How do you test and evaluate the assumptions and hypotheses underlying your product vision?

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Your product vision is the guiding star for your team and stakeholders. It describes the ultimate value and impact of your product, and why it matters to your customers and users. But how do you know if your product vision is valid, feasible, and desirable? How do you test and evaluate the assumptions and hypotheses underlying your product vision? In this article, you will learn how to use a simple framework and some practical methods to validate your product vision and reduce uncertainty and risk.

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Santosh Kookal

Digital Health | General Manager, Chief Product Officer, Head of Product, Vice President, Product Management…

1 Define your assumptions and hypotheses

The first step is to identify and articulate the key assumptions and hypotheses that support your product vision. These are the statements that you believe to be true, but have not yet verified with evidence. For example, you might assume that there is a problem worth solving, that your target market is large enough, that your solution is better than the alternatives, and that your business model is viable. You can use tools like the Lean Canvas, the Value Proposition Canvas, or the Product Vision Board to help you structure and document your assumptions and hypotheses.

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Santosh Kookal

Digital Health | General Manager, Chief Product Officer, Head of Product, Vice President, Product Management, Marketing, R&D | Medical Device, Diagnostics | Healthcare IT, Cloud, IOT, Big Data, AI, ML, SaaS, SaMD
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Most important assumptions and hypotheses are latent and embedded in our world view. Its best to start with the problem space and while it is tempting to jump to solutions, focus on a clearer articulation of the core problem you are solving, who you are solving it for, and how one could know your solution is better. Then you can define the solution space and iterate. People are much better at discussing solutions so your problem space definition will improve as you work through solutions. This is the context for identifying assumptions and hypotheses. Is the problem worth solving? Is the solution desirable? Does it create a product that is viable?

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2 Prioritize and select the most critical ones

The next step is to prioritize and select the most critical assumptions and hypotheses to test. You cannot test everything at once, and some assumptions and hypotheses are more important and risky than others. For example, you might prioritize the ones that have the highest impact on your product vision, the ones that have the lowest level of confidence, and the ones that are easy to test. You can use a simple matrix or a scoring system to help you rank and compare your assumptions and hypotheses.

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Jothi K.
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The crux of effective product management lies in prioritizing critical assumptions using a tool like the ICE matrix - Impact, Confidence, and Ease of testing. Take the target audience's willingness to use AI-based learning: it has a high impact, medium confidence, and high ease of testing. Compute an ICE score like so: (High+Medium+High)/3. This approach manages resources effectively, focusing on the most critical risks first, saving time and resources on less consequential issues. Prioritization is iterative, requiring continuous reassessment based on new insights, reinforcing flexibility and adaptiveness.

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3 Design and run experiments

The third step is to design and run experiments to test your selected assumptions and hypotheses. Experiments are structured ways of collecting and analyzing data to validate or invalidate your assumptions and hypotheses. For example, you might use interviews, surveys, landing pages, prototypes, or MVPs to test your problem, solution, market, or business model assumptions and hypotheses. You can use tools like the Experiment Canvas, the Test Card, or the Experiment Report to help you plan and document your experiments.

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4 Measure and analyze the results

The fourth step is to measure and analyze the results of your experiments. Results are the data and feedback that you collect from your experiments. For example, you might use qualitative or quantitative metrics, such as customer satisfaction, conversion rates, retention rates, or revenue, to measure and analyze your results. You can use tools like the Validation Board, the Learning Card, or the Pivot or Persevere Report to help you summarize and communicate your results.

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5 Learn and decide what to do next

The final step is to learn and decide what to do next based on your results. Learning is the process of extracting insights and implications from your results. For example, you might learn that your assumptions and hypotheses were validated, invalidated, or partially validated, and that you need to revise, refine, or pivot your product vision. Deciding is the process of choosing the best course of action for your next steps. For example, you might decide to continue testing, iterate on your solution, scale up your product, or stop working on your product. You can use tools like the Lean Review, the Retrospective, or the Roadmap to help you learn and decide what to do next.

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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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How do you test and evaluate the assumptions and hypotheses underlying your product vision?

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1 Define your assumptions and hypotheses

2 Prioritize and select the most critical ones

3 Design and run experiments

4 Measure and analyze the results

5 Learn and decide what to do next

6 Here’s what else to consider

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