Case Study — AtlasIQ
How AtlasIQ Helped a Global SaaS Company See Its Revenue Before It Happened
Predictive revenue analytics for a $200M ARR SaaS business
Industry
Enterprise SaaS / Finance
Timeline
16 weeks
Team
6 engineers
Tech
ML + Kafka + React
The Challenge
A $200M ARR SaaS company was flying blind on churn risk, upsell signals, and pipeline health. Their BI stack produced weekly static reports that were obsolete by the time leadership read them. Analysts spent 80% of their time aggregating data and just 20% on analysis — the inverse of what drives business value.
Our Approach
How We Solved It
Real-Time Event Stream Ingestion
We wired product usage events, CRM activity, and billing signals into a unified Kafka stream, giving the platform a live heartbeat of every account.
ML-Based Churn & Upsell Scoring
Trained gradient boosting models on 18 months of historical data to score each account's churn probability and expansion potential daily, with explainable feature attribution.
Automated Alert Pipelines
Built a rule + ML hybrid alerting layer that surfaces high-priority accounts to CSMs in Slack and email the moment a risk threshold is crossed.
Executive Dashboard Consolidation
Replaced 12 separate dashboards with a single executive view showing ARR movement, health scores, and forecast accuracy — updated every 15 minutes.
Engineering Process
How We Built It
Feature Store Architecture
Built a shared feature store so churn, upsell, and segmentation models all consume the same pre-computed account signals — eliminating training-serving skew.
Streaming Aggregation
Apache Flink aggregations on the Kafka stream compute rolling 7/30/90-day engagement metrics in real time without batch jobs or overnight delays.
Model Observability
Every prediction includes a confidence score and the top 3 driving features, making model outputs actionable and auditable rather than black-box scores.
Architecture Decisions
Key Technical Choices
Kafka Over Webhooks
Chose event streaming over webhook polling to handle 50M+ daily events reliably with guaranteed ordering and replay capability for model retraining.
Feature Store vs Ad-hoc Queries
Centralizing features in a dedicated store reduced model training time by 73% and ensured consistency between online and offline scoring environments.
Tiered Alert Fatigue Prevention
Applied a three-tier alert system (critical/watch/informational) with per-CSM throttling to prevent alert fatigue from killing adoption.
Results
What We Delivered
Solution Blueprint
How It All Fits Together
- Kafka event streaming
- Flink aggregations
- Feature store (Redis + PostgreSQL)
- Churn scoring model
- Upsell propensity model
- Anomaly detection
- React executive dashboard
- WebSocket live updates
- Slack / email alert delivery
Lessons Learned
What We Improved
Start With One Model
We shipped churn scoring first, validated it with the CS team for 4 weeks before adding upsell — adoption would have stalled with too much change at once.
Explainability Is Not Optional
CSMs ignored high-risk alerts until we added the top 3 driving factors. Once they could see WHY an account was at risk, engagement with alerts tripled.
Data Quality Before Model Quality
The first 3 weeks were entirely data cleaning. Garbage input features produce confidently wrong predictions — fixing the pipeline was more impactful than model tuning.
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