Feature management and feature experimentation, why you should do both—how it helps both product and software engineering teams

Delivering successful products requires a fine balance between meeting customer needs and maintaining engineering efficiency. Two crucial practices that aid in achieving this balance are feature management and feature experimentation. These practices are not fundamental to DevOps or product management, but they empower product teams and software engineering teams alike by enabling effective feature deployment, iteration, and optimization.   

Put simply, organizations leveraging experimentation and feature management ship products faster, more collaboratively, with more success.  

In this article, we will dive into the world of feature management and feature experimentation, highlighting why both are essential and how their use cases benefit product and engineering teams build a culture of experimentation.  

Understanding feature management  

Feature management refers to the process of controlling the lifecycle of features within a software product for continuous delivery. It allows teams to have granular control over the availability and behavior of features, empowering them to deploy, test, and release features at their desired pace. Feature flags, also known as feature toggles, are a fundamental aspect of feature management workflow. They act as gatekeepers, enabling teams to enable or disable features in real-time, without the need for code deployments.   

Top 3 benefits of feature management for product teams  

1. Progressive feature rollout: Feature management allows product teams to gradually introduce new features to their user base. This approach mitigates the risks associated with large-scale releases and enables teams to gather user feedback, monitor performance, and address any issues proactively.  
2. A/B testing and phased experimentation: Using the combined experimentation and feature management capabilities of Optimizely Feature Experimentation, Feature Flags enable product teams to run A/B tests where different variations of a feature are exposed to different user segments. This approach provides valuable personalization insights; helping build an understanding of user preferences, behavior, and the impact of new features on key metrics. Phased experimentation allows gradual feature expansion based on user feedback, ensuring an optimal user experience whilst minimizing negative impacts.  
3. Reduced time-to-market: By decoupling feature releases from code deployments, feature management allows product teams to accelerate time-to-market. Features can be developed and tested independently, enabling faster iteration cycles and quicker response to market demands.    

Top 3 benefits of feature management for software engineering teams:  

1. Agile development and iteration: Feature management enables engineering teams to work on features independently, allowing for parallel development and faster iteration cycles. This promotes agile development practices, accelerates the feedback loop, and facilitates continuous improvement.  
2. Risk mitigation and quick rollbacks: Teams that use feature flags can toggle feature availability in real-time, reducing the risk of bugs or disruptions. If issues arise, teams can quickly roll back features, minimizing the impact on the overall product and ensuring a more stable user experience.  
3. Efficient collaboration and coordination: Feature management tools facilitate seamless collaboration within engineering teams and between engineering and product teams by decoupling feature releases from code deployments. This promotes better coordination, enables concurrent development, and simplifies merging and integration, enhancing overall team efficiency.  

Understanding feature experimentation  

Feature experimentation, also known as feature testing, involves evaluating the performance and impact of new features by exposing them to a subset of end users. It aims to validate assumptions, gather user feedback, and measure the effectiveness of new functionalities. A robust feature experimentation process requires proper monitoring, data analysis, and an experimentation platform that delivers statistical significance.  

Top 3 benefits of feature experimentation for product teams:  

1. Validation of assumptions and hypotheses: Feature experimentation allows product teams to validate assumptions and test hypotheses before committing to a full-scale release. By exposing features to a subset of users, teams can collect feedback, measure key metrics, and assess whether the new functionality aligns with user expectations and goals. This validation process helps minimize the risk of investing significant resources into features that may not resonate with the target audience.  
2. Improved user experience: Experimenting with new features enables product teams to refine and enhance the user experience based on real-world usage and feedback. By measuring user interactions, behavior, and satisfaction during experimentation, teams can identify pain points, discover bugs, gather insights, and iterate on features (think continuous improvement) to optimize usability, performance, and overall user satisfaction. This iterative process empowers product teams to deliver features that truly meet user needs and expectations.  
3. Faster time-to-market: Feature experimentation allows product teams and stakeholders to accelerate the time-to-market for new features. By testing features with a smaller user segment, teams can gather valuable feedback and iterate on functionality without the need for a full-scale release. This iterative approach minimizes the time spent on developing features that might not resonate with users and enables teams to iterate quickly based on real-time insights, ultimately speeding up the overall product development cycle.  

Top 3 benefits of feature experimentation for software development teams  

1. Data-driven decision-making: Trust empirical evidence over intuition. With statistically significant results, feature experimentation enables software engineering teams to make informed decisions during the release process. It allows them to measure the impact of code changes, understand user preferences, and prioritize feature development based on real-world data.  
2. Risk mitigation: By exposing new features to a limited audience, feature experimentation minimizes the risk of deploying unproven or potentially disruptive functionalities during the development process. Engineers can monitor the performance of features, collect user feedback, and address issues promptly before rolling out features to a broader user base.  
3. Continuous improvement: Feature experimentation fosters a culture of continuous improvement within software engineering teams. By analyzing experiment results, teams can identify opportunities for optimization, enhance user experience, and drive iterative development cycles.  

The synergy between feature management and feature experimentation  

Feature management and feature experimentation are complementary practices that reinforce each other. By leveraging feature flags, product teams can conduct controlled experiments, collect user feedback, and make informed decisions about feature releases. Meanwhile, software engineering teams benefit by having the ability to easily control or even delegate the availability and behavior of features, enabling them to iterate quickly and address any issues identified through experimentation.  

In conclusion, feature experimentation empowers product teams to make data-driven decisions, validate assumptions, enhance user experience, accelerate time-to-market, mitigate risks, and foster continuous improvement and innovation. By leveraging these benefits, product teams can create products that resonate with users, drive customer satisfaction, and achieve long-term success in the market.  

For software engineering teams feature management enables agile development and helps mitigate risks - by embracing a tool like Optimizely Feature Experimentation, which brings both feature management and feature experimentation within the same ecosystem, organizations can achieve this while fostering efficient cross-team collaboration.