1 Define your problem and goal

Before you start experimenting, you need to have a clear problem statement and a specific goal for your product. What is the pain point or opportunity you are trying to address? Who are your target customers and what are their needs and preferences? What are the key metrics and outcomes you want to achieve with your product? These questions will help you frame your problem and goal, and guide your experiment design and analysis.

2 Identify your assumptions and hypotheses

Next, you need to identify your assumptions and hypotheses about your problem, customers, and product. Assumptions are the beliefs or expectations you have that may or may not be true. Hypotheses are the testable statements that express your assumptions in a measurable and falsifiable way. For example, an assumption could be "Customers are willing to pay for a subscription-based service that helps them manage their finances". A hypothesis could be "Customers who sign up for a free trial of our service will convert to paying customers at a rate of 10% or higher".

3 Prioritize and select your experiments

You probably have many assumptions and hypotheses to test, but you can't test them all at once. You need to prioritize and select the most critical and risky ones to validate first. A common framework for prioritization is the ICE score, which stands for Impact, Confidence, and Ease. You can rate each hypothesis on a scale of 1 to 10 based on how much impact it will have on your goal, how confident you are that it is true, and how easy it is to test. Then, you can multiply the scores and rank the hypotheses from highest to lowest. The ones with the highest ICE scores are the ones you should focus on testing first.

4 Choose your experiment methods and tools

Once you have selected your hypotheses to test, you need to choose the appropriate experiment methods and tools to test them. There are many types of experiments you can use, such as surveys, interviews, landing pages, prototypes, MVPs, A/B tests, and more. The best method depends on your hypothesis, your stage of product development, your resources, and your target customers. You also need to choose the right tools to design, run, and measure your experiments, such as online platforms, software, analytics, and feedback tools.

5 Run and analyze your experiments

After you have designed your experiments, you need to run them and collect data from your customers. You should define your sample size, duration, and criteria for success before you launch your experiments. You should also monitor your experiments and make sure they are running smoothly and ethically. Once you have enough data, you need to analyze it and draw conclusions from it. You should compare your results with your hypotheses and see if they are validated or invalidated. You should also look for patterns, insights, trends, and feedback that can help you improve your product and customer understanding.

6 Learn and iterate

The final step of testing your product assumptions and hypotheses with experiments is to learn from them and iterate on your product. You should document your findings, insights, and learnings from your experiments and share them with your team and stakeholders. You should also decide what to do next based on your results. If your hypotheses are validated, you can move forward with your product development and test new hypotheses. If your hypotheses are invalidated, you can pivot your product or test different hypotheses. The key is to keep learning and iterating until you find the best product-market fit for your customers and your goal.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?