- Are feature flags all you need to experiment?
- Is feature experimentation the right option for you?
- A step-by-step guide
Are feature flags all you need to experiment?
Feature experiments using feature flags is a proven way to enable a powerful and data-driven approach for all teams to test hypotheses and validate assumptions before fully releasing new features to all users.
By gradually rolling out experiments to targeted user segments, teams can gather valuable insights and fine-tune their products to meet user needs effectively. This help article will guide you through the step-by-step process of running a feature experiment using Kameleoon, enabling you to optimize user experiences and better drive engagement for your products.
Is feature experimentation the right option for you?
Feature experiments using feature flags offer several advantages over traditional web experimentation approaches. Here are some key reasons why you should also consider making the switch:
- Advanced server-side experimentation: Feature experiments offer all the benefits you may associate with server-side testing such as reduced client-side dependencies, improved performance, enhanced security, and granular targeting. Feature experimentation also ensures consistency across different platforms, version independence, and compliance with data privacy regulations – making it vastly scalable for larger user bases.
- Real-time Control and Safety: With feature flags, you can control the rollout of a new feature in real-time, enabling you to turn it on or off for specific user segments instantly. This provides a safety net to quickly disable the feature if any issues arise, ensuring a smooth user experience and minimizing potential negative impacts – all without having to write or deploy any new code.
- Gradual Rollout and Risk Reduction: Feature flags allow for gradual feature rollout to a subset of users. This controlled release mitigates risks associated with full deployment, allowing you to test the feature in a controlled environment before exposing it to your entire user base.
- Iterative Development: Feature experiments using dynamic variables facilitate iterative development. Product teams can make continuous improvements to a feature based on user feedback and data insights, without requiring a full redeployment.
- Faster Experimentation Cycles: Feature flagging speeds up experimentation cycles since you can quickly introduce, modify, or remove features without redeploying the entire application. This agility fosters a culture of rapid iteration and continuous improvement.
- Reduced Technical Debt: Traditional web experimentation may require maintaining multiple code branches to support different variations of the experiment. Depending on your team’s practices, feature flags centralize this control, reducing technical debt and code complexity.
- Enhanced Collaboration: Feature flagging fosters collaboration between product, engineering, and business teams. It enables cross-functional teams to work together seamlessly on feature development and experimentation. Feature experimentation is also more well-suited for modern development practices.
Overall, feature experiments using feature flags empower your teams with greater control, flexibility, and speed, making them a more efficient and effective approach to validating hypotheses and improving product features.
A step-by-step guide
Getting started with feature flags for experimentation may seem complex but a simple, systematic approach can ensure successful implementation and testing and set you up for long-term success. It helps to clearly outline your experimentation goals and what you want to achieve with feature flagging. Identify specific features or hypotheses you wish to test and validate. This will give you a clear direction and purpose for using feature flags in your experimentation process.
Step 1: Identify the hypothesis
Begin by clearly defining the hypothesis you want to test i.e a change you believe should move your metrics in an expected direction.
For instance, if you have some new ideas for a streamlined checkout process that will increase conversion rates, formulate your hypothesis accordingly.
Step 2: Create a feature flag
Now, create a feature flag in Kameleoon for the new checkout process feature and keep it off for the time being, ensuring that the new checkout flow is initially hidden from all users.
Step 3: Variabilize your feature (Optional)
When your developer codes your new checkout process, they can use Kameleoon’s feature variables to make sure that they won’t have to revisit the source code again. The Kameleoon app can be used to dynamically update values and parameters inside your code using variables if you are testing different versions of the same feature. In that case, it can enable your teams to create several variants to test the hypotheses you drafted and to iterate on them with no code!
Step 4: Targeting and segmentation
Decide on the user groups or segments that will be part of the experiment. Kameleoon allows you to target specific demographics, user attributes, or even beta testers to receive the new feature.
Step 5: Start your experiment
Initiate the experiment by rolling out the variants of your streamlined checkout process to a small percentage of the targeted users. Controlling the rollout percentage enables you to minimize risks and measure the feature’s impact, while ensuring you can still test out all of the ideas you had.
Step 6: Monitor and analyze
As users interact with the new checkout flow, monitor their behavior using the results page. Track essential metrics such as conversion rates, user engagement, and drop-off points to assess the performance. You can now easily compare the performance of the experiment group (users with the new feature) against the control group (users without the feature). Analyze the data to determine if the streamlined checkout process positively influenced user behavior.
If needed, you can also export the results of your experiments to any of the several partnered third-party tools Kameleoon allows integrations with.
Step 7: Iterate and make decisions
Based on the results of the experiment, make informed decisions about whether you want to roll out the best-performing checkout variant to a larger percentage of your users. If the data supports the hypothesis, proceed with a full rollout to all users. Alternatively, if the results are inconclusive or disappointing, iterate on the feature by tweaking your variables and variations from the app, and conduct additional experiments to optimize its conversion rates.
Running feature experiments using feature flags equips all your teams with valuable insights, enabling them to validate hypotheses and optimize user experiences. By leveraging feature management tools to control feature rollout and collect real-time data, teams can refine their products with confidence and deliver exceptional user satisfaction. Remember to utilize data-driven insights from feature experiments to continuously enhance your product, staying responsive to user needs and preferences as your product evolves.