How RSG Group Uses Snowflake Cortex AI to Deliver Member Sentiment Analysis Across 30+ Countries

RSG Group operates one of the world's largest fitness and lifestyle brand networks, with brands spanning Gold's Gym, McFIT, and John Reed spread across more than 1,000 facilities in 30+ countries. Managing analytics at that scale — across multiple brands, markets, languages, and data systems — required a platform that could unify fragmented data and surface actionable insight without a large centralized data science team.

Before migrating to Snowflake, RSG Group's analytics infrastructure was characterized by siloed systems and slow pipelines. Data staging processes that now complete quickly once took hours. Engineers spent time managing pipelines rather than building analytics products, and business users had limited access to data. The organization was not able to operate with the speed or breadth of analysis that a global multi-brand fitness operation requires.

RSG Group migrated to Snowflake's AI Data Cloud running on Azure, centralizing member data across brands and markets into a single governed platform. The team adopted Snowflake Cortex AI to power sentiment analysis across member feedback in more than 30 languages — a capability that previously would have required months of custom model development for each language. Engineers used Snowflake's native AI and machine learning capabilities to build predictive models without requiring dedicated data scientists. Power BI dashboards connected to Snowflake now surface member KPIs and brand performance metrics to business users across the organization.

The impact was measurable across multiple dimensions. Data staging accelerated 80x. Time to insight improved 10x. Total cost of ownership dropped 20x versus the legacy infrastructure. Engineer onboarding became 3x more efficient, and the number of active data users across RSG Group doubled. As Iana Palacheva, Data Engineer at RSG Group, described: "In the past, I used to spend months trying to create different models for different languages, but with Snowflake and Cortex everything just works. If we were to build this from scratch it would take weeks."

For a global fitness organization with members speaking dozens of languages across dozens of markets, the ability to run multilingual sentiment analysis natively within the data platform — without data movement or custom model overhead — represents a qualitative change in what's analytically possible. Christopher Rüge, Head of Data and BI at RSG Group, summarized it: "We can simply provide real value and real insights to our business partners faster, easier, and much more cost-efficiently than before."