Strava is a product built for movement, both for the millions of athletes who track their training on the app and for the internal teams that shape those user experiences.
But behind that movement, there’s always structure. Systems. Questions. Decisions. And for a long time, those decisions were stuck in a bottleneck.
Before 2024, Strava’s data was powerful but difficult to access. Analysts handled all queries, even simple ones. Product managers waited up to two weeks for answers. The tools were too complex for non-technical teams to navigate. And analysts spent a third of their time building dashboards and fielding ad hoc requests.
This wasn’t just inefficient. It was unsustainable.
From overwhelmed analysts to empowered teams
The Growth org at Strava led the change. With support from data leaders and C-level stakeholders, they piloted Amplitude with a clear hypothesis: if analytics became self-serve, decisions could scale without scaling headcount. Analysts could then focus on more strategic work.
They started small, running a pilot to validate performance, data integrity, and usability. A core group of internal champions, some with prior Amplitude experience, helped stress-test the platform and confirm that it could handle the scale of Strava’s user-generated data.
The pilot delivered, both on speed and clarity. Amplitude’s funnel analysis and drag-and-drop dashboards gave every team, from analysts to executives, direct access to insights. No SQL required.
With early use cases established, Strava moved quickly to roll Amplitude out across teams. Adoption ramped up fast thanks to a deliberate onboarding plan and support from experienced internal users. The rollout went beyond just a technical change. It was cultural. A shift toward speed, autonomy, and continuous learning.
Flexing their experiment muscles
Within the Growth org, Amplitude became the backbone for retention and monetization strategy, especially around the free-to-paid experience.
Strava used Amplitude to track key user and business metrics, identify drop-offs in critical funnels, and launch experiments to improve conversion. One recent example started with a dip in trial-to-subscription conversions. Using demographic segmentation, the team discovered that athletes under 35 were converting at a much lower rate, pulling down the overall average.
That insight led to a hypothesis: this cohort needed to perceive value faster during the trial period. It wasn’t just a theory. It became the basis for a new onboarding strategy designed to activate early wins and boost conversions in a targeted way.
It’s one of dozens of experiments now made possible by self-serve analytics. Amplitude helps Strava surface new opportunities, test them rigorously, and measure the incremental value of changes in real time. Even when a test doesn’t yield a win, it yields a lesson. The feedback loop gets tighter, and the company deepens its understanding of what drives user behavior.
Even when a test doesn’t yield a win, it yields a lesson. The feedback loop gets tighter, and the company deepens its understanding of what drives user behavior.
Time savings turned into real business value
Before Amplitude, analysts spent 30% of their time handling basic data requests. After rollout, stakeholders could self-serve answers, and analysts got that time back.
Efficiency tripled. Time-to-insight dropped from weeks to minutes. And with reclaimed analyst time and fewer manual dashboard builds, Strava estimates an annual savings of $100,000. That value is now reinvested into higher-leverage work across the org.
Amplitude also unlocked access to user and event attributes that were previously buried in databases. Now, those same data points are available to anyone through intuitive dashboards. Data became not just accessible, but actionable.
The bigger picture: autonomy at scale
What Strava built with Amplitude is bigger than a new analytics setup. It’s a new rhythm for how the company works: faster feedback, sharper questions, and more confident execution across teams.
Dashboards became starting lines, not finish lines. Teams didn’t just get access to data, they got the ability to explore, experiment, and adjust in real time.
