We've released a new version of the Chrome Extension, with sharing features and a new navigation bar. We hope you'll love it!

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Share templates inside the extension

Before, the only way to share templates with your teammates was to login on Gorgias.io.

If you're on the startup plan, when you create a template, you can choose who has access to it: either only you, specific people, or your entire team.

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The account management section is now available in the extension, under settings.

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New navigation

Tags are now available on the left. It's easier to manage hundreds of templates with them.
You can also navigate through your private & shared templates. Shared templates include templates shared with specific people or with everyone.

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We hope you'll enjoy this new version of our Chrome Extension. As usual, your feedback & questions are welcome!

Today, we’re thrilled to announce that we’ve raised a $1.5 million Seed round led by Charles River Ventures and Amplify Partners, to help build our new helpdesk.

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We’re incredibly grateful to early users, customers, mentors we’ve met both at and Techstars.

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We started the journey with Alex at the beginning of 2015 with our Chrome extension, which helps write email faster using templates. We’ve been pleased all along with customers telling us about how helpful it was, especially for customer support.

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While building the extension, we’ve realized that a big inefficiency in support lies in the lack of integration between the helpdesk, the payment system, CRM and other tools support is using. As a result, agents need to do a lot of repetitive work to respond to customer requests, especially when the company is big.

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That’s why we’ve decided to build a new kind of helpdesk to enable customer support agents to respond 2x faster to customers. You can find out more and sign up for our private beta here.

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When a company has a lot of customers, support becomes repetitive. We want to provide support teams with tools to automate the way they treat simple repetitive requests. This way, they have more time for complex customer issues.

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We'll now focus on this helpdesk and on growing the team, oh, and if you'd like to join, we're hiring! We're super excited about this new helpdesk product. If you’re using the extension, don’t worry.

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Romain & Alex

We've been busy, but not deaf!

Last few months we got lots of feedback about our extension and found to our delight that most people are satisfied, but still a few recurrent issues came up:

• The HTML/WYSIWYG editor sucks.
• No support for Outlook.com.

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We listened and now we're presenting:

• A brand new editor
• Support for outlook.com
• More on the Rich-Text editor

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WYSIWYG editors for the web are notoriously buggy and are just difficult to develop.

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I have yet to see one that is bug free. There are few venerable editors that do a good job like TinyMCE, FKEditor or CKEditor.. but they are big and all have edge cases that break the intended formatting and add a lot of garbage html.

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There are newer good quality editors in town such as Redactor. The one that got my attention and finally landed in Gorgias is this wonderful editor called which is super lightweight, uses modern content-editable (no i-frames) and 'just works' most of the time. That's not to say it's perfect, but it's good enough and I'm satisfied with it's direction in terms of development.

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Enjoy it and as always send us bug-reports or feedback on: support@gorgias.com

TL;DR:

• If your AI is giving wrong answers or getting stuck, it’s likely due to missing or conflicting knowledge. Ensure your AI is trained with up-to-date documents and add guardrails to prevent off-topic replies.
• Loops and escalations usually mean your escalation rules aren’t specific enough. Define when AI should step in, when it should hand over, and create “escape phrases” that trigger human takeover.
• Customers still want human help. Always offer a path to a real person and make sure your agents get full conversation context when a handoff happens.
• Inconsistent tone between AI and agents can make disjointed experiences. Align your brand voice across all support channels and choose tools that let you customize AI tone.
• AI works best when its role is clearly defined. Decide which topics it can handle, train it using real conversations, and review performance regularly to fine-tune your setup.

You’ve chosen your AI tool and turned it on, hoping you won’t have to answer another WISMO question. But now you’re here. Why is AI going in circles? Why isn’t it answering simple questions? Why does it hand off every conversation to a human agent?

Conversational AI and chatbots thrive on proper training and data. Like any other team member on your customer support team, AI needs guidance. This includes knowledge documents, policies, brand voice guidelines, and escalation rules. So, if your AI has gone rogue, you may have skipped a step.

In this article, we’ll show you the top seven AI issues, why they happen, how to fix them, and the best practices for AI setup. 

{{lead-magnet-1}}

1. AI sends the wrong answer — with confidence

AI can only be as accurate as the information you feed it. If your AI is confidently giving customers incorrect answers, it likely has a gap in its knowledge or a lack of guardrails.

Insufficient knowledge can cause AI to pull context from similar topics to create an answer, while the lack of guardrails gives it the green light to compose an answer, correct or not.

How to fix it: 

• Update the AI knowledge base. Create a new document that covers the affected topic in its entirety. To ensure AI follows every step, write your instructions in a when/if/then format.
• Define topics that AI should not handle. As a preventive measure, specify the topics the AI should skip and hand over to a human agent. For example, add words such as ‘disappointed’, ‘bad’, and ‘unacceptable’ to your AI off-limit list, so that human agents automatically handle negative-intent tickets.

2. Customer is stuck in an AI loop 

This is one of the most frustrating customer service issues out there. Left unfixed, you risk losing 29% of customers. 

If your AI is putting customers through a never-ending loop, it’s time to review your knowledge docs and escalation rules.

How to fix it:

• Double-check for conflicts in knowledge. You may have provided multiple resolutions for the same issue across different knowledge sources, such as uploaded documents, website pages, and in-app instructions.
• Add “escape routes”. Choose a set of phrases that automatically escalate conversations from AI to your support team. For example, “it’s not working” or “I already tried that”.
• Set a max number of failed interactions before escalation. Opt for a one-fail-and-escalate approach for every conversation, or specify the number of failed interactions for certain topics.

3. AI escalates too quickly, even for easy questions

It can be frustrating when AI can’t do the bare minimum, like automate WISMO tickets. This issue is likely due to missing knowledge or overly broad escalation rules.

How to fix it:

• Train AI on your FAQs and common issues. Which customer questions do you repeatedly receive? Create a document that lists out every question and its answer.
• Update vague escalation rules. AI works best with specificity. For example, if you told it to escalate conversations about “returns,” it may even escalate frequently asked questions about return eligibility.

4. Customers can’t find a way to reach a human

One in two customers still prefer talking to a human to an AI, according to Katana. Limiting them to AI-only support could risk a sale or their relationship. 

The top live chat apps clearly display options to speak with AI or a human agent. If your tool doesn’t have this, refine your AI-to-human escalation rules.

How to fix it:

• Set phrases to trigger escalation. In your knowledge docs, define which phrases should tell AI to hand a conversation over to your support team. For example, “I want to talk to someone” or “Can I talk to a human?”
• Add a visible option to connect with a human. This can be a button in your chat widget, a note in your contact page, or even a link in your website footer. At minimum, give customers an easy-to-find way to reach a real person.

5. Handoff happens — but the agent gets no context

If your agents are asking customers to repeat themselves, you’ve already lost momentum. One of the fastest ways to break trust is by making someone explain their issue twice. This happens when AI escalates without passing the conversation history, customer profile, or even a summary of what’s already been attempted.

How to fix it:

• Use rules to auto-tag conversations based on AI activity. Set up logic to tag tickets when certain conditions are met — like when AI attempted a specific action, couldn't resolve the issue, or triggered escalation.
• Audit your escalated tickets. Look for patterns where context is missing, and adjust the AI-to-human transition logic accordingly.
• Use an AI platform that provides automated ticket summaries. Choose a tool like Gorgias that provides a quick overview of every ticket.

6. The tone between AI and agent is jarring

Sure, conversational AI has near-perfect grammar, but if its tone is entirely different from your agents’, customers can be put off.

This mismatch usually comes from not settling on an official customer support tone of voice. AI might be pulling from marketing copy. Agents might be winging it. Either way, inconsistency breaks the flow.

How to fix it:

• Create shared brand voice guidelines. Align tone, formality, and language rules across both AI scripts and agent responses.
• Define emojis and punctuation use. A consistent visual style helps conversations feel smoother and more human.
• Use AI tools that allow tone control. Choose platforms that let you customize the voice and personality of your AI to match your brand.
• Train your agents with examples of ideal tone. Give your team brand voice examples of how conversations should continue when handed off.

7. You haven’t defined what AI should actually handle

When AI is underperforming, the problem isn’t always the tool. Many teams launch AI without ever mapping out what it's actually supposed to do. So it tries to do everything (and fails), or it does nothing at all.

It’s important to remember that support automation isn’t “set it and forget it.” It needs to know its playing field and boundaries.

How to fix it:

How to set up AI that actually works

Once you’ve addressed the obvious issues, it’s important to build a setup that works reliably. These best practices will help your AI deliver consistently helpful support.

1. Define clear AI boundaries

Start by deciding what AI should and shouldn’t handle. Let it take care of repetitive tasks like order tracking, return policies, and product questions. Anything complex or emotionally sensitive should go straight to your team.

2. Train it using real customer conversations

Use examples from actual tickets and messages your team handles every day. Help center articles are a good start, but real interactions are what help AI learn how customers actually ask questions.

3. Set up fallback triggers

Create rules that tell your AI when to escalate. These might include customer frustration, low confidence in the answer, or specific phrases like “talk to a person.” The goal is to avoid infinite loops and to hand things off before the experience breaks down.

4. Make sure agents receive full context

When a handoff happens, your agents should see everything the AI did. That includes the full conversation, relevant customer data, and any actions it has already attempted. This helps your team respond quickly and avoid repeating what the customer just went through. 

An easy way to keep order history, customer data, and conversation history in one place is by using a conversational commerce tool like Gorgias.

5. Keep tone and voice consistent

A jarring shift in tone between AI and agent makes the experience feel disconnected. Align aspects such as formality, punctuation, and language style so the transition from AI to human feels natural.

6. Review handoffs regularly

Look at recent escalations each week. Identify where the AI struggled or handed off too early or too late. Use those insights to improve training, adjust boundaries, and strengthen your automation flows.

If your AI chatbot isn’t working the way you expected, it’s probably not because the technology is broken. It’s because it hasn’t been given the right rules.

AI that works your way and knows when to escalate

When you set AI up with clear responsibilities, it becomes a powerful extension of your team.

Want to see what it looks like when AI is set up the right way?

Try Gorgias AI Agent. It’s conversational AI built with smart automation, clean escalations, and ecommerce data in its core — so your customers get faster answers and your agents stay focused.

Get started with Gorgias AI Agent →

TL;DR:

• Position Shopping Assistant as a revenue-driving tool. It boosts AOV, GMV, and chat conversion rates, with some brands seeing up to 97% higher AOV and 13x ROI.
• Highlight its role as a proactive sales agent, not just a support bot. It recommends products, applies discounts, and guides shoppers to checkout in real time.
• Use cross-industry case studies to make your case. Show leadership success stories from brands like Arc’teryx, bareMinerals, and TUSHY to prove impact.
• Focus on the KPIs it improves. Track AOV, GMV, chat conversion, CSAT, and resolution rate to demonstrate clear ROI.

Rising customer expectations, shoppers willing to pay a premium for convenience, and a growing lack of trust in social media channels to make purchase decisions are making it more challenging to turn a profit.  

In this emerging era, AI’s role is becoming not only more pronounced, but a necessity for brands who want to stay ahead. Tools like Gorgias Shopping Assistant can help drive measurable revenue while reducing support costs. 

For example, a brand that specializes in premium outdoor apparel implemented Shopping Assistant and saw a 2.25% uplift in GMV and 29% uplift in average order volume (AOV).

But how, among competing priorities and expenses, do you convince leadership to implement it? We’ll show you.

Why conversational AI matters for modern ecommerce

1) Meet high consumer expectations

Shoppers want on-demand help in real time that’s personalized across devices. 

Shopping Assistant recalls a shopper’s browsing history, like what they have clicked, viewed, and added to their cart. This allows it to make more relevant suggestions that feel personal to each customer. 

2) Keep up with market momentum

The AI ecommerce tools market was valued at $7.25 billion in 2024 and is expected to reach $21.55 billion by 2030. 

Your competitors are using conversational AI to support, sell, and retain. Shopping Assistant satisfies that need, providing upsells and recommendations rooted in real shopper behavior. 

3) Raise AOV and GMV

Conversational AI has real revenue implications, impacting customer retention, average order value (AOV), conversion rates, and gross market value (GMV). 

For example, a leading nutrition brand saw a GMV uplift of over 1%, an increase in AOV of over 16%, and a chat conversion rate of over 15% after implementing Shopping Assistant.

Overall, Shopping Assistant drives higher engagement and more revenue per visitor, sometimes surpassing 50% and 20%, respectively.

How to show the business impact & ROI of Shopping Assistant

1) Pitch its core capabilities

Shopping Assistant engages, personalizes, recommends, and converts. It provides proactive recommendations, smart upsells, dynamic discounts, and is highly personalized, all helping to guide shoppers to checkout. 

Success spotlight

After implementing Shopping Assistant, leading ecommerce brands saw real results:

2) Position it as a revenue driver

Shopping Assistant drives uplift in chat conversion rate and makes successful upsell recommendations.  

Success spotlight

“It’s been awesome to see Shopping Assistant guide customers through our technical product range without any human input. It’s a much smoother journey for the shopper,” says Nathan Larner, Customer Experience Advisor for Arc’teryx. 

For Arc’teryx, that smoother customer journey translated into sales. The brand saw a 75% increase in conversion rate (from 4% to 7%) and 3.7% of overall revenue influenced by Shopping Assistant. 

3) Show its efficiency and cost savings

Because it follows shoppers’ live journey during each session on your website, Shopping Assistant catches shoppers in the moment. It answers questions or concerns that might normally halt a purchase, gets strategic with discounting (based on rules you set), and upsells. 

The overall ROI can be significant. For example, bareMinerals saw an 8.83x return on investment.  

Success spotlight

"The real-time Shopify integration was essential as we needed to ensure that product recommendations were relevant and displayed accurate inventory,” says Katia Komar, Sr. Manager of Ecommerce and Customer Service Operations, UK at bareMinerals. 

“Avoiding customer frustration from out-of-stock recommendations was non-negotiable, especially in beauty, where shade availability is crucial to customer trust and satisfaction. This approach has led to increased CSAT on AI converted tickets."

4) Present the metrics it can impact

Shopping Assistant can impact CSAT scores, response times, resolution rates, AOV, and GMV.  

Success spotlight

For Caitlyn Minimalist, those metrics were an 11.3% uplift in AOV, an 18% click through rate for product recommendations, and a 50% sales lift versus human-only chats. 

"Shopping Assistant has become an intuitive extension of our team, offering product guidance that feels personal and intentional,” says Anthony Ponce, its Head of Customer Experience.

5) Highlight its helpfulness as a sales agent 

Support agents have limited time to assist customers as it is, so taking advantage of sales opportunities can be difficult. Shopping Assistant takes over that role, removing obstacles for purchase or clearing up the right choice among a stacked product catalog.

Success spotlight

With a product that’s not yet mainstream in the US, TUSHY leverages Shopping Assistant for product education and clarification. 

"Shopping Assistant has been a game-changer for our team, especially with the launch of our latest bidet models,” says Ren Fuller-Wasserman, Sr. Director of Customer Experience at TUSHY. 

“Expanding our product catalog has given customers more choices than ever, which can overwhelm first-time buyers. Now, they’re increasingly looking to us for guidance on finding the right fit for their home and personal hygiene needs.”

The bidet brand saw 13x return on investment after implementation, a 15% increase in chat conversion rate, and a 2x higher conversion rate for AI conversations versus human ones. 

6) Provide the KPIs you’ll track 

Customer support metrics include: 

• Resolution rate 
• CSAT score 

Revenue metrics to track include: 

• Average order value (AOV) 
• Gross market value (GMV) 
• Chat conversion rate 

Shopping Assistant: AI that understands your brand 

Shopping Assistant connects to your ecommerce platform (like Shopify), and streamlines information between your helpdesk and order data. It’s also trained on your catalog and support history. 

Allow your agents to focus on support and sell more by tackling questions that are getting in the way of sales. 

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TL;DR:

• Shoppers often hesitate around sizing, shade matching, styling, and product comparisons, and those moments are key revenue opportunities for CX teams.
• Guided shopping removes that friction by giving shoppers quick, personalized recommendations that build confidence in their choices.
• Across 11 brands, guided shopping led to measurable lifts in AOV, conversion rate, and overall revenue.
• Your biggest upsell opportunities likely sit in the same places your shoppers pause, so start by automating your most common pre-purchase questions.

Most shoppers arrive with questions. Is this the right size? Will this match my skin tone? What’s the difference between these models? The faster you can guide them, the faster they decide.

As CX teams take on a bigger role in driving revenue, these moments of hesitation are now some of the most important parts of the buying journey.

That’s why more brands are leaning on conversational AI to support these high-intent questions and remove the friction that slows shoppers down. The impact speaks for itself. Brands can expect higher AOV, stronger chat conversion rates, and smoother paths to purchase, all without adding extra work to your team.

Below, we’re sharing real use cases from 11 ecommerce brands across beauty, apparel, home, body care, and more, along with the exact results they saw after introducing guided shopping experiences.

1. Recommend similar shoes when an old classic disappears

When you’re shopping for shoes similar to an old but discontinued favorite, every detail counts, down to the color of the bottom of the shoe. But legacy brands with large catalogs can be overwhelming to browse.

For shoppers, it’s a double-edged sword: they want to feel confident that they checked your entire collection, but they also don’t want to spend time looking for it.

How Shopping Assistant helps:

Shopping Assistant accelerates the process, turning hazy details into clear, friendly guidance.

It describes shoe details, from colorways to logo placement, compares products side by side, and recommends the best option based on the shopper’s preferences and conditions.

The result is shoppers who feel satisfied and more connected with your brand.

Results:

• AOV uplift: +6.5%

2. Suggest complete outfits for special occasions

Big events call for great outfits, but putting one together online isn’t always easy. With thousands of options to scroll through, shoppers often want a bit of styling direction.

How Shopping Assistant helps:

Shoppers get to chat with a virtual stylist who recommends full outfits based on the occasion, suggests accessories to complete the look, and removes the guesswork of pairing pieces together. 

The result is a fun, confidence-building shopping experience that feels like getting advice from a stylist who actually understands their plans.

Results:

• Chat CVR: 13.02%

3. Match shoppers to the right makeup shade when the formula changes

Shade matching is hard enough in-store, but doing it online can feel impossible. Plus, when a longtime favorite gets discontinued, shoppers are left guessing which new shade will come closest. That uncertainty often leads to hesitation, abandoned carts, or ordering multiple shades “just in case.”

How Shopping Assistant helps:

Shoppers find their perfect match without any of the guesswork. The assistant asks a few quick questions, recommends the closest shade or formula, and offers smart alternatives when a product is unavailable.

The experience feels like chatting with a knowledgeable beauty advisor — someone who makes the decision easy and leaves shoppers feeling confident in what they’re buying.

Katia Komar, Sr. Manager of Ecommerce and Customer Service Operations at bareMinerals UK says, “What impressed me the most is the AI’s ability to upsell with a conversational tone that feels genuinely helpful and doesn't sound too pushy or transactional. It sounds remarkably human, identifying correct follow-up questions to determine the correct product recommendation, resulting in improved AOV. It’s exactly how I train our human agents and BPO partners.”

Results:

• GMV uplift: +6.55%

4. Help find the perfect gift when shoppers don’t know what to buy

When shoppers are buying gifts, especially for someone else, they often know who they’re shopping for but not what to buy. A vague product name or a half-remembered scent can quickly make the experience feel overwhelming without someone to guide them.

How Shopping Assistant helps:

Thoughtful guidance goes a long way. By asking clarifying questions and recognizing likely mix-ups, Shopping Assistant helps shoppers figure out what the recipient was probably referring to, then recommends the right product along with complementary gift options that make the choice feel intentional.

It brings the reassurance of an in-store associate to the online experience, helping shoppers move forward with confidence.

Results:

• Chat CVR: 8.39%

5. Remove the guesswork from bra sizing online

Finding the right bra size online is notoriously tricky. Shoppers often second-guess their band or cup size, and even small uncertainties can lead to returns — or abandoning the purchase altogether.

Many customers just want someone to walk them through what a proper fit should actually feel like.

How Shopping Assistant helps:

Searching for products is no longer a time-consuming process. Shopping Assistant detects a shopper’s search terms and sends relevant products in chat. Like an in-store associate, it uses context to deliver what shoppers are looking for, so they can skip the search and head right to checkout.

Results:

• GMV uplift: +6.22%
• Chat CVR: 16.78%

6. Guide shoppers through jewelry personalization step by step

For shoppers buying personalized jewelry, the details directly affect the final result. That’s why customization questions come up constantly, and why uncertainty can quickly stall the path to purchase.

How Shopping Assistant helps:

Shopping Assistant asks about the shopper’s style preferences and customization needs, then recommends the right product and options so they can feel confident the final piece is exactly their style. The experience feels quick, helpful, and designed to guide shoppers toward a high investment purchase.

Results:

• GMV uplift: +22.59%

7. Recommend furniture that works well together

Decorating a home is personal, and shoppers often want reassurance that a new piece will blend with what they already own. Questions about color palettes, textures, and proportions come up constantly. And without guidance, it’s easy for shoppers to feel unsure about hitting “add to cart.”

How Shopping Assistant helps:

Giving shoppers personalized styling support helps them visualize how pieces will work in their home. 

Shoppers receive styling suggestions based on their existing space as well as recommendations on pieces that complement their color palette. 

It even guides them toward a 60-minute virtual styling consultation when they need deeper help. The experience feels thoughtful and high-touch, which is why shoppers often spend more once they feel confident in their choices.

Results:

• AOV uplift: +97.15%
• Chat CVR: 10.3%

8. Reassure shoppers about flavor before purchase

When shoppers discover a new drink mix, they’re bound to have questions before committing. How strong will it taste? How much should they use? Will it work with their preferred drink or routine? Uncertainty at this stage can stall the purchase or lead to disappointment later.

How Shopping Assistant helps:

Clear, friendly guidance in chat helps shoppers understand exactly how to use the product. Shopping Assistant answers questions about serving size, flavor strength, and pairing options, and suggests the best way to prepare the mix based on the shopper’s preferences.

Results:

• Chat CVR: 12.75%

9. Match supplements to age, lifestyle, and health goals

Shopping for health supplements can feel confusing fast. Customers often have questions about which formulas fit their age, health goals, or daily routine. Without clear guidance, most will hesitate or pick the wrong product.

How Shopping Assistant helps:

Shopping Assistant detects hesitation when shoppers linger on a search results page. It proactively asks a few clarifying questions, narrows down product options, and points shoppers to the best product or bundle for their needs. 

The entire experience feels supportive and gives shoppers confidence they’ve picked the right option.

Results:

• AOV uplift: +16.4%
• Chat CVR: 15.15%

10. Align products with safety needs in kids’ rooms

Shopping for kids’ furniture comes with a lot of “Is this the right one?” moments. Parents want something safe, sturdy, and sized correctly for their child’s age. With so many options, it’s easy to feel unsure about what will actually work in their space.

How Shopping Assistant helps:

Shopping Assistant guides parents toward the best fit right away. It asks about their child’s age, room layout, and safety considerations, then recommends the most appropriate bed or furniture setup. The experience feels like chatting with a knowledgeable salesperson who understands what families actually need as kids grow.

Results:

• GMV uplift: +12.26%
• AOV uplift: +10.19%

11. Clarify technical specs that create hesitation

Even something as simple as choosing a toothbrush can feel complicated when multiple models come with different speeds, materials, and features. Shoppers want to understand what matters so they can pick the one that fits their routine and budget.

How Shopping Assistant helps:

Choosing between toothbrush models shouldn’t feel like decoding tech specs. When shoppers can see the key differences in plain language, including what’s unique, how each model works, and who it’s best for, they can make a decision with ease. 

Suddenly, the whole process feels simple instead of overwhelming.

Results:

• AOV uplift: +11.27%
• Chat CVR: 8.55%

What these results tell us

Across all 11 brands, one theme is clear. When shoppers get the guidance they need at the right moment, they convert more confidently and often spend more.

Here’s what stands out:

• AOV jumps when products are technical or high in consideration. Home decor, supplements, and outdoor gear see the biggest lifts because shoppers feel more confident committing to higher-priced items once the details are explained.
• CVR surges in categories with complex decisions. Lingerie, apparel, and personal styling all showed strong conversion rates because shoppers finally get clarity on fit, shade, or style.
• GMV rises when AI removes friction from the buying journey. Furniture and beauty saw meaningful gains thanks to personalized recommendations that reduce uncertainty and push shoppers toward the right product faster.
• The use cases reveal clear upsell opportunities. If your team sees recurring questions about sizing, shade matching, product differences, or how items work together, that’s a strong signal that guided selling can drive more revenue.

What this means for you:

Look closely at your most common pre-purchase questions. Anywhere shoppers hesitate from fit, shade, technical specs, styling, bundles is a place where Shopping Assistant can step in, boost confidence, and unlock more sales.

Want Shopping Assistant results like these?

If you notice the same patterns in your own store, such as shoppers hesitating over sizing, shade matching, product comparisons, or technical details, guided shopping can make an immediate impact. These moments are often your biggest opportunities to increase revenue and improve the buying experience.

Many of the brands in this post started by identifying their most common pre-purchase questions and letting AI handle them at scale. You can do the same.

If you want to boost conversions, lift AOV, and create a smoother path to purchase, now is a great time to explore guided shopping for your team.

Book a demo or activate Shopping Assistant to get started.

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TL;DR:

• Support chats can now be directly tied to revenue. Brands are measuring conversations by conversion rate, average order value (AOV), and GMV influenced.
• AI resolution rate is only valuable if the answers are accurate and helpful. A high resolution rate doesn’t matter if it leads to poor recommendations — the best AI both deflects volume and drives confident purchases.
• Chat conversion rates often outperform traditional channels. Brands like Arc’teryx saw a 75% lift in conversions (from 4% to 7%) when AI handled high-intent product questions.
• Shoppers who chat often spend more. Conversations lead to higher AOVs by helping customers understand products, explore upgrades, and discover add-ons — not just through upselling, but smarter guidance.

Conversational commerce finally has a scoreboard.

For years, CX leaders knew support conversations mattered, they just couldn’t prove how much. Conversations lived in that gray area of ecommerce where shoppers got answers, agents did their best, and everyone agreed the channel was “important”… 

But tying those interactions back to actual revenue? Nearly impossible.

Fast forward to today, and everything has changed.

Real-time conversations — whether handled by a human agent or powered by AI — now leave a measurable footprint across the entire customer journey. You can see how many conversations directly influenced a purchase. 

In other words, conversational commerce is finally something CX teams can measure, optimize, and scale with confidence.

Why measuring conversational commerce matters now

If you want to prove the value of your CX strategy to your CFO, your marketing team, or your CEO, you need data, not anecdotes.

Leadership isn’t swayed by “We think conversations help shoppers.” They want to see the receipts. They want to know exactly how interactions influence revenue, which conversations drive conversion, and where AI meaningfully reduces workload without sacrificing quality.

That’s why conversational commerce metrics matter now more than ever. This gives CX leaders a way to:

• Quantify the revenue influence of conversations
• Understand where AI improves efficiency — and where humans add the most value
• Make informed decisions on staffing, automation, and channel investment
• Turn CX into a profit center instead of a cost center

These metrics let you track impact with clarity and confidence.

And once you can measure it, you can build a stronger case for deeper investment in conversational tools and strategy.

The 4 metric categories that define conversational commerce success

So, what exactly should CX teams be measuring?

While conversational commerce touches every part of the customer journey, the most meaningful insights fall into four core categories: 

1. Automation performance
2. Conversion & revenue impact
3. Engagement quality
4. Discounting behavior

Let’s dive into each.

Automation performance metrics

If you want to understand how well your conversational commerce strategy is working, automation performance is the first place to look. These metrics reveal how effectively AI is resolving shopper needs, reducing ticket volume, and stepping into revenue-driving conversations at scale.

The two most foundational metrics?

1. Resolution rate: Are AI-led conversations actually helpful?

Resolution rate measures how many conversations your AI handles from start to finish without needing a human to take over. On paper, high resolution rates sound like a guaranteed win. It suggests your AI is handling product questions, sizing concerns, shade matching, order guidance, and more — all without adding to your team’s workload.

But a high resolution rate doesn’t automatically mean your AI is performing well.

Yes, the ticket was “resolved,” but was the customer actually helped? Was the answer accurate? Did the shopper leave satisfied or frustrated?

This is where quality assurance becomes essential. Your AI should be resolving tickets accurately and helpfully, not simply checking boxes.

At its best, a strong resolution rate signals that your AI is:

• Confidently answering product questions
• Guiding shoppers to the right SKU, variant, shade, size, or style
• Reducing cart abandonment caused by confusion
• Helping pre-sale shoppers convert faster

When resolution rate quality goes up, so does revenue influence.

You can see this clearly with beauty brands, where accuracy matters enormously. bareMinerals, for example, used to receive a flood of shade-matching questions. Everything from “Which concealer matches my undertone?” to “This foundation shade was discontinued; what’s the closest match?” 

Before AI, these questions required well-trained agents and often created inconsistencies depending on who answered.

Once they introduced Shopping Assistant, resolution rate suddenly became more meaningful. AI wasn’t just closing tickets; it was giving smarter, more confident recommendations than many agents could deliver at scale, especially after hours. 

That accuracy paid off. 

AI-influenced purchases at bareMinerals had zero returns in the first 30 days because customers were finally getting the right shade the first time.

That’s the difference between “resolved” and resolved well.

2. Zero-touch tickets: How many tickets never reach a human?

The zero-touch ticket rate measures something slightly different: the percentage of conversations AI manages entirely on its own, without ever being escalated to an agent.

This metric is a direct lens into:

• Workload reduction
• Team efficiency
• Cost savings
• AI’s ability to own high-volume question types

More importantly, deflection widens the funnel for more revenue-driven conversations.

When AI deflects more inbound questions, your support team can focus on conversations that truly require human expertise, including returns exceptions, escalations, VIP shoppers, and emotionally sensitive interactions.

Brands with strong deflection rates typically see:

• Shorter wait times
• Higher CSAT
• Lower support costs
• More AI-influenced revenue

Conversion and revenue impact metrics

If automation metrics tell you how well your AI is working, conversion and revenue metrics tell you how well it’s selling.

This category is where conversational commerce really proves its value because it shows the direct financial impact of every human- or AI-led interaction.

1. Chat Conversion Rate (CVR): How often do conversations turn into purchases?

Chat conversion rate measures the percentage of conversations that end in a purchase, and it’s one of the clearest indicators of whether your conversational strategy is influencing shopper decisions.

A strong CVR tells you that conversations are:

• Building confidence
• Removing hesitation
• Guiding shoppers toward the right product

You see this clearly with brands selling technical or performance-driven products. 

Outdoor apparel shoppers, for example, don’t just need “a jacket” — they need to know which jacket will hold up in specific temperatures, conditions, or terrains. A well-trained AI can step into that moment and convert uncertainty into action.

Arc’teryx saw this firsthand. 

Once Shopping Assistant started handling their high-intent pre-purchase questions, their chat conversion rate jumped dramatically — from 4% to 7%. A 75% lift. 

That’s what happens when shoppers finally get the expert guidance they’ve been searching for.

2. GMV influenced: The revenue ripple effect of conversations

Not every shopper buys the moment they finish a chat. Some take a few hours. Some need a day or two. Some want to compare specs or read reviews before committing.

GMV influenced captures this “tail effect” by tracking revenue within 1–3 days of a conversation.

It’s especially powerful for:

• High-consideration purchases (like outdoor gear, home furniture, equipment)
• Products with many options, specs, or configurations
• Shoppers who need reassurance before buying

In Arc’teryx’s case, shoppers often take time to confirm they’re choosing the right technical gear.

Yet even with that natural pause in behavior, Shopping Assistant still influenced 3.7% of all revenue, not by forcing instant decisions, but by providing the clarity people needed to make the right one.

3. AOV from conversational commerce: Do conversations lead to bigger carts?

This metric looks at the average order value of shoppers who engage in a conversation versus those who don’t. 

If the conversational AOV is higher, it means your AI or agents are educating customers in ways that naturally expand the cart.

Examples of AOV-lifting conversations include:

• Recommending complementary gear, tools, or accessories
• Suggesting upgraded options based on needs
• Helping shoppers understand the difference between product tiers
• Explaining why a specific product is worth the investment

When conversations are done well, AOV increases not because shoppers are being upsold, but because they’re being guided. 

4. ROI of AI-powered conversations: The metric your leadership cares most about

ROI compares the revenue generated by conversational AI to the cost of the tool itself — in short, this is the number that turns heads in boardrooms.

Strong ROI shows that your AI:

• Does the work of multiple agents
• Drives new revenue, not just ticket deflection
• Provides accurate answers consistently, at any time
• Delivers a high-quality experience without expanding headcount

When ROI looks like that, AI stops being a “tool” and starts being an undeniable growth lever.

Related: The hidden power and ROI of automated customer support

Engagement metrics that indicate purchase intent

Not every metric in conversational commerce is a final outcome. Some are early signals that show whether shoppers are interested, paying attention, and moving closer to a purchase.

These engagement metrics are especially valuable because they reveal why conversations convert, not just whether they do. When engagement goes up, conversion usually follows.

1. Click-Through Rate (CTR): Are shoppers acting on the products your AI recommends?

CTR measures the percentage of shoppers who click the product links shared during a conversation. It’s one of the cleanest leading indicators of buyer intent because it reflects a moment where curiosity turns into action.

If CTR is high, it’s a sign that:

• Your recommendations are relevant
• The conversation is persuasive
• The shopper trusts the guidance they’re getting
• The AI is surfacing the right product at the right time

In other words, CTR tells you which conversations are influencing shopping behavior.

And the connection between CTR and revenue is often tighter than teams expect.

Just look at what happened with Caitlyn Minimalist. When they began comparing the results of human-led conversations versus AI-assisted ones over a 90-day period, CTR became one of the clearest predictors of success. Their Shopping Assistant consistently drove meaningful engagement with its recommendations — an 18% click-through rate on the products it suggested.

That level of engagement translated directly into better outcomes:

• AI-driven conversations converted at 20%, compared to just 8% for human agents
• Many of those clicks led to multi-item purchases
• Overall, the brand experienced a 50% lift in sales from AI-assisted chats compared to human-only ones

When shoppers click, they’re moving deeper into the buying cycle. Strong CTR makes it easier to forecast conversion and understand how well your conversational flows are guiding shoppers toward the right products.

Discounting behavior metrics

Discounting can be one of the fastest ways to nudge a shopper toward checkout, but it’s also one of the fastest ways to erode margins. 

That’s why discount-related metrics matter so much in conversational commerce. 

They show not just whether AI is using discounts, but how effectively those discounts are driving conversions.

1. Discounts offered: Are incentives being used strategically or too often?

This metric tracks how many discount codes or promotional offers your AI is sharing during conversations. 

Ideally, discounts should be purposeful — timed to moments when a shopper hesitates or needs an extra nudge — not rolled out as a one-size-fits-all script. When you monitor “discounts offered,” you can ensure that incentives are being used as conversion tools, not crutches.

This visibility becomes particularly important at high-intent touchpoints, such as exit intent or cart recovery interactions, where a small incentive can meaningfully increase conversion if used correctly.

2. Discounts applied: Are those discounts actually influencing the purchase?

Offering a discount is one thing. Seeing whether customers use it is another.

A high “discounts applied” rate suggests:

• The offer was compelling
• The timing was right
• The shopper truly needed that incentive to convert

A low usage rate tells a different story: Your team (or your AI) is discounting unnecessarily.

This metric alone often surprises brands. More often than not, CX teams discover they can discount less without hurting conversion, or that a non-discount incentive (like a relevant product recommendation) performs just as well.

Understanding this relationship helps teams tighten their promotional strategy, protect margins, and use discounts only where they actually drive incremental revenue.

How CX teams use these metrics to make better decisions

Once you know which metrics matter, the next step is building a system that brings them together in one place.

Think of your conversational commerce scorecard as a decision-making engine — something that helps you understand performance at a glance, spot bottlenecks, optimize AI, and guide shoppers more effectively.

In Gorgias, you can customize your analytics dashboard to watch the metrics that matter most to your brand. This becomes the single source of truth for understanding how conversations influence revenue.

Here’s what a powerful dashboard unlocks:

1. You learn where AI performs best (and where humans outperform)

Some parts of the customer journey are perfect for AI: repetitive questions, product education, sizing guidance, shade matching, order status checks. 

Others still benefit from human support, like emotional conversations, complex troubleshooting, multi-item styling, or high-value VIP concerns.

Metrics like resolution rate, zero-touch ticket rate, and chat conversion rate show you exactly which is which.

When you track these consistently, you can:

• Identify conversation types AI should fully own
• Spot where AI needs more training
• Allocate human agents to higher-value conversations
• Decide when humans should step in to drive stronger outcomes

For example, if AI handles 80% of sizing questions successfully but struggles with multi-item styling advice, that tells you where to invest in improving AI, and where human expertise should remain the default.

2. You uncover what shoppers actually need to convert

Metrics like CTR, CVR, and conversational AOV reveal the inner workings of shopper decision-making. They show which recommendations resonate, which don’t, and which messaging actually moves someone to purchase.

With these insights, CX teams can:

• Refine product recommendations
• Improve conversation flows that stall out
• Adjust the tone or structure of AI messaging
• Draft stronger scripts for human agents
• Identify recurring questions that indicate missing PDP information

For instance, if shoppers repeatedly ask clarifying questions about a product’s material or fit, that’s a signal for merchandising or product teams. 

If recommendations with social proof get high engagement, marketing can integrate that insight into on-site messaging. 

Conversations reveal what customers really care about — often before analytics do.

3. You prove that conversations directly drive revenue

This is the moment when the scorecard stops being a CX tool and becomes a business tool.

A clear set of metrics shows how conversations tie to:

• GMV influenced
• AOV lift
• Revenue generated by AI
• ROI of conversational commerce tools

When a CX leader walks into a meeting and says, “Our AI Assistant influenced 5% of last month’s revenue” or “Conversational shoppers have a 20% higher AOV,” the perception of CX changes instantly.

You’re no longer a support cost. You’re a revenue channel.

And once you have numbers like ROI or revenue influence in hand, it becomes nearly impossible for anyone to argue against further investment in CX automation.

4. You identify where shoppers are dropping off or hesitating

A scorecard doesn’t just show what’s working, it surfaces what’s not.

Metrics make friction obvious:

Once you identify these patterns, you can run targeted experiments:

• Test new scripts or flows
• Adjust product recommendations
• Add social proof or benefit framing
• Reassess discounting strategies
• Rework messaging on key PDPs

Compounded over time, these moments create major lifts in conversion and revenue.

5. You create a feedback loop across marketing, merchandising, and product

One of the biggest hidden values of conversational data is how it strengthens cross-functional decision-making.

A clear analytics dashboard gives teams visibility into:

• Unclear or missing product information (from repeated questions)
• Merchandising opportunities (from your most popular products)
• Landing page or PDP improvements (from drop-off points)
• Messaging that resonates with real customers (from AI messages)

Suddenly, CX isn’t just answering questions — it’s informing strategy across the business.

CX drives revenue when you measure what matters

With the right metrics in place, CX leaders can finally quantify the impact of every interaction, and use that data to shape smarter, more profitable customer journeys.

If you're ready to measure — and scale — the impact of your conversations, tools like Gorgias AI Agent and Shopping Assistant give CX teams the visibility, accuracy, and performance needed to turn every interaction into revenue.

Want to see it in action? Book a demo and discover what conversational commerce can do for your bottom line.

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TL;DR: 

• AI hallucinations happen when AI models create false information that sounds real. 
• They occur in customer support when chatbots lack proper training data or face unclear questions.
• Main causes include bad training data, overfitting, and confusing prompts. You can prevent them by grounding responses in real data, setting clear limits, and keeping humans in the loop. 
• When managed right, AI reduces support tickets while staying accurate and on-brand.

Your AI chatbot just told a customer their order ships in two days when you actually need five. That's an AI hallucination, or false information delivered with complete confidence.

These fake-but-convincing responses can wreck customer trust and create headaches for your support team. Understanding what triggers these AI mistakes and how to stop them matters for any brand using AI in customer service. 

This guide covers everything support teams need to know about AI hallucinations, from spotting them to building systems that keep your AI honest and helpful.

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What are AI hallucinations?

AI hallucinations are false or made-up responses from AI models that sound completely believable. This means the AI creates information that isn't based on real data but presents it like it's a fact.

The term “hallucination” comes from psychology, but AI hallucinations work differently than human ones. When an AI hallucinates, it's not seeing things that aren't there. Instead, it's filling gaps in its knowledge with creative guesswork.

Large language models (LLMs) power most modern chatbots. These models predict what word comes next based on patterns they learned during training. Sometimes they predict wrong and create entirely made-up information.

The real problem isn't that AI makes mistakes — it's how confidently it delivers wrong answers. Your AI won't say “I think” or “maybe.” It states false information with authority, making errors hard to catch.

For your ecommerce brand, this could mean your chatbot invents return policies or confirms products are in stock when they're not. These confident lies break customer trust and force your human agents to clean up the mess.

What causes AI hallucinations

AI hallucinations are baked into how current models function, caused by training data limitations, missing context, and statistical prediction errors that lead AI to invent convincing but false answers. Knowing why they happen helps you prevent them in your customer support.

Insufficient training data creates the biggest problems. When you ask AI about topics it hasn't learned, it tries to fill knowledge gaps with educated guesses. These guesses often sound reasonable but are completely wrong.

Overfitting happens when AI memorizes training examples instead of learning patterns. The model becomes an expert at repeating what it's seen before but terrible at handling new situations.

Ambiguous prompts confuse AI models. When customers ask vague questions that could mean several things, AI picks one interpretation and runs with it.

Outdated information causes problems because most AI models have knowledge cutoffs. They don't know about new promotions, products, or policy changes after their training ended. Your AI might confidently share old shipping times or discontinued product details.

Pattern misapplication occurs when AI applies learned patterns to wrong situations. The model recognizes linguistic patterns and uses them inappropriately, creating responses that sound right but make no sense in context.

Examples of AI hallucinations in customer support

AI hallucinations create real problems for support teams. When chatbots hallucinate, human agents spend time fixing the damage instead of helping customers with genuine needs.

Here are common ways AI hallucinates in ecommerce support:

• Product feature invention: Customer asks if a jacket is waterproof. AI confidently says "Yes, it's fully waterproof and tested to 10 feet underwater" when it's only water-resistant.
• Fake discount codes: Shopper requests a discount. AI creates "SAVE20NOW" — a realistic-sounding but non-existent code that fails at checkout.
• Wrong return windows: AI tells customers they have 60 days to return items because it hasn’t been fed your return policy, creating expectations you can't meet.
• Fabricated shipping promises: AI promises next-day delivery for standard shipping orders, setting impossible expectations.
• Invented company policies: Customer asks about price matching. AI creates a detailed policy you've never offered, forcing agents to disappoint customers.

Each hallucination creates a cascade of problems. Customers get frustrated when promises don't match reality. Your support team wastes time explaining why the AI was wrong. Your brand reputation takes a hit when customers can't trust your automated responses.

The worst part? These hallucinations often sound more helpful than honest answers. An AI that says “I don't know” seems less useful than one that confidently provides detailed (but wrong) information.

How to prevent AI hallucinations in customer support

You can't eliminate AI hallucinations completely, but you can reduce them. The key is moving from pure text generation to grounded, controlled responses based on your real data.

Limit possible outcomes

Instead of letting AI create any response it wants, give it specific options to choose from. For common questions about returns or shipping, create approved response categories. This prevents AI from inventing policies on the spot.

Decision trees work well for this approach. When customers ask about returns, your AI follows a predetermined path: check order date, verify product type, provide appropriate response. No creativity required.

You can also set response templates for frequent questions. Templates include placeholders for customer names or order numbers while keeping core information consistent and accurate.

Related: How to write Guidance with the “when, if, then” framework

Train models on relevant data

Generic AI training creates generic problems. Train your AI specifically on your business data — past support tickets, verified Help Center articles, and current product information.

Your training data should include:

• Real customer conversations: Use actual support tickets to teach AI how customers ask questions and what answers work
• Current product catalogs: Keep AI updated on what you actually sell, not what it thinks you might sell
• Verified policies: Only include official company policies, not industry standards or competitor information
• Brand voice examples: Show AI how your team actually talks to customers
• Your public Help Center: Use the resources you already provide to customers to teach it your most-asked questions

Quality matters more than quantity. It’s better to train on 1,000 accurate examples than 10,000 low-quality ones.

Create response templates

Templates give AI structure while allowing personalization. Instead of generating responses from scratch, AI fills in blanks within approved frameworks.

A shipping template might look like: 

• "Hi [customer name], your order [order number] shipped on [ship date] and will arrive by [delivery date]. Track it here: [tracking link]."

Templates ensure consistency across all AI responses. Customers get the same quality information whether they chat at 2 pm or 2 am. Your brand voice stays consistent even when AI handles the conversation.

Tell the model what to include and exclude

Set explicit boundaries for your AI through system prompts and instructions. Create lists of topics that always need human agents, like product safety complaints or legal threats.

Specify which information sources AI can use. Point it toward your Help Center and product database while blocking access to general internet information that might be outdated or irrelevant.

You can also create escalation triggers. When AI encounters certain keywords or question types, it automatically passes the conversation to a human agent instead of guessing.

Test and refine the system

AI isn't a set-it-and-forget-it tool. Regular testing catches problems before customers do.

Set up quality checks on AI responses. Review a sample of conversations weekly to spot inaccuracies or areas for improvement. Look for patterns in escalated tickets — they often reveal gaps in AI training.

A/B testing helps optimize AI performance. Try different prompt configurations to see which produces more accurate responses. Monitor customer satisfaction scores for AI-handled tickets compared to human-handled ones.

Customer feedback provides valuable insights. When customers report AI errors, use those examples to improve training data and refine response templates.

Add human oversight and escalation

Build safety nets into your AI system. Set confidence thresholds so the AI only responds when it's confident in its answer. Uncertain responses get escalated to human agents automatically.

Create clear escalation paths for complex issues. AI should recognize when questions go beyond its capabilities and smoothly transfer customers to appropriate team members.

Human oversight doesn't mean micromanaging every AI response. Instead, focus on monitoring patterns and intervening when AI consistently struggles with specific questions.

Ground answers in Shopify and help center data

This strategy delivers the biggest impact for ecommerce brands. Grounding means forcing AI to base responses on verified, real-time data from your tech stack.

When customers ask about order status, grounded AI checks live Shopify data instead of guessing. When they ask about policies, it pulls exact text from your Help Center instead of paraphrasing from memory.

Grounding transforms AI from a creative writer into a reliable assistant. It can only share information that exists in your approved systems, dramatically reducing hallucination risk.

Integration with your ecommerce platform ensures AI always has current information about inventory, shipping, and customer orders. No more promising products you don't have or delivery dates you can't meet.

Turn AI accuracy into competitive advantage

The brands winning with AI have put in the hours to train it. They've built systems that combine AI efficiency with human oversight, creating customer experiences that feel both fast and trustworthy.

Gorgias AI Agent demonstrates this approach in action. It grounds responses in your Shopify data and Help Center content, ensuring every automated interaction reflects your actual business information. Customizable guardrails let you set boundaries while maintaining the speed customers expect.

Ready to see how accurate AI can transform your support operations? Book a demo and discover how to automate customer service without sacrificing quality or control.

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