

TL;DR:
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.
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:
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.
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:
Let’s dive into each.
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?
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:
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.
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:
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:
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.
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:
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.
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:
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.
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:
When conversations are done well, AOV increases not because shoppers are being upsold, but because they’re being guided.
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:
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
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.
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:
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:
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 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.
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.
Offering a discount is one thing. Seeing whether customers use it is another.
A high “discounts applied” rate suggests:
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.
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:
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:
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.
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:
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.
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:
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.
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:
Compounded over time, these moments create major lifts in conversion and revenue.
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:
Suddenly, CX isn’t just answering questions — it’s informing strategy across the business.
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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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.
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:
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:
Read more: How to Optimize Your Help Center for AI Agent
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:
Read more: How to Write Guidance with the “When, If, Then” Framework
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:
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:
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:
Read more: How CX Leaders are Actually Using AI: 6 Must-Know Lessons
Here is Rhoback’s approach distilled into a simple framework you can apply.
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:
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
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.
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% |
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.

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

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:

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 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.
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.
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.
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.
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.
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.
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.
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 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 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 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:
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
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.
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.
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.
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:
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.
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.
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.
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:
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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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.
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.
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.
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:
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:
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.
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.
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
A structured layout helps both AI and shoppers find the right step faster, without confusion or escalation.
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:
This logic helps AI know what to do and how to explain the answer clearly to the customer.
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.
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:
Example: “If your return meets our policy, request your return label here.”
That extra step keeps the conversation moving and prevents unnecessary escalations.
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:
A consistent tone keeps your Help Center professional, helps AI deliver reliable responses, and creates a smoother experience for customers.
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 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.

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.


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’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 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
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.
Break up long text blocks and use descriptive headers (H2s, H3s) so readers and AI Agent can instantly find the right section.
Spell out what happens in each scenario. This logic helps AI Agent decide the right next step without second-guessing.
Make sure your Help Center includes complete, structured articles for high-volume issues like order status, returns, and refunds.
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.
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.
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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In, The State of the Ecommerce Customer Service Industry Report for 2019, we found that a surprising 79% of respondents do not know the cost of a support ticket on the company.
This is quite scary, as this metric helps define the overall profitability of the product, and set reinvestment schedules for the growth of your company.
If costs overtake margin, you lose money with every sale.
While the Gorgias mission is to turn customer service from a cost center into a revenue generator, we do need to acknowledge the raw costs of customer support in order to bake it into our calculation of margin.
What metrics are we going to cover:
Why should you track these metric?
Now, let’s get into it…
Here’s the data you need to collect:
Total cost of customer service. This includes technology, employees, managers, office space, equipment, travel… Everything. This should be easy to calculate if your accounting department is doing their job; they should be able to just hand you over a number. A monthly breakdown of the trailing 12 months is best.
Tickets per month. This can be found in your Gorgias dashboard under “Statistics.
Be sure to set the dates to match appropriately:

Now that we have these two numbers, we can get an understanding of our cost per support ticket.
Simply divide: In this example, we’ve got 1651 tickets in December. We spent ~$4500 on customer service. Therefore each ticket costs us $2.73.
Knowing your average cost per ticket helps you understand the time and value behind resolving customer inquiries. If this number goes up, then you’re inquiries are getting more complex - its either taking more time or more people to answer the same number of questions.
If you ever see this number spike, it’s likely due to a flaw in your product design. Immediately begin looking for commonalities among tickets, inspecting your inventory, and trying to get to the root of the problem before you make even more customers unhappy.
Next up, you need to know your support cost per order, in order to bake customer support into your margin.
Here’s the data you need to collect:
To find this, simply log in to your Shopify dashboard, go to Orders, and add in a couple filters:

You can then “select all” and it will tell you the count of orders. For additional accuracy, you may want completed orders, not including refunds or other issues.
For our example month, we placed 2621 orders. That gives us a cost per order of $1.73
According to our Ecommerce Customer Service data, we estimate that small stores will see 88 support tickets per 100 orders, or roughly 1.1 support tickets for every $100 in revenue.
While large stores, with over $500k revenue/month, will see only 56 support tickets per month and .4 support tickets per every $100 in revenue.
How does your support cost per order compare with these benchmarks? Let us know in the comments below.
Sometimes it's helpful to calculate your cost per revenue as well, which is simply grabbing your net sales number from Shopify and dividing by tickets.
Data you need to collect:
Revenue.
You were previously calculating your margin without including the cost of support…
Even though support drives customer satisfaction, retention, and, in some cases, sales, it also has a clear impact on margin.
How does this new metric affect your COGS? Your margin?
If your average order value is $50, with a $13 margin, you now have only a $11.27 margin.
How does that affect your advertising objectives?
How does it affect your ability to invest into product research?
What can you do to improve your cost per order?
A lot. Mostly, this is called: ticket mitigation.
Here’s some of the more common opportunities:
When you hire a new support agent, or manager, you will see your costs go up.
This is the nature of business: you’re investing in a new hire with the expectation that there will be more demand for them to fulfil.
You’re job, as an operations manager, head of Ecommerce, or COO, is to make sure those costs don’t get out of control while you look to scale your business.
Figure out what margins are acceptable to you and invest in growth cautiously. We’ve seen all too many companies fail because they oversupply and hit stretches of low demand.
That being said, if the business has healthy cashflow, and reasonable growth, I’d invest more in customer service before upping my ad budget.
It takes time to onboard new agents, and if you don’t have someone matching that demand, you’re creating unhappy customers, which is a surefire way to eat your margins even faster.

By Ross Beyeler, Founder and CEO of Growth Spark
Often, a support team answers the same questions over and over…
Or issues returns repeatedly for reasons that could be addressed internally…
Maybe the sizing isn’t well represented, the fulfillment house has mixed up SKUs, or your product images aren’t clear or detailed enough.
If you can lighten the load for your customer support team, you can save significant time and costs, while at the same time improving the buying experience for your customers.
The goals here are to:
The key is to address your customers questions and issues before they ask your support team. Here's how you do that:
91% of shoppers would gladly try to answer their own questions first using an online knowledge base or FAQ page before reaching out to a customer service team, according to a survey by Coleman Parkes for Amdocs.
This means that your FAQ page is a huge opportunity to answer your customers’ most common questions and issues so they don’t need to reach out to customer support.
FAQ information typically falls into one of two distinct buckets: product-specific and buying process.
Product Specific: Common questions about individual products may be better off addressed on the product pages rather than in a broad FAQ page. You may need to provide clearer or more comprehensive product descriptions, or consider more or better photography to clear up common product questions.
Buying Process: Questions about shipping, returns, policies, and other operational topics are best addressed in a single easy-to-find page like an FAQ.
When is the last time you cross-checked the content of your FAQ page with the data from your customer support team?
There are many customer support tools like Gorgias that will make it easy for you to track the reasons behind why users submit a ticket.
Once you begin tracking the topic, or tag, of your questions, you can easily identify the questions that top the list, and permanently add the responses to the FAQ.
Bonus points: Prioritize the FAQ page based on the frequency of each customer service inquiry so that the most relevant answers are closer to the top.
Your next step is to set up a monthly meeting with your head of customer service to review the feedback coming in from your customers and ask yourself:
Remember, an FAQ page is:
For more on FAQ pages, check out this Shopify article.
Now that you have your FAQ page squared away, be sure to track visitors to the page and note any changes in volume, and look for changes in your support ticket volume around those related questions.
Remember: You should never answer a support ticket only by referencing your FAQ page. Always include the information they are asking for directly within your response. After that, let the customer know that there is an FAQ page for more information, to avoid future tickets.
Have you watched actual customers explore your online store to see where they stumble?
Customer behavior tools like Hotjar make it easy to review how customers navigate your website. One way that customer behavior analysis tools can help you understand exactly how your customers are using your site is with heat maps.
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A heat map is a visual representation of the most popular (hot) and unpopular (cold) elements of a website page. They can give you an at-a-glance understanding of how people interact with individual website pages. Elements that get the most views and interaction are shown in red, so you can immediately spot what your users are clicking on. Those that most people tend to ignore appear in blue.
Once you know which parts of your website are most (and least) useful to shoppers, you can tweak those elements to make the on-site experience easier to use.
Customer behavior data can inform on-site improvements, such as:
It may require some A/B testing to ensure your changes deliver results.
According to a recent Shopify post, during the holiday season, Ecommerce returns surge to 30 percent (or as high as 50 percent for “expensive” products).
Return deliveries are estimated to exceed $550 billion by 2020 in the U.S. alone.
Many of those returns are probably associated with a customer support ticket - whether customers are asking questions about the product they received, or need help processing their return.
Anything you can do to reduce the number of returns - and the number of customer support requests associated with them - can mean a huge boost for your bottom line.
So, what causes returns?
Returns can often be traced back to a disconnect between customer expectations and the reality of the product once they receive it. It may be that:
All of these problems (and more) can be prevented in advance with improvements to your website content.
While fit can be a difficult factor to get right online, including detailed dimensions is a big step in the right direction. Some apparel merchants are taking sizing one step further with interactive fit guides, like the one above Nudie Jeans, which uses an app integration called Virtusize:.
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Poor quality or not enough product images can make it difficult for customers to accurately understand what your product will look like when it arrives at their home.
You can easily reduce your return rate by making sure your product photography is clear and high-quality, and illustrates all of the primary parts of each product. More complicated or detailed products can also benefit from a video or 360-view.
Detailed product descriptions can also help address confusion about product appearance and feel. Sol de Janeiro does this with a multi-tab product content area that defaults to a brief product highlight, with additional tabs to provide more details.
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Are orders not being fulfilled to the right customers?
Are deliveries taking longer than they should?
Analyzing your fulfillment data and using that information to make adjustments to your website content - such as average delivery times - can help eliminate a source of customer support calls.
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For example, maybe you want to be able to deliver every order within two days, but your current fulfillment resources simply can’t make that happen consistently. Being up-front and clear about realistic delivery times (like The Black Dog does in their Shipping FAQ page, above) will help set customers’ expectations appropriately.
Bonus: To get setup on two day shipping, consider our partners at ShipBob.
Continue to study your on-site data using Google Analytics or Shopify’s native analytics and look for high exit % pages. These may be pages where prospects or customers are running into a dead end and being forced to turn to support.
You can also create a goal in Google Analytics that corresponds to contacting support, then reverse the user path to determine which pages lead to them submitting a ticket / hitting that “contact” or “support” button.
Chances are, there are a few areas of “low hanging fruit” that can make significant improvements to your customer support load once you find them and address the root concerns. And with those small fixes, you could see a big impact on your bottom line, and a better on-site experience for your customers.
Read more about customer support on our trusted partner’s site, Growth Spark:

Ecommerce has become awash with digital bells and whistles. Technology has no doubt enhanced the shopper experience but the rapid rate of digital innovation has had a profound effect on customer expectations. By 2020, customers expect brands to automatically personalize experiences to address (not just predict) their current – and future – needs.
But, although customers expect more in terms of tech, they still crave the person-to-person connection. In fact, 75% of consumers want to see more human interaction, not less.
At LoyaltyLion we know that bringing back this human-touch depends on providing a good customer experience. Clearly, a worthwhile cause, as studies show that 86% of shoppers who received great customer care are more likely to repeat purchase. By going the extra mile to treat your shoppers as people – rather than numbers – you can secure a faithful, constant customer base.
Here are three insights that will help you bring the human touch back to your online store.
Each customer is unique. They interact with your brand in different ways, all while having their own personal needs and desires. When a customer feels that you have taken the time to understand their unique requirements, they will trust and value your brand more.
Data and personalization go hand in hand. By using member information to learn how customers engage with your loyalty program, you can understand their feelings towards your brand and react accordingly. Being data-driven is the key to true e-commerce success.
One golden opportunity to personalize your communications this is through targeted emails. Use your Gorgias dashboard to identify past interactions and purchases, as well as a customer’s loyalty points balance. You can then use that member data to create bespoke rewards that you can send right to your customer’s inbox.
Maybe you’ve noticed that they keep eyeing a specific product range? If so, give them discounts on new products in that collection to tempt them to back to buy again. Or perhaps you’re aware that they’re just a couple of points away from their next reward. Give them a little nudge to return and receive their reward sooner. For example, LoyaltyLion user Dr. Axe alerts customers when they have rewards waiting to be claimed, and suggests a particular product to redeem that reward on.

Shoppers love to feel that they’re your only priority and that you care about them on a personal level. They want to feel valued as individuals, not just another number in an extensive database.
Loyalty strategies should incorporate ways to surprise and delight customers. For example, making it easy to offer customers points on their birthday or taking a moment to personally congratulate them when they’ve made a certain number of purchases with you. Beauty Bakerie, for example, offers their customers 500 points on their birthday.

With Connectors for Shopify Flow, it’s easy to use LoyaltyLion and Gorgias to set up triggers that automatically create tickets on a customer’s birthday, reminding a representative to get in touch. It’s the thought that counts and going the extra mile will ensure your customers trust and remember you. Plus, you’ll feel good about it too!
Customers get frustrated when they feel their complaints aren’t taken seriously. Dissatisfied customers will tell between nine and 15 people if they have a bad brand experience. Using Gorgias’ helpdesk and macros, you can help resolve complaints whilst maintaining a personal touch. For example, ethical online yarn store, Darn Good Yarn uses the helpdesk to analyse and automate how they solve common customer issues, using a whole database of the shopper’s history to address specific queries in a more informed way.
If you are reacting to customers have had a negative experience, your loyalty program can help you demonstrate you care. You might consider offering bonus points or benefits such as free delivery, or moving them up a loyalty tier so that they can unlock more exclusive rewards in the future. These tokens of appreciation can turn a bitter experience into a sweet deal.

Research shows that 94% of customers who have their issues solved painlessly said they would purchase from that company again. This shows that helping customers to solve their problems is key to securing their long-term loyalty. Treat your most valuable customers well by making their shopping experiences as easy as possible. In return, they’ll give you their loyalty.
In a world where technology and data can give ecommerce stores a competitive edge, there’s a risk that we could lose touch with the human side of retailing. Human exchanges are still, and always will be, the primary driver of loyalty. So, use digital personalization to your advantage and treat your customers as individuals.

It's been over 3 years since we've started working on the Gorgias helpdesk. The engineering team started with just me (Alex) and then gradually grew to a team of 5 people. We're a small team, but we've accomplished a lot during this period. Here are some stats from 0 code/customers/revenue in Oct 2015 to this:
Modest numbers to be sure, but we're very proud that people use our product in a big part of their workday and hopefully are becoming more productive while doing so. The whole idea behind our product is to scale customer support with as little resources as possible. Given this, perhaps it's only natural to build our product with a small team as well?

We've been suffering chronically from "not having enough people" - we still do. That forced us to adopt a certain engineering culture that I want to talk about in this post.
When we first started building Gorgias, having just a few people on the team allowed us to progress at a pace where we could collect real feedback from our customers with things that really mattered to them rather than building every feature they ask for. A lot of their asks seemed legitimate, but because we didn't have a lot of people it forced us to prioritize the critical, high impact things first.
Having a small team can act like a barrier that blocks you from building a bloated product.
I want to make more of a case for the above statement, but first I'd like to get a bit more into what we did during the 3 year period.
Once we've build an initial version of the app and got our first customers we quickly realized that building a "second Gmail" is super-hard:
It takes a lot of effort to get to a point where you can compete with the likes of Gmail or Zendesk - both amazing products btw. This was definitely the case for us, for close to 2 years we had only a couple of customers and our product wasn't that good if we're being honest.
So what changed a year ago? To put it simply: our product didn't suck anymore. Or sucked less. It had that minimum set of features and stability that made it attractive enough to our main customer base (Shopify merchants) that were passionate about productivity in the customer support space. That, and the tenacity of our CEO Romain who was convincing everyone that they should use us.
So we started having our second wave of early adopters and all our hard work was finally starting to pay-off!
Now that we had more and bigger customers we were starting to have performance issues, our app was slow, suddenly we were starting to get bombarded by viral facebook posts events or promotional events via an email campaigns, we didn't have enough monitoring in place, our app was pretty inefficient, the main database was a frequent source of congestion. So we started fixing those issues while still receiving numerous feature requests.
Thankfully we didn't actually optimize our code that much before (no customers!) and there were a lot of low hanging fruits at first, but it still put a lot of stress on the team which was becoming tired and overworked and requested to hire more people to build those features and help with the performance issues.
We all agreed that it would be for the best to have more people on the team, but hiring is hard. Competent coders are not just randomly looking for the next gig. SF is also a very expensive city and for a startup that raised $1.5M and a 2 years of money burned we couldn't really compete with other players in town. We've started working with some great devs in Europe, we worked with a few talented interns as well and we tried to get by until we could have more customers and hopefully raise some more money to hire more people.
I could speak more about hiring in the Bay Area and there are a lot of things we did wrong and still have a lot of things to learn, but that's probably an even longer post than this one. But yeah, it's hard to find someone good, it's expensive, etc...
So what is the situation right now? Well, it's not much better. We've raise d a seed extension round from SaaStr with Jason Lemkin and hired a few people in the Growth team, but we still have a hard time hiring in SF or remote. In the meantime we have a small team and want to talk about that.
I think it's important to realize the advantages of having a smaller team and the single most important super-powers that you're forced to acquire is saying NO more often that you would with a bigger team. If you have a bigger team and say no to a feature, new platform, integration, etc.. it's harder to justify the decision. There are arguments like:
... we have enough devs! They are paid to make features, so what's the problem!?
... the data shows that 50% of our customers are saying that they want this or that feature, we must build it!
But do we absolutely need to build that feature? Are the customers going to be a lot less effective with your product otherwise? Is it going to be a big boost for them or just a nice improvement? Once a feature is there you have to maintain it, fix bugs, improve it, etc.. The thing with data driven decisions is that sometimes it can be biased towards some historical practice that might not have a place in your current world.
Now, I'm not saying that you shouldn't listen to your customers, you absolutely have to, but be sure you understand well what they want before taking action and understanding takes time. Having an artificial brake on your enthusiasm might be a good thing.
Engineers build things, the natural tendency is to accept any technical challenge because of ego, curiosity, fun, etc... It takes discipline to say no and stick by it. A small team is making it easier to do it.
When you have a small team you're forced to automate a lot more often some of your workflows. You don't have the luxury to do repetitive stuff so:
People that work at Gorgias come from different backgrounds and sometimes it can be challenging to be on the same page. In some cases our work processes are similar to many other companies:
But there is so much more than just the above processes to engineering:
These things need time to happen to be embedded in your engineering consciousness and if you're the first-time founder (like myself) you also need the time to understand how to operate in this environment.
Never managed a big team so I can't really speak about it's dynamics, but I would expect that because there are more people there is a lot more bandwidth you have to manage, a lot more people have to agree, a lot more politics have to be settled. I don't look forward to that to be honest, the more time I can get away with hiring as little as possible without a big sacrifice of our growth as a company the more I'll try to delay it.
I conclusion I would say that it's totally fine to have a small team, in fact, I'm considering it a competitive advantage that you should try to keep as long as you can.
I made a point in this post that having a small team is a competitive advantage, but I also think that we are ready to grow our team a bit. Yep, we're hiring!

Facebook Messenger is becoming a new marketing channels for brands. They use it as a way to build personal relationships with customers and to drive higher conversion than traditional email marketing.
Today, we're excited to announce our newest integration: Octane AI.
When a brand launches a marketing campaigns on Messenger, it typically leads to insane conversion rates. That's why the trend is on the rise.
Another consequence is that a lot of customers respond to promotional Messenger communication. This generates a spike of support requests, that your support team has to deal with.
Our integration with Octane AI lets you handle this support spike directly in Gorgias. Your agents have context about the customer: they see the conversation history before the Messenger conversation (did the customer email you last night?), and allow you to take action, like editing or refunding an order
Customers are already using Octane AI and Gorgias. Here's what Live Love Polish has to say about the Octane AI and Gorgias integration:
“We’re really thrilled that Gorgias and Octane AI came together to make the customer service experience over Messenger even better for our customers. Accessible customer service is central to what we do at Live Love Polish. Answering customer questions via Messenger has made our customers happier.”
Do you want to give this a shot? If you use both tools, just connect your Facebook page to your Gorgias account and see the magic happen. If not, create a Gorgias account, or sign up for Octane AI.
Do you have questions? Just hit the chat bubble, our team would love to tell you more about the integration!

Loyalty programs are widely used amongst e-commerce merchants to grow and maintain market share by improving the number of repeat customers and attracting new ones. These programs come in different formats - from loyalty points to surprise gifts depending on the level of loyalty of each customer - and have proven efficient to help brands build a community of consumers based on the emotional attachment to their identity and values.
As a customer support helpdesk, Gorgias is focused on providing the best experience for both end-consumers and support agents. Consequently, giving access to the most accurate information about your customers’ loyalty status enables your support team to adapt their answers to customer requests.
Thus, it seemed only natural that we partner with Smile.io, a rewards platform that has helped over 20,000 merchants reward their most loyal customers for performing profitable actions.
With Smile, you can create and manage reward programs such as loyalty points, referrals and VIP programs, to build a fruitful relationship with your customers.
Because Gorgias is appreciated for its ease of use and automation tools, we have decided to build a strong integration with Smile: not only can your support team have easy access to all the necessary data about your customers, but they can also use Smile variables in canned responses (or “macros”) and automation rules.


By integrating your Smile account to Gorgias, you’ll be able to improve yet again not only your customer support but also your customers’ engagement to your brand. Our early adopters of the integration are already thrilled by it!
"We're loving the Smile integration so far! Having access to the variables in the automation features of Gorgias (macros and rules) is a game-changer, especially now that we're focusing on improving our loyalty program. It would be great if the integration went a little further in the future to enable editing loyalty points!"
Chris Storey, Founder and CEO at Dinkydoo
If you're already a Gorgias customer, you can connect Smile directly from your Gorgias account, in the Integrations section. If not, you can create an account here and get started in a few minutes.

Here at Gorgias, our aim is to provide the best customer support tools to our clients, whatever their specific needs. The more you grow, the more we work to develop our offer so that you can benefit from a tailor-made spectrum of integrations. As your business becomes more successful, you need to adapt your website to a fast-growing community of consumers, especially regarding the quality of your reviews and how they appear.
This is why today we are proud to announce our new partnership with Okendo, a customer-marketing platform perfectly suited for high-performance Shopify businesses.
Okendo helps Shopify’s fastest growing companies like oVertone, Paul Evans and Dormify build vibrant customer communities through product ratings & reviews, customer photos/videos and Q&A.
Along with this, Okendo gives you the tools to leverage customer generated content across other marketing channels such as Google Search, Google Shopping, Facebook and Instagram.
Since one of the key advantages of using Gorgias is to manage all your customer support in one dashboard, we decided to design a straight-to-the-point integration:
If a customer leaves low rating review such as < 3 stars and/or with negative sentiment, Okendo can automatically create a ticket in Gorgias. This way, your staff can quickly engage in a conversation with them to understand what went wrong, and address the issue immediately.

We believe this integration will take your customer support teams to the next level, as Okendo has already convinced some of our key clients.
"One of our biggest assets is our unique customer community, so being able to maintain it as active and engaged as possible is key for our business. And making sure that we address any negative experience efficiently and in no time is just as important: this is exactly what the Okendo integration within Gorgias has enabled us to do, by automatically creating a ticket for these cases with the review displayed right next to it."
Dan Appelstein, Founder & CEO at BeGummy
"Aside from being excellent at building shopper trust, reviews enable us to identify customers who, for whatever reason, have had a less than stellar experience. The Okendo + Gorgias integration enables us to flag these instances and automatically assign a Gorgias ticket to a member of our Client Services Team, so that we can follow up and do our best to assist them with whatever issues they're encountering. This integration, along with Okendo’s consistent availability and unwavering support, have made the integration between these two platforms seamless and successful!"
Jae Sutherland, Director of Client Service at oVertone
If you're already a Gorgias customer, we can introduce you to Okendo to implement the integration directly from your Okendo account. If not, you can create an account here and get started in a few minutes.

The supplement industry is not often the first thing that comes to mind when looking to start a new business. It’s crowded, the barriers to entry are low, the margins are thin, and there are some established and well-known brands with large budgets to outspend competitors.
And yet, Campus Protein, a provider of supplement to college students that started in a dorm room in 2010, has managed to carve itself a highly profitable niche and power its way to millions of dollars in revenue.
No, there’s no magic sauce or secret weapon that helped them do it. They have the same access to resources as everyone else. In fact, they have a smaller team than older brands in the space.
The only difference is they focused on one thing that others in the industry weren’t, the customer experience. This is the story of how they did that and dominated behemoths like GNC in colleges across the US.

Before coming up with the idea, founder Russell Saks was just another sophomore at Indiana University. After joining a fraternity, his new friends convinced him to start hitting the gym.
As Russell started getting into fitness, he noticed that every month his friends would head to the local supplements store to purchase $200 to $300 worth of protein and workout drinks. These were the same people who always complained that they didn’t have beer money on the weekends. Yet here they were, spending hundreds of dollars on supplements without batting an eyelid.
In any industry as crowded as the supplement industry, there are always cheaper options. You can go online and buy your supplements at a much lower price than at the local store. However, the drawback is that you have to wait for it. And, as Russell found out, college students never planned ahead and always needed their next tub of protein powder instantly.
Ever the entrepreneur, Russell figured there was an opportunity here. If he could combine the affordability of online prices with the same-day delivery of the local store, he had a business. All he had to do was bulk order product from a low-cost site in advance, store it locally, and then redistribute it to students when they needed it.
As with any business, those initial days were rough. Yes, there was demand and Russell would often sell out each batch soon after they came in, but the margins were razor thin. To maintain cost-effectiveness, Russell sometimes had to take a loss on certain products.
On top of that, Russell found that his life was getting consumed by the fledgling business. To scale it up, he needed help. His friend and first business partner (now Chief Sales Officer), Mike Yewdell, was a fellow student at Indiana University with lots of connections. With his network, they quickly became the go-to source for supplements on campus.
Russell’s next stop was his high school friend (now business partner and CMO), Tarun Singh, who was studying in Boston University at the time. Tarun noticed the same problems at his school and quickly expanded Campus Protein to his school and then the entire Boston area.
The final piece fell into place when they entered into a business competition and won $100,000 to scale up. With the up-front money, they could negotiate deals with supplement makers to improve their margins, and expand to more college to increase sales.
Today, Campus Protein is in over 300 colleges across the US and shows no signs of slowing down. But none of that would have happened if Russell hadn’t been hyper-focused on a certain type of customer and their needs.

One thing Russell learned early on was that college students had very specific needs. Thus, they craved a personalized experience. They needed help with what supplements to buy based on their goals and budget.
At the local supplement stores, Russell noticed that they couldn’t get any of that. Firstly, they sold to everyone so they didn’t have any expertise specific to the college student market. Secondly, they were trained to sell as much product as possible, so they’d often push supplements that weren’t right for the students.
Russell realized that Campus Protein needed to really understand the needs of a college student to own the market. That meant the company needed to hire students who were into fitness. And so the Campus Rep program was born.
A Campus Rep's main job is sales and marketing. They grow awareness for the brand and encourage help other students achieve their fitness goals.
By recruiting Reps in each college, Campus Protein could keep their core team lean while maintaining a large salesforce on the ground.
This has been the real key to their growth. These Reps are their ideal customers, and they hang out with other prospective customers. Thus, they provide a customer experience that’s far better than anything other brands can offer.
Imagine you’re a college student. Before Campus Protein came along, you had to figure out which products to buy, got pressured into buying unnecessary stuff, and ended up with very little money left over.
Today, you probably have a Campus Protein rep in your gym, wearing a branded tank. He’s giving out free tasters, providing you with workout tips and nutrition advice, listens to your goals, and hands you a card with a link where you can buy exactly what you need for much less. How’s that for customer experience?

Campus Protein may be marketing offline with their campus reps but all their sales come from their Shopify website. That’s the best way for them to scale.
Here’s how it works - they have warehouses across the country where they stock product. Because of their deep customer understanding, they know exactly what to stock and what not to stock. The campus reps then go around building awareness, and students head to the website to make their purchase. Because of the warehouse network, they get their products pretty quickly.
Because the actual sale is made online, the website becomes a crucial part of their strategy. If they don’t provide the same level of customer support and care their reps do, they’ll drop the ball and lose the sale. More importantly, they’ll lose trust. One bad experience could hurt their reputation across an entire college.
To replicate the one-on-one support of their reps, they used website chat. In the early days, they started with Zopim Chat. But as they grew, they found that it was too basic for their needs. They couldn’t tell if someone they were chatting with was an existing customer or a new one. They couldn’t tell if it was a new conversation or a continuation of one that happened in a different channel. It was a poor experience for the customer and the company.
Remember, they have a small core team, so they needed a customer support tool that could do the heavy lifting for them. That’s when they came across Gorgias and it allowed them to create an online experience that increased conversions and revenues.
For starters, Gorgias combines all their customer support channels (chat, email, phone, social media) into one unified view, and builds a profile of each customer. When a student chats with them, Campus Protein know if they are a previous customer, can see all past conversations and sales in their dashboard, and can provide relevant support.
Compare that to the typical support you get when you’re forced to repeat your previous conversations each time you chat with someone.

To speed things up, Gorgias also has macros and templated responses based on the question. For example, if a customer wants to know where their order is, Gorgias presents the support agent with a templated response that pulls in the customer’s order details from Shopify. With just a click, the support agent can answer the question in near real-time.
Automations like this also frees up time for support agents to provide more detailed answers to complicated questions, like when a student asks for nutrition advice. Again, they can provide the same level of caring support that reps do and this helps increase sales.
Another way they increase sales is by detecting if customers are spending a lot of time on a certain page and initiating a chat with them. For example, if someone is on the checkout for too long, Gorgias automatically pops a chat and ask them if they need help. This directly increases conversions.

Perhaps the most important way Campus Protein uses customer support to increase revenues is by converting feedback into website and product changes. For every question that comes in, they try to understand why it wasn’t obvious on the website, and make the appropriate change. This leads to fewer tickets of the same type and higher conversions.
At the end of the day, Campus Protein is just another retailer. In an industry like supplements, anyone can replicate their model, or existing brands like GNC can enter the market. So why hasn’t that happened yet?
Like Warren Buffett says, every business needs to have a moat, something that defends them against competition. In Campus Protein’s case, it’s their deep customer knowledge and the personal level of support they provide.
A college student is introduced to Campus Protein via the local rep. They’re nice, helpful, and remember the student’s name each time. When the student goes online, they have the same experience. Their previous conversations are remembered and even their most complicated questions are answered with care.
Now, you may not be able to create a rep army like Campus Protein for your eCommerce business, but you sure can create an online customer experience that sets you apart from others in your industry.
With Gorgias, whenever a customer creates a ticket on any channel, you have all their information like previous conversations and sales, right there. Instead of asking the customer if they’ve written in before or what their order numbers are, you can get straight to the important stuff. And with all the templates, macros, and automations available, you can do it in minutes.

When a customer has to decide between purchasing at a store where they forget about you after the sale, versus one where they treat you like a friend and remember you a year later, which do you think they’ll choose?
Give your customers a great experience and, like Campus Protein, you’ll have a business that keeps going up.


