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Your Support Team Drives More Revenue Than You Think: Conversational Commerce Metrics

Your chat might be closing more sales than your checkout page. Here’s how to measure it.

0 min read . By Tina Donati

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:

Automation performance
Conversion & revenue impact
Engagement quality
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.

BareMinerals' AI Agent recommends a customer a foundation that matches their skin tone

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.

Arc'teryx uses Shopping Assistant to enable purchases directly from chat

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.

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.

AI Agent recommends a customer with jewelry safe for sensitive skin

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:

Metric Signal	What It Means
Low CTR	Recommendations may be irrelevant or poorly timed.
Low CVR	Conversations aren’t persuasive enough to drive a purchase.
High deflection but low revenue	AI is resolving tickets, but not effectively selling.
High discount usage	Shoppers rely on incentives to convert.
Low discount usage	You may be offering discounts unnecessarily and losing margin.

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.

By Tina Donati

AI in CX Webinar Recap: Turning AI Implementation into Team Alignment

0 min read . By Gabrielle Policella

min read.

By Gabrielle Policella

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Featured articles

How Food & Beverage Brands Can Level Up Self-Service Before BFCM

Before the BFCM rush begins, we’re serving food & beverage CX teams seven easy self-serve upgrades to keep support tickets off their plate.

By Alexa Hertel

0 min read . By Alexa Hertel

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%).

Contact Reason	% of Tickets
🍽️ Subscription cancellation	13%
🚚 Order status (WISMO)	9.1%
❌ Order cancellation	6.5%
🥫 Product details	5.7%
🧃 Product availability	4.1%
⭐ Positive feedback	3.9%

7 ways to improve your self-serve resources before BFCM

Add informative blurbs on product pages
Craft additional help center and FAQ articles
Automate responses with AI or Macros
Get specific about product availability
Provide order cancellation and refund policies upfront
Add how-to information
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.

The Dinner Ladies product page showing parmesan biscuits with tapenade and mascarpone. — The Dinner Ladies includes a drop down menu full of key information on its product pages. *The Dinner Ladies*

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.

Graphic listing benefits of FAQ pages including saving time and improving SEO.

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?”

Everyday Dose FAQ page showing product, payments, and subscription question categories. — Everyday Dose has an extensive FAQ page that guides shoppers through top questions and answers. *Everyday Dose*

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:

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.

Obvi homepage promoting Black Friday sale with 50% off and chat support window open. — *Obvi*

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.

Rebel Cheese product page for Thanksgiving Cheeseboard Classics featuring six vegan cheeses on wood board. — Rebel Cheese warns shoppers that its Thanksgiving cheese board has sold out 3x already. *Rebel Cheese*

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.

Misen order confirmation email with link to change or cancel within one hour of checkout. — Cookware brand Misen follows up its order confirmation email with the option to edit within one hour. *Misen*

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:

Purity Coffee brewing guide showing home drip and commercial batch brewer illustrations. — Purity Coffee has an extensive brewing guide on its website. *Purity Coffee*

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

Butter melting in a seasoned carbon steel pan on a gas stove. — *Misen*

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.

The Dinner Ladies product page featuring duck sausage rolls with cherry and plum dipping sauce. — The Dinner Ladies feature a how to cook section on product pages. *The Dinner Ladies*

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:

Trade Coffee Co offers an interactive quiz to lead shoppers to their perfect coffee match. *Trade Coffee Co*

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.

min read.

What is Conversational AI? The Ecommerce Guide

Learn about the different types of conversational AI and its benefits for ecommerce.

By Gorgias Team

0 min read . By Gorgias Team

min read.

How to Make Your Help Center LLM-Friendly

Your Help Center doesn’t need a rebuild. It just needs a smarter structure so AI can find what customers ask about most.

By Holly Stanley

0 min read . By Holly Stanley

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.

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:

Email thread between AI Agent and customer about skipping a subscription. — AI Agent skipped a customer’s subscription after getting their confirmation.

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:

Rank	Ticket Topic	% of Escalations
1	Order status	12.4%
2	Return request	7.9%
3	Order cancellation	6.1%
4	Product - quality issues	5.9%
5	Missing item	4.6%
6	Subscription cancellation	4.4%
7	Order refund	4.1%
8	Product details	3.5%
9	Return status	3.3%
10	Order delivered but not received	3.1%

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.

Little Words Project Help Center homepage showing six main categories: Orders, Customization, Charms, Shipping, Warranty, and Returns & Exchanges. — Little Words Project's Help Center uses short paragraphs and tightly scoped articles to boost instant answers.

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.

Dr. Bronner's Help Center webpage showing detailed articles, interactive flows, and order management automation for efficient customer support. — The robust, proactively educational Help Center, integrated with interactive flows and order management via Gorgias, streamlines detailed and routine customer inquiries.

Ekster: Building efficiency through automation and clarity

Ekster website and a Gorgias chat widget. A customer asks "How do I attach my AirTag?" and the Support Bot instantly replies with a link to the relevant "User Manual" article. — Gorgias AI Agent instantly recommends a relevant "User Manual" article to a customer asking, "How do I attach my AirTag?", demonstrating how structured Help Center content enables quick, instant issue resolution.

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.

Rowan's Help Center homepage, structured with six clear categories including Piercing Aftercare (19 articles), Returns & Exchanges, and Appointment Information. — Rowan’s Help Center uses a clean, categorized structure (Aftercare, Returns, Shipping) that lets customers and AI Agents jump straight to the right topic.

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.

TUSHY bidet customer help center webpage showing categories: Toilet Fit, My Order, How to Use Your TUSHY, Attachments, Non-Electric and Electric Seats. — Explore articles covering Toilet Fit, My Order, How to Use Your TUSHY, and various Bidet Attachments, all structured for easy retrieval and use.

“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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