Marketing Personalization · Real-time Analytics
Churney needed to improve marketing return for clients while avoiding the engineering burden of running model monitoring, drift detection, and large-scale experimentation on its own infrastructure.
Reported impact
Churney improved customer ad returns and reduced its own MLOps burden, letting the team spend more time improving model quality and less time maintaining infrastructure.
Churney built its causal-AI marketing platform on Google Cloud by combining BigQuery’s warehouse economics with managed MLOps capabilities from Gemini platform. BigQuery gives the company a scalable, cost-efficient base for data handling, while Gemini platform lets it test hundreds of models simultaneously and use managed features such as Model Monitoring and drift detection instead of maintaining that stack internally. This setup is central to Churney’s customer-lifetime-value and ROAS prediction workflows, where causal models help businesses focus spend on their highest-value customers. By using managed services for experimentation and monitoring, Churney can keep moving quickly on model quality instead of dedicating whole teams to operational upkeep. The result is a marketing-optimization platform that delivers stronger returns for customers while saving the vendor dozens of hours of maintenance work every week.
Marketing and customer data is stored in BigQuery, where Churney's causal models are trained and evaluated. Gemini platform manages experimentation, monitoring, and drift detection, and the resulting predictions feed customer lifetime value and ROAS optimization decisions.
BigQuery can run scalable data warehouse operations out of the box.
Gemini platform gives us the ability to speed up experimentation and test hundreds of models simultaneously
One customer saw 2.4 times the return on investment they previously saw.
Our customers are consistently seeing a 20% increase in ROAS with our causal AI predictions.
Without Gemini platform we’d need to spend 80 hours a week maintaining our services.
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
The Organization JSON-LD on the team page lists 22 people under employee.
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