Ecommerce Support Automation Checklist

Export all support tickets from the last 90 days from your helpdesk or inbox. If you do not have clean ticket data, manually code the most recent 200 conversations and note that the sample is directional.

Categorize by the primary customer question, not only the agent tag. Add columns for channel, order state, product category, resolution type, policy source, first response time, full resolution time, escalation, refund involved, and whether human judgment was needed.

Identify the top categories by volume and risk. A high-volume, low-risk factual category is a good automation candidate.

A lower-volume category involving chargebacks, fraud, legal threats, or high-value refunds should stay human-led. Do not proceed until you have a baseline, a sample transcript set, and a written list of categories you will not automate.

Write or rewrite help center articles for every top ticket category. One article per question.

Title articles with customer phrasing and include the policy owner, effective date, exact next step, and escalation path. Add help center links where the question forms: order confirmation, shipping notification, product page, customer account, returns page, and checkout policy links.

Set up or improve self-service order tracking and returns initiation. Then QA the foundation from a customer's phone: search the help center using the same phrases from your audit, click every link in every transactional email, start a return, check tracking, and confirm the customer can recover if the order number or email is entered incorrectly.

Do not proceed until the self-service path works without agent knowledge.

Document requirements before demos. Required channels should reflect actual customer behavior, not a vendor's channel list.

Record ecommerce platform, store count, order volume, support volume, required languages, return workflow, data residency constraints, and must-have integrations. For every shortlisted tool, create a source-backed evidence row: official pricing page checked, integration docs checked, requested Shopify scopes or WooCommerce endpoints checked, data retention or security page checked, and demo evidence captured.

During demos, bring real scenarios from your audit and require a live order lookup or staging-store equivalent. Do not accept a slide deck, marketplace badge, or integration logo as proof of workflow depth.

Confirm pricing at projected conversation volume and ask what happens when usage exceeds plan limits.

Connect the tool to your ecommerce platform with least-privilege access. For Shopify, review requested scopes against the workflows you are enabling.

For WooCommerce, create a dedicated REST API key and start read-only unless write actions are explicitly in scope. Upload approved knowledge sources and maintain a source register with owner and review date.

Build a test matrix before configuring the widget: order lookup, guest order, multiple shipments, missing tracking, return outside window, excluded product, product variation availability, refund status, angry customer, human request, and out-of-scope legal or fraud question. Each test should record expected answer, actual answer, source used, data accessed, escalation result, and pass or fail.

Do not go live until critical tests pass and non-critical failures have an owner.

Enable AI automation for one query type on one channel. Order status on web chat is usually the cleanest first pilot if the audit supports it.

Announce the launch internally and show agents where escalations appear, what context the AI passes, and how to flag bad answers. Launch during business hours first.

Review every conversation for the first three days, then move to sampled QA only after there are no critical errors. Critical errors include wrong order data, leaked private data, invented policy, refusal to hand off after a human request, or executing an action without confirmation.

Keep scope narrow for at least one full week before adding a second query type. The launch goal is not maximum containment.

It is stable, correct resolution inside a clearly defined boundary.

Track metrics weekly for the first 90 days, but read them with caveats. Automation rate should rise only inside approved workflows.

CSAT should be separated into AI-resolved, AI-escalated, and human-only. Escalation rate should be reviewed by reason, not treated as good or bad on its own.

Resolution time should exclude abandoned conversations and bot waiting time. Ticket volume by category should be normalized against order volume and annotated for sale periods, carrier delays, stockouts, policy changes, and channel changes.

Weekly actions: review failed AI responses, update knowledge content, adjust escalation rules, and run the regression test pack. Monthly actions: audit permissions, pricing/usage, source freshness, and the top new ticket categories.

Quarterly actions: compare against the original baseline and decide whether to expand, pause, or narrow automation.