A 90-Day Validation Plan Before You Build the SaaS

A product can be technically impressive and still fail because the problem was weak, the buyer was unclear, or the cost of changing behavior was underestimated. A better starting point is to treat the first ninety days as an evidence-gathering program rather than a development sprint.

The objective is not to prove that the original idea is correct. It is to learn whether a specific group of people has a recurring problem, already spends time or money trying to solve it, and will make a meaningful commitment to a better approach.

Days 1–15: define the narrowest credible problem

Write a one-page problem brief before creating a feature list. It should identify the user, the moment the problem appears, the current workaround, the cost of the problem, and the observable outcome a better solution would create.

A useful starting statement looks like this:

Operations managers at small membership organizations lose several hours each week reconciling disconnected payment, member, and communication systems, causing delayed support and unreliable reporting.

Avoid broad claims such as “teams need better productivity.” Broad problems produce generic products. Narrow problems create testable conversations.

Days 16–35: interview behavior, not enthusiasm

Speak with at least fifteen people who closely match the intended customer. Ask about recent examples, not hypothetical interest. Strong questions include:

• When did this problem last happen?
• What did you do to resolve it?
• Who else was involved?
• What did the delay or error cost?
• Which tools or services have you already tried?
• Who controls the budget for solving it?

Do not lead with a product pitch. The goal is to understand the workflow, vocabulary, urgency, and buying constraints. Record patterns in a simple research table so the strongest evidence is visible across interviews.

Days 36–55: deliver the result manually

Before automating the workflow, offer the outcome as a service. A reporting SaaS might begin as a manually prepared weekly report. An AI support product might begin with a human-assisted triage service. A scheduling platform might begin with a structured intake form and a shared calendar.

Manual delivery reveals hidden requirements that are difficult to discover in a mockup. It also shows whether customers value the result enough to provide data, attend onboarding, change a process, or pay for the service.

Days 56–70: test the buying decision

Pricing should be discussed before the product is complete. Present a clear package, expected outcome, onboarding effort, and price range. Ask what approval process would be required and what could prevent the purchase.

Useful commitment signals are stronger than compliments:

• A paid pilot.
• A signed letter of intent with concrete conditions.
• Access to representative data for implementation.
• An introduction to the budget owner.
• A scheduled onboarding date.

An email address on a waitlist is a weak signal. Time, data, reputation, and money are stronger.

Days 71–82: define the smallest complete product

Use the evidence to identify one complete customer journey. The first version should solve a narrow problem end to end rather than expose many unfinished capabilities.

For each proposed feature, ask:

1. Which validated customer problem does this address?
2. Is it required for the first successful outcome?
3. Can the team perform it manually during the pilot?
4. What risk does automation introduce?

Anything that cannot be connected to observed behavior belongs in a later hypothesis, not the first release.

Days 83–90: make a deliberate decision

At the end of the cycle, choose one of four outcomes:

• Build: the problem is recurring, the buyer is identifiable, and customers made meaningful commitments.
• Continue validating: the problem appears real, but pricing or buying authority remains uncertain.
• Change direction: interviews revealed a more urgent adjacent problem.
• Stop: the evidence does not justify further investment.

Stopping is not a failed validation cycle. It is the return on doing the research before spending months on the wrong product.

The final validation scorecard

A credible decision should include the number of qualified interviews, repeated problems observed, current alternatives, estimated cost of the problem, buyer role, sales-cycle risks, pilot commitments, pricing reactions, and the smallest workflow customers need.

The purpose of validation is not to eliminate uncertainty. It is to replace assumptions with enough evidence that the next investment is intentional.