Ecommerce Chatbot Examples for Australian Retailers

Good ecommerce chatbot examples show a customer getting a useful answer from the store’s product data, delivery policy or order record. The conversation must also show what happens when the answer needs a person. You can use the examples below to show your founder what a retail chatbot must do before choosing a tool.

The unnamed store conversations are hypothetical. Their products, prices and policies are sample inputs for an Australian store, not records of real customer chats. The Adore Beauty exchange is from its public support chat.

Product Questions and Recommendations

A product answer must connect the customer’s needs to details you actually hold in the catalogue. A plausible answer is useless if it recommends the wrong size or an unavailable variant.

In this hypothetical conversation, a store sells two running tops. All product prices are AUD and include GST.

Customer: I’m usually a women’s size 12. Which top will fit a 96 cm bust for running?

Chatbot: The Coast top’s size 12 chart covers a 94 to 98 cm bust. It has a relaxed cut and costs $59. The Track top in size 12 covers the same measurement. It has a closer cut and costs $69. Both are in stock in navy. Would you prefer a loose or close fit?

Customer: Loose, please.

Chatbot: The Coast top matches that preference. Its size chart puts your measurement in size 12. Fit also depends on your preferred amount of room.

For that answer, the store supplied each product’s size chart and cut. The feed also held the variant’s colour, current stock and GST-inclusive price. A generic label such as “true to size” wouldn’t explain the recommendation.

Keep product advice separate from a guarantee of fit. If a size chart is missing, the chatbot must ask staff rather than invent a measurement.

For a local example, Adore Beauty’s public support site has an assistant named ABi. Its regional delivery answer shows the same discipline: a specific question gets a specific store answer. The captured exchange appears in the next section.

Delivery, Returns and Policy Answers

Delivery answers must distinguish dispatch time from time in transit. A regional postcode also needs the right service estimate, not a metropolitan promise.

This hypothetical store sends regional orders with Australia Post. Its sample policy estimates five to eight business days in transit after dispatch.

Customer: I’m in Albany, postcode 6330. Will standard delivery arrive in two days?

Chatbot: Our delivery policy says “Regional standard delivery takes 5 to 8 business days after dispatch.” That estimate applies to postcode 6330. Two-day arrival isn’t promised.

Customer: Can I return the top if I change my mind?

Chatbot: Our policy says “Change-of-mind returns are accepted within 30 days of receipt for unworn items with tags attached.” Return postage is your responsibility. A product problem is assessed separately under your consumer rights.

The quotation must match the store’s policy, including eligibility and who pays postage. Don’t turn an estimate into a guarantee or remove exclusions to shorten the answer. Our guide to change-of-mind returns explains that distinction in more detail.

The ACCC’s refund guidance distinguishes changing your mind from a product failing consumer guarantees. A business doesn’t have to accept a change-of-mind return unless it promises to do so. Its voluntary policy cannot remove rights for faulty goods.

An Australian retailer’s public chat

We asked Adore Beauty’s ABi a delivery question on its public support site. This is the exchange, with the answer reproduced verbatim.

Question: How long does standard delivery take to regional Australia?

ABi: Standard delivery to regional and remote Australia takes 3 to 11 business days after your order has been dispatched.

Adore Beauty’s shipping information lists the same standard-delivery range for regional and remote areas. The answer preserves the dispatch condition. That small phrase stops a customer counting transit days from the moment they pay.

Order-Status Conversations After the Sale

An order-status chatbot retrieves the customer’s order and tracking record after the required identity check. It must give the actual carrier status, not predict a parcel’s location from elapsed time.

This hypothetical customer is signed in to the store’s account. The order number and carrier status are fictional.

Customer: Where’s order 4826?

Chatbot: Order 4826 has been dispatched with Australia Post. The latest tracking event is “In transit”. Open the tracking link on your order card to see the carrier’s updates.

Customer: Do I need to email support?

Chatbot: You can follow the parcel from this chat. If tracking stops updating or you need help with the delivery, I can pass the conversation to our team.

An unsigned customer needs the store’s approved verification route before receiving private order details. An order number alone must not expose another customer’s address or purchases. Keep the tracking link attached to the retrieved order so the customer doesn’t have to copy a number into an email.

Handing Over to a Person

A complaint needs a handover when a person must investigate or decide a remedy. The customer service chatbot example below carries the customer’s issue into the staff inbox.

This hypothetical conversation continues after order 4826 arrives.

Customer: My top arrived with a torn seam. I want someone to sort it out.

Chatbot: I’ll pass this to our support team with the order details and your description of the torn seam. Would you like to attach a photo?

Customer: Yes. I’ve uploaded one. Please replace it.

Chatbot: I’ve included the photo and your replacement request. Our team will assess the problem and reply through this conversation.

The staff member receives this exact hypothetical summary:

Order: 4826. Customer identity: verified through signed-in account. Item: Coast running top, navy, size 12. Issue: customer reports a torn seam on arrival. Evidence: one uploaded photo. Requested remedy: replacement. Action taken: conversation handed to support. Refund or replacement authorised: none.

The summary records what the customer reported and what the chatbot actually did. It must not mark a refund approved because the customer asked for one. Preserve the full conversation beside the summary so staff can read the original wording.

Amazon Rufus: A Retailer-Owned Shopping Assistant

Amazon introduced Rufus as a shopping assistant inside the Amazon Shopping app. Amazon now calls the assistant Alexa for Shopping. The Rufus example still explains how product questions can sit beside the products being considered.

Amazon’s Rufus announcement gives the sample question “is this jacket machine washable?” It says product-page answers use listing details, customer reviews and community questions and answers.

A customer asks about washing a jacket. Rufus uses the product’s listing and the customer material attached to it to answer inside the shopping experience. The customer can then ask a follow-up without starting another search.

Amazon also describes using information from across the web. Rufus AI therefore cannot accurately be described as answering only from Amazon’s catalogue. An Australian store can borrow the product-page context while setting a narrower rule: product recommendations come from its own catalogue and policy answers from its own documents.

Walmart Sparky

Walmart Sparky helps customers find and compare products in the Walmart app. Walmart’s Sparky announcement describes review summaries and recommendations for occasions, including celebrations and sporting events.

An occasion gives the assistant requirements that a product name alone would miss. A customer planning a celebration needs suitable items across categories. A review summary then helps explain recurring praise or complaints about a shortlisted product.

Walmart’s 2026 earnings remarks describe Sparky as starting in the US. Sparky is a US Walmart shopping experience, rather than a local Australian storefront assistant. Australian retailers can borrow its occasion-based recommendations without treating Walmart’s fulfilment options as Australian delivery services.

Klarna AI Assistant

Klarna’s app combines cross-store product discovery with payments and purchase support. Its shopping tools let customers compare a product across retailers. That changes the store’s role: your product can appear beside the same item sold elsewhere.

Klarna’s shopping announcement describes an AI shopping lens that identifies items from a photo. Customers use the app’s search and compare tool to compare retailers and prices for those items.

Keep that shopping function distinct from the Klarna AI assistant for customer service. Klarna’s company filing describes the in-app assistant handling payment questions, refunds and returns. These are related experiences, but a payment-support chat isn’t evidence of a product-search conversation.

A cross-store assistant helps a customer decide which retailer to buy from. A chatbot on your own site helps the customer choose within your range and understand your policies. The merchant completes the sale, so accurate stock and delivery details still need to come from that merchant.

Ecommerce Chatbot Templates You Can Adapt

Use chatbot templates to define the information an answer needs. Replace each sample with your store’s actual wording before it goes live. These are hypothetical messages, not claims about any named retailer.

• Greeting: “Hi, I can help you choose a product or check a store policy. What are you looking for?”
• Sizing: “Which product are you considering? I’ll use its size chart to compare your measurements with the available sizes.”
• Delivery: “What’s your delivery postcode? I’ll show the estimate for that service and explain when the delivery period starts.”
• Handover: “I’ll send our team this conversation and the details you’ve already shared. You can continue here when they reply.”

A scripted chatbot flow follows branches you write in advance. This short outline shows a greeting leading to a product question or handover:

1. Greet the customer and ask what they need.
2. If they choose product advice, ask for the product and their requirements.
3. If the catalogue has the required detail, answer and suggest two suitable options.
4. If the detail is missing or the customer asks for staff, attach the conversation and hand it over.

An AI chatbot can handle freely worded questions within the same boundaries. It must still use the right source and stop when a person needs to decide.

In a chatbot demo, try a size question with a follow-up that changes the colour. Ask about a regional postcode, then request a delivery guarantee the policy doesn’t make. Finish with an order question and a complaint. The expected result is a sourced answer or a useful handover, with the earlier context preserved.

Use our chatbot testing guide for a fuller test set. When you’re ready to compare the tools that run these conversations, see AI chatbots for ecommerce.

Examples That Go Wrong

A chatbot goes wrong when it creates a commercial promise that the store never made. This hypothetical failure invents both a promotion and a delivery deadline.

Customer: Can you take 20% off and get it to Albany tomorrow?

Incorrect chatbot answer: Yes, use SAVE20 for 20% off. Your order will arrive tomorrow.

Corrected answer: There’s no active SAVE20 promotion in our store’s offers. Standard delivery to postcode 6330 takes an estimated 5 to 8 business days after dispatch. I can show the available delivery services before you pay.

The fix is to read discounts from active promotions and delivery estimates from the applicable policy or checkout service. Missing information must lead to a question or handover, not a promise. Run the same request again and confirm that the chatbot refuses the invented offer while giving the supported delivery answer.