How Strava Tripled Analytics Efficiency to Propel Athlete Excellence

There’s only one thing better than completing a great workout: sharing that progress and accomplishment with others. Strava helps more than 135 million users to do just that.

We are the app for active people. We empower users to track and share indoor and outdoor fitness activities, including yoga, cycling, running, and swimming. Our users range from everyday athletes to WorldTour riders, professional and amateur triathletes, and fitness buffs from every walk of life, who all leverage our powerful mapping and analytics tools to plan routes, monitor their active journeys, and consolidate training data from real-world and virtual workouts.

To best support our users, our internal teams analyze large volumes of user-generated data to understand how athletes use the app, find gaps in our product offerings, and build features and experiences that increase value. It sounds simple, but the task used to be anything but.

Complicated tools slowed our pace

Our analytics and data science teams used to handle all data queries and share the results with product managers and other stakeholders, simply because our platforms were too complex for other teams to use.

This method was impossible to scale. I’m the senior analytics manager for Strava’s Growth organization, and my analysts spent a third of their time building dashboards and answering ad hoc questions from PMs, who had to wait up to two weeks for answers. Many of these requests were simple in principle but tedious and cumbersome to execute.

My biggest obstacle was ensuring my team focused on the right things at the right time. There was no shortage of questions, but our tools prevented us from identifying and quickly answering the high-priority queries that would move our strategy forward.

At the same time, I wanted to empower our PMs and other stakeholders outside our analytics and data science teams to ask and answer questions independently. That meant moving to a self-serve analytics platform that unlocked behavioral and user data in our databases and made it available to everyone at Strava.

After evaluating our options, I championed Amplitude Analytics based on its expansive feature set and self-serve capabilities to our C-suite and data teams, who approved the partnership. I was excited to get started, and I had the help of Amplitude’s Bennett Brownlow to implement the platform at Strava.

Going the distance with data

Amplitude answers many stakeholder questions right out of the box. It offers funnel analysis and built-in frameworks that allow users to drag and drop different types of event data, automatically generate funnels, and visualize data in easy-to-understand dashboards. These features are available to our analytics and data science teams and non-technical users, including project managers and C-level executives.

Some of our key data and product team members had experience with Amplitude from previous positions, and their existing knowledge helped us ramp up quickly. However, we still weren’t sure whether to expand its use beyond the Growth org because we didn’t know if the platform could handle our high volume of user-generated data.

We ran a pilot to confirm that Amplitude could ingest user data and output insights at speed. The platform surpassed our expectations, so we worked with Bennett to establish use cases, deploy Amplitude Analytics across our teams, and expedite its adoption by a broad spectrum of users.

Flexing our experiment muscles

Within the Growth org, we use Amplitude to help us retain users and demonstrate value to athletes so they’ll upgrade to the paid subscription. Amplitude helps us track key business and user metrics to find optimization opportunities across key funnels, and it helps us run and analyze experiments that bring us closer to our goals.

A recent experiment came from noticing a decline in our trial-to-subscription conversion rate. Using demographic data and other attributes, we leveraged Amplitude to analyze the conversion funnel and segment users. We discovered a cohort of under-35 athletes converting at a lower rate, dragging down the overall average. We’re now working on strategies to activate that cohort during the trial period to increase its perception of value and, hopefully, lead to more subscriptions.

This is just one of dozens of opportunities we’ve identified to run experiments across the athlete onboarding and free-to-paid funnels. Amplitude surfaces insights that lead to A/B tests, the results of which allow us to determine incremental revenue gains generated by altering elements of the user experience. Even if an individual experiment doesn’t lead to quantitative business results, we are learning faster, establishing use cases, and building customized dashboards that help us understand and improve Strava’s performance.

Self-serving saves $100K annually

Amplitude liberates user and event attributes that used to live in our database and makes them accessible to everyone at Strava through intuitive dashboards. Amplitude is so quick and easy to use that anyone can log in and track user activity.

Enabling self-serve analytics also empowered Strava’s teams and allowed us to push the boundaries of our analytics capabilities. Before Amplitude, my analysts spent a third of their time answering ad hoc questions for PMs and other stakeholders and building dashboards. Often, these PMs would have to wait up to two weeks for answers. Now, stakeholders get the answers they need up to two weeks faster, and my analysts have regained that time, increasing efficiency by 3x.

These time savings equate to roughly $100,000 annually, which we can re-invest in other projects and initiatives.

Unlocking gains, one step at a time

Strava has an active and engaged user base. I like to think of the athletes who use our platform as product managers because they all have strong opinions about the new features we should build. They are as invested in our product as we are. Amplitude helps us mobilize our teams to do the right things at the right time to create more value for these athletes, enhance the user experience, optimize our features, grow specific cohorts, and give our customers a voice.

Before Amplitude, Strava’s analytics and data science teams were overworked and underpowered. Our PMs and other stakeholders were in the dark about user behavior, and our event data was sitting unused in our database.

Amplitude opened our eyes, unlocking the analytics and data science team’s potential to focus on high-value initiatives and giving our PMs the power to answer their own questions and move faster. It’s helping us chase the same excellence that our global community strives to achieve on their active journeys.