Fitness and Wellness

San Francisco, CA


Quality Challenge
Slow manual processes, lack of data-driven insights, misaligned internal priorities

  • 41% improvement in the unitQ Score
  • 13% improvement in Google Play Store ratings
  • 94% reduction in manual user feedback efforts
  • Potential for 20% reduction in incoming support tickets
Strava is one of the most popular fitness apps in the world, with over 80 million global athletes using its platform to record activities, share photos, challenge friends, and compete in running and cycling events. It’s a social network where athletes can connect and share adventures. With such a large and loyal user base, Strava wanted to ensure a great user experience while pushing fixes and new features through product operations. In a highly competitive niche where product experience and product quality count, Strava needed a way to prioritize user requests alongside the development roadmap.

An Uphill Climb to Balance Product Quality and Product Development

Strava, like most product-forward companies, has to juggle product development goals as well as trailing fixes, product quality issues, and pending enhancements. These streams flow in parallel to keep existing users happy while outpacing competitors with new and novel capabilities. With finite engineering and development resources, it’s always difficult to get alignment on what the community of users demands versus what’s needed to remain the market leader.

Running Towards Data-Driven Alignment

Strava was already collecting user feedback from public and internal sources, like Twitter, Reddit, Zendesk, the Apple App Store, Google Play Store, and Samsung Galaxy Store. But, searching for, identifying, and categorizing product quality issues was a decidedly manual effort, with tagging that could vary across individuals. The team would search keywords, dig into individual Zendesk tickets, and try to manually piece together the volume, scope, and frequency of product quality issues. The effort consumed as much as 8 hours per week from the community management team. And, the resulting manually-derived insights left Strava focused on just the biggest apparent issues.
Strava knew they needed alignment on product quality issues and resolution, both within the technical support team and with Development, but it could only happen if they adopted a data-driven approach. They also wanted to improve app store ratings, especially in the Google Play Store. So, in addition to needing a more quantitative prioritization engine, Strava also needed comprehensive, data-driven insights into what users were saying. That user feedback data would guide development prioritizations, help them fix the right issues faster, and provide a better experience for users.

Winning the Product Quality Race With unitQ

Strava had a great view into user feedback, but manual processes kept them from seeing the whole picture and blocked community insights from influencing development priorities. With unitQ’s product quality monitoring platform, Strava has a comprehensive, single source of truth to provide indisputable, data-driven direction for development prioritizations, and guidance on improvements to product quality, user experience, and overall product direction.

Insights from unitQ have helped Strava reduce their manual analysis time by up to 94%, raise their Google Play Store rating from 3.9 to 4.4, and increase their unitQ Score, the Quality Metric, by 41%. Other benefits of unitQ, according to Strava, include:
  • unitQ Monitor enables a data-driven, analytical approach to everything from support to development to product roadmap planning.
  • The top trending quality issues report in unitQ gives Strava product quality insights that could reduce incoming support volumes by as much as 20%. That’s the type of tangible, actionable information both Support and Development can use to refocus their efforts to benefit both Strava and their users.
  • unitQ Saved Searches allow faster, easier sharing of user feedback on specific issues.
  • Increased collaboration, and the confidence of using unitQ’s unbiased data, enable faster, better decision making.
  • unitQ Monitor allows for deeper investigation of user trends, issue scope, and the story behind eventual quality issue prioritizations.

unitQ Fuels Strava’s Ongoing Product Quality Efforts

The deep and wide set of global data collected by unitQ ensures Strava can decode the nuance of user comments, and weigh social media and app store reviews against what users are submitting via Zendesk support tickets. Those insights let Strava see what users are saying to Strava and amongst themselves on Twitter and Reddit. They’ve already started using this cross-channel data to drive collaboration in product operations and give the product management team user insights to help guide product roadmap planning. This mix of automated user feedback capture and analysis, data-driven insights, and indisputable product quality prioritizations gives Strava the user feedback clarity required to keep everyone focused on the athletes they strive to delight.

“We wanted to get alignment between what the community wants and what our developers have planned. We couldn’t quantify the community’s demand for feature requests, so it was difficult to prioritize development. That lack of alignment was starting to impact our app store scores.”

— Stephen Young, Technical Support Lead, Strava

In order to provide long-term scalability and security for customers like Strava, unitQ’s architecture leverages several AWS services. Amazon Kinesis Data Streams serve as a pipeline for the millions of pieces of feedback we process and help ensure no data is lost as it winds its way through various redundant microservices which run in Amazon Elastic Kubernetes Service (EKS). Amazon OpenSearch Service then makes it possible to quickly aggregate millions of data points into our UI, allowing customers to create charts and dashboards for easy reporting on their user feedback and to share insights across the organization.

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