Article

12min read

Feature Experimentation Best Practices

Welcome to the first post within our new ‘Feature Experimentation’ series, where we’ll be broaching different topics related to this modern and essential practice in modern product development.

In this series, we’ll be introducing various scenarios where you can reap the benefits of feature experimentation as well as other relevant guides to help you on your experimentation journey.

In this first post, we will list and discuss some essential best practices when it comes to feature experimentation to ensure that your experiments run smoothly and successfully.

Why running experiments should be a central part of your product development process

Running experiments has become a growing, popular trend and a necessity to develop high quality features and products.

Such experiments are key in helping you uncover usage patterns and to give you insight on how your users interact with your products.

Therefore, experiments are a great way, particularly for product managers and product teams, to validate product quality and to ensure that a product aligns with business objectives.

To measure the outcome of your experiments, metrics can be used to help gauge how your customers are reacting to the new feature and whether it meets their expectations.

This means that experiments help you build and optimize your products so you can make sure that you’re releasing products that can guarantee customer satisfaction.

Experiments are also a great way to learn and prioritize resources so that product teams can focus on the most impactful areas for further iteration.

Experiments can come in different forms and these include tests such as A/B testing and multi-armed bandits.

What exactly is feature experimentation?

We talked generally about experiments in the previous section but in this series we will focus on a specific type of experimentation.

As the name suggests, feature experimentation involves feature testing or running experiments on developed or modified features with live users in order to see whether they’re performing as intended.

When we talk about feature experimentation, we’re referring to certain areas within your product that may have issues and need further optimization and improvement.

These features are ones that define the functionality of your software which make the product as a whole more effective and the overall user experience better such as a sign-up flow, a referral program, a purchase funnel or pricing offers, for example.

In other words, features refer to complete parts of your product that often involve multiple stakeholders or teams and are tied to your internal processes or business logic.

These are the features that often have a major impact, positive or negative. As a result, such features need to be tested to avoid the risks associated with blindly launching them into the wild without a clear understanding how they will perform or what their impact will be on revenue and sales or on product usage, for example.  

Thus, your team can compare different variations of features with users, instead of going for a full bang release, and see which one confirms your initial hypothesis and shows a positive impact.

This way, only your best features reach your customers after looking at the data that points to the better performing variation.

Experimentation will essentially give you the data you need to do exactly that. Once the winning feature is determined, it can then be rolled out to the rest of your users with the promise of a great user experience. 

Some essential best practices for running impactful experiments

As we’ve just seen, feature experimentation and experimentation in general is an indispensable tool for any modern tech and product teams. 

In this section, we will discuss some general best practices when it comes to running experiments so you can achieve the best results and avoid any missteps in your experimentation journey. 

Create a culture of experimentation

This should go without saying but in order to get started with experimentation, you need to build and nurture a culture of experimentation within your organization.

Some factors will come into play during this process such as your company size, your team’s workflow and capabilities and the type of industry and market you’re operating in.

What this essentially means is that you primarily need to have a clear strategy and roadmap in place so that your teams are aware of the main business objectives to build efficient tests.

We will look into building an experimentation roadmap in another post within our Feature Experimentation series so stay tuned for that! 

In the meantime, what is important to note is that this roadmap will serve as the key to link business objectives with product managers’ ideas in order to execute tests and experiments and to be able to set and track the right metrics.

Furthermore, having a culture of experimentation will enable you to make data-driven decisions.

The data gathered from your experiments will allow you to determine and measure the impact of your ideas to see how they resonate with your customers, enabling you to have a clearer understanding of your target audience’s needs.

Building such a culture means you will need to have the right tools in place to help you segment your audience accordingly and tools that will also help you to collect the appropriate metrics and to analyze the results.

Just as important is having and investing in the right people, management and infrastructure to get the most out of experimentation. 

However, keep in mind that building this culture of experimentation doesn’t happen overnight.

It requires time and effort but with the right mindset, you can start nurturing this kind of culture within your organization and motivating your team to get started on their roadmaps.  

Make it a team effort

To embrace experimentation as part of your company culture, all the relevant teams need to be involved in product or feature testing and not just engineers and developers.

It is important to remember that a good experiment comes as a result of well-defined, shared goals and metrics by all stakeholders.

For example, as mentioned previously, experimentation is a great way for product teams to test out their ideas so everyone needs to be part of the brainstoming process and to look at experiments as a learning experience even if they failed.

In fact, sometimes, it is failed experiments that give the best insight. Any data and learnings gathered from experiments, then, will need to be shared widely among teams so everyone gets a chance to review the results and take the necessary action.

Increasing experiment visibility will allow more people within an organization to clearly see the benefits and processes underlying this practice and highlighting the success and areas of improvement boosts engagement so that they can share their own inputs thereby further instilling a culture of experimentation.

Product managers, in turn, can empower the rest of the teams to be part of the decision-making process on how to improve and optimize products so experimentation becomes a collaborative effort. 

It also holds them accountable for the experiments they run so that there is a shared sense of commitment. The earlier a team is involved, the more invested they’ll be in the experiment.

Make it easy

You want to build a culture of experimentation, great, but it’s also important not to make it too complex or a time consuming process that ends up discouraging your team from running their own experiments. 

Remember, experimentation should be a collaborative effort, as mentioned previously. Often, experiments may involve cross-functional teams depending on the type and the scope of the experiment you’re looking to launch.

At the same time, there shouldn’t be too much dependence among teams. We already mentioned that every team, and not just development and engineering teams, should be able to run their own experiments.

Feature flags are one way to decrease risk of running experiments by decoupling release from deployment so that all teams feel confident enough to execute experiments. We will go into further detail on that later.

Set realistic experimentation goals 

The goal of running experiments is to improve your product for your customers. The results gathered should give you sufficient data to enable you to make informed decisions to optimize your products. 

To be able to obtain relevant data, you will need to have a specific goal or objective that will lead you to create a viable hypothesis that you can prove (or disprove).

This is why having a roadmap, as mentioned previously, will be important to allow you to focus your tests so you can get the right data with statistically significant results.

Also, remember that it’s not always possible to test everything. This means you will need to channel your testing energy into running experiments that are relevant to your goals and objectives.

Additionally, some companies may not have a high volume of traffic or users to be able to test everything. This is especially true for feature experiments. A feature needs to receive enough traffic when running A/B tests on this feature in order to generate efficient results.

In sum, good tests or experiments should be focused enough that they give you relevant results and data to improve your products to ultimately ensure customer satisfaction.

Learn from failure

If an experiment goes wrong for any reason and you don’t obtain the results you were expecting, this doesn’t mean that the experiment was a waste of time.

Failures when it comes to experimentation can be considered as a learning experience. This encourages your team to take more risks and boosts creativity.

As a result, implementing experimentation as part of your company culture, regardless whether your experiments turn out to be successful or not, means that it becomes embedded within your team’s natural workflow. 

Also, remember knowing what not to do will actually help in improving your product by preventing you from implementing ideas that didn’t perform well so that you know it’s time to move on to the next idea.

Consider the metrics

If you want to make the most out of your experiments by making data-driven decisions then you need to carefully consider the metrics you will track to help you judge whether your feature was a success such as clicks, registrations or sales.

This is an essential best practice as good, efficient experiments are built around a specific goal or metric- the key is to keep a certain focus during experiments, as already mentioned, so as not to deviate from the original goal and lose sight of why you were conducting the experiment in the first place. 

This all means that you need to basically tie your experiments to specific KPIs so you can track and analyze the impact of your experiments.

Choosing the right metrics serve as a baseline for your KPIs to enable you to track the results of your experiments so you can make sound decisions.

Target the right audience

This may seem like a no-brainer but to get the results you need to improve your products, you need to choose the right audience to give you those results. 

Proper targeting will allow you to see what kind of changes you need to make to your feature variations and consequently, you will be able to tailor the user experience according to the needs of a specific set of users.

This way, product managers can gain valuable insight into their target audience by observing how they interact with different variations of a feature, allowing these managers to validate theories and assumptions about a certain audience.

There are many ways you can go about segmenting your audience, which includes by region, company, device, etc. It will ultimately depend on your own unique objectives.

Remember that to target the right audience, gather the data and analyze the results, you will need to have the appropriate tools at hand depending on your business objectives and teams’ preferences.

Consider the duration of the tests

With feature experimentation, you need to run these experiments for long enough time so you can gather enough data to yield statistically significant results.

Click here to read more about statistical significance and type 1 and type 2 errors which may occur during experiments.

This is important because statistical significance indicates that the results of your experiments can be attributed to a specific cause or trend and are not just a random occurrence.

Therefore, as you start to build your roadmap, you will need to include guidelines for the scheduling and duration of your tests in order to standardize workflows for your team.

However, keep in mind that having a sufficient sample size will be more important than the amount of time an experiment runs.

Use feature flags for safer experiments

For some, the idea of testing in production seems risky and stressful. 

However, there is a way to run feature experiments safely without any headaches.

Feature flags are software development tools that decouple deployment from release giving you full control over the release process. In that sense, feature flags can be considered as the foundation of a good experiment.

Feature flags allow you to safely conduct experiments by turning on features for certain users and turning them off for everyone else. If anything goes wrong during your experiment, then you can easily turn off the faulty feature until it’s fixed. 

Using feature flags alongside feature experimentation will help you maintain the continuous delivery momentum that is required from modern software development while minimizing the risk of disgruntled customers due to an unstable release.

Furthermore, once you have completed your experiment and obtained the results, you can implement the necessary changes through progressive rollout to further test how these new changes perform with users.

Therefore, through progressive delivery using feature flags, you can introduce changes slowly to your users to ensure a smooth user experience before releasing them to everyone else.

Embrace feature experimentation as part of your company DNA

Some of the biggest companies have achieved their market leadership position precisely because they have embraced experimentation as part of their culture. 

Therefore, feature experimentation, when done right, will allow you to make more powerful decisions based on quantifiable data straight from your users.

This means that instead of making decisions on a whim, experimentation will demonstrate what works and what doesn’t based on mathematically-sound data.

Experimentation is one of the most important capabilities offered by many feature management tools

Our own feature flagging solution, for example, offers an experiment platform that runs A/B tests to track the business impact of feature releases. 

This means that everyone has the tools and confidence to take part in experimentation.

For product managers, in particular, it gives them the power to set up, monitor and release confidently without waiting on engineering teams to run the experiments for them through a simple, easy-to-use dashboard. 

Our platform focuses specifically on more advanced server-side experiments that allow you to test deeper modifications tied to your back-end architecture using feature flags where you can then measure their impact on the user experience and business.

Flagship's Report Interface for Experimentation & A/B Tests

Find out how AB Tasty can help you transition seamlessly into the world of experimentation by signing up for a free trial.

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Article

8min read

Alpha vs. Beta Testing: What are the Differences?

Prior to the launch of a product, a number of tests are usually run to ensure that a software is ready for release and provides a good user experience. The purpose of these tests would be to validate the software before going ahead with a final release to your end-users.

These sorts of tests are essential to make sure that the software you’re releasing is free of bugs and meets the quality and requirements expected by your customers.

Among such tests are alpha and beta tests. These tests are conducted towards the end of the software development life cycle (SDLC) to test releases outside the development team to help uncover any issues that would otherwise not show up in previous tests that are run in more controlled environments.

What is alpha testing?

Alpha testing is typically run on internal users by the QA team (Quality Assurance) to make sure that the software meets all expectations and is working as it should. Thus, it represents an opportunity to evaluate the performance and functionality of a product release as well as obtain feedback from technical users.

In other words, the main purpose of this test is to uncover any bugs or issues to resolve them before the final product is released to users. It helps ensure bug-free functionality by carrying out tasks that a typical user may perform.

This test is usually performed when your product is nearly complete towards the end of the software development cycle in a staging environment, which attempts to mimic an actual production environment as closely as possible, but before beta testing, which we’ll get to later.

It seeks to answer the question whether your product actually works.

Alpha testing involves two main phases:

  • The first phase is run by software developers using debugging tools or software to catch bugs quickly.
  • The second phase is performed by the QA team and may involve ‘white box’ and ‘black box’ testing. A white box test will test the software system’s design and internal structure allowing QA testers to ‘look inside’ the product. A black box test, meanwhile, will test the system’s input and output functionality. 

 

The advantages of this type of testing are clear.
 

It allows teams to locate bugs and issues that managed to escape previous tests so that they may be fixed before they reach your end-users. 

Up until that point, tests were focused on testing specific parts of the software but alpha testing, on the other hand, looks to see if the software as a whole functions correctly.

In other words, it enables teams to validate the quality and functionality of their releases before it is released to customers. Put simply, Alpha testing opens up the application to receive initial feedback.

This results in improved software quality as the software is tested in an environment that is a very close replica of the environment it will eventually be used in, hence creating realistic testing conditions. This also allows the QA team to understand how the software will behave when it is later released to end-users.

To sum up, alpha testing provides an opportunity to put your product in real user environments but with technical users who are more adept at identifying and discovering bugs before conducting beta tests with actual real-world users.

However, conducting alpha testing may prolong the test execution cycle thereby delaying the release of the product to your end-users. Also, keep in mind that since the software is still in the development stage, alpha testing doesn’t provide in-depth testing of the functionality of the software.

Now, we will move on to the next testing phase, beta testing.

What is beta testing?

Beta testing involves releasing the software to a limited number of real users external to the organization. As a result, this type of testing is done in a production environment.

These users will then be asked to provide their feedback on the release, also named “beta version”. Beta testing, then, is an opportunity that allows users to test out the product to uncover any bugs or issues related to user experience (UX) before it is rolled out to the rest of your users.

In other words, it represents the final stage of testing before releasing the product to a wider audience. 

It also enables teams to run security and reliability tests as those tests cannot be conducted in a staging or lab environment.

There are many ways to implement beta testing. For example, often, companies will ask a select number of users to willingly op-in to get early access to the software. The advantage of this is that these users will be aware that the beta version may not be very stable and so they are more forgiving of any potential bugs and are happy to provide the feedback you need to optimize your product.

To be more specific, you may go for a closed or open beta test. In an open test, anyone can use the product but users are given a clear indication that the product is a beta version so they know that it’s still a work in progress. 

Meanwhile, in a closed test, as in the example given above, the testing is limited to a specific set of users, which would be by invite only. These users would be composed of early adopters, current customers or even paid beta testers.

Such exclusivity is one way to build close relationships with specific users as you are demonstrating that you value their opinion in particular before doing a wider release.

The advantage of this testing is clear. It is the first chance to test how the software will behave in real-world settings and observe how your end-users interact with it and what the user experience looks like.

Product managers, in particular, can make use of the feedback received to collect ideas and suggestions when planning future releases.

Beta testing is a way these managers can observe usage behavior and analytics to confirm that users are interacting with the product as expected. They may also run experiments and A/B tests of features to decide which one to choose for a general release. 

This, in turn, allows developers to uncover any bugs in real production and less controlled environments so that they may be fixed before a full launch.

Many bugs may have been discovered during alpha testing by your internal users but nothing can truly simulate real world users, which is why beta testing is necessary after alpha testing.

However, as we’ve seen, beta testing is conducted in real environments as opposed to controlled environments during alpha testing and so the former is more difficult to control.

Feature flags and beta testing: safer testing in production

During beta testing, you are essentially testing in production, which doesn’t come without its risks but luckily there is a way to mitigate those risks: by using feature flags.

A feature flag is a software development tool that helps decouple deployment from release, giving you full control over the release process. With feature flags, you can perform beta tests by enabling features for certain users and turning them off for everyone else.

Feature flags also act as a kill switch so that you can gradually roll out features to users to test performance and if something goes wrong, you can just as easily roll it back or turn off the buggy feature.

Feature flags are a great way for all teams within an organization to carry out beta testing as using feature flags for beta testing means even non-technical users such as product and marketing teams can turn on features for specific users, which means they’re not so reliant on development teams anymore.

Alpha vs beta testing

The major advantage of such types of testing is that it helps the development team to identify issues in advance before it goes to launch, allowing them to fix these issues early on before going for a full release. 

However, as already alluded to in above sections, there are still major differences between these two types of testing, some of which are summarized in the table below.

 Alpha αBeta β
TestersInternal employeesEnd-users or customers not part of the organization
Environment Requires a specific environment for testingDoes not require a testing environment
What’s testedFunctionality and usability are tested while security and reliability tests are not carried out in depthReliability, security and stability tests are key aspects of a beta test
Testing techniqueUses both white and black box testing techniquesFocuses mainly on black box testing 
WhenRun before the product officially launches to the marketRun after the product is launched
PurposeTest that the product is working as it should to evaluate product qualityUnderstand how real users interact with the product and to evaluate customer satisfaction 
DurationLong execution cycleShort process usually only lasting a few weeks
Post-test actionsAny bugs or issued discovered will be immediately rectifiedMost issues identified and feedback received will be implemented as improvements future versions of the product

Conclusion

Clearly, testing is important to ensure the delivery of high quality, bug-free releases. There are a number of tests carried out throughout a software’s life cycle, each of which serves a unique purpose. 

Read more: all about test environments and the type of tests that may be run as the software is being developed.

Here we looked at two important ones that occur towards the end of a software’s life cycle: alpha and beta testing.

Both alpha and beta tests are an important part of the testing process as they provide a valuable means to highlight crucial issues with your releases and provide user feedback, both internally and externally.

Alpha testing helps validate the quality of your software while beta testing allows you to obtain real-world feedback to ensure you’re building products that your customers actually like.

Therefore, in the testing lifecycle, both alpha and beta testing are essential.