Successful metrics in experimentation: what to track and why

Navigating the world of experimentation can feel like setting sail without a map. How do you know you're heading in the right direction? The key lies in choosing the right metrics that truly reflect your goals.

In this blog, we'll dive into how to select meaningful metrics that not only guide your experiments but also drive real growth. Let's explore how to move beyond basic metrics, implement and monitor them effectively, and continuously adapt your strategy for lasting success.

The foundation of successful experimentation: choosing the right metrics

Metrics are the compass guiding your experimentation journey. They help you navigate toward meaningful growth by measuring what truly matters. Focusing on superficial metrics like win rates can lead you astray, celebrating minor tweaks with minimal impact.

So, how do you choose successful metrics? Start by aligning them with your business goals and hypotheses. This ensures your experiments yield actionable insights that drive real progress. Break down your hypothesis into a direct action (behavioral metric) and a business outcome (business metric).

Develop a portfolio of secondary metrics to capture potential downsides and provide context. These metrics help you anticipate negative consequences and explain primary metric movements. But be cautious—over-generating behavioral metrics can confuse your analysis.

Regularly review your metrics to ensure they remain relevant to your current objectives. As your business evolves, so should your metrics. Double-loop learning involves continuously challenging the relevance of your metrics and retiring those that no longer serve your goals.

At Statsig, we emphasize the importance of picking metrics that make or break your experiments. By focusing on meaningful metrics, you can drive real impact and avoid getting lost in a sea of irrelevant data.

Beyond basic metrics: journey and compound metrics

As your experimentation program matures, it's crucial to move beyond basic metrics. Journey metrics capture the entire customer experience across all interactions, providing a holistic view of an experiment's impact. These metrics consider the complete user journey, from acquisition to retention and beyond.

Compound metrics combine multiple data points for deeper insights. For example, pairing customer acquisition cost with lifetime value provides a more comprehensive understanding of an experiment's ROI. Other examples include combining feature adoption with retention metrics or analyzing engagement alongside revenue.

Incorporating these advanced metrics into your experimentation program is essential for driving long-term growth. They provide a more nuanced understanding of how experiments affect user behavior and business outcomes. By focusing on journey and compound metrics, you can make data-driven decisions that optimize the entire customer experience.

To effectively track and analyze these metrics, consider leveraging warehouse-native analytics. This approach allows for seamless data integration across various platforms, enabling sophisticated analysis without complex data pipelines. With warehouse-native analytics, you can easily create and monitor journey and compound metrics, empowering faster and more informed decision-making.

Successful metrics go beyond surface-level insights and dive deep into the customer journey. By embracing journey and compound metrics, you can unlock a wealth of knowledge about your users and make decisions that drive sustainable growth.

Implementing and monitoring metrics effectively

Warehouse-native analytics enable seamless data integration and sophisticated analysis. This approach facilitates tracking journey metrics and creating compound metrics, ultimately enabling faster, more informed decision-making.

Monitoring process, action, and people metrics helps ensure experiment health and team engagement. Process metrics focus on adherence to governance frameworks, while action metrics measure the real-world application of experiment results. People metrics address individual involvement and activity within the program.

Regular monitoring is crucial for maintaining data integrity and the validity of experimentation results. Effective monitoring involves watching trends, understanding influences, and knowing when to replace or drop measures as progress is made, as discussed in An Appropriate Use of Metrics.

At Statsig, we understand that successful metrics align with your hypothesis and business objectives. As outlined in How to pick metrics that make or break your experiments, focusing on one primary behavioral metric aligned with a business metric is key. Anticipating and measuring negative consequences, using secondary metrics to fill understanding gaps, and ensuring your experiment is powerful enough to yield reliable results are also essential for success.

Evolving your metrics strategy: continuous learning and adaptation

Regularly reviewing metrics ensures they remain relevant to current business goals. Outdated metrics can waste resources and lead to misguided decisions. Retire irrelevant metrics and focus on those explicitly linked to current objectives.

Avoid misusing metrics by not substituting them for critical thinking and context. Management by numbers alone is insufficient for effective decision-making. Metrics should inform, not replace, strategic analysis and domain expertise.

Adapt metrics to match the maturity stage of your experimentation program for optimal impact. Early-stage programs may focus on basic conversion metrics, while advanced programs use experimentation as a strategic driver. Successful metrics evolve alongside your program's growth and sophistication.

Embrace a culture of continuous learning and adaptation in your metrics strategy. Regularly challenge the relevance of each metric and its link to clear goals. Monitor trends, understand influences, and be prepared to replace or drop measures as progress is made.

By adopting a dynamic approach to metrics, you can ensure your experimentation program remains aligned with business objectives and drives meaningful impact. Successful metrics are those that evolve with your organization, providing actionable insights and guiding evidence-based decision-making.

Closing thoughts

Choosing the right metrics is the cornerstone of any successful experimentation program. By aligning metrics with your business goals, moving beyond basic metrics, and continuously adapting your strategy, you set your experiments up for meaningful impact. Remember, metrics are your compass—let them guide you toward growth and success.

If you're looking to deepen your understanding of effective metrics, check out Statsig's insights on picking metrics that make or break your experiments. Hope you find this useful!