MVP Experiment Plan Template (Riskiest Assumption Test)

Lean MVP experiment: risk-ranked assumptions, falsifiable hypothesis, MVP type, success and kill criteria, run plan, results, decision

スタートアップ標準Lean Startup (Ries 2011), build-measure-learnmvp experiment plan更新 2026/10/29

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使い方: Use it when you want to test a startup's riskiest assumption before building. Fill the assumption ranking and write success and kill criteria before running anything. The main pitfall is measuring opinions instead of behaviour, or moving the goalposts after the data arrives.

プレビュー

MVP Experiment Plan: [Product or idea]

A plan for testing a startup's riskiest assumption with the smallest possible product before building the full thing: the assumptions ranked by risk, the hypothesis in falsifiable form, the MVP type chosen, the metric and success and kill criteria fixed in advance, the run plan, results, and the persevere, pivot or stop decision. Structure aligned with the build-measure-learn loop and minimum viable product practice described in Eric Ries, The Lean Startup (2011). Replace every [bracketed] value and every row marked Example. The example is fictional.

Document control

Field	Value
Document ID	[EXP-NNN]
Version	[0.1]
Status	[Planned / Running / Concluded]
Owner	[@founder]
Reviewers	[@co-founder], [@advisor]
Last updated	YYYY-MM-DD
Classification	Internal
Related	`startup/customer-discovery`, `business/lean-canvas`

Revision history

Version	Date	Author	Change
0.1	YYYY-MM-DD	[@founder]	Example: Criteria agreed before launch
1.0	YYYY-MM-DD	[@founder]	Example: Results and decision recorded

1. Assumptions and riskiest assumption

ID	Assumption	Type	Impact if wrong (1 to 5)	Evidence today (1 to 5, 5 = strong)	Risk = impact x (6 minus evidence)
ASM-01 (Example)	Independent bicycle shops will pay monthly for stock forecasts	Desirability, viability	5	1	25
ASM-02 (Example)	Shop point-of-sale data can be exported weekly	Feasibility	4	3	12
ASM-03 (Example)	Shops can be reached at trade shows at low cost	Viability	3	2	12
ASM-04 (Example)	Forecasts beat the owner's own reorder habit	Desirability	4	2	16

Riskiest assumption to test now: Example: ASM-01.

2. Hypothesis

We believe that [customer segment] will [observable behaviour] at [threshold] within [period], because [reason from discovery].

Example: "We believe that independent bicycle shops with 1 to 3 locations will pay a 150 USD pilot fee for six weeks of hand-made stock forecasts, with at least 6 of 12 invited shops paying within two weeks of the offer, because 9 of 15 discovery interviews named overstock of slow-moving parts as a top-three problem."

3. MVP type

MVP type	Tests	Cost and speed	Chosen
Landing page or fake door	Interest, message, channel	Very cheap, days	[ ]
Concierge (service by hand)	Willingness to pay, value delivered	Cheap, weeks, does not scale	Example: [x]
Wizard of Oz (looks automated, run by people)	Value and usability of the future product	Medium	[ ]
Pre-sale or letter of intent	Willingness to pay	Cheap	[ ]
Single-feature product	Retention on the core job	Higher, needs code	[ ]

Example: Concierge MVP. A founder builds each shop's forecast in a spreadsheet from its exported sales data and sends a reorder list every Monday for six weeks. In parallel, a fake-door page tests the channel (ASM-03) as a separate experiment, EXP-002.

4. Metrics, success and kill criteria

Item	Value
Primary metric	Example: Shops paying the 150 USD pilot fee, out of 12 invited
Success (persevere)	Example: 6 or more of 12 pay
Kill (stop or pivot)	Example: 2 or fewer pay
Inconclusive zone	Example: 3 to 5 pay: rerun with 12 more shops and a changed offer
Secondary metrics	Example: Shops acting on at least half of weekly reorder lists; pilot shops asking to continue after week 6
Guardrail	Example: Founder time under 10 hours a week
Sample and duration	Example: 12 shops, offer open 2 weeks, pilot 6 weeks
Budget	Example: Founder time plus 300 USD for data cleaning tools

5. Run plan

Step	Action	Owner	Date
1	Invite 12 shops from discovery list with the same written offer	[@ceo]	YYYY-MM-DD
2	Collect fees and sales exports	[@ceo]	YYYY-MM-DD
3	Send weekly forecasts for 6 weeks, log usage	[@cto]	YYYY-MM-DD
4	Exit interview with every shop	[@ceo]	YYYY-MM-DD
5	Results review against criteria	Both founders	YYYY-MM-DD

6. Results

Metric	Criterion	Result (Example)
Shops paying the pilot fee	6 or more of 12	7 of 12 (58%)
Plausible range for the paying rate	Information only	About 32% to 81% (95% Wilson interval, n = 12)
Shops acting on at least half of reorder lists	Secondary	5 of 7
Asked to continue after week 6	Secondary	6 of 7
Founder time	Under 10 hours a week	About 8 hours a week

Example: Separate experiment EXP-002 (fake door): 36 of 400 visitors from trade-show follow-ups booked a demo (9%, about 6.6% to 12.2% at 95%).

7. Decision and next experiment

Item	Value
Decision	Example: Persevere: success criterion met
What we learned	Example: Shops pay for the outcome; owners trust a list more than a dashboard
Next riskiest assumption	Example: ASM-04, forecasts beat the owner's reorder habit, tested with a 12-week accuracy comparison
Follow-on work	Example: Price test and automated data import, feeding the revenue assumptions in `startup/financial-projections`

8. References

Eric Ries, The Lean Startup (Crown Business, 2011): minimum viable product and the build-measure-learn loop. Publisher and book information: https://theleanstartup.com/book
Related templates: startup/customer-discovery, startup/pivot-memo, business/lean-canvas, business/value-proposition-canvas, product/ab-test-plan, product/opportunity-solution-tree.

Standard and licence note

Structure aligned with the minimum viable product and build-measure- learn practice described in Eric Ries's The Lean Startup (2011). The book is named and cited only; no text is copied and all wording here is original. Official text: the publisher's edition, purchase or licensed access via https://theleanstartup.com/book. The example company and its figures are fictional. Template licence: MIT.