Step-by-Step Guide to Better Product Experimentation

There’s no such thing as a perfect product. Even successful products need to adapt and innovate as customer expectations change. 

Customers expect continuous product improvement and refinement. After all, every company has a competitor, and customers go elsewhere at the drop of a hat.  

Are you in the earliest stages of product development or changing an existing product to appeal to your target audience? You need a way to measure the impact of the decisions you make. 

Product experimentation enables you to continually test ideas to change your product and improve the user experience. 

Instead of making changes based on guesswork, you can generate a feedback loop of measurable data. This can help your company make informed decisions about product changes. 

Product experimentation is an ongoing process. Doing it properly helps you deliver the best possible solution and delight your customers with impactful changes. 

Establishing a solid process and avoiding common pitfalls is essential to master product experimentation. Let’s learn more about product experiments and experiment frameworks. 

Product experimentation applies scientific principles to determine which changes will most likely improve a product. Development teams go beyond educated guesses and test various hypotheses in a controlled environment. 

Running routine experiments can reduce risks and optimize product designs based on concrete data. 

The ideal user experience is a fickle concept. The wording on your call-to-action buttons or navigation menu placement can make your product easier or harder for your customers to use. 

Product experimentation allows you to use data from customer feedback to learn what customers like most. It is also ideal for complex changes like the addition of new features. You can run several experiments to optimize your product in different ways.

Why do you need to run experiments?

Experimentation is an essential part of agile product development. Ideas that seem like good business decisions don't always appeal to users. Instead, they can result in customer disappointment and wasted investments. 

A firm understanding of your target audience gives you an idea about market fit and product features. Experiments take this knowledge further, enabling development teams to validate assumptions and make data-driven decisions.

Running experiments enables project managers and development teams to meet these objectives:

Understand the real impact of a single change

It can be tricky to understand a single change’s impact when many teams are taking various actions to improve customer service and user experience. 

Experimentation cuts the noise and uses hard data to help teams identify the drivers of change in essential key performance indicators (KPIs). 

Reduce risks

Running various tests can help product development teams understand the cause and effect of a specific change to a product and how users interact with it. 

Gathering data surrounding the impact of a single change on all KPIs ensures developers can reduce the likelihood of making changes that will negatively affect customers. 

Implement continuous improvement

In a competitive business environment, companies must constantly evolve to meet customer requirements. 

When development teams test different ideas, they can identify what works and what doesn't. This is key to delivering the best products for their target audience. 

Develop customer-centric products

According to Qualtrics, 63% of customers think brands need to do a better job of listening to customer feedback about their experience. 

Gathering data from experiments ensures product managers can tailor products to better meet their target audience's needs. 

Prove value

Business budgets leave little room for error. To avoid costly errors in judgment, it's often essential to prove the potential return on investment (ROI) of product features or changes.

Product experimentation allows product managers and teams to make data-informed decisions about products and how the customer experience should evolve. 

Experiments can test almost anything about a product. For an experiment to generate valuable insights, it needs a few specific components. 

Problem

If developers want to include a feature in a product or make a change, it should solve a specific user problem. Devs should identify the problem to solve to conduct an effective experiment.

Users

A feature will often impact how a segment of users interact with your site or application. It's essential to identify the size of your user group and their typical behaviors. You'll also need to define the group of users who will experience the test. 

Solution

An experiment typically tests a hypothesis about the effect of a suggested feature to solve the problem you've identified. This could be a single solution (such as a new feature) or multiple solutions (to test the best option) for creating value for users. 

Benefit

Good experiments revolve around the projected outcome of the solution. What is the benefit you expect to give to users? What is the business goal you're trying to achieve? 

It's also wise to identify potential trade-offs that could impact the user experience in other ways. 

Data

While all experiments generate data, you need to know how you'll measure success. Identify the metric you want to improve and how much you expect it to change.  

Is product experimentation the same as product validation?

Although they both use testing methods to evaluate how a product meets a customer's needs, product validation is not the same as product experimentation. 

Product validation tests and evaluates a product to ensure it will serve customers' needs. 

Companies often use it during the early stages of development to see if the product has a place in the market. You can also use it to make necessary changes before a product goes to market. 

Experimentation improves upon the value of an existing product. It’s a part of ongoing research and product evolution to improve the customer journey. 

Since product experimentation can test a wide range of initiatives, several types of experiments cover different uses. 

These are some of the most common types of experiments in the product development lifecycle:

A/B testing

Typically for testing design variants, this experiment involves showing two versions of a product to different groups. Let’s say group A and group B. Randomly assigning users to different product experiences lets you see which variant users interact with the most. 

This method is ideal for testing metrics like conversion rate, pricing strategies, new features, and UX concepts.

Split testing

Similar to A/B testing, split testing allows you to show different versions to users. However, the implementation is slightly different. 

Instead of testing single-variable changes like a different call-to-action button, split testing tests a control sample against a completely different page variation with the same goal.  

Funnel testing

Funnel testing is an experimentation method that allows teams to make changes across multiple pages to improve the customer journey.

It’s ideal when you have components that must stay consistent across pages. Funnel testing can help you identify variations that prevent users from dropping off at specific points during the journey.

Multivariate testing

Similar to A/B testing, multivariate testing allows developers to test the impact of a specific feature or image. However, it includes the use of multiple variants at a time. 

This method is best when you have several components to consider and are trying to find a combination of variables to drive optimal results. Instead of testing one variation of a single variable, teams can test multiple variations of multiple variables on a single page. 

Click tracking

This method shows how users navigate through your website by monitoring the actions that users take. You can monitor actions like clicks, mouse hovering, form field inputs, forms completed, etc. 

Measuring these events lets you see where users struggle to navigate your website. From there, you can make changes to reduce friction. 

Product experimentation provides concrete data to support informed decisions about product changes. 

Conducting routine experiments shows how your users interact with your products so any changes produce multiple benefits. Depending on your goals, you may realize one or more of these benefits:

Streamline the decision-making process

Development teams often have different ideas for product innovation. The resulting back and forth can prolong decisions and slow down taking a product to market. Product decisions based on hard data eliminate guesswork and reduce discussions for a faster process overall. 

Increase customer retention

Product experimentation enables companies to make innovative changes to products that meet the evolving needs of their target audience. 

When you use experiments that make the most of customer feedback, you can implement changes that improve the customer journey and user experience. 

Identify what users like and dislike to improve customer satisfaction

Without user testing, deciding which changes to implement is based on assumptions. Product experimentation enables development teams to see exactly which features appeal to customers and those that slow down user navigation.  

As a result, they can spot opportunities to improve products in ways that will substantially improve customer satisfaction. 

Cut costs related to mistakes

Meeting decisions may meet specific business criteria but fail to align with user needs. When an idea flops after rollout, the company loses money. Experimentation allows teams to prove hypotheses before taking that step. 

Increase conversions

Website pages depend on many elements for users to complete the customer journey and make a purchase. Product experimentation can help teams determine which website features increase conversions or result in user frustration or hesitation. 

Making changes that align with testing data can increase conversions. 

Product experimentation enables development teams to use factual data to improve products to meet user needs. While product experimentation yields many benefits, testing can require an abundance of time and resources. 

When you implement a defined framework to guide your product experiments, you can use an effective and repeatable process for testing. 

Use these steps to develop a framework for effective product experiments:

1. Define a product objective

Every experiment should begin with a goal in mind. With an objective, you can define what you hope to achieve for your customers and which elements to include in your experiment. 

Set specific goals that outline the solution to an existing problem, how the solution will impact users or the company, and the KPIs you'll use to measure results. 

For example, you may want to increase conversions or improve site navigation.

2. Build a hypothesis relevant to your goal

A hypothesis isn't just an idea. It's an educated guess based on research you're trying to validate or disprove. Your hypothesis should be based on your goal and state your expectations of the results. 

For instance, if you're trying to improve conversions, you may suggest that a simplified checkout process will increase conversions by 30%. 

3. Choose KPIs to measure your experiment

Product experimentation yields hard data that you can use for informed business decisions. However, without a structured process, you may fail to gather data that proves or disproves your hypothesis. 

KPIs are various forms of measurable data that can prove the impact of product changes. Identifying the KPIs you'll use means you can craft an actionable experiment that measures whether your results match your hypothesis. 

Some KPIs used in product experimentation include conversion increases, form completion rates, customer satisfaction rates, and customer churn rates.

4. Define experiment parameters

Without structure, an experiment will only allow you to make the most of random data. This step defines exactly how you’ll carry out the experiment, providing a specific objective and a solution to reach your goal. 

Outlining the parameters of your experiment will define the resources you need, the method of testing, user group size, and more. 

When identifying your experiment parameters, define these elements: 

• The audience you're trying to serve (who will benefit from the change)

• Experiment type or method

• Size of your control group/testing groups

• The variations you'll use in testing

• How long testing will last

5. Conduct your experiment and gather data

Set up and implement your experiment according to your parameters. You'll need to identify: 

• A control group that you won’t expose to the changes

• The tools you'll use to carry out the experiment

• The variations you're testing

When setting up the experiment, ensure you correctly set up tools, variations, and user segments to yield accurate data. 

Throughout your experiment, collect relevant data and use analytics tools to measure the impact of variations on KPIs. Depending on your goals and testing methods, you may consider primary and secondary metrics to examine multiple insights. 

6. Review results

Once you've run the experiment for the intended length, it's time to analyze the data you've collected. On the surface, you can determine whether you've confirmed or disproved your hypothesis. 

You might use additional data (secondary metrics) to identify insights about your target audience's preferences. 

Even if your experiment was successful, it's a good idea to dig deeper into metrics to identify why your idea was a hit for your target audience. Even learning that you disproved your hypothesis is a valuable result. 

Consider how the results of this experiment can help you identify additional experiments to continue product evolution. 

Creating a continuous feedback loop allows you to prioritize future product updates and additional features most likely to appeal to your target audience.

Although you can use various experiments in different settings, the process isn't ideal for every situation. Product experiments don't replace initial product research and can waste time if you use them incorrectly. 

Consider running product experiments in these situations:

• Innovating to better serve your customers' needs with an existing product

• Developing a new feature

• Troubleshooting issues with one or more solutions for an existing user issue

• Prioritizing future product updates or features

• Optimizing the user interface

Experiments are unlikely to be helpful in these situations:

• Determining market placement

• Conducting initial product research

• Seeking metrics from successful tests to use for marketing purposes

• Making strategic management decisions

What is a minimum viable test?

A minimum viable test (MVT) is a hypothesis research process. It can help you determine whether to run a more extensive product experiment. 

In some cases, determining whether a product experiment is the best option may not be clear. Product experimentation requires time and resources, and there's no guarantee the experiment will be successful. 

A minimum viable test can determine whether an experiment is worth the investment. 

Data from MVTs can also set the parameters for more complex experiments. 

To conduct an MVT, break your hypothesis into small assumptions and plan for a test with a smaller scope. 

Use available resources to conduct in-house testing until the outcome of an experiment becomes more predictable. 

You can use the data from the MVT to run the experiment as planned or course-correct for a more effective product experiment. 

According to Harvard Business School, 95% of the 30,000 new products introduced each year fail. Businesses need a way to avoid such costly errors. Product experimentation can be a solution, but only if the experiment serves the intended purpose. 

These tips can help you avoid common pitfalls and run a successful experiment:

Develop a strong hypothesis with specific metrics

Without a strong hypothesis that defines the KPIs you'll use, you can end up with a lot of random data that doesn't help you reach your goal. 

A clear hypothesis identifies your goal and aligns the team to a singular objective.

Define the experiment’s scope before starting

Develop an approximate timeline for how long the experiment will run to engage stakeholders and clarify when you’ll deliver results. 

It's best to confine the experiment's length to the minimum time required to achieve the expected change.

Experiment in phases to reduce risks

When companies develop a culture of experimentation, it becomes an ongoing part of development. However, innovative ideas that offer high rewards come with greater risk. 

When you start with experiments that test smaller components, you avoid taking on larger risks. 

Identify counter metrics to include

New features and other changes can indirectly cause adverse effects in a different area. Before starting the experiment, identify the business metrics that could be negatively affected. Define the metrics you'll track to ensure the positive effects outweigh the negative. 

Make sure you're tracking enough data

If an unexpected error results in reduced data collection, an otherwise successful experiment may fail. When you begin collecting data, check metrics to ensure you'll get the data you need for accurate results. 

Trust the data

It's easy to agree with the outcome of a test when it goes as expected. However, when a test yields an unexpected result, you might want to dismiss the data and skip the recommended change. You should always follow conclusive data.

As technology grows and changes, consumers expect brands to anticipate and meet their needs. According to a Salesforce survey of 6,000 people, 66% of customers expect companies to understand their needs. 

To achieve this, development teams must constantly innovate. Establishing a culture of experimentation ensures you’re on top of customer expectations. You’ll be able to constantly innovate to better meet customers' needs. 

Here’s some tips to help you create a culture of experimentation:

Create an environment of continuous discovery

Continuous discovery is consistent research using user insights supplied by feedback generated from frequent, short research methodologies. 

It enables development teams to constantly research user needs and prioritize changes most beneficial for a specific target audience. 

Constantly communicating with users lets companies find opportunities for improvement, validate ideas, and prioritize solutions. 

Regularly collecting user feedback and analyzing the data creates an environment of continuous discovery and focus on improvements with the greatest impact.

Encourage company-wide creativity

When experimentation becomes a mindset for everyone, from the top executives to each employee, teams will seek opportunities to improve products. 

Share the value of data-backed development and encourage employees to participate in generating new ideas. 

As stakeholders begin to recognize the rewards of experimentation, they'll be more likely to allocate resources for ongoing experiments. 

Establish processes that lead to success

Many experiments fail due to poor planning. Developing a firm understanding of the components required for a successful experiment and using a proven framework can reduce the chances of error. Ensure all employees know how the company conducts experiments and make resources available.