What Is Product Experimentation and Why Is It Important?

Product experimentation means systematically testing different versions of your product to determine which option works best. As a product manager, it’s one of the most powerful tools in your arsenal. Running experiments helps you make data-driven decisions to improve user experience and key metrics.

Why Experiment?

Product experimentation provides concrete data to help teams make better decisions. Some of the key benefits include:

• Reduced risk: Testing with a subset of users first minimizes the impact of releasing changes that negatively impact metrics or user experience.

• Continuous improvement: Regular experimentation creates a feedback loop to constantly optimize and enhance your product.

• Better user experience: Seeing how real users interact with different options helps teams build what users really want and need.

• Increased innovation: The ability to quickly test new ideas without fear of failure leads to more creativity and outside-the-box thinking.

• Growth: Conversion rate optimization and funnel optimization experiments can have a huge impact on key growth metrics like signup completion, retention, and revenue.

• Competitive advantage: A culture of experimentation and optimization helps companies outpace competitors in meeting customer needs and building the best user experience.

Types of Product Experiments to Run

1) A/B Tests

A/B testing involves comparing two versions of something to see which performs better. You could test anything from button colors to whole user experiences. The key is to only change one element at a time so you know exactly what's making the difference. A/B tests are easy to set up and can provide quick wins.

2) Multivariate Tests

Once you've optimized individual elements, multivariate testing lets you combine the high-performing options to find the very best overall combination. Say you tested button color, size, and placement individually. A multivariate test could try different combos to see which combo gets the most clicks. Multivariate testing requires more traffic but can uncover unexpected interactions between elements.

3) Feature Flags

Feature flags allow you to release a new feature to some users and not others. You can then analyze how the feature impacts key metrics before rolling it out to everyone. Feature flags give you flexibility and control. You can turn a feature on for 1% of users, 50%, or 100%. And you can instantly turn it off if needed. Feature flags do require engineering work to implement, but the insights they provide are invaluable.

4) Funnel Tests

Making changes to parts of a conversion funnel to optimize flow and increase conversions. This could be redesigning an onboarding flow or changing the checkout process.

5) Cohort Analysis

Cohort analysis looks at how groups of users behave over time. You might analyze how often new users from January use your product vs. those from February or March. Or look at how engagement differs between users who sign up for a free trial vs. those who convert to paying customers. Cohort analysis provides a long-term view of how well your product is satisfying and retaining different types of users. The insights can shape both short- and long-term product strategy.

Best Practices for Setting Up and Running Successful Experiments

1) Clearly define your hypothesis

Before running any experiment, define what you want to test. Form a hypothesis that is specific and measurable. Rather than “Users will engage more with a redesigned homepage,” try “Redesigning the homepage will increase time on page by 25%.” A focused hypothesis will help determine the right metrics and audience for your test.

2) Choose your metrics wisely

Select metrics that actually measure the impact of your changes, not just overall site traffic or engagement. If your hypothesis is around increasing conversion rates, measure conversion rates. If it’s improving user experience, look at metrics like time on page or churn rates. Don’t rely only on vanity metrics like page views. Measure what really matters for your business goals.

3) Start with a small test group

When launching an experiment, start small by exposing it to only a subset of your users. A smaller test means fewer risks if something goes wrong, and it’s easier to analyze the results. Aim for testing on at least 1-5% of your total traffic to get meaningful data and insights. You can always expand from there once you’ve proven the concept.

4) Run the test long enough

Give your experiment enough time to generate statistically significant results. The length will depend on your site’s traffic levels, but aim for at least 1-2 weeks. Shorter tests often don’t provide enough data to draw strong conclusions. But don’t run an experiment indefinitely either - check in on the results regularly and be prepared to end it if the impact is clear.

5) Analyze, learn, and iterate

The most important part of experimentation is analyzing your results and learning from them. Look at the data to see if your hypothesis was correct and how users responded. Make changes and run follow-up tests as needed. Over time, you’ll get better at crafting experiments and interpreting results to optimize your product.