5 Inspirational Examples of Chatbots in eCommerce
- E-commerce chatbots take over recurring sales, support and marketing jobs: product recommendations, returns, refund questions and lead qualification.
- Sephora, Snaptravel and Toyota Hong Kong show the first wave of messaging-app bots; Toyota's Messenger bot with live chat hand-off became a long-term project.
- Zalando's assistant, live in all 25 Zalando markets since October 2024, and Amazon's Rufus, now Alexa for Shopping, show the large language model era of shopping assistants.
- In PwC's 2017 Bot.Me report, 27% of consumers were not sure whether their last customer service interaction was with a human or a chatbot.
- A chatbot does not need to be complex to bring results: start with one job, a clear hand-off to humans and a measured test.
An e-commerce chatbot is a program that talks with shoppers in a chat window or a messaging app. It answers questions, recommends products, handles returns and collects leads, at any hour and without a human on the other side. The five examples below show what that looks like in practice: Sephora's advisory bot for Gen Z, Snaptravel's chat-based hotel booking, Toyota Hong Kong's lead generation bot, Zalando's AI fashion assistant and Amazon's AI shopping assistant.
Ray Kurzweil predicted in 2016 that computers would reach human-level language ability by 2029, which means we would be able to have a meaningful conversation with AI that year. Even with the technology of 2017, 27% of consumers in PwC's Bot.Me report were not sure whether their last customer service interaction was with a human or a chatbot.
The first wave grew fast. Facebook Messenger had 100,000 bots in 2017 and passed 300,000 by May 2018. In this post you will learn which support, sales and marketing tasks chatbots can take over, what a multi-brand shopping assistant looks like, and which types of tools you can use to create your own chatbot.
What are the benefits of using chatbots in e-commerce?
Take the first e-commerce use case: placing orders online. If a shopper is not quite sure what they want, or is just looking around, an assistant can be very valuable. Based on a few questions or a short quiz, the chatbot presents the offers that suit their taste best.
What if the products don't meet expectations? A blouse is too big and the pants are too tight. The customer can turn to the chatbot and start the return without any human interaction. A well-optimized return procedure saves a lot of time, and nobody has to wait on the hotline for a consultant.
Now take it to the next stage of the customer journey. The order is returned, but the money is not in the customer's account yet. They could check the FAQ, but on a complex site that takes a while, and the frustration may show up later in the reviews they leave you. With a chatbot, customers do not browse through a wall of text. They ask for the information in a small chat window and get it.
Well-designed chatbots can have a positive effect on the customer experience. The table below sums up the jobs they typically take over.
| Team | Job the chatbot takes over | How to read the result |
|---|---|---|
| Sales | Asks a few questions or runs a quiz, then recommends products | Compare conversion rate and order value for shoppers who used the bot with those who did not |
| Support | Starts returns, answers refund and delivery status questions | Watch how many conversations end without a hand-off to a human, and customer satisfaction after the chat |
| Marketing | Collects preferences and contact details in a conversation | Count qualified leads and how many of them reach sales |
| All teams | Stays available outside working hours | Look at the share of conversations and orders that happen when the team is offline |
How exactly do big brands use chatbots? Let's have a closer look.
What are 5 inspirational examples of chatbots in e-commerce?
| Chatbot | Job | Channel | Era |
|---|---|---|---|
| Sephora | Beauty advice and engagement for Gen Z | Kik | First wave (2016–2017) |
| Snaptravel | Quick hotel search and booking | SMS, WhatsApp, Messenger | First wave (from 2016) |
| Toyota Hong Kong | Lead generation with live chat hand-off | Facebook Messenger | First wave, kept as a long-term project |
| Zalando Assistant | Fashion advice across many brands | Zalando app and website | LLM era (from 2023) |
| Amazon (Rufus, now Alexa for Shopping) | Product questions, comparisons, recommendations | Amazon app and website | LLM era (from 2024) |
1. Sephora: an advisory chatbot that engaged Gen Z
How do you reach Generation Z? Sephora asked the same question. Gen Z sees email as an outdated way to communicate: a 2016 Forbes report on Gen Z found they were 3 times more likely to open a chat message pushed out by a notification.
If you have ever visited a Sephora store, online or offline, you have probably been overwhelmed by the variety of products. This is where Sephora's chatbot came in. The brand launched it on Kik, one of the top messaging apps among young customers at the time. The bot helped shoppers find their way through the assortment with advice, photos and tutorials. Its main business achievement was a large increase in teen engagement, thanks to an experience that felt unique.
"We were able to engage in real time with our Gen Z customers, helping her learn, get inspired and play with beauty, for an important moment… With Kik, we are able to participate in her social and mobile experience in a way that's natural and useful." Deborah Yeh, then SVP Marketing & Brand at Sephora
Best for: brands with a large assortment and a young audience that lives in messaging apps. Why it matters: the bot met customers in the channel they already used, instead of asking them to come to the brand.
2. Snaptravel: quick and easy trip planning over chat
Planning a trip takes time. There are hundreds of hotel and flight offers to compare on different sites. Snaptravel, launched in 2016, made the process much simpler with a chatbot that worked over SMS, WhatsApp and Facebook Messenger and found the hotel offer that fit your needs.
You gave the bot your budget, the city and your preferences, and it searched hundreds of offers and returned the most suitable ones. When one of my office colleagues tested it, it took him 20 seconds to find accommodation in London. Users want to save time at every step, and here the only thing they had to do was type a few phrases.
In October 2022 the company rebranded as Super.com and grew into a broader app, with hotel deals still at its core. The lesson from the chat-only version still holds: keep the conversation short and ask only what you need to give a good answer.
Best for: categories where customers compare many similar offers. Why it matters: the chatbot replaced a long comparison session with a few messages.
3. Toyota Hong Kong: a lead generation chatbot
Toyota's Hong Kong branch did something unconventional in the automotive industry. Together with the chatbot agency Sanuker, it created a Facebook Messenger chatbot to market the then-new Toyota Sienta.
The bot collected the preferences of each person over a friendly conversation, similar to a quiz. Based on those details, it assigned users to the right sales team members on live chat, and those conversations led to brochure requests and test drive bookings.
The hybrid of chatbot and live sales chat worked so well that, according to Sanuker, Toyota Hong Kong turned it into a long-term project that handles all enquiries coming through its Facebook page. When the bot identifies an interest, such as a test drive or a price plan, it creates a ticket and alerts the sales team.
Best for: high-consideration purchases where a human still closes the sale. Why it matters: the bot does the qualifying, so sales people spend their time on conversations that are ready to move forward.
4. Zalando Assistant: a multi-brand fashion shopping assistant
The original version of this post featured Masha.ai, a startup Messenger bot that brought hundreds of brands into one chat window, like a shopping mall. Masha.ai is no longer online, but the idea of a multi-brand shopping assistant is now live at scale.
Zalando launched a fashion assistant powered by ChatGPT in 2023, first in Germany, Austria, the UK and Ireland. Customers describe what they need in their own words, for example what to wear to a wedding in Santorini in July. The assistant understands that this is a formal event in hot weather, explains its advice and suggests products, and the customer can refine the results in the same conversation. In October 2024 Zalando brought the assistant to all 25 of its markets, in local languages, for logged-in customers.
Best for: retailers with a very large, multi-brand assortment. Why it matters: shoppers search by occasion and need, not by product name, and the assistant turns that into a product list.
5. Amazon: from Rufus to Alexa for Shopping
The original post also featured Nexc, an electronics shopping bot that asked about your usage, operating system, screen and budget before it recommended a laptop. Nexc is no longer online either. Today the clearest example of that job, at the largest scale, is Amazon's AI shopping assistant.
Amazon introduced Rufus in February 2024 as a conversational shopping assistant trained on its product catalog. Shoppers can ask what to consider when they buy a product, compare options and get recommendations, then ask follow-up questions in the same chat. In May 2026 Amazon merged Rufus into Alexa for Shopping, one assistant that works across the Amazon app, the website and Echo Show devices in the US.
Best for: technical categories where shoppers need help to compare specifications. Why it matters: the assistant answers the questions a good sales associate would ask and answer, inside the store.
How have large language models changed e-commerce chatbots?
In 2020 Facebook removed the Discover tab from Messenger and demoted chatbots. Many of the first-wave bots, including two of the startups in the original version of this post, did not survive. The examples that lasted solved one clear job: Toyota's lead qualification and Snaptravel's fast hotel search.
The Zalando and Amazon assistants show the second wave. They work inside the store rather than in a separate messaging app, and they handle open questions such as "what should I wear to…" instead of fixed menus. Three things did not change:
- The bot is only as good as its data. Product information, stock, prices and return policies must be correct, or the bot gives wrong answers with confidence.
- Customers still want a human for some cases. Toyota's hand-off from bot to sales team is still a good model.
- You still have to measure the result. A chatbot changes the shopping experience, so test it like any other change to your site.
How do you create a chatbot for your e-commerce store?
The examples in this post are useful for inspiration: the questions they ask, the flows they use and the business value they bring. But they are based on complex logic or advanced AI. The good news is that you can create your own chatbot without external experts or developers. There are three types of tools:
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Chatbot development platformsThese applications exist only to create chatbots. You build the logic of your bot there, then publish it where the conversation happens: Facebook Messenger, Instagram, WhatsApp or your website. Examples include Chatfuel and Botsify.
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Marketing automation platformsIf you already use a marketing automation platform, first check whether it includes a chatbot builder. Many do, for example HubSpot and User.com. These bots run in the chat widget on your website and, in some cases, connect to popular messaging apps.
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A combination of bothIf you use a marketing automation platform but want a chatbot that works across apps and keeps all important data in one place, connect a chatbot development platform to your marketing automation tool with an integration tool such as Zapier.
Whichever tool you choose, keep the flow clear, quick and convenient. Start with one job, define the questions the bot must answer, decide when it hands off to a human, and measure the result before you add more. The data a bot collects, such as preferences and purchase intent, is most useful when you connect it to what you already know about each customer. Nexus by Omniconvert groups customers into RFM segments, from Soulmates and Loyal customers to About-to-Dump and Breakups, so you can see which customers your chatbot is talking to and what each segment is worth.
To learn more about analyzing what customers say in chat, read our guide to conversation analytics.
Frequently Asked Questions
An e-commerce chatbot is a program that talks with shoppers in a chat window on a website or in a messaging app. It can answer product questions, recommend products, start returns, give order and refund status and collect leads, at any hour and without a human agent.
Chatbots save time for customers and employees and can increase revenue at a relatively low additional cost. They help undecided shoppers find the right product, let customers solve return and refund questions on their own, qualify leads for the sales team and keep serving customers at night, on public holidays and in busy periods.
Five well-known examples are Sephora's Kik bot, which advised Gen Z shoppers; Snaptravel, which booked hotels over SMS and Messenger; Toyota Hong Kong's Messenger bot, which qualified leads for live sales agents; the Zalando Assistant, which gives fashion advice across many brands; and Amazon's AI shopping assistant, first launched as Rufus and now part of Alexa for Shopping.
Yes, but less than during the 2017 and 2018 peak, when Messenger passed 300,000 bots. In 2020 Facebook removed the Discover tab from Messenger and demoted chatbots, and many startup bots closed. Bots that solve one clear job, such as Toyota Hong Kong's lead qualification bot, are still in use.
First-wave chatbots followed decision trees with buttons and fixed paths. Chatbots built on large language models, such as the Zalando Assistant, understand questions in the customer's own words and answer from the product catalog. They still depend on correct product data, a clear hand-off to humans and measured results.
Start with one job, such as product recommendations or return questions. Then choose a chatbot development platform such as Chatfuel or Botsify, use the chatbot module of your marketing automation platform, or connect both with an integration tool such as Zapier. Define when the bot hands off to a human, and measure the result before you add more.
No, not for most use cases. Chatbot development platforms and the drag-and-drop chatbot builders in marketing automation tools let you create a simple bot without developers. Complex bots that connect to live inventory, orders or a large language model may need technical help.
Measure the job you gave the bot. For sales, compare conversion rate and order value for shoppers who used it with those who did not. For support, track how many conversations end without a human hand-off and customer satisfaction after the chat. For lead generation, count qualified leads that reach sales. An A/B test gives the clearest answer.
If you have been thinking about using chatbots in your business, pick one job from the examples above and start there. It does not have to be an advanced chatbot: with a chatbot development platform or the drag-and-drop builder of your marketing automation tool, you can have a simple bot ready quickly. Chatbots take over recurring tasks from your support team and keep selling and serving during nights, public holidays and busy periods. Keep the flow clear, quick and convenient, test different bot types as your business grows, and remember that these team members work 24/7 and never ask for a pay raise.
Test whether your chatbot really lifts conversions
Omniconvert Explore lets you A/B test on-site experiences, personalize them for each segment and run targeted surveys to learn what shoppers need. It is built on 70,000+ experiments across 7,000+ websites, with a 23.2% average uplift.