TL;DR:

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

Conversational commerce finally has a scoreboard.

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

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

Fast forward to today, and everything has changed.

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

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

Why measuring conversational commerce matters now

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

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

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

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

These metrics let you track impact with clarity and confidence.

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

The 4 metric categories that define conversational commerce success

So, what exactly should CX teams be measuring?

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

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

Let’s dive into each.

Automation performance metrics

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

The two most foundational metrics?

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

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

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

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

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

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

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

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

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

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

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

That accuracy paid off. 

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

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

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

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

This metric is a direct lens into:

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

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

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

Brands with strong deflection rates typically see:

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

Conversion and revenue impact metrics

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

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

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

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

A strong CVR tells you that conversations are:

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

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

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

Arc’teryx saw this firsthand. 

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

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

2. GMV influenced: The revenue ripple effect of conversations

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

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

It’s especially powerful for:

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

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

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

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

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

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

Examples of AOV-lifting conversations include:

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

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

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

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

Strong ROI shows that your AI:

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

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

Related: The hidden power and ROI of automated customer support

Engagement metrics that indicate purchase intent

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

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

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

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

If CTR is high, it’s a sign that:

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

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

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

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

That level of engagement translated directly into better outcomes:

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

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

Discounting behavior metrics

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

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

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

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

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

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

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

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

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

A high “discounts applied” rate suggests:

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

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

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

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

How CX teams use these metrics to make better decisions

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

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

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

Here’s what a powerful dashboard unlocks:

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

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

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

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

When you track these consistently, you can:

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

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

2. You uncover what shoppers actually need to convert

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

With these insights, CX teams can:

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

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

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

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

3. You prove that conversations directly drive revenue

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

A clear set of metrics shows how conversations tie to:

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

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

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

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

4. You identify where shoppers are dropping off or hesitating

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

Metrics make friction obvious:

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

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

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

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

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

A clear analytics dashboard gives teams visibility into:

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

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

CX drives revenue when you measure what matters

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

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

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

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TL;DR:

• Implement quickly and iterate. Rhoback’s initial rollout process took two weeks, right before BFCM. Samantha moved quickly, starting with basic FAQs and then continuously optimizing.  
• Train AI like a three-year-old. Although it is empathetic, an AI Agent does not inherently know what is right or wrong. Invest in writing clear Guidance, testing responses, and ensuring document accuracy. 
• Approach your AI’s tone of voice like a character study. Your AI Agent is an extension of your brand, and its personality should reflect that. Rhoback conducted a complete analysis of its agent’s tone, age, energy, and vocabulary. 
• Embrace AI as a tool to reveal inconsistencies. If your AI Agent is giving inaccurate information, it’s exposing gaps in your knowledge sources. Uses these early test responses to audit product pages, help center content, Guidance, and policies.
• Check in regularly and keep humans in control. Introduce weekly reviews or QA rituals to refine AI’s accuracy, tone, and efficiency. Communicate AI insights cross-functionally to build trust and work towards shared goals.

When Rhoback introduced an AI Agent to its customer experience team, it did more than automate routine tickets. Implementation revealed an opportunity to improve documentation, collaborate cross-functionally, and establish a clear brand tone of voice. 

Samantha Gagliardi, Associate Director of Customer Experience at Rhoback, explains the entire process in the first episode of our AI in CX webinar series.

Top learnings from Rhoback’s AI rollout  

1. You can start before you “feel ready”

With any new tool, the pre-implementation phase can take some time. Creating proper documentation, training internal teams, and integrating with your tech stack are all important steps that happen before you go live. 

But sometimes it’s okay just to launch a tool and optimize as you go. 

Rhoback launched its AI agent two weeks before BFCM to automate routine tickets during the busy season. 

Why it worked:

• Samantha had audited all of Rhoback’s SOPs, training materials, and FAQs a few months before implementation. 
• They started by automating high-volume questions such as returns, exchanges, and order tracking.
• They followed a structured AI implementation checklist. 

2. Audit your knowledge sources before you automate

Before turning on Rhoback’s AI Agent, Samantha’s team reviewed every FAQ, policy, and help article that human agents are trained on. This helped establish clear CX expectations that they could program into an AI Agent. 

Samantha also reviewed the most frequently asked questions and the ideal responses to each. Which ones needed an empathetic human touch and which ones required fast, accurate information?  

“AI tells you immediately when your data isn’t clean. If a product detail page says one thing and the help center says another, it shows up right away.” 

Rhoback’s pre-implementation audit checklist:

• Review customer FAQs and the appropriate responses for each. 
• Update outdated PDPs, Help Centre articles, policies, and other relevant documentation.
• Establish workflows with Ecommerce and Product teams to align Macros, Guidance, and Help Center articles with product descriptions and website copy. 

Read more: How to Optimize Your Help Center for AI Agent

3. Train your AI Agent in small, clear steps

It’s often said that you should train your AI Agent like a brand-new employee. 

Samantha took it one step further and recommended treating AI like a toddler, with clear, patient, repetitive instructions. 

“The AI does not have a sense of good and bad. It’s going to say whatever you train it, so you need to break it down like you’re talking to a three-year-old that doesn’t know any different. Your directions should be so detailed that there is no room for error.”

Practical tips:

• Use AI to build your AI Guidance, focusing on clear, detailed, simple instructions. 
• Test each Guidance before adding new ones.
• Treat the training process like an ongoing feedback loop, not a one-time upload.

Read more: How to Write Guidance with the “When, If, Then” Framework

4. Prioritize Tone of Voice to make AI feel natural

For Rhoback, an on-brand Tone of Voice was a non-negotiable. Samantha built a character study that shaped Rhoback’s AI Agent’s custom brand voice.

“I built out the character of Rhoback, how it talks, what age it feels like, what its personality is. If it does not sound like us, it is not worth implementing.”

Key questions to shape your AI Agent’s tone of voice:

• How does the AI Agent speak? Friendly, funny, empathetic, etc…?
• Does your AI Agent use emojis? How often?
• Are there any terms or phrases the AI Agent should always or never say?

5. Use AI to surface knowledge gaps or inconsistencies

Once Samantha started testing the AI Agent, it quickly revealed misalignment between Rhoback’s teams. With such an extensive product catalog, AI showed that product details did not always match the Help Center or CX documentation. 

This made a case for stronger collaboration amongst the CX, Product, and Ecommerce teams to work towards their shared goal of prioritizing the customer. 

“It opened up conversations we were not having before. We all want the customer to be happy, from the moment they click on an ad to the moment they purchase to the moment they receive their order. AI Agent allowed us to see the areas we need to improve upon.” 

Tips to improve internal alignment:

• Create regular syncs between CX, Product, Ecommerce, and Marketing teams.
• Share AI summaries, QA insights, and trends to highlight recurring customer pain points.
• Build a collaborative workflow for updating documents that gives each team visibility. 

6. Build trust (with your team and customers) through transparency 

Despite the benefits of AI for CX, there’s still trepidation. Agents are concerned that AI would replace them, while customers worry they won’t be able to reach a human. Both are valid concerns, but clearly communicating internally and externally can mitigate skepticism. 

At Rhoback, Samantha built internal trust by looping in key stakeholders throughout the testing process. “I showed my team that it is not replacing them. It’s meant to be a support that helps them be even more successful with what they’re already doing," Samantha explains.

On the customer side, Samantha trained their AI Agent to tell customers in the first message that it is an AI customer service assistant that will try to help them or pass them along to a human if it can’t. 

How Rhoback built AI confidence:

• Positioned AI as a personal assistant for agents, not a replacement.
• Let agents, other departments, and leadership test and shape the AI Agent experience early.
• Told customers up front when automation was being used and made the path to a human clear and easy.

Read more: How CX Leaders are Actually Using AI: 6 Must-Know Lessons

Putting these into practice: Rhoback’s framework for an aligned AI implementation 

Here is Rhoback’s approach distilled into a simple framework you can apply.

1. Audit your content: Ensure your FAQs, product data, policies, and all documentation are accurate.
2. Start small: Automate one repetitive workflow, such as returns or tracking.
3. Train iteratively: Add Guidance in small, testable batches.
4. Prioritize tone: Make sure every AI reply sounds like your brand.
5. Align teams: Use AI data to resolve cross-departmental inconsistencies and establish clearer communication lines.
6. Be transparent: Tell both agents and customers how AI fits into the process.
7. Refine regularly: Review, measure, and adjust on an ongoing basis.
   

Watch the full conversation with Samantha to learn how AI can act as a catalyst for better internal alignment. 

📌 Join us for episode 2 of AI in CX: Building a Conversational Commerce Strategy that Converts with Cornbread Hemp on December 16.

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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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