Ergatta - Coach AI

The Opportunity

This started off as the founders pet project that had the potential for exclusive intellectual property, positioning Ergatta as a leader in the fitness industry. The hope was that this positioning in the industry would translate to increased growth and win rate against our competitors. Rowing is a pretty niche exercise modality. As Ergatta transitioned from early adopters with rowing experience, to the mainstream members with no rowing experience. This created a new education and support gap in our experience.

Growth and justification for a higher subscription price were the main objectives.

The Audience

Members who are new to fitness or new to rowing were concerned about their potential for injury without proper form.

The Role

Director:

• I acted as the biomechanics expert leading Research and Development with our Data Science Engineer.

  • Led a mass data collection effort for capturing rowing technique at scale across a variety of body types.

  • Led a strategic pivot shifting our machine learning model from power curve data to computer vision data.

  • Led model development with our Data Science Engineer.

  • Led model validation with testing and spot checking model outputs against real world rowing inputs.

• Transitioning from R&D to product execution, I acted as a consultant for the product team.

  • I gave the product manager enough context to run with for implementation of the feature set.

Key Performance Indicators

• Increased TAM and our head to head comparison with competitors

• Feature usage

  • X% of companion app users use form analysis

  • Y% of users use form analysis multiple times

• Form improvement

  • Z% of users see a reduction in form issues over time

The Outcome

• 50% of new customer specifically referenced the importances of rowing form and this Coach AI feature being a key reason why they purchased Ergatta.

• We did see an initial usage spike at launch, with quick drop off.

  • Adoption = 2%

  • Repeat Usage = 30%

• Repeat users saw their Average Form Score increase from an initial 85% to 95%

• Patented technology that adds value to Ergatta in exit opportunities.

• Added perceived value to our paid subscription in preparation for a subscription price increase.

Lessons Learned

• Power curve data is noisy and led to a lot of conflation among different form errors. Initially we thought maybe we just need more data and better quality data, but in the end we found power curves weren’t a great proxy measurement for biomechanical feedback in our application.

• Pivoting to computer vision ended up saving us like 6 months of R&D time.

• Customer video recording can open pandora’s box with privacy and storage concerns. We chose not to store customer video records to avoid privacy concerns, but did limit the product use cases for leveraging replay of their video in our experience.

Key Skills

• Product Management for Machine Learning

• Data Collection, Tagging, Cleaning

• Biomechanics

• Computer Vision