Class and Content Recommendation Engine for a 40-Studio Yoga Chain
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Problem
Generic schedules driving churn
A 40-studio yoga chain with a companion on-demand video library watched members disengage within their first months. Every member received the same class schedule emails and an unsorted content feed, new instructor formats went undiscovered, and the marketing team had no way to match suggestions to a member's level, preferred style, or attendance pattern.
Solution
Cross-studio recommendation engine
We developed a recommendation engine spanning in-studio classes and the on-demand library, combining collaborative filtering on booking history with content embeddings of class style, intensity, and instructor. Recommendations personalise the app home screen, weekly emails, and post-class follow-ups, with cold-start handling for new members driven by an onboarding quiz and their first bookings.
Measurable Impact
What changed after launch
Members receiving personalised recommendations booked 31% more classes than a hold-out control group over 90 days
First-90-day member churn reduced from 27% to 19% after rollout across all 40 studios
On-demand library plays per active member increased 44% within one quarter
Weekly class email click-through rose from 6% to 15% with personalised line-ups
Tech & Tools Used
What powered the build
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