Strava has always caught my attention. It’s not just an app for tracking workouts. It’s a platform that managed to build a habit. More than 135 million people use it to log, analyze, and share their activity. And that doesn’t happen by accident.
Behind that experience, there’s something key: data-informed product decisions.
While researching how Strava built one of the most engaging apps in the world, I found more information about its Amplitude implementation and the impact it had on the Growth team. One of the most important changes was moving to a self-serve analytics model, which enabled product and growth teams to access data directly, reduced their dependency on the analytics team, and accelerated decision-making based on real user behavior. You can find the original source of the case published by Amplitude at the end of this article.
But it wasn’t always that simple.
When data exists but isn’t accessible, growth slows down
Strava generated massive volumes of data every day. Every activity, every interaction, every feature used was an opportunity to better understand its users.
The problem was that access to this data was centralized within the analytics team.
Product and growth teams depended entirely on them to get answers. Something as basic as understanding a funnel or analyzing a segment’s behavior could take up to two weeks. Meanwhile, the analytics team spent close to a third of its time building dashboards and responding to operational requests.
The result was clear: the organization had the data, but it didn’t have the speed to act on it.
The shift: democratizing data access with Amplitude
To solve this problem, Strava decided to implement Amplitude with a very clear goal: democratize access to data.
Amplitude enabled product, growth, and business teams to access dashboards directly, analyze funnels, segment users, and understand behavior—without constantly relying on the analytics team.
This completely changed how decisions were made.
Instead of waiting weeks, teams could answer questions in minutes. They could identify friction points, spot opportunities, and validate hypotheses much faster.
And that had a direct impact on their ability to grow.
The impact: understanding behavior better and optimizing conversion
One of the clearest examples came when they detected a drop in conversion from the free trial to the paid subscription.
Using Amplitude, the team analyzed the funnel and segmented users to understand what was happening. They discovered that athletes under 35 had a lower conversion rate than the rest.
This kind of insight is what allows teams to design more relevant experiences, optimize onboarding, and increase the perceived value of the product.
Amplitude not only helped them understand the problem, but also run experiments, measure results, and continuously optimize the experience.
The result: 3x more efficiency and much faster decisions
The impact was significant.
The analytics team increased its efficiency by 3x. Product teams could access data directly. Decisions started being made faster and with greater confidence.
In addition, this efficiency improvement generated an estimated annual savings of USD 100,000, by freeing up the team’s time to focus on strategic initiatives instead of operational tasks.
But beyond the savings, the real impact was this: Strava went from having data to truly using it as a competitive advantage.
The real takeaway: access to data changes how products grow
This case highlights something we see every day when working with digital teams.
The problem usually isn’t a lack of data. The problem is lack of access—and the ability to act on it.
When teams can understand user behavior, identify friction, and experiment quickly, the product evolves faster. And growth stops being a matter of luck and becomes the result of data-informed decisions.
That’s exactly the approach that makes it possible to build products that sustain engagement over time.
Original source and author
This article is an adaptation and Spanish translation of the success story originally published by Amplitude. Original source:
https://amplitude.com/blog/strava-analytics-efficiency
Paige DeRaedt
Senior Analytics Manager, Strava
Paige has experience in data analytics, product design, and quality assurance engineering, and leads analytics initiatives that help Strava optimize its product and improve the user experience.
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