How to Automate Shopify FAQs Without Automating the Wrong Questions

1. Start with repeatable information requests

Review recent support conversations and list the questions customers ask repeatedly. Strong starting points include:

• shipping destinations and delivery estimates
• return windows and exchange rules
• size-guide and product-care questions
• payment methods and discount rules
• opening hours and contact details

Choose questions with a documented answer that is consistent for most customers. Do not begin with requests that require a judgment, account change, or refund decision.

2. Give the chatbot one approved source for each answer

Remove contradictions between your policy pages, product descriptions, FAQ page, and support macros. If two sources disagree, an AI chatbot cannot know which one your business considers current.

Write direct answers with the condition, timeframe, and next step together. For example, state the return window, the item conditions, and where the customer should start the return.

3. Treat order information differently from general FAQs

A shipping-policy question is general information. A request for the status of a specific order uses customer data and needs identity verification.

Keep these flows separate. For order tracking, Farnsla asks for the customer email, verifies identity, and then checks Shopify for live fulfillment and tracking information.

4. Define questions that should always reach a person

Set a clear human boundary before launch. Typical escalation categories include:

• refund and replacement decisions
• address or order changes
• policy exceptions
• complaints or damaged deliveries
• questions with missing or conflicting source information

The chatbot should say what it can do next instead of inventing an answer. Farnsla can capture the conversation for follow-up, and Shopify Direct Messaging can support a human handoff on connected channels.

5. Test real wording and keep answers current

Test each topic with short questions, misspellings, follow-up questions, and requests that fall just outside the policy. Confirm that the answer stays grounded and the escalation path remains visible.

Review unanswered conversations after launch. Update the approved source whenever a policy changes, rather than patching individual chatbot replies.