Strava Social AI
Strava Social AI aggregates activity data (heart rate, elevation and pace) across activities posted in Strava clubs and provides averages to help members in finding new events and clubs where they will feel most comfortable participating. All data is abstracted and anonymized before being made public, and athletes can choose at any time to not share activity data
Problem Space
Problem Statement
How might we improve the accuracy of average abilities in clubs in Strava so that more people can feel confident and comfortable choosing social exercise options?
Problem Background
Most people on Strava are using the app for a variety of fitness-related purposes, including logging activities, tracking their fitness progress, and finding social exercise options. When it comes to logging activities, Strava does a great job with a clean and usable interface that received no comments for improvement in my survey. This is the reason for focusing on the next most-cited reasons for using Strava: fitness tracking and social options.
It is inherent human nature to want social interactions, and there is plenty of anecdotal (and scientific) evidence that exercising in groups has motivational and emotional benefits. However, it can be difficult to know how your abilities fit in with a given group or club, and many will attest that it makes social exercise much less fun if the majority of your group has significant fitness differences.
Research Insights
User Pain Points
- Finding clubs and/or groups to exercise with when moving to new places and/or going on vacation
- Creating motivation for themselves to exercise more regularly
- Finding ways to make exercise more enjoyable
Supporting Data
Data was collected through a Google Form: Survey Link
- 70% of users surveyed stated that they use Strava for general activity logging & tracking.
- 50% of the users surveyed stated that they use Strava for social aspects (seeing what friends are up to, commenting, motivation).
- 40% stated that they use Strava to see their training and/or fitness progress.
- 20% of users stated they use Strava to find group events to attend.
Feedback
Preliminary user research has suggested that Strava users enjoy the app, but further additions to features (more detail in heart rate analysis, improved pace analysis for running, etc.) are desired. Unfortunately, most survey respondents did not answer the open-form questions about what they would like to see in future versions of the app.
Landing on the Solution
Based on our target users’ feedback, we knew we wanted to work on the following features:
- Improving the social aspects of Strava, especially in the area of increasing the motivational capability of the app. This can be done through the Groups features or the Friends feature.
- Creating more opportunities for customization. This can be done within the Groups feature and the personal activities analytics.
Note: Improvement of general activity logging & tracking was considered as a solution idea, but Strava already does quite a wonderful job of this, so areas with more potential were chosen instead.
Explanation of Solution
Strava Social AI is an anonymous aggregation of data from activities posted within Strava clubs using artificial intelligence to quickly and accurately produce useful metrics for each club. The statistics that would be involved in the initial version of the feature are:
- Pace
- Elevation
- “Effort” Score (using heart rate in conjunction with the above 2 metrics).
The goal of showing these statistics on each club’s homepage is to encourage and promote social exercise for everyone, regardless of ability. It also enhances the customizability of Strava by increasing the possible use cases for the Groups feature. Athletes may use Strava to find a club to help get them out the door once a week; they may also use it to choose clubs to attend based on their goals for the day, week or month.
Because the data will be strictly anonymized, with no location or identity information attached, there will be less concern about privacy within this feature than with other features that Strava already maintains. However, there will always be the option for athletes to individually hide some or all of their activities, even if they’re already part of multiple clubs that their activities were being posted in previously. Club creators/moderators will also be able to disable Strava Social AI for their club if they wish to do so.
The motivation for integrating AI into this feature comes down to how quickly and accurately AI can summarize and produce results from very large amounts of data. Given that it is rapidly becoming cheaper and easier to use AI for data-related tasks, it makes more sense to utilize its capabilities as opposed to a more difficult and time-consuming method of aggregating data that may have been used before AI was available.
User Flows/Mockups
Figure 1: User flow sketch for Experience 1.
A draft of this user experience can be found here:
DTTP AI Product Management Program - User Flows
Figure 2: Sketched wireframe for an example club after the proposed changes.
Future Steps
Additional Problems/Areas to Explore in the Future
- Within Strava Social AI: Further improving the customizability of the feature by having it provide recommendations for clubs or groups in your area based on your aggregated Strava activity data over a certain timespan.
- Within Other Areas Expressed in User Research: Exploring possible improvements to Strava’s route planning feature as this was an area of the app that 30% of survey respondents cited using.
- Within Further Surveys and Customer Interviews: Myriad possibilities!
Images
Learnings
Product Manager Learnings:
Ava Peacock
I have really enjoyed the process of finding my own problem space and learning how to properly diagnose the pain points of users in an effort to propose a useful solution.
Some of the most valuable things I’ve learned include how to get extremely specific when it comes to my problem space and my user personas as well as becoming aware of just how many tools are out there that can assist with the role of a Product Manager.
I feel extremely lucky to have had Ananya as my mentor as she has given feedback that is specific and actionable, and she has also been someone who is easy to talk to and relate to.
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.