Ecommerce Support Automation Checklist

Export all support tickets from the last 90 days from your helpdesk or inbox. If you do not have ticket data, export customer email threads and manually log 200 recent conversations.

Categorize every ticket by the primary customer question. Use categories such as order status, shipping questions, return requests, product questions, payment issues, account access, cancellations, complaints, and other.

Count tickets per category. Calculate percentage of total.

Identify the top three to five categories by volume. For each top category, note whether the resolution is factual and repeatable or requires human judgment.

Document current baseline metrics: average first response time in minutes or hours, average full resolution time, and customer satisfaction score if available. Calculate what percentage of total ticket volume is repetitive and factual.

This number defines your maximum addressable automation scope. Do not proceed to Phase 2 until you have a categorized ticket dataset and documented baselines.

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

Title articles with customer phrasing. Write in plain language with specific timelines and steps.

Add a search function to your help center that handles natural language queries. Add help center links in your store navigation, footer, order confirmation emails, shipping notification emails, product pages, and checkout page.

Set up a self-service order tracking page where customers enter their order number and email to see real-time status. Build or improve the returns portal so customers can initiate returns, print labels, and check return status without contacting support.

Create or update shipping, returns, and privacy policy pages in plain language with exact timelines and processes. Add FAQ sections to product pages covering sizing, materials, care, and availability.

Configure or improve automated email notifications for order confirmation, shipping confirmation, out-for-delivery alert, delivery confirmation, and return window reminder. Do not proceed to Phase 3 until your self-service pages are live and at least two automated email flows are active.

Document your requirements. Required channels: web chat, email, WhatsApp, Messenger, Telegram, LINE.

Which of these do customers actually use? Do not add channels customers do not want.

Ecommerce platform: Shopify, WooCommerce, Magento, BigCommerce, or custom. Note the version.

AI capabilities needed: knowledge retrieval for answering policy and FAQ questions, platform integration for pulling live order and customer data, conversation classification for routing, human handoff with context transfer. Budget range per month.

Volume projection: how many conversations per month do you expect the AI to handle. Assemble a shortlist of three to five tools.

For ecommerce-specific tools, evaluate Gorgias, Re:amaze, Tidio, and Help Scout. For broader AI agent platforms with omnichannel support, evaluate YourGPT, Intercom, Zendesk, and Freshdesk.

Request demos of your top three. During demos, ask for a live demonstration of the integration with your specific ecommerce platform.

Do not accept a slide deck as proof. Verify the tool can pull real-time order data.

Confirm pricing at your projected volume with all required channels included. Check data handling practices and data processing location for GDPR or other regulatory compliance.

Make a selection and sign up for the minimum-tier plan that covers your launch scope.

Connect the tool to your ecommerce platform. For Shopify, install through the Shopify App Store or connect via API.

For WooCommerce, create REST API keys and configure the connection. For other platforms, follow the vendor's integration guide.

Test real-time data access. Run these queries through the AI and verify responses: look up an order by number and confirm it returns correct status, items, and tracking; look up a customer by email and confirm order history is correct; check a product's stock status and confirm accuracy; verify the AI correctly identifies orders outside the return window.

Upload knowledge sources: help center articles, policy pages, product FAQs, size charts, and shipping information. Configure the chat widget: brand colors, greeting message, placement on page, and proactive trigger rules.

Set up routing: which query types the AI handles autonomously, which query types auto-escalate. Configure escalation triggers: customer requests human, negative sentiment detected, query outside scope, high-risk scenarios.

Set the AI's tone and response guidelines. Test the full flow: simulated customer message, AI response, escalation to human agent with context.

Fix issues before proceeding. Do not go live until all test cases pass.

Enable AI automation for one query type on one channel. Order status on web chat is the recommended first automation.

Announce the new AI support capability to your team. Show them exactly how the AI works and where escalations will appear.

Enable the AI during business hours first so human agents are available if something goes wrong. Monitor every AI conversation for the first three days.

Read every response. Fix knowledge gaps and configuration errors immediately.

After three days of stable performance with no critical errors, expand to 24/7 operation if desired. Keep the scope narrow for at least one full week before adding a second query type.

Do not expand to a second channel until the first query type on the first channel has run for two weeks with acceptable accuracy (this phased rollout approach is consistent with Shopify's recommended app deployment practices in the Shopify App Store listing requirements, 2024, which emphasize controlled rollouts and monitoring). During this phase, the primary goal is stability, not coverage.

A small, accurate automation footprint is better than a broad, inaccurate one.

Track these metrics weekly for the first 90 days. Automation rate: the percentage of total conversations the AI resolves without human help.

Target an increasing trend. CSAT for AI-resolved conversations: measure separately from human-handled conversations.

Flag any drop below your pre-automation baseline. Escalation rate: the percentage of AI conversations handed to a human.

Target below 25 percent. If higher, narrow the AI scope or improve knowledge content.

Average resolution time: AI-resolved time versus human-resolved time. The gap should be significant.

Ticket volume by category: compare against your audit baseline. Categories with AI automation should show clear volume reduction.

Monthly actions: review failed AI responses and update knowledge content. Adjust escalation rules based on escalation patterns.

Check for new ticket categories that emerged and decide whether to automate them. Verify the platform integration still works by running test queries.

Quarterly actions: full audit of automation performance against original baselines. Evaluate whether to switch plans or platforms based on usage and costs.

Expand to new channels or query types based on data-driven confidence. Schedule training for human agents on any new automation features or workflows.

Support automation requires continuous attention. Make it part of your weekly support operations routine.