Playlist Launches AI Analytics Tools
Krissy Vann | Host, All Things Fitness and Wellness
Playlist is expanding its use of artificial intelligence across its fitness and wellness software portfolio with the launch of AI Insights, a new set of analytics tools designed to help operators interpret business performance, identify retention risks and forecast revenue.
The features will initially be available to Mindbody customers and use machine learning to analyze data including sales, bookings, visits, membership activity and client behavior. Playlist is the parent company of Mindbody, ClassPass, Booker, EGYM and Kite.
AI Insights includes weekly performance summaries that translate business data into plain-language reports, including sales and utilization trends and revenue by class or service type. Operators can view the reports through the Weekly AI Insights dashboard or opt to receive them by email.
The company is also developing an AI Assistant, currently in beta, that allows Mindbody customers to ask questions about their business in natural language. An operator could, for example, ask which memberships are approaching expiration and be directed to the relevant data.
Another component, Comparative Analytics, allows operators to compare sales, visits, membership, classes and appointments with similar businesses based on factors including category and region. Playlist said the benchmarking will draw on anonymized data from more than 110,000 venues across its portfolio. The feature is currently available to Mindbody Accelerate and Ultimate customers.
Retention and customer value are another focus. The Clients at Risk feature uses predictive models to identify customers considered more likely to lapse and estimates the potential revenue associated with losing them. A separate Big Spenders feature identifies customers predicted to become higher-value clients. Both are refreshed daily and can be used to create audience segments for outreach through Attentive, the SMS and email marketing platform integrated with Mindbody.
Playlist reports that customers using Clients at Risk have recorded a 19% higher client rebooking rate than customers that do not use the feature. The company did not provide additional methodology or data in the announcement to establish whether use of the feature itself caused the difference.
Revenue forecasting is also being tested. The Forecasted Revenue dashboard uses machine learning to estimate monthly revenue and indicate whether a business is tracking below, at or above expectations. It is currently in beta with select U.S. studios, with international availability planned for later this year.
"Every operator in this industry has been told AI is going to change their business, but a solo owner shouldn't need a data team to understand how," said Jess Huang, Chief Product Officer of Playlist. "AI Insights helps every business owner make sense of the numbers and name their next move. Now, operators can see when retention is slipping against comparable studios in their market, which clients are about to lapse, and what message to send to re-engage them. Playlist is uniquely positioned to do this because of the huge number of fitness and wellness locations informing our competitive dataset. With these new features, we're putting that data to work in a way the businesses we serve can actually use."
For fitness operators, the launch reflects a broader shift in how management software companies are incorporating AI into existing operational data. Rather than requiring owners and managers to interpret dashboards themselves, the emerging model uses predictive analytics and natural-language interfaces to surface potential problems and opportunities.
AI Insights is Playlist's third AI-focused product launch since July, following AI Concierge and Customized Websites. The latest release puts a greater emphasis on business intelligence, particularly retention, benchmarking and revenue forecasting, as fitness technology providers increasingly compete on their ability to turn operator data into actionable information.
