Idea Testing

Overview

Use this skill to test a business idea or major assumption before heavy execution. It structures experiments, evidence review, and decision rules so the team can persevere, pivot, or stop with discipline.

Use When

• Use when a business idea, offer, or market hypothesis still needs proof.
• Use when risky assumptions should be tested before building, funding, or scaling.
• Use when the outcome should be an experiment plan or validation decision.

Do Not Use When

• Do not use after decisive evidence already exists unless re-testing is needed.
• Do not mistake opinion gathering for disciplined validation.
• Do not continue testing forever when a clear decision threshold has been crossed.
• Use meta-business-model-design instead to design, compare and GEL-score the business model itself; this skill then tests the chosen model's riskiest assumptions.
• For idea-testing, route to the relevant plan-section skill instead when the request is section drafting rather than cross-section analysis.

Required Inputs

• Idea, hypothesis, or risky assumption to test
• Target customer and context
• Available evidence, resources, and time constraints
• Decision threshold for what counts as validation or failure

Workflow

1. Define the exact assumption or idea being tested.
2. Choose the lightest credible experiment that can produce signal.
3. Set success, failure, and learning criteria before running the test.
4. Capture results and interpret them honestly.
5. Decide whether to persevere, pivot, or stop.
6. Flag what still remains unvalidated.

Decision, stop, and recovery controls

• Decision point: confirm that the requested output is the assumption test card and that the decision concerns persevere, pivot, or kill after the experiment.
• Stop condition: halt the affected conclusion if required evidence is missing (named risky assumption, target user, and observable behaviour) or if the work could lead to this identified risk: building the product before testing the failure-prone assumption.
• Recovery: obtain the missing record or reviewer, repeat the affected check, and update the exception record before release.

Quality Bar

• The test targets a real assumption, not a vague ambition.
• Success criteria are observable and decision-useful.
• The experiment is proportionate to the risk and stage.
• Outcomes lead to a clear next decision.

Anti-Patterns

• Testing too many hypotheses at once.
• Running expensive experiments before cheap ones.
• Redefining success after seeing weak results.
• Confusing interest or compliments with willingness to pay or adopt.
• Treating a generic idea testing template as a conclusion. Correction: tie each choice to the named audience, evidence, and operating constraint.
• Applying the wrong neighbouring route to idea testing. Correction: confirm the decision and route to the named neighbour before analysis.
• Treating an assumption as verified evidence. Correction: label it, cite its source or owner, and assign a verification action.
• Recommending action without a decision threshold. Correction: state the measurable acceptance condition and review trigger.
• Recording an unavailable check as passed. Correction: mark it not assessed and state the consequence for the decision.
• Mutating or publishing during an analysis-only task. Correction: remain read-only until the owner gives explicit authority.

Outputs

• A validation plan, experiment set, or decision memo
• Clear pass, fail, or pivot criteria
• Remaining unknowns and follow-up tests

Overview

Testing Business Ideas uses structured experiments to gather evidence before committing to building. Every business idea rests on assumptions about desirability (do customers want it?), feasibility (can it be delivered?), and viability (can it make money?). Test the riskiest assumptions first with the cheapest experiments possible, increasing evidence strength as confidence grows.

Business Design Loop: Ideate Business Prototype Assess risk Experiment Learn Decide (Persevere/Pivot/Kill) repeat

Three Risk Types

Any experiment can test one or more risk types. Always prioritise desirability first.

Test Card Design Every Experiment

Before running any experiment, complete a Test Card:

We believe:        [the hypothesis  what we assume to be true]
To verify this:    [the experiment  specific action we will take]
We will measure:   [the metrics  what data we will collect]
We are right if:   [the criteria  specific pass/fail threshold]

After the experiment, complete a Learning Card:

We observed:       [what actually happened  raw facts]
From this:         [the insight  what it means]
Therefore:         [the next action  build, iterate, pivot, kill, or test more]
We need to:        [remaining open hypotheses to test next]

See references/test-learn-decide.md for full templates, evidence strength guide, and Decide framework.

Experiment Selection Two Phases

Phase 1: Discovery (go from uncertainty initial direction)

Use cheap, fast experiments to learn whether you are solving a real problem for real customers. Aim for 15–20 customer conversations before drawing conclusions. Evidence from discovery is generally weak-to-moderate it is directional, not confirmatory.

Rule: Start with exploration (interviews, observations). Layer in interest data (ads, landing pages, surveys). Use prototypes to test concepts quickly.

See references/discovery-experiments.md for all 20+ discovery experiments with EA adaptations.

Phase 2: Validation (confirm direction with stronger evidence)

Once you have a clear direction, use higher-fidelity experiments that generate strong evidence real actions, real payments, real commitments. These cost more but reduce risk significantly before full build.

Rule: Escalate fidelity progressively. Never skip directly to building use Wizard of Oz, Concierge, or Presale to validate before investing in infrastructure.

See references/validation-experiments.md for all 15 validation experiments.

Evidence Strength

Stronger evidence = more reliable signals. Always push for the strongest evidence your constraints allow.

Confidence levels:

• Not Really Confident opinions, one-off conversations, single data point
• Somewhat Confident multiple interviews, landing page clicks, prototype feedback
• Very Confident real purchases, LOIs signed, multiple experiment types aligned

Decide: What to Do With Evidence

After each experiment, decide:

Pivot triggers: customers want a different feature, wrong segment, pricing mismatch, delivery cost makes viability impossible.

Kill triggers: multiple experiments all refute desirability, feasibility is technically impossible, no viable revenue model found after thorough testing.

Experiment Ceremonies (for ongoing testing)

Funding by Stage (Innovation Portfolio Model)

EA Context Adaptations

• Online ad platforms: Facebook Ads dominates in Uganda; Google Search Ads for business-to-business
• Email campaigns: WhatsApp broadcast lists are more effective than email in most EA B2C markets
• Presale / payment: Use Mobile Money (MTN MoMo, Airtel Money) more friction-free than bank transfer for small amounts
• Pop-up stores: KCCA market stalls, church/school trade fairs, roadside stalls low permit cost
• Letter of Intent: Widely understood in Ugandan B2B and institutional procurement especially for government/NGO buyers
• Concierge MVP: Ideal for service businesses deliver manually first, automate once validated
• Interviews: In-person visits to potential customers are expected and welcomed; cold calls less effective than warm introductions

Common Pitfalls

Reference Files

• references/test-learn-decide.md Test Card, Learning Card, evidence guide, decide framework
• references/discovery-experiments.md All 20+ discovery experiments with EA adaptations
• references/validation-experiments.md All 15 validation experiments with EA adaptations

Evidence Produced

Capability and Permission Boundaries

Read supplied records and use non-mutating checks to produce the assumption test card; recording experiments in the approved learning backlog is permitted when requested. Do not publish, contact third parties, alter live systems, commit funds, or claim legal, tax, audit, valuation, ESG, or investment assurance without the owner's explicit authorisation and the appropriate reviewer.

Degraded Mode

If named risky assumption, target user, and observable behaviour cannot be obtained, return a qualified assumption test card covering only the checks that remain supportable. Leave this decision unresolved: persevere, pivot, or kill after the experiment. Record the evidence owner and next check; an inaccessible source, tool, or reviewer is never a pass.

Decision Rules

Quality Standards

Accept the assumption test card only when evidence is sufficient for this decision: persevere, pivot, or kill after the experiment. Assumptions and countercases remain visible, calculations and cross-references reconcile, and the reviewer can see how the recommendation addresses the risk of building the product before testing the failure-prone assumption.

Worked Example

A founder plans six months of development for a supplier marketplace. First test whether ten target buyers will submit real orders under the proposed fulfilment terms, with the pivot threshold agreed before outreach.

References

• AGENTS.md - repository routing, evidence, finance, and release rules.
• Business model design and GEL scoring - read first when the assumption under test is the business model's customer, sales or margin logic; its open-assumption list is this skill's input.

Market-to-commitment validation ladder

Before expensive build or launch, test in stages: define the target user and problem; inspect demand, trends, alternatives, and saturation; run interviews or observation; test a concept/landing page; prototype or deliver manually; then seek real use, payment, renewal, LOI, or another material commitment where appropriate. Search volume, compliments, registrations, and portfolio examples are signals, not validation.

For every stage record the hypothesis, sample/segment, behaviour, threshold, guardrail, time window, decision, and remaining unknowns. Escalate evidence only when the next investment decision requires it. This keeps validation reversible and prevents a launch plan from substituting for product proof.

Practitioner cross-check: Eleken product-idea validation and UX ideas; corroborating practice: GOV.UK plan user research.