TL;DR:

• Conversational commerce uses real-time messaging to turn support into sales opportunities
• Brands see higher conversion rates through instant answers to pre-purchase questions
• AI and automation handles repetitive inquiries while agents focus on complex issues
• Personalized recommendations and proactive messaging increase average order value
• Customer data from conversations powers smarter marketing and loyalty programs

Ecommerce and retail accounted for over 35% of conversational commerce spend in 2023, totaling $9 billion globally. This isn't surprising — conversational commerce delivers what customers demand nowadays: immediate, personalized responses wherever they shop. 

We’ll explain what conversational commerce is, its benefits for ecommerce brands, and how to implement it effectively.

What is conversational commerce?

Conversational commerce is the practice of using real-time, two-way conversations as your storefront, turning every customer interaction into an opportunity to sell, support, and build relationships through instant messaging.

The key difference from traditional ecommerce is the interactive element. You're not just displaying products and hoping customers buy. You're actively answering questions and guiding shoppers through their experience in real time.

These conversations happen across four main channels:

• Live chat: A chat widget on your site where shoppers get immediate answers from human agents or automation. One agent can manage multiple chats simultaneously, boosting efficiency while keeping things personal.
• AI assistants: Smart helpers that use Natural Language Processing (NLP) to understand customer intent. They guide shoppers through questions, offer product suggestions, handle FAQs, and can complete transactions or post-purchase support right in the chat.
• Messaging apps: WhatsApp, Facebook Messenger, and SMS. Instead of sending customers to your website, you bring the shopping to them in channels they already trust.
• Voice assistants: AI-powered voice support that delivers natural phone conversations without needing a call center. These agents answer questions, route calls, handle returns and exchanges, and personalize support based on customer behavior.

Read more: Conversational commerce: A complete beginner's guide

The benefits of conversational commerce for ecommerce brands

Conversational commerce delivers measurable results that impact both revenue and operational efficiency. Here are the seven key benefits you can expect.

1. Higher conversion rates from instant answers

When shoppers have questions, they want answers immediately. Making them wait for email replies often means losing the sale.

Conversational commerce removes this barrier by providing instant responses. Questions about sizing, product features, or shipping policies get answered in seconds. This is especially critical for mobile shoppers who have less patience for complex navigation.

Real-time answers work because they catch customers at the moment of highest intent. When someone is actively considering a purchase and asks a question, an immediate helpful response often provides the final push they need to buy.

2. Bigger average order value from personalized recommendations

Conversations create natural opportunities for upselling that are often hard to come by when a customer just wants to know where their order is. Based on what customers ask or what's in their cart, you can make relevant recommendations that feel helpful rather than pushy.

• Context-based suggestions: If someone buys a camera, suggest a compatible lens or carrying case
• Bundle recommendations: Offer complete outfits when customers buy individual clothing items
• Upgrade opportunities: Present premium versions when customers ask about basic products

These recommendations work because they're contextual and helpful. Customers see them as expert advice rather than sales pitches, leading to natural increases in average order value.

3. Lower cart abandonment with proactive messaging

Cart abandonment affects nearly every ecommerce store. Conversational commerce gives you powerful tools to combat this problem through proactive engagement.

You can set up triggers that automatically engage shoppers showing signs of abandonment. A simple message like "Questions about the items in your cart?" can re-engage hesitant buyers. You can also offer time-sensitive discounts or clarify shipping information that might be causing hesitation.

The key is timing. Catching customers at the right moment with the right message can recover significant revenue that would otherwise be lost.

Related: Why campaign timing matters: 4 ways to get it right

4. Faster resolutions with automation

Many support inquiries are repetitive and simple to resolve. Questions about order status, return policies, or shipping information can easily be handled by AI agents.

Automating these responses provides several benefits:

• Instant answers: Customers get help immediately, even outside business hours
• Agent efficiency: Human agents focus on complex issues that require personal attention
• Consistent quality: AI provides accurate, on-brand responses every time
• Scalability: Handle volume spikes without increasing headcount

This automation doesn't replace human agents. It frees them to do more work that drives actual business value.

5. Lower support costs with self-service

Self-service capabilities significantly reduce support ticket volume. AI-powered chatbots and well-structured help centers can deflect common questions before they reach your team.

This approach allows you to scale support operations without proportionally increasing costs. You can handle seasonal volume spikes like Black Friday Cyber Monday without overwhelming your team or sacrificing service quality.

The cost savings compound over time. Every automated resolution reduces the load on human agents, allowing smaller teams to support larger customer bases effectively.

6. Richer customer data for smarter campaigns

Every conversation generates valuable zero-party data — information customers willingly share with you. Through natural dialogue, you learn about preferences, pain points, and purchase motivations.

This data becomes a goldmine for marketing teams:

• Targeted segments: Create highly specific customer groups based on expressed interests
• Personalized content: Tailor website and email content to individual preferences
• Product development: Use customer feedback to inform future product decisions
• Campaign optimization: Understand what messaging resonates with different customer types

The more you understand your customers through conversations, the more effective all your marketing becomes.

7. Stronger loyalty through consistent, human-like support

Conversational commerce builds relationships through every interaction. When customers feel heard and valued, they become repeat buyers and brand advocates.

Fast, helpful, and personalized interactions create memorable experiences that build trust. By maintaining consistent brand voice across all channels and providing support that feels human, you foster emotional connections with customers.

These relationships are the foundation of long-term business success. Loyal customers have higher lifetime value, make more frequent purchases, and refer others to your brand.

High-impact use cases for DTC brands

DTC brands thrive by turning the online shopping experience into a competitive advantage. Maximizing each touchpoint with conversational commerce is how you do it. Focus on these use cases for quick, measurable impact.

Pre-purchase consultative selling for guidance

Products requiring education — like skincare, supplements, or technical apparel — hugely benefit from conversational selling. Chat acts as a virtual consultant, helping customers find the product made for them.

How to implement: Create guided flows that ask about customer needs and recommend perfect products. This consultative approach builds confidence and helps shoppers feel certain about their choices.

Order status and returns self-service for deflection

Order status and returns questions dominate most support queues. Automating these inquiries reduces the load of day-to-day tasks, benefiting long-term efficiency.

How to implement: Set up self-serve order management on your website. Guide customers through return initiation directly within chat and link to your returns portal. This deflects huge volumes of repetitive tickets.

Cart and discount recovery for saved revenue

Proactively engaging cart abandoners delivers some of the highest ROI in conversational commerce. When customers have items in cart but haven't checked out, trigger helpful messages.

How to implement: Offer to answer questions or provide time-sensitive discounts to create urgency. This simple intervention can recover significant otherwise-lost revenue.

Best practices to get started without overwhelming your team

Implementing conversational commerce doesn't require massive overhauls. Start small, prove value, and expand based on results.

Start with top intents like WISMO and returns

Don't automate everything immediately. Begin with your highest-volume, most repetitive inquiries — typically order status questions and return policy inquiries.

Build solid automation for these top intents first. Measure impact on ticket volume, resolution time, and customer satisfaction. This creates clear wins and builds momentum for future expansion.

Launch on one high-impact channel first

Choose one channel based on where your customers are most active. Analyze your data to understand whether that's website chat, Instagram DMs, or SMS.

Master that channel before expanding to others. This allows you to test, learn, and optimize in a controlled environment. Apply these learnings as you scale to ensure consistent, high-quality experiences everywhere.

The future of conversational commerce for ecommerce teams

Generative AI is making support conversations more natural than ever.

The future focuses on proactive and predictive engagement, where brands anticipate customer needs before they're expressed. As privacy concerns grow, owned channels and first-party data from conversations become increasingly valuable for building direct customer relationships.

Ready to see how leading ecommerce brands turn every customer conversation into growth opportunities? Book a demo to see Gorgias in action and learn how you can transform your customer experience.

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TL;DR:

• Most food & beverage support tickets during BFCM are predictable. Subscription cancellations, WISMO, and product questions make up the bulk—so prep answers ahead of time.
• Proactive CX site updates can drastically cut down repetitive tickets. Add ingredient lists, cooking instructions, and clear refund policies to product pages and FAQs.
• FAQ pages should go deep, not just broad. Answer hyper-specific questions like “Will this break my fast?” to help customers self-serve without hesitation.‍
• Transparency about stock reduces confusion and cart abandonment. Show inventory levels, set up waitlists, and clearly state cancellation windows.

In 2024, Shopify merchants drove $11.5 billion in sales over Black Friday Cyber Monday. Now, BFCM is quickly approaching, with some brands and major retailers already hosting sales.

If you’re feeling late to prepare for the season or want to maximize the number of sales you’ll make, we’ll cover how food and beverage CX teams can serve up better self-serve resources for this year’s BFCM. 

Learn how to answer and deflect customers’ top questions before they’re escalated to your support team.

💡 Your guide to everything peak season → The Gorgias BFCM Hub

Handling BFCM as a food & beverage brand

During busy seasons like BFCM and beyond, staying on top of routine customer asks can be an extreme challenge. 

“Every founder thinks BFCM is the highest peak feeling of nervousness,” says Ron Shah, CEO and Co-founder of supplement brand Obvi. 

“It’s a tough week. So anything that makes our team’s life easier instantly means we can focus more on things that need the time,” he continues. 

Anticipating contact reasons and preparing methods (like automated responses, macros, and enabling an AI Agent) is something that can help. Below, find the top contact reasons for food and beverage companies in 2025. 

Top contact reasons in the food & beverage industry 

According to Gorgias proprietary data, the top reason customers reach out to brands in the food and beverage industry is to cancel a subscription (13%) followed by order status questions (9.1%).

7 ways to improve your self-serve resources before BFCM

1. Add informative blurbs on product pages 
2. Craft additional help center and FAQ articles 
3. Automate responses with AI or Macros 
4. Get specific about product availability
5. Provide order cancellation and refund policies upfront
6. Add how-to information
7. Build resources to help with buying decisions 

1) Add informative blurbs on product pages

Because product detail queries represent 5.7% of contact reasons for the food and beverage industry, the more information you provide on your product pages, the better. 

Include things like calorie content, nutritional information, and all ingredients.  

For example, ready-to-heat meal company The Dinner Ladies includes a dropdown menu on each product page for further reading. Categories include serving instructions, a full ingredient list, allergens, nutritional information, and even a handy “size guide” that shows how many people the meal serves. 

2) Craft additional Help Center and FAQ articles

FAQ pages make up the information hub of your website. They exist to provide customers with a way to get their questions answered without reaching out to you.   

This includes information like how food should be stored, how long its shelf life is, delivery range, and serving instructions. FAQs can even direct customers toward finding out where their order is and what its status is. 

In the context of BFCM, FAQs are all about deflecting repetitive questions away from your team and assisting shoppers in finding what they need faster. 

That’s the strategy for German supplement brand mybacs. 

“Our focus is to improve automations to make it easier for customers to self-handle their requests. This goes hand in hand with making our FAQs more comprehensive to give customers all the information they need,” says Alexander Grassmann, its Co-Founder & COO.

As you contemplate what to add to your FAQ page, remember that more information is usually better. That’s the approach Everyday Dose takes, answering even hyper-specific questions like, “Will it break my fast?” or “Do I have to use milk?”

While the FAQs you choose to add will be specific to your products, peruse the top-notch food and bev FAQ pages below. 

Time for some FAQ inspo:

• Juice Plus+ FAQ
• Everyday Dose FAQ
• Chamberlain Coffee FAQ
• Jurassic Fruit FAQ
• Rebel Cheese FAQ
• The Dinner Ladies FAQ

3) Automate responses with AI or macros

AI Agents and AI-powered Shopping Assistants are easy to set up and are extremely effective in handling customer interactions––especially during BFCM.  

“I told our team we were going to onboard Gorgias AI Agent for BFCM, so a good portion of tickets would be handled automatically,” says Ron Shah, CEO and Co-founder at Obvi. “There was a huge sigh of relief knowing that customers were going to be taken care of.” 

And, they’re getting smarter. AI Agent’s CSAT is just 0.6 points shy of human agents’ average CSAT score. 

Here are the specific responses and use cases we recommend automating: 

• WISMO (where is my order) inquiries 
• Product related questions 
• Returns 
• Order issues
• Cancellations 
• Discounts, including BFCM related 
• Customer feedback
• Account management
• Collaboration requests 
• Rerouting complex queries

Get your checklist here: How to prep for peak season: BFCM automation checklist

4) Get specific about product availability

With high price reductions often comes faster-than-usual sell out times. By offering transparency around item quantities, you can avoid frustrated or upset customers. 

For example, you could show how many items are left under a certain threshold (e.g. “Only 10 items left”), or, like Rebel Cheese does, mention whether items have sold out in the past.  

You could also set up presales, give people the option to add themselves to a waitlist, and provide early access to VIP shoppers. 

5) Provide order cancellation and refund policies upfront 

Give shoppers a heads up whether they’ll be able to cancel an order once placed, and what your refund policies are. 

For example, cookware brand Misen follows its order confirmation email with a “change or cancel within one hour” email that provides a handy link to do so. 

Your refund policies and order cancellations should live within an FAQ and in the footer of your website. 

6) Add how-to information 

Include how-to information on your website within your FAQs, on your blog, or as a standalone webpage. That might be sharing how to use a product, how to cook with it, or how to prepare it. This can prevent customers from asking questions like, “how do you use this?” or “how do I cook this?” or “what can I use this with?” etc. 

For example, Purity Coffee created a full brewing guide with illustrations:

Similarly, for its unique preseasoned carbon steel pan, Misen lists out care instructions: 

And for those who want to understand the level of prep and cooking time involved, The Dinner Ladies feature cooking instructions on each product page. 

7) Build resources to help with buying decisions 

Interactive quizzes, buying guides, and gift guides can help ensure shoppers choose the right items for them––without contacting you first. 

For example, Trade Coffee Co created a quiz to help first timers find their perfect coffee match: 

Set your team up for BFCM success with Gorgias 

The more information you can share with customers upfront, the better. That will leave your team time to tackle the heady stuff. 

If you’re looking for an AI-assist this season, check out Gorgias’s suite of products like AI Agent and Shopping Assistant. 

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TL;DR:

• Conversational AI combines natural language processing, machine learning, and generative AI to create human-like interactions
• For ecommerce, it automates customer service, drives sales through personalized recommendations, and scales support 24/7
• Key types include chatbots, voice assistants, and AI agents that handle both support and sales tasks
• Implementation requires defining clear goals, choosing an ecommerce-ready platform, and connecting your tech stack

Conversational AI changes how ecommerce brands interact with customers by enabling natural, human-like conversations at scale, helping reduce customer churn. 

Instead of forcing shoppers through rigid menus or making them wait for support, conversational AI understands questions, detects intent, and delivers instant, personalized responses. 

This technology powers everything from customer service chatbots to voice assistants, helping brands automate repetitive tasks while maintaining the personal touch customers expect. 

For ecommerce specifically, it means handling order inquiries, providing product recommendations, and recovering abandoned carts — all without adding headcount.

What is conversational AI?

Conversational AI is a type of artificial intelligence that allows computers to understand, process, and respond to human language through natural, two-way conversations. This means your customers can ask questions in their own words and get helpful answers that feel like they're talking to a real person.

Unlike basic chatbots that only recognize specific keywords, conversational AI actually understands what your customers mean. It can handle typos, slang, and complex questions that have multiple parts. The AI learns from every conversation, getting better at helping your customers over time.

Think of it as having a super-smart team member who never sleeps, never gets frustrated, and remembers every detail about your products and policies. This AI team member can chat with customers on your website, answer questions through social media, or even handle phone calls.

What are the key components of conversational AI?

Conversational AI works because several smart technologies team up to understand and respond to your customers. Each piece has a specific job in making conversations feel natural and helpful.

Natural Language Processing (NLP) is the foundation that breaks down human language into pieces a computer can understand. This means when a customer types "Where's my order?" the AI can identify the important words and grammar structure.

Natural Language Understanding (NLU) figures out what the customer actually wants. This is the smart part that realizes "Where's my order?" means the customer wants to track a shipment, even if they phrase it differently like "I need to check my package status."

Natural Language Generation (NLG) creates responses that sound human and helpful. Instead of robotic answers, it crafts replies that match your brand's voice and provide exactly what the customer needs to know.

The dialog manager keeps track of the entire conversation. This means if a customer asks a follow-up question, the AI remembers what you were just talking about and can give a relevant answer.

Your knowledge base stores all the information the AI needs to help customers. This includes your return policy, product details, shipping information, and any other facts your team would use to answer questions.

How does conversational AI work?

Conversational AI follows a simple three-step process that happens in seconds. Understanding this process helps you see why it's so much more powerful than old-school chatbots.

1) It processes input across voice and text with NLP

When a customer sends a message or asks a question, the AI first needs to understand what they're saying. For text messages from chat, email, or social media, the system breaks down the sentence into individual words and analyzes the grammar.

For voice interactions like phone calls, the AI uses speech recognition to turn spoken words into text first. Modern systems handle different accents, background noise, and natural speech patterns without missing a beat.

2) It detects intent and context with NLU

Once the AI has the customer's words, it needs to figure out what they actually want. The system looks for the customer's intent — their goal or what they're trying to accomplish.

For example, when someone asks "Can I return this sweater I bought last week?" the AI identifies the intent as wanting to make a return. It also pulls out important details like the product type and timeframe.

The AI also uses context from earlier in the conversation. If the customer mentioned their order number earlier, the AI remembers it and can use that information to help with the return request.

3) It generates responses with NLG

After understanding what the customer wants, the AI creates a helpful response. It might pull information from your knowledge base, personalize the answer with the customer's specific details, or generate a completely new response using generative AI.

The system also checks how confident it is in its answer. If the AI isn't sure about something or if the topic is too complex, it knows to hand the conversation over to one of your human agents.

What are the types of conversational AI?

Different types of conversational AI work better for different situations in your ecommerce business. Understanding these types helps you choose the right solution for your customers and team.

Chatbots handle scripted and AI-driven chat

Chatbots are the most common type you'll see on websites and messaging apps. Early chatbots followed strict scripts — if a customer's question didn't match the script exactly, the bot would get confused and give unhelpful answers.

Modern AI-powered chatbots understand natural language and can handle much more complex conversations. The best systems combine both approaches: using simple rules for straightforward questions and AI for everything else.

These chatbots work great for answering common questions about shipping, returns, and product details. They can also help customers find the right products or guide them through your checkout process.

Voice assistants manage speech-based requests

Voice assistants bring conversational AI to phone support and other voice channels. These aren't the old phone trees that made customers press numbers to navigate menus.

Instead, customers can speak naturally and get helpful answers right away. Voice assistants can look up order information, explain your return policy, or even process simple requests like address changes.

This works especially well for customers who prefer calling over typing, or when they need help while their hands are busy.

Read more: How Cornbread Hemp reached a 13.6% phone conversion rate with Gorgias Voice

AI agents and copilots assist teams and customers

AI agents are the most advanced type of conversational AI. Unlike chatbots that mainly provide information, AI agents can actually take action on behalf of customers.

These systems connect to your other business tools like Shopify, your shipping software, or your returns platform. This means they can do things like:

• Process returns: Start a return and send the customer a shipping label
• Update orders: Change a shipping address or add items to an existing order
• Handle refunds: Issue refunds for eligible orders automatically
• Manage subscriptions: Skip shipments or update subscription preferences

Copilots work alongside your human agents, suggesting responses and pulling up customer information to help resolve issues faster.

Read more: How AI Agent works & gathers data

What are the benefits of conversational AI for ecommerce?

Conversational AI delivers real business results for ecommerce brands. The benefits go beyond just making your support team more efficient — though that's certainly part of it.

24/7 availability means you never miss a sale or support opportunity. Customers can get help at 2 a.m. or during holidays when your team is offline. This is especially valuable for international customers in different time zones.

Instant responses prevent cart abandonment and customer frustration, improving first contact resolution. When someone has a question about sizing or shipping, they get an answer immediately instead of waiting hours or days for an email response.

Personalized interactions at scale drive higher average order values. The AI can recommend products based on what customers are browsing, their purchase history, and their preferences, just like your best salesperson would.

Cost efficiency comes from handling repetitive questions automatically. Your human agents can focus on complex issues, VIP customers, and revenue-generating activities instead of answering the same shipping questions over and over.

Multilingual support helps you serve global customers without hiring native speakers for every language. The AI can communicate in dozens of languages, opening up new markets for your business.

What are the most valuable conversational AI use cases for ecommerce?

Certain moments in the shopping experience create the biggest opportunities for conversational AI to drive results. Focus on these high-impact use cases first.

Pre-purchase questions are your biggest conversion opportunity. When someone is looking at a product but hasn't bought yet, quick answers about sizing, materials, or compatibility can close the sale. The AI can also suggest complementary products or highlight features the customer might have missed.

Order tracking makes up the largest volume of support tickets for most ecommerce brands. Customers want to know where their package is, when it will arrive, and what to do if there's a delay. AI handles these WISMO requests instantly by pulling real-time tracking information.

Returns and exchanges can be complex, but AI excels at the initial screening. It can check if an item is eligible for return, explain your policy, and start the return process. For straightforward returns, customers never need to wait for human help.

Cart recovery works best when it's immediate and personal. AI can detect when someone abandons their cart and reach out through chat or email with personalized messages, discount offers, or answers to common concerns that prevent purchases.

Post-purchase support keeps customers happy after they buy. The AI can send order confirmations, provide care instructions, suggest related products, and handle simple issues like address changes.

How do you implement conversational AI in an ecommerce tech stack?

Getting started with conversational AI doesn't require a complete overhaul of your systems. The key is starting with clear goals and building your capabilities over time.

Step 1: Define goals and KPIs for automation

The best automation opportunities are found in your tickets. Look for questions that come up repeatedly and have straightforward answers. Common examples include order status, return policies, and basic product information.

Set realistic goals for your first phase. You might aim to automate 30% of your tickets or reduce average response time by half. Track metrics like:

• Automation rate: Percentage of tickets resolved without human intervention
• Customer satisfaction: How happy customers are with AI interactions
• Revenue impact: Sales influenced by AI recommendations or cart recovery

Step 2: Choose an ecommerce-ready AI platform

Not all conversational AI platforms understand ecommerce needs. Look for a platform that integrates directly with Shopify and your other business tools. This connection is essential for pulling real-time order data, customer history, and product information.

Your platform should come with pre-built actions for common ecommerce tasks like order lookups, return processing, and subscription management. This saves months of custom development work.

Make sure you can control the AI's behavior through clear guidance and rules. You need to be able to set your brand voice, define when to escalate to humans, and update the AI's knowledge as your business changes.

Step 3: Connect Shopify and key tools, then iterate

Start your implementation by connecting your Shopify store to give the AI access to order and customer data. Don’t forget to integrate the rest of your tech stack like shipping software, returns platforms, and loyalty programs.

Launch with a few core use cases like order tracking and basic product questions. Monitor the AI's performance closely and gather feedback from both customers and your support team. Use this data to refine the AI's responses and gradually expand its capabilities. 

The best approach is iterative — start small, learn what works, and build from there.

What are the challenges and risks of conversational AI?

While conversational AI offers significant benefits, you need to be aware of potential challenges and plan for them from the start.

Accuracy concerns arise when AI systems provide incorrect information or "hallucinate" facts that aren't true. Prevent this by using platforms that ground responses in your verified knowledge base and product data rather than generating answers from scratch.

Brand voice consistency becomes critical when AI represents your brand to customers. Set clear guidelines for tone, style, and messaging. Test the AI's responses regularly to ensure they align with how your human team would handle similar situations.

Data privacy requires careful attention since conversational AI handles sensitive customer information. Choose platforms with strong security measures, data encryption, and compliance with regulations like GDPR. Look for features like automatic removal of personal information from conversation logs.

Over-automation can frustrate customers when complex issues require human empathy and problem-solving. Design clear escalation paths so customers can easily reach human agents when needed. Train your AI to recognize when a situation is beyond its capabilities.

Integration complexity can slow down implementation if your chosen platform doesn't work well with your existing tools. This is why choosing an ecommerce-focused platform with pre-built integrations is so important.

Turn conversations into revenue with conversational AI

The brands winning with conversational AI start with clear goals, choose the right platform, and iterate based on real performance data. They don't try to automate everything at once. They focus on high-impact use cases that deliver real results.

Ready to see how conversational AI can transform your ecommerce support and sales? Book a demo with Gorgias — built specifically for ecommerce brands.

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TL;DR:

• You don’t need to rebuild your Help Center to make it work with AI—you just need to structure it smarter.
• AI Agent reads your content in three layers: Help Center, Guidance, and Actions, following an “if / when / then” logic to find and share accurate answers.
• Most AI escalations happen because Help Docs are vague or incomplete. Start by improving your top 10 ticket topics—like order status, returns, and refunds.
• Make your articles scannable, define clear conditions, link next steps, and keep your tone consistent. These small tweaks help AI Agent resolve more tickets on its own—and free up your team to focus on what matters most.

As holiday season support volumes spike and teams lean on AI to keep up, one frustration keeps surfacing, our Help Center has the answers—so why can’t AI find them?

The truth is, AI can’t help customers if it can’t understand your Help Center. Most large language models (LLMs), including Gorgias AI Agent, don’t ignore your existing docs, they just struggle to find clear, structured answers inside them.

The good news is you don’t need to rebuild your Help Center or overhaul your content. You simply need to format it in a way that’s easy for both people and AI to read.

We’ll break down how AI Agent reads your Help Center, finds answers, and why small formatting changes can help it respond faster and more accurately, so your team spends less time on escalations.

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How AI Agent uses your Help Center content

Before you start rewriting your Help Center, it helps to understand how AI Agent actually reads and uses it.

Think of it like a three-step process that mirrors how a trained support rep thinks through a ticket.

1. Read Help Center docs

Your Help Center is AI Agent’s brain. AI Agent uses your Help Center to pull facts, policies, and instructions it needs to respond to customers accurately. If your articles are clearly structured and easy to scan, AI Agent can find what it needs fast. If not, it hesitates or escalates.

2. Follow Guidance instructions

Think of Guidance as AI Agent’s decision layer. What should AI Agent do when someone asks for a refund? What about when they ask for a discount? Guidance helps AI Agent provide accurate answers or hand over to a human by following an “if/when/then” framework.

3. Respond and perform

Finally, AI Agent uses a combination of your help docs and Guidance to respond to customers, and if enabled, perform an Action on their behalf—whether that’s changing a shipping address or canceling an order altogether.

Here’s what that looks like in practice:

This structure removes guesswork for both your AI and your customers. The clearer your docs are about when something applies and what happens next, the more accurate and human your automated responses will feel.

A Help Center written for both people and AI Agent:

• Saves your team time
• Reduces escalations
• Helps every customer get the right answer the first time

What causes AI Agent to escalate tickets, and how to fix it

Our data shows that most AI escalations happen for a simple reason––your Help Center doesn’t clearly answer the question your customer is asking.

That’s not a failure of AI. It’s a content issue. When articles are vague, outdated, or missing key details, AI Agent can’t confidently respond, so it passes the ticket to a human.

Here are the top 10 topics that trigger escalations most often:

Each of these topics needs a dedicated, clearly structured Help Doc that uses keywords customers are likely to search and spells out specific conditions. 

Here’s how to strengthen each one:

• Order status: Include expected delivery timelines, tracking link FAQs, and a clear section for “what to do if tracking isn’t updating.”
• Return request: Spell out eligibility requirements, time limits, and how to print or request a return label.
• Order cancellation: Define cut-off times for canceling and link to your “returns” doc for shipped orders.
• Product quality issues: Explain what qualifies as a defect, how to submit photos, and whether replacements or refunds apply.
• Missing item: Clarify how to report missing items and what verification steps your team takes before reshipping.
• Subscription cancellation: Add “if/then” logic for different cases: if paused vs. canceled, if prepaid vs. monthly.
• Order refund: Outline refund timelines, where customers can see status updates, and any exceptions (e.g., partial refunds).
• Product details: Cover sizing, materials, compatibility, or FAQs that drive most product-related questions.
• Return status: State how long returns take to process and where to check progress once a label is scanned.
• Order delivered but not received: Provide step-by-step guidance for checking with neighbors, filing claims, or requesting replacements.

Start by improving these 10 articles first. Together, they account for nearly half of all AI Agent escalations. The clearer your Help Center is on these topics, the fewer tickets your team will ever see, and the faster your AI will resolve the rest.

How to format your Help Center docs for LLMs

Once you know how AI Agent reads your content, the next step is formatting your help docs so it can easily understand and use them. 

The goal isn’t to rewrite everything, it’s to make your articles more structured, scannable, and logic-friendly. 

Here’s how.

1. Use structured, scannable sections

Both humans and large language models read hierarchically. If your article runs together in one long block of text, key answers get buried.

Break articles into clear sections and subheadings (H2s, H3s) for each scenario or condition. Use short paragraphs, bullets, and numbered lists to keep things readable.

Example:

How to Track Your Order

• Step 1: Find your tracking number in your confirmation email.
• Step 2: Click the tracking link to see your delivery status.
• Step 3: If tracking hasn’t updated in 3 days, contact support.

A structured layout helps both AI and shoppers find the right step faster, without confusion or escalation.

2. Write for “if/when/then” logic

AI Agent learns best when your Help Docs clearly define what happens under specific conditions. Think of it like writing directions for a flowchart.

Example:

• “If your order hasn’t arrived within 10 days, contact support for a replacement.”
• “If your order has shipped, you can find the tracking link in your order confirmation email.”

This logic helps AI know what to do and how to explain the answer clearly to the customer.

3. Clarify similar terms and synonyms

Customers don’t always use the same words you do, and neither do LLMs. If your docs treat “cancel,” “stop,” and “pause” as interchangeable, AI Agent might return the wrong answer.

Define each term clearly in your Help Center and add small keyword variations (“cancel subscription,” “end plan,” “pause delivery”) so the AI can recognize related requests.

4. Link to next steps

AI Agent follows links just like a human agent. If your doc ends abruptly, it can’t guide the customer any further.

Always finish articles with an explicit next step, like linking to:

• A form
• Another article
• A support action page

Example: “If your return meets our policy, request your return label here.”

That extra step keeps the conversation moving and prevents unnecessary escalations.

5. Keep tone consistent

AI tools prioritize structure and wording when learning from your Help Center—not emotional tone. 

Phrases like “Don’t worry!” or “We’ve got you!” add noise without clarity.

Instead, use simple, action-driven sentences that tell the customer exactly what to do:

• “Click here to request a refund.”
• “Fill out the warranty form to get a replacement.”

A consistent tone keeps your Help Center professional, helps AI deliver reliable responses, and creates a smoother experience for customers.

LLM-friendly Help Centers in action

You don’t need hundreds of articles or complex workflows to make your Help Center AI-ready. But you do need clarity, structure, and consistency. These Gorgias customers show how it’s done.

Little Words Project: Simple formatting that boosts instant answers

Little Words Project keeps things refreshingly straightforward. Their Help Center uses short paragraphs, descriptive headers, and tightly scoped articles that focus on a single intent, like returns, shipping, or product care. 

That makes it easy for AI Agent to scan the page, pull out the right facts, and return accurate answers on the first try.

Their tone stays friendly and on-brand, but the structure is what shines. Every article flows from question → answer → next step. It’s a minimalist approach, and it works. Both for customers and the AI reading alongside them.

Dr. Bronner’s: Making tools work for the team

Customer education is at the heart of Dr. Bronner’s mission. Their customers often ask detailed questions about product ingredients, packaging, and certifications. With Gorgias, Emily and her team were able to build a robust Help Center that helped to proactively give this information.

The Help Center doesn't just provide information. The integration of interactive Flows, Order Management, and a Contact Form automation allowed Dr. Bronner’s to handle routine inquiries—such as order statuses—quickly and efficiently. These kinds of interactive elements are all possible out-of-the-box, no IT support needed.

Read more: How Dr. Bronner's saved $100k/year by switching from Salesforce, then automated 50% of interactions with Gorgias 

Ekster: Building efficiency through automation and clarity

When Ekster switched to Gorgias, the team wanted to make their Help Center work smarter. By writing clear, structured articles for common questions like order tracking, returns, and product details, they gave both customers and AI Agent the information needed to resolve issues instantly.

"Our previous Help Center solution was the worst. I hated it. Then I saw Gorgias’s Help Center features, and how the Article Recommendations could answer shoppers’ questions instantly, and I loved it. I thought: this is just what we need." —Shauna Cleary, Head of Ecommerce at Ekster

The results followed fast. With well-organized Help Center content and automation built around it, Ekster was able to scale support without expanding the team.

“With all the automations we’ve set up in Gorgias, and because our team in Buenos Aires has ramped up, we didn’t have to rehire any extra agents.” —Shauna Cleary, Head of Ecommerce at Ekster

Learn more: How Ekster used automation to cover the workload of 4 agents 

Rowan: Clean structure that keeps customers (and AI) on track

Rowan’s Help Center is a great example of how clear structure can do the heavy lifting. Their FAQs are grouped into simple categories like piercing, shipping, returns, and aftercare, so readers and AI Agent can jump straight to the right topic without digging. 

For LLMs, that kind of consistency reduces guesswork. For customers, it creates a smooth, reassuring self-service experience. 

TUSHY: Balancing brand voice with automation

TUSHY proves you can maintain personality and structure. Their Help Center articles use clear headings, direct language, and brand-consistent tone. It makes it easy for AI Agent to give accurate, on-brand responses.

“Too often, a great interaction is diminished when a customer feels reduced to just another transaction. With AI, we let the tech handle the selling, unabashedly, if needed, so our future customers can ask anything, even the questions they might be too shy to bring up with a human. In the end, everybody wins!" —Ren Fuller-Wasserman, Senior Director of Customer Experience at TUSHY

Quick checklist to audit your Help Center for AI

Ready to put your Help Center to the test? Use this five-point checklist to make sure your content is easy for both customers and AI to navigate.

1. Are your articles scannable with clear headings?

Break up long text blocks and use descriptive headers (H2s, H3s) so readers and AI Agent can instantly find the right section.

2. Do you define conditions with “if/when/then” phrasing?

Spell out what happens in each scenario. This logic helps AI Agent decide the right next step without second-guessing.

3. Do you cover your top escalation topics?

Make sure your Help Center includes complete, structured articles for high-volume issues like order status, returns, and refunds.

4. Does each article end with a clear next step or link?

Close every piece with a call to action, like a form, related article, or support link, so neither AI nor customers hit a dead end.

5. Is your language simple, action-based, and consistent?

Use direct, predictable phrasing. Avoid filler like “Don’t worry!” and focus on steps customers can actually take.

By tweaking structure instead of your content, it’s easier to turn your Help Center into a self-service powerhouse for both customers and your AI Agent.

Make your Help Center work smarter

Your Help Center already holds the answers your customers need. Now it’s time to make sure AI can find them. A few small tweaks to structure and phrasing can turn your existing content into a powerful, AI-ready knowledge base.

If you’re not sure where to start, review your Help Center with your Gorgias rep or CX team. They can help you identify quick wins and show you how AI Agent pulls information from your articles.

Remember: AI Agent gets smarter with every structured doc you publish.

Ready to optimize your Help Center for faster, more accurate support? Book a demo today.

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TL;DR:

• How AI for customer support works: machine learning and natural language processing to automate repetitive tickets and assist human agents
• AI for customer support benefits: Reduced response times, lower support costs, and increased conversion rates 
• Common AI uses: Automating WISMO inquiries, returns processing, and product recommendations
• How to implement: Pay attention to data privacy, brand voice consistency, and measuring ROI

The days of waiting for support to respond for hours or days are gone now that AI is here to stay. In the ecommerce world, AI has become an essential part of CX team’s toolkits, addressing common questions about orders, returns, and products without losing personalized service.

This technology combines natural language processing with your brand's specific knowledge to deliver accurate, on-brand responses across email, chat, and other channels. The result is faster support that drives sales while slashing operational costs.

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What is AI for customer support

AI for customer support is software that uses machine learning to understand and respond to customer questions automatically. This means your customers get instant answers to common questions without waiting for a human agent to respond.

Unlike basic automation that follows pre-defined rules, AI actively learns from every conversation. It gets smarter over time and can handle more complex questions as it processes more data from your support tickets.

The technology works through several key parts:

• Natural language processing: AI understands what customers mean, not just the exact words they type
• Intent classification: The system figures out what the customer actually wants to accomplish
• Knowledge base integration: AI pulls answers from your product information, policies, and past conversations
• Omnichannel deployment: The same AI works across email, chat, social media, and phone support

AI doesn't replace your human agents. Instead, it handles repetitive questions so your team can focus on problems that require a unique human touch.

Related: What is conversational AI? The ecommerce guide

What are the benefits of using AI for customer support

AI delivers immediate improvements to both your customer experience and your bottom line. Your customers get faster responses, and your business saves money while increasing sales.

The most important benefits include:

• Instant responses: Customers get answers in seconds instead of hours or days
• Lower costs: Each ticket costs less to resolve while maintaining high quality
• 24/7 availability: Support never stops, even when your team is offline
• Consistent quality: Every response follows your brand guidelines and policies
• Revenue growth: AI turns support conversations into sales opportunities

You'll see improvements in key metrics like customer satisfaction (CSAT) scores and first contact resolution rates. Your average handle time (AHT) drops because AI resolves simple questions instantly. During busy seasons like Black Friday, AI helps you meet service level agreements (SLAs) without hiring temporary staff.

Most importantly, AI creates revenue. By providing instant product recommendations and helping customers complete purchases, your support team becomes a sales channel.

How ecommerce brands use AI for customer support

Smart ecommerce brands use AI to handle their most common and time-consuming support requests. This frees up human agents to focus on building relationships and solving complex problems.

Resolve where-is-my-order requests

"Where is my order" questions make up the biggest chunk of ecommerce support tickets. AI completely automates these by connecting to your order management system and pulling real-time tracking information.

When a customer asks about their order, AI instantly checks the status and provides tracking details. If there's a delay, it explains what happened and gives an updated delivery estimate.

Automate returns and exchanges

AI guides customers through your entire returns process without human help. It checks if items qualify for returns based on your policy, generates return shipping labels, and processes exchanges.

For brands using returns platforms like Loop Returns, AI can automatically send customers to their returns portal with all their order information pre-filled.

Handle cancellations and order edits

Speed matters when customers want to cancel or change orders. AI checks if an order has shipped yet and processes cancellations automatically for orders still in your warehouse.

For shipped orders or complex changes like address updates, AI gathers all the necessary information and routes the ticket to the right human agent with full context.

Related: Why faster isn’t always better: The pitfalls of fast-only customer support

Answer product and sizing questions

Shoppers need answers before they buy. AI acts as a personal shopping assistant, using your product catalog and sizing guides to answer questions about fit, materials, and features.

When items are out of stock, AI suggests similar alternatives to save the sale instead of losing the customer.

Troubleshoot shipping and delivery issues

Delivery problems create frustrated customers who need immediate help. AI tracks packages in real-time, identifies issues like delays or failed deliveries, and provides resolution options.

For serious problems like lost or damaged packages, AI escalates to human agents with all the tracking details and customer information ready.

Manage discounts and promotions

AI handles all types of discount questions, from explaining promotion terms to troubleshooting codes that won't work. It can even apply forgotten discount codes retroactively if your policies allow it.

During busy sale periods, AI prevents your team from getting overwhelmed with promo code questions.

Recommend products and drive upsells

AI doesn't just solve problems — it creates sales opportunities. By analyzing what customers are browsing and their purchase history, AI suggests relevant products they might want.

This personalized approach increases your average order value (AOV) and turns routine support conversations into revenue-generating interactions.

Give callers fast answers

AI phone solutions keep your phone channel helpful, fast, and cost-efficient without sacrificing the personal feel callers prefer. It takes care of simple, high-volume requests, such as order status, subscription updates, address changes, so your team can focus on calls that move revenue or require empathy.

It picks up on tone and frustration, then routes customers to a person before the situation escalates. This matters most when:

• The purchase is high-value and shoppers want to hear a voice
• Your team is buried during peak season
• You need a reliable fallback when chat and email queues are full

How to use AI for customer support to improve speed and sales

Getting the most from AI requires a strategic approach that is both efficient and beneficial to your bottom line. Start by analyzing your support data to find the highest-volume, most repetitive questions, then build automated workflows to resolve them.

What to consider before implementing AI for customer support

Success with AI requires planning around several key areas that affect both performance and customer trust.

Protect customer data and privacy

AI systems process sensitive customer information, so security is critical. Choose platforms that comply with privacy regulations like GDPR and have strong security certifications.

Be transparent with customers about how you use their data. This builds trust and ensures you meet legal requirements in all the markets where you sell.

Read more: Should brands disclose AI in customer interactions? A guide for CX leaders

Maintain brand voice and accuracy

Your AI must sound like your brand in every interaction. Train the AI on your specific brand voice, style, and terminology so responses feel authentic to your customers.

Set up guardrails to prevent off-brand or incorrect responses. Create a process for monitoring conversations and making corrections when needed.

Measure time to value and ROI

Define success metrics before you start. Identify which numbers you want to improve, like response time or cost per ticket, and establish baseline measurements.

Track both cost savings and revenue generation to calculate your full return on investment (ROI). This helps justify the investment and guide future improvements.

Align people, process, and workflow

AI works best when it complements your human team, not replaces them. Plan for change management and train agents on working alongside AI.

Redesign your workflows to create smooth handoffs between AI and human agents. This ensures customers get consistent service regardless of who helps them.

How to get started with AI for customer support

If you’re ready to go all in with AI, you don’t need to complete overhaul your support operations. 

Follow this practical roadmap to see value quickly while building toward more advanced capabilities:

1. Analyze your ticket data to identify automation opportunities
2. Define success metrics and ROI targets
3. Choose an AI platform built for ecommerce
4. Run a pilot with one high-volume use case like order tracking
5. Expand based on pilot results and learnings

How to choose an AI platform for customer support

Not all AI platforms work well for ecommerce brands. Focus on solutions built specifically for online retail with deep integrations into your existing tech stack.

Look for platforms that connect natively to Shopify, your shipping providers, and other essential tools. Strong API capabilities let you build custom workflows for unique business needs.

Consider these essential features:

• Ecommerce integrations: Native connections to your order management, shipping, and customer data systems
• Brand customization: Ability to train AI on your specific voice, policies, and product information
• Performance tracking: Clear reporting on AI effectiveness, resolution rates, and revenue impact
• Scalability: Handles peak season traffic without performance degradation
• Vendor expertise: Deep understanding of ecommerce support challenges and best practices

Pay attention to total cost of ownership beyond subscription fees. Factor in implementation time, training requirements, and ongoing maintenance needs.

Support your support team with AI

The brands winning with AI start with clear goals, choose the right platform, and focus on delivering value to customers while improving operational efficiency.

Book a demo with Gorgias to see how AI can transform your support operations and drive more revenue from every conversation.

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TL;DR:

• Suggestion turns browsing into buying by gently guiding action instead of forcing it.
• Fewer, clearer choices reduce decision fatigue and help shoppers move forward with confidence.
• A well-timed prompt with a friendly tone can make automation feel like a real conversation.
• Good design earns trust by being subtle, approachable, and easy to engage with.
• Small, thoughtful cues create moments of connection that make shoppers feel understood.

Shopping today isn’t a linear funnel. It’s a fluid conversation. Browse → question → help → buy → return → repeat.

Every step is a dialogue between the shopper’s intent and the brand’s response. 

But what bridges the gap between “just looking” and “I’m buying” isn’t persuasion or urgency — it’s suggestion: the subtle design, timing, and language cues that guide action without forcing it.

When done well, suggestion becomes the architecture of trust. It’s also the best way to make AI-powered experiences feel human-first, not tech-first.

This article explores how the power of suggestion — rooted in behavioral psychology and UX design — shapes modern conversational commerce. 

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Why suggestion matters in the age of conversational commerce

The average ecommerce shopper faces thousands of micro-decisions from the moment they land on a site. Which product? Which variant? Which review to trust? Which shipping method? Each one adds cognitive weight.

Psychologist Barry Schwartz coined the term The Paradox of Choice to describe how abundance often leads to paralysis. In his research, participants faced with too many options were less likely to make a choice and less satisfied when they did.

In ecommerce, that means overload costs conversions. When shoppers must evaluate too many variables, they hesitate, second-guess, or abandon.

Shoppers today expect empathy and ease, not persuasion. When you suggest rather than push, you signal empathy and support.

This is especially important for conversational commerce. Suggestion humanizes automation by making AI interactions feel like conversations rather than transactions.

When you push and persuade, you create a memorable experience for customers — but it’s not the kind you want them to remember.

One Reddit thread perfectly captures the problem: a user tried to cancel their Thrive Market membership and had to ask nine times before the chatbot complied.

Each time, the AI assistant tried to talk them out of it (offering deals, guilt-tripping responses, or irrelevant messages) until the customer’s frustration boiled over.

The thread exploded not just because it was mildly infuriating, but because it illustrated what customers fear most about automation: a lack of empathy.

Suggestion is how you design for trust, ease, and interaction. And for ecommerce and CX professionals, suggestion bridges browsing and buying by prompting dialogue in a gentle, psychologically sound way.

5 ways to use suggestion with agentic AI

The magic of suggestion is that it works with human psychology, not against it. It bridges the space between what a shopper wants to do and what helps them do it.

That’s the foundation of the Fogg Behavior Model, developed by Stanford’s Dr. BJ Fogg. The model states that behavior happens when three things intersect:

1. Motivation — the user wants to do something.
2. Ability — they can do it easily.
3. Prompt — they’re nudged at the right moment.

When these three align, the likelihood of action skyrockets.

In conversational commerce, suggestion is the gentle push that turns intent into interaction.

Below are five ways to apply suggestion with agentic AI (think chat, assistants, and marketing tools) to drive trust, dialogue, and conversion.

1. Build trust with a friendly invitation

A first impression shapes the entire interaction.

A greeting like “Need help?” or “Looking for something special?” signals availability without applying pressure. It’s the digital equivalent of a store associate smiling and saying, “Let me know if you need anything.”

This works because of linguistic framing, which is a form of persuasive language that subtly shapes how people interpret intent.

• Sentences using personal pronouns (“you,” “we”) increase perceived warmth and empathy.
• Questions (vs. imperatives) activate a conversational schema in the brain, inviting a cooperative response.
• Short, low-stakes phrasing signals that engagement is voluntary.

In practice, this means:

• Replace “Start chat now” with “Need a hand finding the right fit?”
• Use punctuation and tone cues that convey friendliness.
• Let the chat invite linger rather than pop up suddenly — this gives users agency.

Take a look at Glamnetic. Its shopping assistant sits at the bottom-right corner of every page. While shoppers scroll on the homepage, a prompt appears: “Shop with AI.” It’s transparent about being an AI chat, but subtle enough to be there for shoppers when they’re ready to use it at their own leisure. 

Gorgias Shopping Assistant is an easy way to do this. At the right moment, Shopping Assistant appears with a greeting such as “Need help?” or “Chat with our AI!” It’s friendly, low-pressure, optional, more “Hey I’m here if you need” than “Buy now!”

2. Make decisions easier by offering fewer choices

If you’ve ever scrolled through 80 product filters and given up, you’ve experienced choice overload. This is the Paradox of Choice in action: 

More options = higher cognitive effort = lower satisfaction.

Suggestion works because it reduces mental effort. When an AI assistant limits quick-reply options to just a few (say, “Long sleeve,” “Short sleeve,” “Sleeveless”), it transforms chaos into clarity.

Each small tap provides forward momentum, a concept known as the goal-gradient effect: the closer we feel to completing a goal, the faster and more positively we act.

How can you apply this to agentic AI? 

• Keep quick replies between 3–5 choices — enough to feel personalized, not overwhelming.
• Present them as progressive steps, not isolated decisions (e.g., “Show me styles” → “Show me colors” → “Add to cart”).
• Always include a “Something else” or “Other” option to preserve user autonomy.
• Refresh options dynamically based on prior selections — a technique known as choice scaffolding.

Gorgias’s Shopping Assistant does this well, surfacing only the most relevant next steps. Instead of forcing open-ended typing, it guides shoppers through mini-decisions that build confidence. Here’s an example from Okanui, showing four clear options to reply to Shopping Assistant.

3. Encourage interaction with a user-friendly design 

Before a shopper reads a single word of text, their brain has already judged whether your interface feels safe to engage with.

That’s the Aesthetic–Usability Effect — when people perceive something as visually appealing, they assume it will be easier and more trustworthy to use.

Design psychologist Don Norman put it best: “Attractive things work better because they make people feel better.”

Here’s why visual subtlety matters:

• Rounded edges and soft shapes signal continuity and friendliness (the human brain associates curves with safety; sharp angles with danger).
• Muted palettes and neutral contrast lower visual stress, allowing the interface to fade into the background until needed.
• Micro-animations — like a gentle glow or slide-in — trigger attention without hijacking focus.
• Minimizable elements give users a sense of control, reducing resistance to engagement.

OSEA’s product description page is a beautiful example of unintrusive design in action. The buttons have rounded edges, the 10% offer isn’t covering other page elements, and the chat sits in the bottom-right corner, making it easily accessible if a shopper has questions about the product.

4. Match your timing to the customer’s pace

Timing is everything in suggestion-based design. Even the most thoughtful interaction will fail if it appears at the wrong moment.

That’s where the Fogg Behavior Model becomes tactical: Behavior = Motivation × Ability × Prompt

When shoppers are motivated (interested in a product) and able (engaging is easy), a well-timed prompt (chat bubble, message, or offer) turns potential into action.

But mistime it, and you risk the opposite. A chat that appears too early feels like spam. Too late, and the user’s interest window closes.

Here’s how to align the timing sweet spot:

• Use behavioral triggers: Fire prompts after meaningful engagement (e.g., 25–30 seconds on a product page, reaching 70% scroll depth, or idling for 15 seconds).
• Match prompt to context: Offer size guidance on apparel pages, warranty info near checkout, or live help on return pages.
• Respect frequency: One well-placed nudge beats five redundant ones.
• Localize timing: Adjust based on device and location. Mobile users often need faster cues due to shorter browsing sessions.

Gorgias Shopping Assistant does all of the above. Using context — such as the current page, conversational context, and cart behavior — helps the AI trigger prompts like “Need help choosing a size?” or “Have questions about shipping?”

5. Aim to educate, reassure, or inspire — not just sell

Every small suggestion — a phrase, a button shape, a pause, a tone — creates what behavioral economists call a moment of micro-trust.

Individually, these moments may feel insignificant. But together, they turn a static interface into a relationship.

When greeting, choices, design, and timing align, conversation becomes the natural outcome — not the goal. That’s what conversational commerce gets right: it reframes success from “did they convert?” to “did they connect?”

For CX teams, this shift requires designing for the emotional continuity of the experience:

• Did each prompt respect the shopper’s autonomy?
• Did the interaction feel like a two-way exchange?
• Did the system adapt to intent rather than dictate it?

We love this example from Perry Ellis to drive this tip home:

Designing for trust in an age of AI

As AI continues to shape how people shop, brands face a choice: Design for control, or design for trust.

Suggestion is the path to the latter.

The right cue, delivered at the right time, reminds people that even in automated spaces, there’s still room for empathy and understanding.

Gorgias was built on the belief that great commerce starts with conversation, not conversion.

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TL;DR:

• AI Agent is getting more accurate every month: It’s improved 14.9% this year thanks to better LLMs, constant updates, and user feedback.
• It writes more correctly than most humans: With a 4.77/5 language score, it’s nailing grammar, tone, and clarity better than human agents.
• It’s empathetic, too: AI Agent now shows more empathy and listens better than human agents.
• Brands are gaining confidence fast: Quality scores jumped from 57% to 85% in just a few months, and CX teams are noticing.
• Customers are almost as happy with AI as with humans: AI Agent’s CSAT is just 0.6 points shy of human agents’ average CSAT.

Handing trust over to AI can be intimidating. One off-brand reply and you undo the reputation and customer loyalty you’ve worked so hard to build. 

That’s why we’ve made accuracy our top priority with Gorgias AI Agent.

For the past year, the Gorgias team has been hard at work fulfilling the pressing  demand for accuracy and speed. AI Agent is getting smarter, faster, and more reliable, and merchants and their customers are happier with the output. 

Here’s the data.

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AI Agent delivers more accurate answers than ever

This year, AI Agent’s accuracy rose from 3.55 to 4.08 out of 5, a 14.9% improvement from January. This average score is based on CX agents' ratings of AI Agent responses in the product, on a scale of 1 to 5.

In the past year, we’ve improved knowledge retrieval, added new integrations, expanded reporting features, and asked for more feedback in-product.

We saw the steadiest leap in July, right after the release of GPT-5. AI Agent began reaching levels of consistency and accuracy that agents could trust.

AI Agent writes with more linguistic precision than humans

Clear, easy-to-understand language helps people trust what they’re reading. Website Planet found that 85% more visitors bounced from a page when typos were present. That’s why we’ve made it a priority for AI Agent to respond to customers with correct grammar, syntax, and tone of voice. 

The efforts have paid off: AI Agent scores a high 4.77 out of 5 in language proficiency compared to 4.4 for human agents. The result is error-free messages that are easy to read and consistent with your brand vocabulary.

AI Agent shows that empathy can be scaled

Accuracy isn’t just about saying the right thing; it’s also about how a message lands. For that reason, we track AI Agent’s communication quality. Did it reply with empathy? Did it exhibit active listening and respond with clear phrasing?

Recently, AI Agent is even scoring slightly above humans with 4.48 out of 5 in communication, compared to 4.27. This means AI Agent captures the nuance of every message by considering the background context and acknowledging customer frustration before it gives customers a solution. 

AI Agent resolves every part of a customer’s question

What happens when a ticket ends without a clear answer? Customers feel neglected and leave the chat still unsure. This can make your brand look out of touch, leaving customers with the lingering feeling that you don’t care.

But don’t worry, we built AI Agent to close that loop every time: AI Agent’s resolution completeness score sits at a perfect 1 out of 1, compared to 0.99 out of 1 for human agents. 

In practice, this means customers feel cared for and understood, while your team receives fewer follow-ups, giving them more time to focus on strategic, high-priority tasks.

Read more: A guide to resolution time: How to measure and lower it

Brand confidence is on the rise

Building a great product is a two-way conversation between our engineers and the people who use it. We listen, review feedback, ship changes, and measure what improves.

From January to November 2025, AI Agent quality rose from about 57% to 85%. August was the first big step up, and September kept climbing. Brands are seeing fewer low-quality or incorrect answers and more steady decisions.

This is proof that merchants and their shoppers are witnessing the improvements we’ve been making, for the better.

Related: The engineering work that keeps Gorgias running smoothly

Shoppers are rating AI support almost as high as human support

At the end of the day, what matters is how customers feel when they talk to support. Do they trust the answer? Do they find it helpful? Are they running into more friction with AI than without it?

Our data shows that customers are appreciating AI assistance more and more. Since the start of 2025, AI Agent on live chat has gotten a CSAT score 40% closer to the average CSAT of human agents. For email, the gap has narrowed by about 8%.

The goal is to eventually achieve a gap of zero. At this point, AI’s support quality is indistinguishable from that of humans. To get there, we’re focusing on practical improvements like accuracy, clear language, complete answers, and better handoff rules.

How we measure CSAT gap: The CSAT gap is calculated by subtracting AI CSAT from human CSAT. When the number is closer to zero, AI is catching up. When it’s negative, AI is still below human results.

Reliable AI interactions start with accuracy

Behind every accurate AI reply is a team that cares about the details. AI Agent doesn’t make up answers—it follows what you teach it. The more effort your team puts into maintaining an up-to-date Help Center and Guidance, the better the customer experience becomes.

As we look ahead to 2026, we’re focused on fine-tuning knowledge retrieval logic, refining Guidance rules, and continuously learning from feedback from you and your customers.

We’re proud of the strides AI Agent continues to make, and can’t wait for more brands to experience the accuracy for themselves.

Want to see how AI Agent delivers exceptional accuracy without sacrificing speed? Book a demo or start a trial today.

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TL;DR:

• Fast ≠ good. Chasing faster replies without accuracy or empathy leads to frustrated customers, burned-out agents, and declining CSAT.
• Speed-only AI backfires. Quick but wrong responses damage trust and increase ticket volume.
• Train your AI like a new hire. The best results come when AI learns your tone, workflows, and policies—not when it’s treated as plug-and-play.
• Balance speed with quality. Brands like Boody, Cocorico, and TUSHY show that when AI is trained thoughtfully, teams can scale automation and keep the human touch.
• Adopt an accuracy-first mindset. The future of CX belongs to brands that prioritize reliability, empathy, and consistency over being the fastest.

Speed gets all the glory in customer support. The faster the reply, the happier the customer. That’s not always true. When CX teams chase response times at the expense of accuracy or empathy, they often end up with the opposite effect. Frustrated customers, burned-out agents, and slipping CSAT are common when speed is the only priority.

As more teams adopt AI tools that promise instant results, the risk grows. Quick responses mean nothing if they’re wrong or robotic. 

In this post, we’ll unpack why “fast” doesn’t always mean “good” and how an accuracy-first approach to AI leads to better support, and stronger customer relationships in the long run.

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The speed trap: why CX teams fall for it

Response time has become the go-to measure of “good” support. Dashboards light up green when messages are answered in seconds, and teams celebrate shaved-down handle times. 

But focusing on speed alone can create a dangerous blind spot.

When “fast” becomes the only KPI that matters, CX leaders make speed-at-all-costs decisions. They may roll out untrained AI tools, overuse canned replies, or push agents to close tickets before solving real problems.

On paper, the metrics look great. In reality, customer sentiment quietly drops.

It’s no surprise that 86% of consumers say empathy and human connection matter more than a quick response when it comes to excellent customer experience. 

Fast support might satisfy your dashboard, but thoughtful, accurate service is what satisfies your customers.

Pitfall #1: Maximizing speed and sacrificing quality

A chatbot replies instantly, but gives the wrong answer. The customer follows up again, frustrated. Now your ticket volume has doubled, your agents are backlogged, and the customer’s confidence in your brand has dropped.

That’s the hidden cost of speed-first support. When teams prioritize quick replies over correct ones, CSAT falls, costs rise, and trust erodes. Customers remember the experience, not the timestamp.

They want to feel understood and confident that their issue is solved. A fast reply that misses the mark doesn’t deliver reassurance, empathy, or clear next steps. It’s not speed they value. It’s resolution, accuracy, and a sense that someone genuinely cared enough to get it right. 

Bad AI answers sting more than slow ones because they feel careless. Especially when they repeat the same mistakes. Accuracy builds credibility; speed without it breaks it.

How Boody delivers high-quality replies while maintaining speed

Boody, for example, found the balance. With AI trained on their tone of voice and workflows, they reduced response times from hours to seconds while maintaining a high CSAT score and freeing agents for meaningful work. 

The bamboo apparel brand uses Gorgias AI Agent to reassure the customer that someone is on the way to help, especially for urgent situations. It’s been instrumental in collecting preliminary information for more nuanced situations, like photos and product numbers for warranty claims.

As Boody’s CX Manager, Myriam Ferraty, explained the key is using AI to provide instant low-effort answers when customers need a prompt response. 

“If a customer reaches out about product feedback or issues, AI Agent prompts the customer to give us all the information we need. When an agent gets to the ticket, they can jump into solution mode right away.” —Myriam Ferraty, CX Manager at Boody

Boody found a way to avoid the “fast but frustrating” trap by pairing speed with quality, and the numbers prove it:

• 99.88% faster first-response times: Boody’s AI Agent reduced average response times from 7 hours to just 31 seconds.
• 9+ hours shorter resolution times: Within one month of implementation, resolution times dropped significantly while accuracy stayed high.
• 26% of all interactions handled by AI: Their AI agent took on repetitive queries, freeing human agents for higher-value conversations.
• 10% revenue lift from support: With agents focused on community engagement and brand experience, customer interactions began driving measurable revenue.

These results show what happen when CX teams train AI thoughtfully, it can becomes a trusted extension of the support team, instead of only increasing speed booster.

Takeaway: Fast and good is possible, but only when your AI is trained, guided, and measured for precision, not just speed.

Read more: How CX leaders are actually using AI: 6 must-know lessons

Pitfall #2: Treating AI as plug-and-play

Many CX teams expect AI to “just work” out of the box. They install a shiny new tool, flip the switch, and hope it starts solving tickets overnight. But AI isn’t a magic button. It’s a new team member. And like any new hire, it needs training, context, and feedback to perform well.

Untrained AI can quickly go off-script. It might give inconsistent answers, slip into the wrong tone, or worse, hallucinate information altogether. The consequences are confused customers, damaged trust, and more cleanup work for your human agents.

AI performs best when it’s trained on your brand voice, policies, and knowledge base. The best CX teams don’t settle for default settings or cookie-cutter templates. They invest time to train their AI. That’s what turns it from a generic chatbot into a genuine brand representative.

How Cocorico’s well-trained AI led to customer trust (and laughter)

Cocorico, a French fashion brand, shows what this looks like in practice. Instead of setting AI loose, their team invested time in teaching it how to communicate naturally and on-brand. Within just a few months, they achieved:

• 48% automation rate, handling nearly half of all customer requests.
• 22-second average first-response time, without losing personalization.

At first, Cocorico’s Ecommerce Manager, Margaux Pourrain, admitted she was hesitant to trust AI, “We were apprehensive about launching AI. On the technical side, I thought, ‘Would the AI respond professionally? Would it respond appropriately? Could it create more work by requiring constant verification?’ On the customer experience side, I was nervous it would feel impersonal.”

Her doubts didn’t last long. Once trained on Cocorico’s workflows and brand tone, AI transformed how the team engaged with customers, “AI Agent responds so personally that customers often don’t realize they’re talking to AI. We’ve even seen customers interacting playfully and joking around with Maurice.”

Takeaway: With proper training and oversight, AI can become a trusted teammate that enhances customer experience rather than diluting it.

Read more: How AI Agent works & gathers data

Pitfall #3: Losing the human touch

When CX teams chase faster replies above all else, it’s easy to forget that great support involves connection. Agents and AI start focusing on closing tickets instead of solving problems.

Speed-only goals create fast but flat experiences that technically help customers but don’t feel human.

Over-automation can strip away the warmth and personality that make a brand memorable. Customers might get an answer in seconds, but if it lacks empathy or context, trust takes a hit. Research supports that brands that prioritize emotional intelligence in support interactions see stronger loyalty and retention rates.

How TUSHY keeps their AI playful, not robotic 

TUSHY, the bidet brand known for its witty tone, took a more thoughtful approach to automation. With Gorgias Shopping Assistant, pre-sale questions about compatibility, installation, and recommendations are handled automatically. This frees up human agents to focus on relationship-building conversations.

As Ren Fuller-Wasserman, TUSHY’s Senior Director of Customer Experience, explained, keeping conversations authentic was central to their approach:

“Too often, a great interaction is diminished when a customer feels reduced to just another transaction. With AI, we let the tech handle the selling, unabashedly, if needed, so our future customers can ask anything, even the questions they might be too shy to bring up with a human. In the end, everybody wins!”

That human touch has paid off. TUSHY’s Shopping Assistant mirrors their playful brand voice and delivers real results:

• +20% increase in chat conversion rate overall
• 81% higher conversion rate compared with human agents
• 13× ROI from the Shopping Assistant investment

“Shopping Assistant has been a game-changer for our team, especially with the launch of our latest bidet models,” Fuller-Wasserman said. “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.”

Takeaway: Automation shouldn’t erase your brand’s humanity, it should amplify it. When AI is trained to reflect your tone and values, it can boost both efficiency and emotional connection.

The smarter path forward: accuracy-first AI

The future of customer support doesn’t involve being the fastest. Instead it means being the most reliable. Accuracy-first AI reframes automation from a race to respond into a strategy to build trust.

When customers get the right answer, in the right tone, every time, they’re more likely to stay loyal, even if it takes a few seconds longer.

So what does accuracy-first AI actually look like?

• Starts with training and clear guardrails: Like any new team member, your AI needs onboarding. These guardrails include context, escalation rules, and examples of what “great” looks like.
• Learns from past tickets and feedback: Continuous improvement keeps your AI sharp and aligned with evolving customer expectations.
• Reflects your tone and knowledge base: Every response should sound like you, not a generic script.
• Complements instead of replaces human agents: AI should take the repetitive load so humans can focus on empathy, problem-solving, and connection.

Accuracy-first AI is a mindset shift. Teams that treat AI as a coachable teammate, not a plug-and-play tool, will unlock faster resolutions and higher CSAT in the long run.

Read more: Coach AI Agent in one hour a week: SuitShop’s guide 

Build for accuracy, instead of speed

Speed might win you a customer’s attention, but accuracy is what earns their trust. Fast replies mean little if they’re wrong, off-brand, or robotic. The real differentiator in modern CX isn’t how quickly you respond, it’s how effectively you resolve issues and make customers feel understood.

AI should enhance your team’s expertise, not replace it. Train it on your tone, coach it like a new hire, and measure it on quality as much as efficiency.

The brands that will thrive in the AI era won’t always be the fastest. They’ll be the most reliable, human, and consistent. 

Looking for AI-led support that’s fast and human? Book a demo with Gorgias to see how accuracy-first automation can elevate your support.

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