Proactive Alert System: catching problems before customers escalate

Why proactive alerts

Until now, the Howzer pipeline analyzed feedback reactively: a message comes in, gets scored, and results are returned. Pattern detection could surface trends over time, but there was no system that evaluated each individual feedback against real-time conditions and immediately flagged when something needs attention. The Proactive Alert Engine changes that.

By the time you see a pattern in a dashboard, the damage is already done. Alerts need to fire on the first signal, not the hundredth.

How it works

Every feedback signal that enters the pipeline is now evaluated by the Proactive Alert Engine, which runs alongside the existing pattern detection and customer history engines. It checks two categories of rules: per-signal rules (instant checks on individual feedback) and window rules (aggregated checks over sliding time windows).

Per-signal rules

These rules evaluate individual feedback the moment it arrives. Each rule checks specific risk signals and triggers an alert if thresholds are exceeded.

• Churn signal: fires when churn risk > 0.5 and sentiment < -0.3. A customer showing both high churn probability and negative sentiment is at immediate risk of leaving.
• Legal threat: fires when the feedback contains an explicit legal threat flag or legal risk exceeds 0.7. Always classified as crisis severity. The legal team needs to know immediately.
• Critical escalation: fires when the risk tier is "critical". These are the highest-priority cases that need immediate human attention.
• High-risk compound: fires when 3 or more risk components (churn, escalation, legal, reputation, financial) simultaneously exceed moderate thresholds. Multiple moderate risks are often worse than a single high one.

Window rules

Window rules aggregate data over sliding time windows to detect systemic issues that no single feedback would reveal on its own.

• Volume spike: tracks hourly feedback volume against an exponential moving average baseline. Fires when volume exceeds baseline + 2 standard deviations. A sudden flood of complaints often signals a service outage or product issue.
• Sentiment cliff: monitors average sentiment in a 1-hour sliding window. Fires when the window average drops more than 0.4 below the long-term baseline. Something has changed that is making customers significantly unhappier.
• Geographic concentration: detects when 5+ feedbacks with the same root cause cluster within 50km in a 2-hour window. This catches localized incidents like a regional service partner failure or a local outage.

Alert lifecycle

Every alert follows a clear lifecycle: created, acknowledged, resolved. The API provides full management capabilities: list active alerts, filter by type or severity, acknowledge to signal awareness, and resolve to close. Deduplication prevents alert fatigue: the same alert type for the same customer within the cooldown window is suppressed.

API capabilities

• List active alerts with optional type and severity filters.
• View alert counts grouped by type and severity.
• Get full alert detail including recommended action and metadata.
• Acknowledge an alert to signal awareness.
• Resolve an alert to close it.

German-language actions

Every alert includes a recommended action in German, tailored to the alert type. For churn signals, the recommendation is to initiate personal contact. For legal threats, the legal department should be informed before any automated response is sent. These are suggestions, not automations. The human always decides.

What's next

• WebSocket/SSE support for real-time dashboard push (currently REST polling).
• External event correlation: weather events, holidays, and service calendars as context for alert rules.
• Alert routing: auto-assign alerts to teams based on type and severity.
• Historical alert analytics: track resolution times and alert-to-action conversion rates.