How do you test and validate your pricing assumptions and hypotheses?

2 Generate and prioritize your pricing hypotheses

Once you have your pricing objectives and metrics, you can start generating and prioritizing your pricing hypotheses. A pricing hypothesis is a statement that expresses a causal relationship between a pricing variable and a pricing metric. For example, a pricing hypothesis could be: "Increasing the price of the premium plan by 10% will increase the revenue per user by 5% without affecting the conversion rate." To generate your pricing hypotheses, you can use various sources of information, such as customer feedback, competitor analysis, market research, or value-based pricing methods. To prioritize your pricing hypotheses, you can use a framework such as the ICE score, which evaluates the impact, confidence, and ease of each hypothesis.

3 Choose your pricing experiment method

After you have your prioritized list of pricing hypotheses, you need to choose the best method to test and validate them. There are different types of pricing experiments, such as A/B testing, conjoint analysis, surveys, or interviews. Each method has its advantages and disadvantages, depending on your goals, resources, and constraints. For example, A/B testing is a powerful way to measure the actual behavior of your customers in response to different prices, but it requires a large and representative sample size, a reliable testing platform, and a careful design to avoid confounding factors. Conjoint analysis is a sophisticated technique to estimate the willingness to pay of your customers for different features and benefits, but it requires a complex statistical analysis and a high-quality questionnaire.

4 Run and analyze your pricing experiment

Once you have chosen your pricing experiment method, you need to run and analyze your pricing experiment. This involves setting up your experiment according to the best practices of your chosen method, collecting and cleaning your data, and applying the appropriate statistical methods to evaluate the results. You should also monitor and control your experiment to ensure its validity and reliability. For example, you should check for any biases, errors, or outliers in your data, and use methods such as randomization, segmentation, or control groups to reduce the noise and increase the signal in your experiment.

5 Learn and iterate from your pricing experiment

The final step of testing and validating your pricing is to learn and iterate from your pricing experiment. This means interpreting and communicating your findings, drawing actionable insights, and making data-driven decisions. You should also consider the limitations and assumptions of your experiment, and identify any gaps or opportunities for further testing. For example, you should ask yourself questions such as: What did you learn from your experiment? How confident are you in your results? How generalizable are they to your target market? How do they align with your pricing objectives and metrics? What are the next steps to improve your pricing?

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?