Walla’s New Software Tool Uses AI To Predict When Members Might Churn 

WallaPredict scans data points such as class bookings, attendance and payment activity to generate member risk scores, and recommend targeted retention tactics

Walla has unveiled WallaPredict, an AI-powered tool designed to help boutique fitness studios and franchises boost member retention through early interventions. 

Developed with Amazon Web Services (AWS), the new feature analyzes member behavior and provides actionable strategies before clients drop off.

Launched in beta mode, WallaPredict scans data points such as class bookings, attendance frequency, purchase patterns and payment activity to generate member risk scores, highlight root causes of declining engagement and recommend targeted retention tactics. Studio operators can then act on these insights directly within Walla’s all-in-one management platform.

“The era of reacting to member loss is over,” said Walla CEO and co-founder Doug Hecht. “WallaPredict doesn’t just flag members who might leave — it shows you why, and what to do next. It’s like having a retention expert in your back pocket, powered by AI, making sure your community stays strong and your business keeps growing.”

Studios in the beta program have reported early success, according to Walla, noting easier client outreach and more clarity around retention efforts. One early adopter described the tool as a “game-changer,” citing its ability to replace complex spreadsheets and automate messaging to at-risk members, per Walla. 

Built on AWS infrastructure, WallaPredict offers secure data handling and scalability designed to support single-location studios as well as growing multi-site operations. The launch marks the start of Walla’s broader AI strategy for the boutique fitness market, which the company says will extend to sales and marketing in the future.

Walla, which makes business management software for boutique fitness studios, raised an $8 million Series A round in 2022, adding to a previous $5 million seed round for $13 million in total funding.