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What's the best way to test your product hypotheses?

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As a product manager, you have many ideas and assumptions about what your customers need and want, and how your product can solve their problems. But how do you know if your ideas are valid and worth pursuing? How do you avoid wasting time and resources on features that nobody uses or values? The answer is to test your product hypotheses with data and feedback from real users. In this article, you'll learn what product hypotheses are, how to formulate them, and how to design and run experiments to validate or invalidate them.

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1 What are product hypotheses?

A product hypothesis is a statement that expresses your belief about how a certain feature or change will affect your customers' behavior, satisfaction, or outcomes. For example, you might have a hypothesis that adding a chatbot to your website will increase conversions, or that simplifying your sign-up process will reduce churn. A product hypothesis should be specific, measurable, and testable, and it should include a clear cause-and-effect relationship. For example, a bad product hypothesis is "Our customers will love our new design." A good product hypothesis is "By redesigning our landing page with more clear and compelling value propositions, we will increase the number of visitors who sign up for a free trial by 10%."

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2 How to formulate product hypotheses

The first step to formulate product hypotheses is to identify the problem or opportunity that you want to address with your product. Use various sources of information, such as customer feedback, market research, competitor analysis, user data, or your own intuition. The next step is to brainstorm possible solutions or features that could solve the problem or seize the opportunity. Try techniques such as ideation, prototyping, or user stories to generate and refine your ideas. The final step is to prioritize your ideas and select the most promising ones to test. Criteria such as impact, feasibility, cost, risk, or alignment with your vision and goals can help rank your ideas and choose the ones that have the highest potential value for your customers and your business.

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3 How to design product experiments

Once you have selected your product hypotheses, you need to design experiments that will test them with real users and data. An experiment is a method of collecting evidence that supports or rejects your hypothesis. Depending on your hypothesis, product stage, and resources, there are a variety of experiments you can use. For instance, A/B tests involve creating two or more versions of your product or feature, and randomly assigning users to each version. You then measure and compare the outcomes of each version. User interviews involve talking to users or potential users of your product or feature and asking them questions about their needs, preferences, expectations, or opinions. Surveys involve sending out a questionnaire to users or potential users of your product or feature and asking them to rate, rank, or choose from different options. All these experiments help validate or invalidate your hypothesis by providing valuable insights into user behavior.

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4 How to run product experiments

Once you have designed your product experiments, you need to run them and collect data and feedback from your users. To ensure that your experiments are reliable and valid, you should define your metrics, set your sample size, choose your segments, run your experiment, and monitor it. Your metrics should be aligned with your hypothesis, relevant to your customers and business, and easy to track and analyze. Your sample size depends on factors such as expected effect size, margin of error, confidence level, and user base; you can use online calculators or tools to estimate it. Your segments should be relevant to your hypothesis, representative of the target market, and easy to identify and reach. Try tools such as Google Optimize or SurveyMonkey to create and run tests or surveys; you can also use tools such as Zoom or Calendly to schedule user interviews. Google Analytics or Excel can monitor and visualize metrics, and tools such as Notion or Airtable can organize and synthesize feedback.

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5 How to interpret product experiments

The last step of testing your product hypotheses is to interpret your experiments and draw conclusions from them. Evaluate the results of your experiments and decide if they confirm or contradict your hypothesis and what they mean for your product decisions. To do this, ask yourself what the main outcome of the experiment was, what key insights or learnings you have gained, and what the next steps should be. Testing product hypotheses is essential for product managers, as it helps them build products that solve real problems and deliver real value. Through a systematic and data-driven process, you can test hypotheses with confidence and make better product decisions.

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6 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?

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What's the best way to test your product hypotheses?

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1 What are product hypotheses?

2 How to formulate product hypotheses

3 How to design product experiments

4 How to run product experiments

5 How to interpret product experiments

6 Here’s what else to consider

Product Management

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What's the best way to test your product hypotheses?

1

2

3

4

5

6

1 What are product hypotheses?

2 How to formulate product hypotheses

3 How to design product experiments

4 How to run product experiments

5 How to interpret product experiments

6 Here’s what else to consider

Product Management

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