7th post in the A2Z Product Management Series!

Hypotheses are like Sherlock Holmes' magnifying glass. They help us uncover valuable insights, make informed decisions, and drive product improvements.

These are some of my learnings from creating and testing hypotheses both as a Product Analyst and as a Product Manager working on Product-Led Growth:

  1. As a Product Analyst, when you’re tackling a problem for the first time, it’s better to start by creating hypotheses instead of immediately delving into writing queries or slicing and dicing the data.

    1. After considering and formulating hypotheses, you may realize that you don’t need to retrieve data for some of them, while others may require a more in-depth examination. This saves you time and helps you focus on the most crucial aspects.

    2. You can also run your hypotheses by a PM or other stakeholders to gather their feedback before pulling the data. This will help identify which hypotheses are more significant and which ones can be safely discarded.

  2. As a PM, when developing products, begin by identifying a specific growth challenge or opportunity. Clearly define what you aim to achieve or enhance in terms of user adoption, retention, and more.

  3. Ensure that your hypothesis is linked to measurable growth metrics. Define key performance indicators (KPIs) or success criteria that will enable you to assess the impact.

  4. Incorporating both data-driven and user research-driven insights into creating hypotheses is key to having more successful tests over time.

  5. Make sure your hypothesis suggests a change that can be tested and measured within the framework of your product-led growth strategy.

  6. You will likely have multiple hypotheses when addressing a problem, a product flow, or a growth loop. Prioritize them based on the most important factors in your product's growth equation.

  7. A/B testing is an efficient way to quickly test hypotheses, but many people misunderstand it. Learn more about statistical significance, confidence intervals, and other important concepts that help you conduct and measure the results correctly. It's better to keep the product the same than change it and potentially worsen the user experience based on a flawed experiment or misinterpretation of results.

  8. Involve cross-functional teams, including designers, developers, and analysts, in the process. Collaborative input can lead to more robust hypotheses.

  9. Maintain a record of your growth-focused hypotheses, tests, and results. Sharing this knowledge with your team and stakeholders is essential for making insights accessible to everyone, fostering a culture of data-driven decision-making, and avoiding the repetition of the same experiments, which can consume time and resources.

  10. While individual hypotheses focus on short-term improvements in product-led growth, consider how they align with your long-term growth strategy. Ensure that your hypothesis testing supports your overarching goals for sustained growth.