How Strava uses Snowplow data to drive a culture of continuous improvement



Strava at a glance

Launched in 2009 in California, Strava is a platform for athletes to exercise together and share their sport activities.

Key results

  • 4bn

    Events collected and processed per day

  • Ability to democratize data to empower analysts and product teams

  • Data engineers free to focus on meaningful projects

“We would not have achieved our current level of self-serve data without Snowplow. It has enabled us to democratize our data culture, significantly improving our analytics coverage and deepening our insights.”

– Daniel Huang, Data Engineer at Strava

The challenge

The data team at Strava capture huge volumes of data, with anywhere from 3bn to 4bn events entering their data warehouse on a daily basis. Capturing and operationalizing data on such a huge scale is a challenge in itself, but Strava also required robust infrastructure and tooling in place to enable their analysts to move quickly, self-serving data without relying on support from engineers. With their existing data stack, implementing tracking for new features was also cumbersome. Strava needed a way to set up tracking quickly so they could drive a culture of continuous product optimization without running into analytics ‘blind spots’.

The solution

Unlike their previous vendor, Snowplow enabled Strava to manage their behavioral at scale without incurring huge costs. Once they became familiar with defining their own custom events and entities, data analysts at Strava could rapidly implement end-to-end tracking without needing help from data engineers. Strava’s data team are now free to focus on valuable projects, and since tracking is much easier to implement, no new features and product iterations go missing under the analytics radar.

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