How to run a Growth Experiment - Learn 4 steps framework

Happy Monday!

Today, I want to share an exciting approach that can help Product, Marketing, and Sales teams validate new strategies before making significant resource investments.

It's called Growth experiments, a powerful tool to test assumptions in the real world. It is a four-step process.

Growth Experiment Framework

• Hypothesis

• Trigger

• Action

• Measure

Case: Hypothetical SaaS startup called FinPro, challenges - low product engagement, low activation rate % and high churn.

Step 1: Hypothesis

We start by identifying a customer struggle and the desired business outcome we aim to achieve.

The key is to remain open-minded and focus on the struggle rather than becoming too attached to a specific solution.

For instance, in our case, we have:

Hypothesis: "Providing personalized onboarding tutorials to new users will improve product adoption and reduce churn rate."

Step 2: Trigger

We can leverage email and direct calls to communicate with the customers about their struggles and our proposed solution.

Consistency in messaging is crucial, and we need to choose the best time to reach out.

Here's an example of a trigger for the growth experiment:

Audience: Email new users who have completed fewer than three onboarding steps within their first two weeks of signing up.

Message: "Unlock the full potential of our platform with personalized onboarding tutorials just for you."

Timing: We'll send the email when users are most active on the platform, such as during business hours.

Step 3: Action

To gauge the success of the growth experiment, we define a measurable action for users to take based on the trigger. This action will provide valuable insights.

For example, we can create personalized tutorial videos addressing their needs and challenges.

The email will include a call-to-action button like "Watch Your Personalized Tutorial Now."

Step 4: Measure

To assess the performance of the trigger and action stages, we'll track essential metrics such as:

• email open rates

• click-through rates

• tutorial completion rates

• user engagement

We can use spreadsheets or analytics tools to gather and evaluate relevant data. This will help determine whether the hypothesis holds true or adjustments are needed.

Positive Scenario

It validates the hypothesis if the engagement metrics show increased onboarding completion and improved user engagement.

This positive outcome will provide a more robust business case for investing more resources in personalized onboarding.

Negative Scenario

If the metrics indicate low engagement or no significant improvement, we must reassess the hypothesis and explore alternative solutions.

Conclusion

Running growth experiments empowers to make informed decisions, validate assumptions, and drive business growth.

We can effectively optimize growth strategies by following these four steps and adapting them to specific contexts.

If you have any questions or ideas, please feel free to reach out. Let's make data-driven decisions that propel us forward.

Wishing you a successful and productive week!

Thanks,
Chintan Maisuria

P.S. Come say hi on Twitter and Linkedin 👋🏼