Agile teams use real-time behavioral data to accelerate experimentation cycles, validate hypotheses faster, and make data-driven product decisions.

Real-Time Data in Agile Experimentation:

Faster Feedback Loops:

• Monitor experiment results in real time, not after daily data refreshes
• Detect statistical significance faster with streaming data
• Identify issues or unexpected behavior immediately
• Iterate on features within the same sprint

Data-Driven Decision Making:

• A/B test feature variations with accurate behavioral metrics
• Measure feature impact on engagement, retention, and conversion
• Validate user stories with actual usage data
• Prioritize roadmap based on real user behavior, not assumptions

Continuous Experimentation:

• Run multiple experiments simultaneously with proper segmentation
• Test personalization algorithms with real-time feedback
• Optimize ML models with immediate performance data
• Build experiment culture with accessible, trusted data

Strava Example: Strava's product team used Snowplow behavioral data to run A/B tests on their Routes feature. With real-time tracking and analysis through Snowflake and Tableau, they measured experiment impact with pinpoint accuracy—resulting in significant increases in Route page views, downloads, saves, and shares per user.

"With Snowplow data, we were able to measure project success through an A/B test... we delivered increased value to members of the Strava community." — Lauren Gray, Senior Product Analyst, Strava

With Snowplow, agile teams gain the real-time, granular behavioral data needed to experiment continuously. Snowplow's comprehensive tracking, real-time delivery, and dbt data models make it easy for product analysts to self-serve insights without waiting on engineering—keeping pace with rapid iteration cycles.