# How do you run an MVP experiment - Part 1

December 26, 2017

There are 6 steps to a successful MVP experiment.

- Determine the problem you’re trying to solve and the solution your product provides.
- Identify your assumptions about the problem and solution.
- Build testable hypotheses based on your assumptions.
- Figure out how you’ll determine whether the test was successful or not.
- Pick the best MVP type for your unique situation.
- Execute the MVP experiment and collect the data. Decide if your hypothesis is true, false, or somewhere in between.

We will cover the first three in this tutorial, and the next three in the next one.

## Determining the problem and solution

This should be done during the brainstorming and planning stages of product development. If you don’t have a clear idea of the problem or solution, you aren’t ready to make MVP experiments.

Once you have them, the next step is to identify your assumptions.

## Identifying your assumptions

You now have an idea of your target customer, the problem they’re facing, and a product that will solve the problem. But how much do you really know?

You need to realize that most of your ideas are based on broad assumptions about the market. If your assumptions are true, your idea will probably succeed. But if your assumptions are false, your idea will fail.

MVPs are designed to test each assumption one by one to determine if your idea is on the right track.

The four most common assumptions are:

- “My user has a problem with ___.”
- “My customers care about ___.”
- “___ will pay for this.”
- “There are no good substitutes for this idea.”

To find out your own underlying assumptions, start with this statement: “In order for this to be successful, the following must be true…”

List every assumption you have, then prioritize your assumptions in order from most to least risky.

## Identifying your high-risk assumptions

As a Product Manager, your job is to mitigate risk. It makes sense to start from the assumptions that could cause the most damage if they turned out to be false.

Go through your list of assumptions one by one and imagine what would happen if the assumption was wrong.

- “There are no good substitutes for this idea.”

If this is false, and there *are* other substitutes for your idea, what will happen?

It may still work out, because consumers’ appetites can change. Each time a new, more high-definition TV is released, many people upgrade to a newer TV because they want the better picture (even though they already have a TV at home).

- “___ will pay for this.”

If this is false, and people aren’t willing to pay for your product, what will happen?

Even if nobody pays, you can find other ways of monetizing—like getting sponsors or placing ads in your content.

- “My customers care about ___.”

If this is false, and people don’t care about solving their problem with your product, what will happen?

You may still have hope. Marketing is designed to influence people to want products they never knew they needed.

- “My user has a problem with ___.”

If this is false, and your target users don’t actually have the problem you’re trying to solve, what will happen?

This is a fatal flaw. Your product will never survive if it doesn’t solve the target user’s problem. This is the riskiest assumption.

Once you’ve prioritized your assumptions, you can begin to make hypotheses based on them. Start with the riskiest one.

## Creating hypotheses

A hypothesis is a single statement of what you believe to be true. Unlike assumptions, which are vague descriptions of your beliefs, hypothesis are specific and testable.

You’ll need to specify all the details: the *who*, *how* and *why* of your test.

A hypothesis follows this template:

If we (action), we believe (subject) will (predicted outcome) because (reason).

The reason should include the problem you are trying to solve (or at least a benefit for the user).

Also, make sure to define a quantifiable outcome in your hypothesis. Don’t use vague expressions like “buy more.” Should it increase by 30% a month, or 5 orders per day, etc.? Be specific and make your outcomes quantitative.

An even more detailed hypothesis (for a more established company) would be:

We believe (subject) has a (problem) because (reason). If we (action), this (metric) metric will improve.

This makes it clear what the problem is and what metric needs to change in order to prove your hypothesis.

The more specific your hypothesis is, the easier it will be to evaluate the results.

Move on to the next section to learn about the final three steps of running an MVP experiment.