Predictive analytics for consumer networking brand, Ahmedabad

Explore how Inferenz helped a global consumer-networking brand overcome chaotic event data and outdated churn models by unifying tens of millions of raw JSON events per day under one common schema, cutting across clickstreams, alerts, and device logs. By introducing a low-code rule engine and predictive models based on gradient-boosted trees, they enabled real-time detection of churn risk days earlier. The results? A 25% increase in customer renewals through more timely offers, an 80% reduction in time needed to onboard new event types, and a 40% reduction in processing costs thanks to a shared event-processing pipeline

Driving this transformation was a tech stack featuring Kafka, Snowflake, Databricks, Python/PySpark, AWS Lambda, and Apache NiFi, combined with live dashboards via Tableau. The integrated pipeline directly streamed churn risk scores into CRM and push-notification systems, enabling proactive campaign adjustments and measurable campaign-to-sale conversion improvements

Check the full case study here.

https://inferenz.ai/resources/case- …