Product Experimentation: The Definitive Guide

In this guide about product experimentation you’ll learn:

• What product experimentation is
• Why it matters for businesses
• How to get started
• And lots more…

If you’re excited to start experimenting like a pro, this guide is for you.

Let’s dive right in.

• What is product experimentation?
• Why is product experimentation important for businesses?
• How to run product experiments
  • Step 1: Define the hypothesis
  • Step 2: Run the experiment
  • Step 3: Iterate
• Best practices for product experimentation
• How to encourage a culture of experimentation within your organization
• How Artificial Intelligence enables product experimentation
• Conclusion

What is product experimentation?

Product experimentation is the process of testing and validating different hypothesis to continuously improve the customer experience of a product or service.

Product experiments can be run with A/B tests, but it can also be done by making multiple small bets, that is, a bet where the downside is minimal but the potential upside is significant.

Why is product experimentation important for businesses?

Technology, consumer preferences and the global economy are constantly changing and so businesses must continuously evaluate, iterate and adapt to keep up.

By running experiments, companies can stay ahead in today’s competitive market by continuously testing new ideas, improving their products and capitalizing on new opportunities.

A company culture with a strong focus on product experimentation encourages iteration and continuous improvement. Rather then seeing product development as a project with a clear start and end date, it’s seen as an ongoing process. Launching a feature or product is not the end goal — it’s the starting point! Once it’s launched, companies can follow up and improve the product based on customer feedback.

Additionally, product experimentation helps businesses mitigate risk by testing various hypotheses before investing in development. Product experimentation can identify whether there is a market for a feature or product before committing to months of development. Based on the outcome, businesses can make data-informed decisions to avoid costly mistakes.

How to run product experiments

To conduct product experiments, follow these steps:

1. Define the hypothesis
2. Run the experiment
3. Iterate

Let’s go through each step in detail.

Step 1: Define the hypothesis

A hypothesis is a testable statement that explains the expected outcome of an experiment. It should be measurable and focused on a single variable.

For example, if you want to test the impact of a new feature on user engagement, your hypothesis might be: “Adding a personalized recommendation feature to our app will increase user engagement by 10%.”

Step 2: Run the experiment

After defining the hypothesis, design the experiment to test it.

This might involve creating multiple versions of your product (A/B testing) or implementing various features to compare their effects.

For example, you could roll out two versions of your app: one with the personalized recommendation feature and one without it. Measure user engagement across both versions and compare the results.

For small companies, A/B tests don’t make much sense, though. You’d be better off by making small bets on new features and improvements.

Also, always make sure to respect user privacy and comply with regulations like GDPR and CCPA when running experiments.

Step 3: Iterate

Based on the results of the experiment, decide whether to roll out the tested changes on a larger scale, iterate on the hypothesis, or discard the idea altogether.

If the hypothesis was supported, consider doing additional tests to confirm it, or revise it to become more specific. Once you’re confident that the hypothesis is proven true, consider rolling it out on a larger scale.

For example, if the personalized recommendation feature successfully increased user engagement by 10%, you may want to test different variations of the feature to further optimize its impact. Experiment with different algorithms and designs to find the most effective implementation.

If the hypothesis was not supported, learn from the experiment and come up with a new hypothesis. Remember, experimentation is an ongoing process, and if your assumptions never prove to be incorrect, it’s likely that you’re not challenging yourself to explore new possibilities.

For example, if the personalized recommendation feature did not increase user engagement as expected, you might revise the hypothesis to focus on refining the recommendation algorithm, test other ways to package the feature, or perhaps explore other ways to boost engagement.

Finally, you learn much more from a failed test than a successful one. In the words of inventor and entrepreneur James Dyson:¹

“Failure is […] part of making progress. You never learn from success, but you do learn from failure.”

Best practices for product experimentation

To make the most of your product experimentation efforts, follow these best practices…

Start small and iterate

Begin with smaller experiments to test your hypotheses and learn from the results before moving on to larger-scale changes.

This approach allows you to make adjustments as you go and minimize potential risks.

Make data-informed decisions

Base your decisions on data and evidence rather than personal opinions or assumptions.

However, don’t solely make decisions based on data. Develop good taste and let data guide your decisions.

Embrace failure

Not every experiment will yield positive results, and that’s okay.

Treat failures as learning opportunities and use the insights gained to come up with new hypothesis and improve your experimentation process.

Document your experiments

Keep a record of your experiments, including hypotheses, results, and insights gained. This documentation will help you track progress, learn from past experiments, and inform future decisions and strategies.

Run multiple experiments simultaneously

It’s difficult to know in advance what will work, so it’s a good practice to conduct multiple experiments simultaneously. That way, you increase the chances of discovering valuable insights.

How to encourage a culture of experimentation within your organization

Create a supportive environment

Establish a learning culture where employees feel comfortable making mistakes and learning from them.

As a manager, it’s important to never punish failures. Instead, celebrate them as opportunities to learn and grow! Encourage team members to take ownership over their work, including learning from failures.

Provide resources and tools

Equip employees with the tools, resources, and training they need to conduct experiments.

(Why not send them this guide? 😉)

Reward experimentation

As a manager, recognize and reward teams and employees who take the initiative to conduct experiments.

Also, this motivate others to follow suit.

Encourage teaching and learning

Create opportunities for teams that conduct experiments to share their failures and learnings with other teams in the organization and foster cross-functional collaboration.

This could include presentations, workshops, or internal blog posts.

How Artificial Intelligence enables product experimentation

In the next decade, Artificial Intelligence will lower the bar A LOT for businesses to experiment.

Tools like Github Copilot is already making developers more productive by not only automating code writing but also enable shorter feedback loops. It’s like having your own personal assistant, always there to help!

Just think about the possibilities!

Somebody learning programming can have his or her own teacher in the code editor. How cool is that!?

An experienced programmer could delegate the code writing to the AI and spend more time on the stuff that humans do better than computers: coming up with creative solutions to problems.

Just like computers have previously enabled humans to do more high-level work by leveraging automation, AI will accelerate the development and have a 10x impact.

Additionally, AI will lower the cost to experiment. What would previously require a team of software engineers will soon be possible to achieve by a single developer with AI-powered tools.

Software companies that move fast and continuously test new ideas will get ahead.

Conclusion

In conclusion, product experimentation allows companies to continuously test new ideas, improve their products, and capitalize on new opportunities.

Those that embrace continuous experimentation will lead the way.

Happy experimenting!