How IONOS Uses Snowflake to Retain 30% of At-Risk Customers
IONOS, a web hosting provider serving 6.6 million customers, consolidated over 150 data sources into Snowflake’s AI Data Cloud to create a unified customer intelligence platform. The company uses machine learning models and real-time streaming to identify churn risk, power next-best-offer recommendations, and resolve service issues proactively. The result is a 30% retention rate among customers who call to cancel and up to 2x conversion rate improvement from AI-driven upselling.
Impact
150+
Data sources consolidated
15,000+
Call transcripts analyzed daily
50%+
Churn risk detection accuracy
30%
Customer retention at cancellation point
2x
Upsell conversion rate uplift
Challenge
IONOS’s customer data was spread across disconnected silos in multiple departments, making it impossible to detect at-risk customers before they cancelled or to surface relevant upsell recommendations during service calls.
Solution
IONOS consolidated 150+ data sources into Snowflake’s AI Data Cloud with real-time ingestion via Snowpipe Streaming, then layered machine learning models for churn prediction, Next Best Offer recommendations, and call transcript analysis at scale.
Tools & Technologies
What Leaders Say
“Our various brands had data silos spread across departments like customer care, sales and finance at various brands. Our plan was to unite that data so we could gain the most detailed insights into customer activities.”
“Before we could analyze around 1% of those transcripts, because it all had to be done manually. Now, we can do 100%. It helps us automate the analysis of churn risk with at least 50% accuracy, so we’re now much better at reaching out to customers where they are and when we most need to.”
“If we hadn’t invested in Snowflake, we simply wouldn’t be as successful as we are now. Having that central data and intelligence platform is driving revenue and success. And its simplicity means we can do more with a small data team.”
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Full Story
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.