How do you choose the right metrics for prototyping?

1 Why metrics matter

Metrics are quantitative or qualitative indicators that help you evaluate the performance, value, and impact of your prototypes. They can help you answer questions like: How well does your prototype solve the problem? How do users interact with it? How viable is your solution in the market? Metrics can also help you compare different prototypes, identify areas for improvement, and communicate your results to stakeholders.

2 How to define your goals

Before you choose your metrics, you need to define your goals for prototyping. What are you trying to achieve, learn, or test with your prototype? Your goals should be specific, measurable, achievable, relevant, and time-bound (SMART). For example, your goal could be to validate your value proposition, test your usability, or explore different features or scenarios.

3 How to select your metrics

Once you have your goals, you can select the metrics that best align with them. Different types of metrics for prototyping include behavior metrics, such as clicks, time spent, completion rate, or satisfaction; outcome metrics, such as revenue, retention, referrals, or impact; and learning metrics, such as insights, feedback, or hypotheses. The right metrics for you will depend on your goals; for example, if you are testing usability you may use behavior metrics like completion rate or satisfaction, and if you are validating your value proposition you may use outcome metrics like revenue or retention. Ultimately, the chosen metrics should be relevant, meaningful and actionable.

4 How to collect your data

When selecting metrics, it's important to determine how to collect the data. There are various methods and tools for data collection, such as analytics software applications, surveys, interviews, and observations. It's essential to choose the methods and tools that are most reliable and efficient for your data collection. For example, analytics or observations can be used to collect behavior metrics, while surveys or interviews can be used to collect outcome or learning metrics.

5 How to analyze your data

Once you collect your data, you need to analyze it and draw conclusions. Descriptive analysis summarises your data using statistics, graphs, or tables, such as mean, median, mode, frequency, or distribution. Inferential analysis tests your data using hypotheses, significance, or confidence, such as t-test, ANOVA, or chi-square. Exploratory analysis examines your data using patterns, correlations, or outliers, such as cluster analysis, regression analysis, or factor analysis. You must select the most appropriate approach depending on the type of data; for instance, if you want to analyze behavior metrics you could utilize descriptive or inferential analysis. Alternatively, if you're analyzing outcome or learning metrics you may opt for exploratory or inferential analysis.

6 How to iterate your prototype

After you analyze your data, you need to iterate your prototype based on your findings. Iteration is the process of making changes to your prototype and testing it again until you reach your desired outcome. You can choose from different ways to iterate your prototype, such as pivoting (a major change involving changing your value proposition, target market, or business model), persevering (a minor change involving improving features, design, or usability), or killing (deciding to stop working on the prototype and move on to another idea). You should select the iteration that is most aligned with your goals, metrics, and feedback. For example, if your metrics show that your prototype isn't solving the problem or generating value, you might pivot or kill it. Otherwise, if it's solving the problem or generating value but needs some refinement, persevere it.

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?