Support ticket classification and draft response

Demonstration, not a client case study E-commerce Customer service Advanced · 4 credits

Before

A small support team handles several hundred tickets a week in Zendesk. Most are order-status, returns and product questions, but they arrive mixed with complaints and supplier emails. Agents spend the first minutes of every ticket working out what it is, opening Shopify to find the order and searching the help centre for the right policy text.

Trigger

New ticket in Zendesk or Freshdesk

Connected systems: Zendesk or Freshdesk, Shopify, knowledge base, Slack

Automated steps

Classify intent (order status, return, product question, complaint, other), urgency and language (AI step, confidence threshold)
Find the customer and the most recent order in Shopify by email or order number
Retrieve the relevant help-centre articles and policy text from the knowledge base
Draft a reply that quotes the real order status and cites the articles used (AI step)
Tag and prioritise the ticket; assign to the right queue
Present the draft with sources and confidence to the agent inside the ticket
On approval, send the reply and log the classification, sources and decision
Escalate complaints, low-confidence cases and anything mentioning legal terms to a senior agent via Slack

Human checkpoint

The agent sees the classification, the matched order, the draft and the articles it was based on. They send, edit or reject. Complaints and low-confidence cases skip the draft and go to a senior agent. No reply is sent to a customer without an agent's approval.

Output

Every ticket tagged, prioritised and routed within a minute of arrival; routine tickets arrive with an approved-in-one-click draft; complaints reach a senior person immediately; a log of classifications and sources for quality review.

What is measured

First-response time
Handling time per routine ticket
Escalation rate and reasons
Draft acceptance rate (sent unchanged, edited, rejected)
Customer satisfaction on automated-draft tickets versus manual

Risks and limitations

Drafts can be fluent and wrong; the agent must check the order details, not just the tone
Knowledge base must be current or the draft cites outdated policy
Customer data flows to a model provider; data minimisation and the provider's terms must be reviewed
High volume makes model and platform costs material; cost caps are required
Advanced scope: retrieval, three systems and volume mean more testing and maintenance than a standard workflow

Demonstration using sample data — not a client case study

Walkthrough

A customer writes: “Ordered two weeks ago, still nothing, order 48213. Can I cancel?” The workflow classifies the ticket as order status with a cancellation intent, medium urgency, English. It finds order 48213 in Shopify, sees it shipped four days ago and has a tracking number, and retrieves the delivery-times and cancellation-policy articles from the help centre.

The draft reply apologises for the wait, gives the actual tracking link and dispatch date, explains that a shipped order cannot be cancelled but can be returned, and links the returns article. The two articles used are listed under the draft. The ticket is tagged, given medium priority and placed in the standard queue with the draft attached.

The agent opens it, checks that the tracking number matches, softens one line and sends. Total handling time is under a minute, compared with the several minutes it took to find the order and the policy text by hand.

Later that day a ticket arrives containing the words “damaged”, “third time” and “consumer rights”. The workflow classifies it as a complaint, skips drafting entirely, and alerts a senior agent in Slack with the order history attached. That is the point of the design: the system speeds up the routine cases and gets out of the way on the ones that need a person’s full attention.

Next step

Start with one workflow worth fixing

Answer nine short questions and get an honest read on whether your process is a good automation candidate — and which package fits. No sales call required.

Run the free Opportunity Scan

Book a 20-minute fit call