What are some of the best practices for conducting lean experiments to validate your assumptions?

1 Define your hypotheses

Before you design and run any experiment, you need to have a clear idea of what you want to learn and why. A hypothesis is a statement that expresses your assumption about a specific aspect of your product, market, or customer behavior. For example, "I believe that X feature will increase Y metric by Z%". A good hypothesis should be specific, measurable, achievable, relevant, and testable. You can use tools like the business model canvas or the lean canvas to map out your hypotheses and identify the most critical ones to test.

2 Choose your experiment type

Depending on your hypothesis, you can choose from a variety of experiments to validate or invalidate it. Interviews are useful for exploring customer segments and problem-solution fit, while surveys are helpful for measuring customer satisfaction and behavior. Landing pages are great for testing customer demand and acquisition channels, while prototypes are beneficial for testing product usability, feasibility, and desirability. MVPs can be used to test product-market fit, revenue streams, and growth strategies.

3 Design your experiment

Once you have chosen your experiment type, you need to design it in a way that guarantees its validity, reliability, and efficiency. To do this, you must define the metrics you will measure and how you will measure them. These metrics should be SMART (specific, measurable, achievable, relevant, and time-bound) and should be aligned with your hypothesis and goals. You also need to decide who to target and how to reach them. Your sample should be representative of your customer segment and sizeable enough to produce meaningful results. Additionally, you must decide how you will run your experiment and collect data. Your method should be suitable for your experiment type and metrics and should reduce bias and noise. For example, if you are running an interview, you may use a semi-structured script, record the session, and take notes. If you are running a landing page, you may use a web analytics tool, a split test, and a feedback form.

4 Run your experiment

When you have designed your experiment, you need to execute it and gather your data. Depending on your experiment type and method, this might involve different steps and tools. For example, if you are running an interview, you might schedule a time and place, prepare the equipment, conduct the session, and thank the participant. If you are running a landing page, you might set up the web page, launch the campaign, monitor the traffic, and analyze the data.

5 Analyze your results

After you have run your experiment, you need to analyze your results and draw conclusions. You need to compare your data with your hypothesis and see if it supports or contradicts it. You also need to look for patterns, trends, insights, and surprises that might reveal something new or unexpected about your product, market, or customers. You can use tools like charts, graphs, tables, or dashboards to visualize and communicate your results.

6 Learn and iterate

The final step of conducting a lean experiment is to learn from your results and decide what to do next. You need to evaluate your hypothesis and see if it is validated or invalidated. If it is validated, you can move on to testing another hypothesis or scaling your product. If it is invalidated, you can pivot or persevere. Pivoting means changing one or more aspects of your product or business model based on your learning. Persevering means continuing to test and refine your product or business model based on your learning. You can use tools like the build-measure-learn loop or the pivot or persevere framework to guide your decision making.