Product discovery

De-risk product bets before building

Discovery's job is to fail fast and cheaply — identify wrong assumptions before they're baked into shipped software.

Opportunity Solution Tree (Teresa Torres)

Desired outcome (metric to move)
  └── Opportunity (unmet user need / pain / desire)
       └── Solution idea (intervention)
            └── Experiment (cheapest test)

Rules:

• Opportunities come from user evidence — interviews, support tickets, analytics — not internal opinions.
• One desired outcome per tree. Multiple outcomes = no prioritisation.
• Solutions are hypotheses; experiments are the cheapest way to test each hypothesis.
• The tree is a living document — update as evidence accumulates.

Assumption mapping

For each solution idea, map its assumptions across four risk dimensions:

Dimension	Question	Example assumption
Desirability	Do users want this?	"Users will pay $10/month for this feature"
Viability	Does this create sustainable business value?	"This will reduce churn by 5%"
Feasibility	Can we build this?	"The API supports the required event granularity"
Usability	Can users use this without training?	"Users will understand the new onboarding flow without docs"

Score each assumption: Risk (1–3) × Certainty (1–3 inverse — low certainty = high score). Highest scores = test first.

Validation methods (choose by cost)

Method	Cost	Validates
Desk research	Hours	Market size, competitor landscape, existing solutions
Customer interview (problem)	Days	Pain existence, frequency, severity, willingness to solve
Fake-door test	Days	Demand signal (click-through to a "coming soon" page)
Prototype usability test	Days–week	Usability, core interaction
Wizard-of-Oz / concierge	Week	Desirability + willingness to pay without building the feature
A/B experiment	Week–months	Behavioural impact on a metric

Fail fast principle: choose the cheapest method that can kill or confirm the assumption. Don't spend a month building a prototype when 5 interviews would do.

Discovery sprint structure (1–2 weeks)

1. Week 1 — hypothesis formation + cheapest validation plan.
2. Daily evidence reviews — what did we learn today? Does it change the tree?
3. End-of-sprint decision gate — proceed (evidence supports the bet), pivot (evidence points to a different opportunity), or stop (evidence shows no opportunity).

Steps

1. Define the desired outcome. One metric; baseline and target; time horizon.
2. Build the OST. Map opportunities from existing user evidence. Diverge before converging — generate many opportunities; then cluster and score by frequency × severity.
3. Map assumptions for top opportunities. Use the four dimensions. Identify the assumptions that would kill the bet if wrong.
4. Plan experiments. For each killer assumption: cheapest method that provides signal in < 1 week.
5. Run experiments. Document: hypothesis, method, result, what we concluded.
6. Decision gate. Proceed / pivot / stop with explicit reasoning. Capture in lore.

Review checklist

• Desired outcome specific — one metric; measurable; time-bounded.
• Opportunities from user evidence — not internal brainstorms.
• Killer assumptions identified — the ones that, if wrong, kill the bet.
• Cheapest validation method chosen — not over-invested in expensive experiments for low-risk assumptions.
• Decision gate explicit — proceed/pivot/stop with documented reasoning.

Rules

• Discovery debt is real: every feature shipped without discovery is a bet placed blind.
• User interviews discover problems; prototypes test solutions. Don't use interviews to validate solutions.
• A "proceed" decision without documented evidence is not a discovery output — it's a guess with extra steps.