| name | indie-saas-validation-master |
| description | Validate and de-risk an indie SaaS from problem discovery through paid pilot, MVP, retention, repeatable acquisition, and scaling decisions. Use whenever the user is evaluating a SaaS idea, planning an MVP, interviewing customers, pre-selling, running a fake-door test, seeking product-market fit, or deciding whether to persist, pivot, pause, or scale. |
| category | business |
| license | MIT |
Indie SaaS Validation Master
Turn an idea into a sequence of falsifiable decisions. Progress by evidence and
risk reduction, not by a fixed calendar, signup target, or MRR milestone.
Operating Rules
- Use the current conversation, repository, landing page, analytics, and prior
customer evidence before asking questions.
- Identify the riskiest assumption first. Do not validate the easiest part
while ignoring the assumption that can kill the business.
- Prefer observed behavior and costly commitment over stated enthusiasm.
- Record counter-evidence and failed tests. Do not reinterpret every result as
validation.
- Never fabricate customers, demand, market size, quotes, or conversion data.
- Fake doors, pre-sales, deposits, and pilots must accurately disclose what
exists, what is planned, delivery timing, cancellation/refund terms, and how
data will be used.
- Treat timelines and benchmarks as planning inputs, not universal pass/fail
rules.
Evidence Ladder
Use the strongest evidence feasible:
- target customer repeatedly experiences the problem;
- customer uses a costly workaround or pays an alternative;
- customer gives time, access, data, an introduction, or implementation effort;
- customer agrees to a concrete pilot or letter of intent;
- customer pays under clear terms;
- customer reaches value and returns;
- multiple customers in the same segment retain and refer;
- acquisition repeats with acceptable economics.
A large low-intent waitlist can be weaker than three well-qualified paid pilots.
Workflow
1. Create an Assumption Map
Summarize:
- customer, user, and buyer;
- painful job and triggering event;
- current alternative and its cost;
- promised outcome and differentiated wedge;
- acquisition path and sales motion;
- value metric and plausible price;
- feasibility, data, regulatory, and platform dependencies.
Classify assumptions as desirability, viability, feasibility, usability,
distribution, or compliance. Rank each by uncertainty × impact. Use the
validation checklist for a fuller
inventory.
2. Gather Problem Evidence
Research recent customer language and conduct interviews with people who match
the target segment. Ask about the last real occurrence:
- What triggered the task?
- What did they do step by step?
- What did it cost in time, money, risk, or delay?
- Who approved the current solution?
- What have they already tried?
- What would block switching?
Do not lead with a feature pitch or ask only “Would you use this?” Summarize
evidence by segment and explicitly note non-problems, low urgency, and
inaccessible buyers.
3. Test the Offer
Create a narrow offer for one segment and one outcome. Include an actual price
or pricing mechanism early enough to test willingness to pay.
Choose the smallest credible test:
- concierge/manual service;
- clickable or coded prototype using realistic data;
- design-partner agreement;
- paid pilot with defined success criteria;
- transparent waitlist or fake-door page;
- outbound offer to a small, qualified account list.
Use fake-door templates as scaffolding. Do
not imply that an unavailable product is currently usable. Define pass, revise,
and stop conditions before traffic arrives.
4. Run a Decision Gate
Produce a gate memo:
| Area | Evidence | Confidence | Main risk | Next test |
|---|
| Problem | Observed behavior | High/medium/low | ... | ... |
| Buyer and budget | Commitment or workaround | ... | ... | ... |
| Solution | Prototype/pilot result | ... | ... | ... |
| Distribution | Reach and response | ... | ... | ... |
| Economics | Price, cost, support | ... | ... | ... |
| Feasibility/compliance | Technical and legal review | ... | ... | ... |
Choose one: proceed, proceed with constraint, revise, pivot segment/problem, pause, or stop. State what evidence would reverse the
decision.
5. Scope the Learning MVP
Build only what is necessary to deliver the promised outcome and measure whether
users reach it repeatedly. Define:
- target workflow and excluded use cases;
- activation event and time-to-value;
- must-have reliability, security, privacy, and support controls;
- manual operations hidden behind the product where acceptable;
- instrumentation and feedback capture;
- rollback and customer communication plan;
- explicit non-goals.
A four-week build can be useful, but scope should follow risk and safety rather
than an arbitrary deadline.
6. Validate Retention and Economics
After launch, analyze cohorts rather than cumulative signups. Track:
- activation and time-to-first-value;
- repeated use tied to the core job;
- logo and revenue retention;
- expansion, contraction, pauses, and reactivation;
- support effort and gross margin;
- acquisition source, sales time, and payback;
- reasons for non-use and churn.
Do not claim product-market fit from launch traffic. Look for repeated value in
a coherent segment and evidence that distribution can continue.
7. Choose the Next Stage
Use community platforms and growth
phases when the evidence supports expansion.
Possible paths:
- deepen one segment and use case;
- improve activation or retention before acquisition;
- raise or restructure pricing;
- add a second repeatable channel;
- remain a profitable focused product;
- hire or automate a proven bottleneck;
- stop or sell when expected return no longer justifies attention.
Output Contract
Return:
- executive decision summary;
- assumption map ranked by risk;
- evidence ledger with sources and dates;
- customer/competitor/workaround findings;
- one or more validation experiment cards;
- gate memo with proceed/revise/stop logic;
- learning MVP scope and instrumentation;
- 30-day action plan based on the current stage.
Sources
