TechnologyCustomer Service

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

IONOS SE, the German web hosting company serving 6.6 million customers, built a unified data foundation on Snowflake to eliminate customer data silos across all brands. AI and machine learning power automated analysis of 15,000 daily call transcripts, ML-driven upsell recommendations, and proactive churn detection — retaining 30% of customers at the point they call to cancel.

Impact

150+

Data sources consolidated

15,000

Daily call transcripts analyzed

30%

Customers retained at cancellation point

Up to 2x

Upsell conversion rate uplift

50%+

Automated churn risk analysis accuracy

Challenge

IONOS operated customer data across disconnected silos spanning multiple brands and departments, preventing unified visibility into customer behavior, limiting transcript analysis to 1% of call volume, and leaving churn risk and upsell opportunities largely undetected across 6.6 million customers.

Solution

IONOS migrated to Snowflake’s AI Data Cloud, consolidating 150+ data sources with Snowpipe Streaming for real-time ingestion and Dynamic Tables for pipeline automation, then used Snowflake Notebooks to feed 15,000 daily call transcripts into AI models — enabling automated churn detection, ML-powered upsell recommendations, and proactive customer retention at scale.

Tools & Technologies

What Leaders Say

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.

Laurent Beck, Manager of Data Engineering, IONOS

With Snowflake, we collect data on failed actions and provide that information to agents before they even pick up the phone. This means we have a head start in resolving issues and can instantly escalate more technical issues to different levels of support, without the customer having to be passed from pillar to post.

Thomas Krug, Senior Manager of Group Data Management and Applications, IONOS

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.

Thomas Krug, Senior Manager of Group Data Management and Applications, IONOS
Get the full context.

Sign up to read complete case studies, access detailed metrics, and unlock all use cases.

Full Story

IONOS SE operates one of Europe’s largest web hosting platforms, serving over 6.6 million customers with domains, hosting, website tools, and cloud services across multiple brands. For years, customer data was siloed: care, sales, finance, and product teams each maintained separate data systems with no shared view of a customer’s full journey. Service agents had limited context when fielding calls, marketing teams couldn’t target effectively, and churn often went undetected until it was too late.

The structural problem was both technical and operational. With data fragmented across brands and departments, IONOS could not generate overarching insight into customer behavior, predict which customers were at risk, or identify upsell opportunities at scale. Transcript analysis was manually performed on roughly 1% of the call volume — leaving the other 99% as untapped intelligence.

Six years ago, IONOS migrated to Snowflake’s AI Data Cloud to consolidate over 150 data sources into a single platform. Snowflake Snowpipe Streaming enables real-time, low-latency data ingestion, replacing the prior once-a-day ETL approach. Dynamic Tables automate data transformation and pipeline management, cutting setup time from hours to minutes. Snowflake Notebooks are used to route 15,000 daily call transcripts to AI models for automated analysis, with results stored centrally and surfaced to agents and sales teams.

The results span the entire customer lifecycle. Automated churn risk analysis identifies at-risk customers before they call to cancel, achieving 50% accuracy and enabling proactive outreach with discounts or alternative product recommendations. The Next Best Offer system provides contact center agents with prioritized upsell recommendations based on ML signals, delivering up to 2x conversion rate uplift. At the point a customer calls to cancel, Snowflake-powered data helps agents retain approximately 30% of those customers through targeted offers.

Looking ahead, IONOS is building custom AI agents within Snowflake to allow senior management to query data directly through natural language, reducing dashboard engineering overhead and enabling more dynamic data exploration. The platform investment has become foundational: as Thomas Krug, Senior Manager of Group Data Management and Applications, put it, without Snowflake the company simply would not be as successful as it is today.

Similar Cases

O
Omilia
33% faster
deployment time improvement

Omilia, the Cyprus-based conversational AI company helping enterprises replace legacy IVR systems with AI-first contact centers, adopted Snowflake’s AI Data Cloud on AWS to centralize analytics and streamline data operations. Snowflake’s managed platform delivered 33% faster deployment times and saved hundreds of DevOps hours per month, enabling near real-time visibility into AI model performance, call volumes, and operational trends across Omilia’s global enterprise customer base.

TechnologySSnowflake
I
Intercom
$1.4M
annual savings from sales team efficiency

Intercom, the AI-first customer service platform, built a Sales Cockpit on Snowflake’s AI Data Cloud powered by Cortex AI to give sales reps a unified view of customer data and AI-generated insight decks. The tool saves more than 2,000 hours per month across the sales organization, equivalent to $1.4 million in annual savings, and reduced the time to produce customer insight reports by 96%.

TechnologySSnowflakeSCSnowflake Cortex AI
E
Ensono
54–70%
reduction in mean time to resolution (mttr)

Ensono, a managed services provider handling over 60 billion retail transactions and government platforms for 24 million constituents, built two AI-powered systems on Snowflake to shift IT operations from reactive to predictive. The Envision Predictive Engine (EPE) and DiagnoseNow application reduced mean time to resolution by 54–70%, cut major incidents by 22%, and improved SLA performance by 38% across its enterprise client base.

TechnologySMSnowpark MLSSnowflake
P
Pfizer
93%
database reduction

Pfizer achieved a 93% database reduction and 20% cost avoidance by migrating their global SAP environment to S/4HANA on IBM Power10 infrastructure.

PharmaceuticalsTechnologyICIBM ConsultingIPIBM Power Virtual Server
A
Allspice
20% → 97%
ingredient matching accuracy

Allspice, a food technology startup building a kitchen operating system for consumers and recipe publishers, deployed Pinecone’s vector database to solve the inherent messiness of ingredient data that traditional text search could not handle. The implementation raised ingredient matching accuracy from roughly 20% to 97%, enabling the launch of recipe importing as a core product feature and expanding into a platform-wide semantic layer for search, recommendations, and conversational AI.

TechnologyTtext-embedding-3-largePPinecone
J
Jamf
Under 45 minutes
performance review skill build time

Jamf deployed Claude Enterprise across 16 departments, then built interactive workflow skills using Claude Cowork that transformed manual spreadsheet-based processes into guided, conversational experiences. Performance reviews that previously required months of effort are now built in under 45 minutes, and non-engineering teams independently create custom data dashboards.

TechnologyCEClaude EnterpriseCCClaude Cowork
R
Rappi
40%
search response latency reduction

Rappi, Latin America’s fastest-growing on-demand delivery app serving over 300 cities, replaced its keyword-based search engine with Oracle AI Vector Search and Oracle Cloud Infrastructure Generative AI to enable semantic and image-based product discovery. The upgrade reduced search response latency by 40% and improved conversion rate by 25%, driving higher engagement and order volumes across the platform.

TechnologyOAOracle AI Vector SearchOAOracle Autonomous AI Database
C
Confluent
15,000+
hours saved monthly

Confluent, a data streaming platform company with 2,000+ employees and 4,000+ customers, deployed Glean to solve the knowledge fragmentation that came with rapid growth from 250 to 2,000+ employees across 20+ systems. Glean indexed the company's full tool stack — Slack, Salesforce, Confluence, and more — enabling instant knowledge retrieval across all teams. The result: 15,000+ hours saved monthly, a 13% increase in support team satisfaction, and over 70% employee adoption.

TechnologyGGlean