Pulse
Agentic segment drift and reach intelligence — continuous monitoring with intelligent explanations when audiences degrade.
Anomaly DetectionSegment DriftAgentic DiagnosisBullMQStreaming IngestionObservability
Context
Segments silently degrade over time — data changes, user behavior shifts, upstream schema changes, product feature changes. Teams only notice when CTR drops, delivery fails, or someone complains. This is reactive, not intelligent.
Pulse continuously monitors segments and answers: "Is this segment still behaving the way the business expects?" It's observability for audiences.
Problem
- Segments silently erode due to upstream data changes, behavior shifts, and schema evolution.
- Teams only discover degradation after campaign underperformance — reactive, not intelligent.
- No tooling exists to continuously monitor audience health with automated diagnosis.
What I Built
- Segment Snapshotting — daily size tracking, attribute distributions, entry/exit velocity.
- Drift Detection — sudden size changes, attribute distribution skew, conversion rate anomalies, inactivity creep.
- Agentic Diagnosis — LLM-powered explanation of what changed, why it likely changed, and what to do next.
- Recommendations Engine — adjust rule thresholds, split into sub-segments, archive unused, create alerting rules.
- Alert Channels — Slack, Email, Teams notifications.
Guardrails & Constraints
- Statistical baselines prevent false positives — drift detection uses adaptive thresholds.
- Explanations are grounded in observable data changes, not speculation.
- Alert fatigue mitigation through severity scoring and batching.
Outcomes
- Caught 42% silent segment erosion caused by a schema change in demo scenario.
- Shifted segment monitoring from reactive to proactive.
What's Next
- Predictive drift modeling using historical patterns.
- Cross-segment correlation analysis.
- Integration with CDP platforms for automated remediation.