups, workshops, and networking events. Observe which topics and problems repeatedly come up. Then approach individuals for a short, focused conversation.
• Use trusted introductions:Ask colleagues, friends, mentors, or industry contacts to connect you with suitable interview partners. Be specific: “Who do you know who regularly deals with X?” is far more effective than “Do you know anyone I could interview?”
• Look beyond the obvious:Depending on your target group, you might find valuable interview partners through professional associations, coworking spaces, universities, local businesses, or even at the places where the problem occurs in practice.

 

The goal is not to interview as many people as possible. It is to speak with people who have recently experienced the problem and can describe what they actually DID (and not what they think they might do!).

 

 

The Big Takeaways

 

1. Kill the Pitch:
   If you mention your idea during the interview, you’ve already lost.
   Focus entirely on their current reality and past actions.
2. Hunt for Friction:
   Ignore compliments and „would you“ questions.
   Seek out the specific, messy details of how they currently struggle.
3. Evidence over Intent:
   A customer saying „I would pay for that“ is a guess.
   A customer showing you a spreadsheet they built to solve the problem themselves is a fact.

Start this week: schedule 5 Mom Test interviews and let real customer behavior (not polite opinions!) guide your next move.