How IONOS Uses Snowflake to Retain 30% of At-Risk Customers

IONOS SE operates at a scale that makes manual customer management impossible. With more than 6.6 million customers across domains, web hosting, e-commerce, and cloud services, the company’s ability to retain customers and grow revenue depends entirely on the quality of its data and the speed at which insights reach service agents. Six years ago, that meant accepting significant blind spots.

Before centralizing on Snowflake, IONOS ran on a fragmented data infrastructure where customer care, sales, finance, and product departments each maintained separate silos. Analysts could only examine a fraction of customer interactions, and the company had no way to see an at-risk customer coming. When a customer called to cancel, agents were often starting from scratch.

The shift to Snowflake addressed this at the architecture level. IONOS consolidated over 150 data sources into a single platform, building what the team describes as a 360-degree view of customers, contracts, and domains. To move beyond batch processing, the company added Snowflake Snowpipe Streaming for continuous, low-latency data ingestion and dynamic tables to simplify transformation pipelines, cutting setup time from hours to minutes per table.

The most consequential results came from applying machine learning directly to this unified data. IONOS now analyzes over 15,000 call transcripts per day using AI models hosted in its cloud, storing the results in Snowflake. Churn risk scoring, which previously required manual review of 1% of calls, now runs at 100% coverage with at least 50% accuracy. A Next Best Offer model surfaces the most relevant upsell products for each customer in real time, delivering up to 2x conversion uplift. Agents serving at-risk customers see recommended retention offers—discounts, alternative products—backed by behavioral data. About 30% of customers who call to cancel are retained at that point of contact.

Looking ahead, IONOS is building AI agents within Snowflake Intelligence to let senior managers query business data through natural language, reducing the engineering burden of dashboard creation. The platform has become the operating system for the company’s commercial decisions—and according to the team, a prerequisite for the customer support quality that has earned them industry recognition.