Research: Why Product Programmes Fail

95% of new products fail to meet their commercial targets

£88M wasted per £1B spent on projects with poorly defined problems

42% of startup failures attributed to building something nobody needs

0 standalone tools exist to score a problem definition before committing resource

Q1 PERSONA VALIDATION

What best describes your role?

Q2 PROBLEM EXISTENCE

In the last 3 years, have you seen a product programme fail or pivot significantly because it was aimed at the wrong problem?

Yes; more than once Yes; once I suspect so, but it was never labelled that way No

Q3 DETECTION PATTERN

When did the "wrong problem" realisation hit?

Select all that apply

At a design review, months into development When customers saw a prototype and said "that's not what we need" After launch, when the market didn't respond When a competitor launched something better aimed Someone had a gut feeling from the start but couldn't evidence it During regulatory submission, when the intended use didn't hold up

Q4 CURRENT WORKAROUNDS

How does your organisation currently validate problem definitions before committing resource?

Select all that apply

Stage-gate review process Design review meetings Commissioned market research Internal spreadsheets or checklists Direct customer visits or interviews External design / innovation consultancy Senior leadership intuition We don't; we commit based on the original brief

Q5 GOVERNANCE EFFECTIVENESS

How confident are you that your current process catches a bad problem definition before significant resource is committed?

Very confident; our process is rigorous Somewhat; it catches the obvious ones Not really; it's mostly a tick-box exercise Not at all; we find out too late

Q6 SPEED THRESHOLD

If an external audit could score your problem definition against evidence and flag gaps; what's the longest you'd wait for it?

15 minutes (instant triage) Same day 48 hours Up to a week Depends on the programme value

Q7 WILLINGNESS TO PAY

Would you pay for a standalone problem definition audit; separate from a full design consultancy engagement?

Yes; I'd buy this as a standalone service Yes, if it was fast and the output was actionable Maybe; I'd need to see an example output first No; I'd expect this bundled into a consultancy engagement No; we handle this internally

Q8 DATA TRUST

Would you share internal project data (problem briefs, requirements docs) with an external scoring tool?

Yes, via a secure cloud platform Yes, under NDA with a named consultant Only if the data was anonymised or stayed on-premise No; the audit should use external evidence only

Q9 LANGUAGE FIT

If you were searching for help validating a problem definition, which term would you search for?

Pick up to 2

Problem engineering Problem audit / problem review Requirements validation Front end of innovation Problem framing / problem definition Innovation audit Something else (please specify below)

Q10 OPEN SIGNAL

What's the most expensive "wrong problem" mistake you've witnessed?

Optional. One or two sentences is perfect. This is the evidence we can't find in published research.

OPTIONAL

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Survey responses are anonymised and used for research purposes only (lawful basis: legitimate interest). Contact details (name, company, job title, LinkedIn, email) are collected only when you opt in and used solely to deliver findings, send product updates, or vet beta access; whichever you select (lawful basis: consent). Beta access is vetted individually; we may decline applications without giving a reason. All data is stored securely by ProblemSmith Ltd (UK). Survey data is deleted after 12 months; contact details are retained until you withdraw consent. To request deletion or withdraw consent at any time, email [email protected]. Full privacy notice.

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The findings come back to you

This is not a marketing exercise dressed as research. We're building a dataset that does not exist yet; how product design, development and engineering teams in high-risk industries actually handle problem definition. Everyone who contributes gets the results.

Anonymised benchmark data: how your sector compares on problem definition governance
The most common "wrong problem" patterns and when they're typically caught
What workarounds teams are using and how effective they report them to be
The gap map: where current processes fail, backed by your industry's own data

This research is led by Lee Smith; 24 years in high-risk product development across industries where the cost of getting things wrong is high. It feeds a broader investigation into why product programmes fail at the problem definition stage, and what structured tools could prevent it. Learn more about Problemsmith.

Your engineering isn't the problem. The problem is the problem.

What does your organisation actually do when the problem is wrong?

Noted.

The findings come back to you