Product Growth Strategy: Experiment-Driven Product Innovation

Product experimentation is an invaluable tool for product teams looking to build great products. It helps them identify what users love, hate, and feel indifferent about so they can fix issues fast and spot product opportunities that’ll really move the needle. It also saves money and resources by understanding the real impact of changes with minimal investment and disruption. As a product manager, it's important to understand the three key layers of effective product experiments: setting realistic goals, getting buy-in and aligning with your team, and establishing processes, infrastructure, and technology. By understanding these three layers, you can ensure that your product experiments are successful and help you reach your desired outcomes.

?Setting Realistic Experimentation Goals

The main goal behind any experiment is to improve your product for your users. To do this, you must look at every failure as a learning opportunity and embrace a product experimentation culture. This encourages your team to take risks, be curious, and experiment as a day-to-day process. When creating a hypothesis and testing it, remember that knowing what not to do also makes your product better.

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Getting Buy-In and Aligning With Your Team

The most innovative companies have embedded experimentation into their culture. As a product manager, the best thing you can do is to empower your whole team (including senior stakeholders) to think and act creatively, and make sure everyone understands that experimentation is a worthy investment. Creating a shared understanding amongst the team on how users experience your product will help you prioritize brilliantly.

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Establishing Processes, Infrastructure, and Technology

Part of a product manager’s job is to establish essential processes and technology that make experimentation easy. Think through what tools you use to influence user behaviors: copy, design, UX, or features. Each solution is a test worth running. Establish a process that defines how you run an experiment, as well as how you analyze and implement its results. By training everyone on how experiments work and sharing the same tools, you build trust in the process and the results.

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By understanding the three key layers of effective product experiments, you can ensure that your product experiments are successful and help you reach your desired outcomes. Setting realistic goals, getting buy-in and aligning with your team, and establishing processes, infrastructure, and technology are all essential components of successful product experiments. To get the most out of product experiments, it’s important to have a framework in place. Below it outlines a 6-step framework for effective product experiments.

Step 1: Define a Product Goal and Its Impact on Your Users

Great experiments start with the end goal in mind. Without a goal to work towards, you’ll struggle to analyze your experiment’s results (and even to build it). When defining your goal and its impact on customers, consider these five experiment components: the problem, the (possible) solution, the benefit, the users, and the data. Great product goals come from existing customer experience and feedback. Quantitative and qualitative data from your product will reveal goals worth pursuing.

?Step 2: Build a Hypothesis Relevant to Your Goal

A product experiment hypothesis helps remove emotional involvement from your product goals and ideas. The structure of a hypothesis considers the goal you’ve set and the insights you’ve used to set it, which helps your team understand why a product change did or didn’t work. Here’s the structure you can follow to build your product experiment hypothesis: "We believe that [a solution] will [the change that will happen] for [audience segment] because [reason for change]."

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Step 3: Choose KPIs to Measure Your Experiment

Key performance indicators (KPIs) are values that allow you to measure the impact of your product change. KPIs are essential to either prove or disprove your hypothesis—it’s how you know whether you’ve achieved the goal you’ve set in step one. To set your key performance indicator(s), ask yourself: what needs to change to prove or disprove a hypothesis?

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Step 4: Set Up Experiment Parameters

Next up are the sample size and length of your product experiment. A sample size is the total number of data points collected in your experiment. To define your sample size, it’s important to consider statistical significance and whether the data you collect accurately reflects the population as a whole. The length of your experiment will be tied to your sample size based on the number of customers who use your product (or a relevant section of it) in a day, week, or month.

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Step 5: Run Your Product Experiment

For this, you’ll need to set up some product experimentation tools. Here are some suggestions: Google Optimize, Optimizely, and Omniconvert. Make sure your user segments, variations you’re testing, and the tools you’re using to collect data and insights are correctly set up so you get the most accurate, actionable results possible.

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Step 6: Review Results to Prioritize Product Updates and Inform Future Experiments

There are two main possible outcomes of your experiment: you’ve confirmed your hypothesis, or you’ve disproved your hypothesis. Both scenarios are important and valuable opportunities to understand your users better, empathize with them, and inform future goals, hypotheses, and experiments. First, be sure to share your experiment results with your team and map out next steps. Then, dig into the why of your experiment results (yes, even if you’ve proven your hypothesis!).

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Product experimentation is a powerful tool for product teams. By understanding the three key layers of effective product experiments, you can ensure that your product experiments are successful and help you reach your desired outcomes. And by following a 6-step framework for effective product experiments ensures that your team is able to get the most out of product experiments. Product Experiment layers and framework includes setting a product goal and its impact on your users, building a hypothesis relevant to your goal, choosing KPIs to measure your experiment, setting up experiment parameters, running your product experiment, and reviewing results to prioritize product updates and inform future experiments. By running regular experiments, product teams are able to explore possibilities for new features and product improvements. With the right combination of a growth mindset, data-driven decisions, and useful tools, you can drive product growth and make your users successful.