Support

Support automation should reduce repetitive tickets without hiding the human handoff. This guide covers ticket fit, escalation rules, quality metrics, and an 8-agent rollout.

A support bot can appear efficient while repeat contacts and bad handoffs erase the savings. Define verified deflection, calculate fully loaded cost per resolution, set quality stop rules, and build an honest ROI case.

A failed renewal needs more than another email. Learn how to coordinate eligible retries, make card updates reach the right subscription, hand exceptions to support, and measure recovered cash without double counting.

AI resolution claims can overstate the work your team avoided. Use a randomized holdout, reconcile repeat contacts across channels, and compare human effort before turning ticket deflection into an ROI estimate.

Refund tickets can reveal weak listings, damaged packaging, and product gaps. Use an item-level return taxonomy to separate reports from causes, assign a fix, and compare mature shipment cohorts before claiming savings.

Decide when info@ needs a help desk, what a shared inbox can still handle, and which work to move first. Use an ownership check, a seven-step cutover, current USD pricing, and a clearly labeled planning example.

An outdated AI knowledge base quietly teaches your support bot the wrong rules. This guide shows small teams how to run knowledge base QA: assign owners, set review cadences, test answers weekly, and catch policy drift before customers do.

First-come-first-served queues make agents answer easy tickets while churn-risk customers wait. Score every ticket on intent, urgency, and revenue at risk, route by rules, and let AI handle labeling — an SMB playbook with costs and a working matrix.

A practical escalation playbook for teams under 10: a three-tier escalation matrix with named owners, time-based auto-escalation timers, and a short owner-alert list — plus real cost data showing escalated tickets run 3-5x the price of tier-1 fixes.