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10 Chatbot Strategies eCommerce Brands Use to Boost Sales In 2023

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Online shopping isn’t just about silent category browsing. It is about customer communication first. Hearing and in-time guiding customers at each step of their journey is key to sales growth. 

Sounds like a task for a 24/7 customer service team, heh? It’s a good thing that a chatbot tool for business can automate part of these processes. 

Moreover, 40% of shoppers are ready to use it. Tommy Hilfiger is one of the many brands that use that knowledge. Its chatbot brings the brand an 87% rate of returning customers. Another case is the Just Eat chatbot, with a 266% conversion rate.

Intrigued? There are more examples in the article! Find out ten chatbot strategies eCommerce brands use to convert customers on websites, messengers, and social media 👇

24/7 assistance on FAQs 

Imagine a never-sleeping support manager answering repeating customer queries around the clock, with no vacation or coffee break. 

It is an automated chatbot. Think about such an employee when building your customer service 😉

Launch it and:

  • Provide visitors with instant self-service at any time. 
  • Save budget by focusing managers’ time on solving high-priority issues.

Example from the Hitee chat👇

In addition to simple questions, this FAQ chatbot can provide customers with information about insurance options.

Notify consumers about new products

This case is popular in fashion and luxury retail. Instead of mainstream emails, they talk about new collections in messengers. And for a reason! For instance, compared to the 25% Open Rate of email, Facebook has an impressive 80%.  

Thus, when the new collection is live, its subscribers see +1 in DMs. It is a company chatbot telling customers about new items in stock. Casually and cheerfully, it engages them to browse for more pieces directly in a messenger without switching to a website. 

Example from Burberry👇

This luxury retail brand implemented a Facebook Messenger chatbot to introduce customers to their latest collection of bags.

A chatbot by Burberry on MessengerImage source.

Recommend products

The ability to generate endless chatbot ideas makes it an ideal tool for businesses. And this scenario is a good confirmation of that. Launch a chatbot that will define customers’ preferences in an up to five-question dialog. 

Examples of product recommendations from Lego👇

The company launched Ralph the Gift Bot to help its customers choose the perfect gift: 

Process orders 

Allowing customers to order in a chatbot is a great idea to save your managers time and follow the introverts’ desire to avoid direct communication. 

Here is how it works. Customers choose an item and place an order without leaving a chat. For this, people share personal details like name, telephone number, and billing address, and a chatbot will route them to the checkout page on a company website. 

An example from the 1-800-Flowers store

In addition to the gift choice, its users can also submit their order information. A chatbot is like your inbound lead conversion administrator who collects recipients’ addresses, names, and phone numbers, billing addresses and only then routes them to a website checkout page.

Finally, the best thing here – to make the customer experience better, the chatbot offers to save this data.

Tell about sales and promotions

Enhance your sales campaign with a proactive chatbot message. Choose a segment you want to send it to and launch a personalized offer, for instance, 20% off on a new dress collection for customers who visit relevant store categories. 

As for the conversation scenarios, there are two options:

  • Showing products on sale and routing to a checkout or shopping cart.
  • Offer personalized recommendations of items on sale.
  • Capturing customers’ emails in exchange for a coupon.

Here is an example of how it can work 👇

This chatbot engages customers with a bright image, and then shares coupon codes.

Recover shopping carts

70% of online buyers leave items in their carts instead of buying. The fix?

  • Launch a website chatbot to engage visitors when they are trying to leave.
  • Launch a messenger or social media chatbot to re-engage those who left. 

E-commerce marketers switch to this strategy because of the low Open Rate of the classic follow-up emails and the high price of the SMS channel. 

An example of a cart-recovering chatbot 👇

Perfuel Pet Suppliers sends follow-ups in a Facebook Messenger chatbot for registered customers who left the store without a purchase. 


Image source

Upsell and cross-sell

Depending on the product page customers visit, or their shopping cart, a chatbot can suggest additional products or upgrades.

Here is an example of how it works on Shopify👇

When a customer is on a particular product page like jeans, in some time a chatbot message appears “I see you eyeing our new black Levis jeans..” and offers to discover matching items.

Gobot eCommerce Chatbot
Gobot eCommerce Chatbot

It is a great example of how businesses transform customer experience and personalize it. 

Help customers track orders

In a short conversation, a chatbot will define the issue, capture the order number, and share its status instantly. All you have to do is to integrate it with the logistics system. 

Order tracking chatbot example👇

MR.DIY, a Malaysia-based home improvement retailer, launched such a chatbot for its website visitors. In real-time, the chatbot delivers information on where is a customer’s order: 

It brought MR D.I.Y an 80% growth in its containment rate. 

Collect customers’ feedback

There are several challenges that e-commerce businesses face when trying to gather customer feedback:

  • A low response rate of the marketers’ attempts to get customers’ feedback via email.
  • Customers post negative feedback on socials or review websites.
  • A lot of time is spent collecting, managing, and analyzing customer feedback. 

The fix? Automate the process with a chatbot.

For example, contact them on checkout after the payment or after a conversation with a customer manager. 

For example 👇

You can send a short survey with stars and a comment field or turn the process into a conversation by reacting to the rating the customer gave you.

Image source.Image source.

Engage customers in the loyalty program

Use a chatbot to automate the way you:

  • Engage customers to join your loyalty program.
  • Register them.
  • Provide loyalty points updates.
  • Suggest rewards they can redeem.
  • Answer FAQs.

Loyalty program chatbot examples 👇

The first case is about loyalty program registration. The chatbot collects customers’ contacts and promises to notify them about discounts.

Image source.

The second is about points updates and announcements. It actually does what the first promised – send loyalty program updates and engage to continue shopping.

To sum up

Inspiring examples, right? When correctly set up, chatbots provide personalized interactions, resolve queries swiftly, and bring you an army of loyal customers. But to make any examples work in your business, mind the following rule – segment and personalize its workflow with the info about customers’ behavior and preferences. 

About the Author: Evelina Carillo is a friendly and skilled writer and blogger with more than a decade of experience in crafting all sorts of content for the marketing and business world. She’s also spent five years diving into the exciting world of EdTech, where she’s continued to learn and grow in her field.

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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