Arclight Metrics

Support triage and renewal-risk tracking managed by manual tagging.

Fictional example: illustrative composite, not an anonymized client case.

Timeline

6 weeks to production readiness.

Value

Projected annual impact: $224k protected renewals and labor leverage.

Challenge

Customer teams spent too much time sorting tickets and building renewal narratives manually, so high-risk accounts were often identified too late for meaningful intervention.

Scope Defined in Blueprint

Classifier layer for inbound ticket intent and urgency.
Risk scoring model combining support, usage, and billing signals.
CRM task creation with account-specific intervention playbooks.
Executive dashboard with weekly SLA and churn-risk trends.

Automation Delivered

AI-assisted ticket classification with confidence scoring and fallback rules.
Account health signal aggregation from support, product usage, and billing.
Renewal-risk alerts pushed to CRM tasks with playbook suggestions.
Leadership dashboard for weekly SLA performance and risk pipeline.

5-Step Delivery Breakdown

Step 1

Discovery & Scope Lock

Define support taxonomy and baseline SLA/churn-signal delays.

Output: Approved metric tree for triage quality and risk response.

Step 2

Solution Blueprint

Specify model thresholds, CRM actions, and governance controls.

Output: Blueprint covering confidence rules and manual override path.

Step 3

Build + Validate

Train and validate classifier against historical support tickets.

Output: UAT sign-off on routing precision and alert usefulness.

Step 4

Production Cutover + Hypercare

Enable live triage with supervised rollback and metric watch.

Output: Stabilized production model with tuning log.

Step 5

Handover + Continuous Improvement

Formalize retraining cadence and account-health governance.

Output: Owned operating model with monthly model-review protocol.

Outcome Metrics

SLA breaches

28% → 6%

-79%

CSM prep time

9h/week → 2.5h/week

-72%

Risk detection lead time

6 days → same day

+6 days

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