Case Study — AtlasIQ

Revenue Intelligence Platform

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

34%

Net Revenue Retention Increase

58%

Faster Pipeline Visibility

$12M

Upsell Revenue Identified

92%

Churn Prediction Accuracy

Solution Blueprint

How It All Fits Together

Data Layer

Kafka event streaming
Flink aggregations
Feature store (Redis + PostgreSQL)

AI Layer

Churn scoring model
Upsell propensity model
Anomaly detection

Application Layer

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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