1. Map the labels and handoffs you actually need
Start by listing the real email types you get and the person or team who must action each one. Keep labels short (6 or fewer): for example "urgent-support", "billing", "booking", "sales-enquiry", "feedback", "other". For each label write one sentence: who owns it, expected SLA (e.g. 4 hours), and the minimum info needed to close the ticket.
Run a quick 30–60 minute workshop with whoever answers email. Use three sample emails per label from the last week to check the labels make sense in practice. This prevents over‑fitting your classifier to imaginary categories and keeps routing operationally useful.
2. Choose simple rules + a conservative classifier and add CRM flags
Use a rule‑first approach: add obvious rules (from-address, subject keywords, common phrasing) and then layer a lightweight classifier where rules can’t decide. You can implement this with Zapier/Make webhooks, a small serverless function, or native features in HubSpot, Salesforce, Marketo or Pardot — the operational principle is the same: conservative automation, clear provenance, and fail‑safe human review.
Use one short bullet list in your CRM for implementation fields and thresholds:
- email_class (dropdown: urgent-support, billing, booking, sales-enquiry, feedback, other)
- email_confidence (number 0.00–1.00)
- email_processing_state (pending, auto-routed, under-review, completed)
- email_provenance (text: rule-kit-v1 or classifier-v1-2026-07-26)
- thresholds: auto-route if confidence >= 0.90; request human review if confidence < 0.60; suggest but hold if 0.60–0.90
Always anonymise or redact PII before sending content to any third‑party classifier or AI service: strip full names, account numbers, sort codes, postcodes and email addresses where possible, or replace digits with X. Keep a processing timestamp and provenance tag so you can trace why a decision happened.
3. Build a tiny human‑review queue, test fast and add a rollback plan
Create a human‑review queue in your CRM or a shared board filtered on email_processing_state = under-review or email_confidence < 0.60. Make the queue one page and visible: reviewer, time received, suggested label, and a one‑line reason for low confidence. Keep decisions quick — reviewer applies final email_class and sets email_processing_state = completed.
Week‑one tests (practical, repeatable): run the system on 50 recent emails. Sample 20 high‑confidence auto‑routes and confirm how many are correct; aim for >90% precision before widening auto‑route. Sample 20 low‑confidence items to check whether the human queue is catching the real exceptions. Track the daily misroute count and time saved versus manual routing.
Finally, add simple monitoring and a one‑page rollback: a toggle to pause routing, a timestamped snapshot (CSV) of recent automated changes, and a short drill: pause automation -> revert the last 24 automated routes -> notify owners. Review metrics weekly for two weeks, then relax thresholds slowly if accuracy holds. If you want a hand mapping labels, setting the flags or running the first‑week tests, Optira can help.