Track Early Warning Metrics

Track Early Warning Metrics

What this is for

What you get

• A tailored set of high-signal early warning metrics

• Thresholds/triggers for escalation

• Mapping from signal to action

• A lightweight monitoring cadence to surface drift fast

Core logic

Step-by-step

1. Identify risk domains

Common areas: revenue, customer health, acquisition efficiency, cost structure, operations, product/offer performance, competitive pressure, and team execution.

2. Select 1–2 early-warning metrics per domain

Examples:

• Revenue: Weekly revenue vs. forecast deviation, concentration drift (top customer share increasing)
• Customer: Drop in repeat rate, rising support tickets per customer, NPS decline
• Acquisition: CAC creeping up, conversion rate slipping, new lead quality drop
• Unit Economics: LTV:CAC ratio shrinking, margin compression before price change
• Operations: Cycle time increases, backlog growth, fulfillment error spike
• Product/Offer: Increased refund/return rate, feature usage decay, offer fatigue (declining engagement)
• Competitive/Market: Competitor pricing changes, share-of-voice shifts, inbound customer objections citing alternatives
• Team Execution: Missed milestone rate rising, decision delays, owner overload signals

3. Define healthy baselines and thresholds

Use historical data to set “normal” ranges. Establish trigger thresholds (e.g., conversion rate down 10% for two consecutive weeks, CAC up 15%, repeat purchase interval lengthens by 20%).

4. Attach responses

For each metric and trigger, define the immediate action:

• Triage (investigate cause)
• Short-term remedy (e.g., temporarily pull spend, reroute fulfillment)
• Escalation (bring in leadership, pause new commitments)
• Preventive adjustment (e.g., tighten onboarding, add buffer capacity)

5. Build a simple dashboard or pulse check

Surface these metrics weekly (or more frequently for fast-moving areas) with clear color-coded status: green (normal), yellow (warning), red (action required).

6. Review and refine

After incidents or near-misses, backfill: did the early warning metrics flash? Adjust thresholds or add missing signals.

Decision thresholds / guardrails

• Warning persists beyond tolerance window (e.g., signal in yellow for >2 cycles) → Escalate to root-cause investigation.

• Multiple early warnings align (e.g., rising CAC + falling conversion + higher refund rate) → Treat as compound risk; activate broader contingency plan.

• Metric spikes without action → Audit response process; ensure ownership and accountability.

• Signal noise too high → Recalibrate or replace with higher-precision indicator to avoid alert fatigue.

Examples

• E-commerce: Cart abandonment rate increases 12% while traffic is stable—trigger checkout copy and UX review before revenue drops.

• SaaS: Trial-to-paid conversion falls for three weeks; early warning prompts product onboarding audit, preventing churn wave.

• Service: Average time to deliver client value creeps up—early flag leads to process simplification before delivery delays damage reputation.

• Marketing: Paid channel CAC drifts up 20%; early alert pauses scaled spend and revalidates creative/targeting.

Thinking checks

• Do you have a small set of leading indicators for each critical risk area?

• Are thresholds defined so deviations aren’t ignored or overreacted to?

• Is ownership and response mapped for each warning?

• Do you learn from past slips by tuning your warnings?

What to track (minimum)

• Signal status trend (green/yellow/red over time)

• Trigger events and response time

• Incidents prevented vs. blind spots discovered

• Threshold accuracy (false positives/negatives)