

TL;DR:
Your AI sounds like a robot, and your customers can tell.
Sure, the answer is right, but something feels off. The tone of voice is stiff. The phrases are predictable and generic. At most, it sounds copy-pasted. This may not be a big deal from your side of support. In reality, it’s costing you more than you think.
Recent data shows that 45% of U.S. adults find customer service chatbots unfavorable, up from 43% in 2022. As awareness of chatbots has increased, so have negative opinions of them. Only 19% of people say chatbots are helpful or beneficial in addressing their queries. The gap isn't just about capability. It's about trust. When AI sounds impersonal, customers disengage or leave frustrated.
Luckily, you don't need to choose between automation and the human touch.
In this guide, we'll show you six practical ways to train your AI to sound natural, build trust, and deliver the kind of support your customers actually like.
The fastest way to make your AI sound more human is to teach it to sound like you. AI is only as good as the input you give it, so the more detailed your brand voice training, the more natural and on-brand your responses will be.
Start by building a brand voice guide. It doesn't need to be complicated, but it should clearly define how your brand communicates with customers. At minimum, include:
Think of your AI as a character. Samantha Gagliardi, Associate Director of Customer Experience at Rhoback, described their approach as building an AI persona:
"I kind of treat it like breaking down an actor. I used to sing and perform for a living — how would I break down the character of Rhoback? How does Rhoback speak? What age are they? What makes the most sense?"
✅ Create a brand voice guide with tone, style, formality, and example phrases.
Humans associate short pauses with thinking, so when your AI responds too quickly, it instantly feels unnatural.
Adding small delays helps your AI feel more like a real teammate.
Where to add response delays:
Even a one- to two-second pause can make a big difference in a robotic or human-sounding AI.
✅ Add instructions in your AI’s knowledge base to include short response delays during key moments.
Generic phrases make your AI sound like... well, AI. Customers can spot a copy-pasted response immediately — especially when it's overly formal.
That doesn't mean you need to be extremely casual. It means being true to your brand. Whether your voice is professional or conversational, the goal is the same: sound like a real person on your team.
Here's how to replace robotic phrasing with more brand-aligned responses:
|
Generic Phrase |
More Natural Alternative |
|---|---|
|
“We apologize for the inconvenience.” |
“Sorry about that, we’re working on it now.” (friendly) |
|
“Your satisfaction is our top priority.” |
“We want to make sure this works for you.” (friendly) |
|
“Please be advised…” |
“Just a quick heads up…” (friendly) |
|
“Your request has been received.” |
“Got it. Thanks for reaching out.” (friendly) |
|
“I will now review your request.” |
“Let me take a quick look.” (friendly) |
✅ Identify your five most common inquiries and give your AI a rewritten example response for each.
One of the biggest tells that a response is AI-generated? It ignores what's already happened.
When your AI doesn't reference order history or past conversations, customers are forced to repeat themselves. Repetition can lead to frustration and can quickly turn a good customer experience into a bad one.
Great AI uses context to craft replies that feel personalized and genuinely helpful.
Here's what good context looks like in AI responses:
Tools like Gorgias AI Agent automatically pull in customer and order data, so replies feel human and contextual without sacrificing speed.
✅ Add instructions that prompt your AI to reference order details and/or past conversations in its replies, so customers feel acknowledged.
Customers just want help. They don't care whether it comes from a human or AI, as long as it's the right help. But if you try to trick them, it backfires fast. AI that pretend to be human often give customers the runaround, especially when the issue is complex or emotional.
A better approach is to be transparent. Solve what you can, and hand off anything else to an agent as needed.
When to disclose that the customer is talking to AI:
For more on this topic, check out our article: Should You Tell Customers They're Talking to AI?
✅ Set clear rules for when your AI should escalate to a human and include handoff messaging that sets expectations and preserves context.
We're giving you permission to break the rules a little bit. The most human-sounding AI doesn't follow perfect grammar or structure. It reflects the messiness of real dialogue.
People don't speak in flawless sentences every time. We pause, rephrase, cut ourselves off, and throw in the occasional emoji or "uh." When AI has an unpredictable cadence, it feels more relatable and, in turn, more human.
What an imperfect AI could look like:
These imperfections give your AI a more believable voice.
✅ Add instructions for your AI that permit variation in grammar, tone, and sentence structure to mimic real human speech.
Human-sounding AI doesn’t require complex prompts or endless fine-tuning. With the right voice guidelines, small tone adjustments, and a few smart instructions, your AI can sound like a real part of your team.
Book a demo of Gorgias AI Agent and see for yourself.
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TL;DR:
You’ve chosen your AI tool and turned it on, hoping you won’t have to answer another WISMO question. But now you’re here. Why is AI going in circles? Why isn’t it answering simple questions? Why does it hand off every conversation to a human agent?
Conversational AI and chatbots thrive on proper training and data. Like any other team member on your customer support team, AI needs guidance. This includes knowledge documents, policies, brand voice guidelines, and escalation rules. So, if your AI has gone rogue, you may have skipped a step.
In this article, we’ll show you the top seven AI issues, why they happen, how to fix them, and the best practices for AI setup.
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AI can only be as accurate as the information you feed it. If your AI is confidently giving customers incorrect answers, it likely has a gap in its knowledge or a lack of guardrails.
Insufficient knowledge can cause AI to pull context from similar topics to create an answer, while the lack of guardrails gives it the green light to compose an answer, correct or not.
How to fix it:
This is one of the most frustrating customer service issues out there. Left unfixed, you risk losing 29% of customers.
If your AI is putting customers through a never-ending loop, it’s time to review your knowledge docs and escalation rules.
How to fix it:
It can be frustrating when AI can’t do the bare minimum, like automate WISMO tickets. This issue is likely due to missing knowledge or overly broad escalation rules.
How to fix it:
One in two customers still prefer talking to a human to an AI, according to Katana. Limiting them to AI-only support could risk a sale or their relationship.
The top live chat apps clearly display options to speak with AI or a human agent. If your tool doesn’t have this, refine your AI-to-human escalation rules.
How to fix it:
If your agents are asking customers to repeat themselves, you’ve already lost momentum. One of the fastest ways to break trust is by making someone explain their issue twice. This happens when AI escalates without passing the conversation history, customer profile, or even a summary of what’s already been attempted.
How to fix it:
Sure, conversational AI has near-perfect grammar, but if its tone is entirely different from your agents’, customers can be put off.
This mismatch usually comes from not settling on an official customer support tone of voice. AI might be pulling from marketing copy. Agents might be winging it. Either way, inconsistency breaks the flow.
How to fix it:
When AI is underperforming, the problem isn’t always the tool. Many teams launch AI without ever mapping out what it's actually supposed to do. So it tries to do everything (and fails), or it does nothing at all.
It’s important to remember that support automation isn’t “set it and forget it.” It needs to know its playing field and boundaries.
How to fix it:
AI should handle |
AI should escalate to a human |
|---|---|
Order tracking (“Where’s my package?”) |
Upset, frustrated, or emotional customers |
Return and refund policy questions |
Billing problems or refund exceptions |
Store hours, shipping rates, and FAQs |
Technical product or troubleshooting issues |
Simple product questions |
Complex or edge‑case product questions |
Password resets |
Multi‑part or multi‑issue requests |
Pre‑sale questions with clear, binary answers |
Anything where a wrong answer risks churn |
Once you’ve addressed the obvious issues, it’s important to build a setup that works reliably. These best practices will help your AI deliver consistently helpful support.
Start by deciding what AI should and shouldn’t handle. Let it take care of repetitive tasks like order tracking, return policies, and product questions. Anything complex or emotionally sensitive should go straight to your team.
Use examples from actual tickets and messages your team handles every day. Help center articles are a good start, but real interactions are what help AI learn how customers actually ask questions.
Create rules that tell your AI when to escalate. These might include customer frustration, low confidence in the answer, or specific phrases like “talk to a person.” The goal is to avoid infinite loops and to hand things off before the experience breaks down.
When a handoff happens, your agents should see everything the AI did. That includes the full conversation, relevant customer data, and any actions it has already attempted. This helps your team respond quickly and avoid repeating what the customer just went through.
An easy way to keep order history, customer data, and conversation history in one place is by using a conversational commerce tool like Gorgias.
A jarring shift in tone between AI and agent makes the experience feel disconnected. Align aspects such as formality, punctuation, and language style so the transition from AI to human feels natural.
Look at recent escalations each week. Identify where the AI struggled or handed off too early or too late. Use those insights to improve training, adjust boundaries, and strengthen your automation flows.
If your AI chatbot isn’t working the way you expected, it’s probably not because the technology is broken. It’s because it hasn’t been given the right rules.
When you set AI up with clear responsibilities, it becomes a powerful extension of your team.
Want to see what it looks like when AI is set up the right way?
Try Gorgias AI Agent. It’s conversational AI built with smart automation, clean escalations, and ecommerce data in its core — so your customers get faster answers and your agents stay focused.
The best in CX and ecommerce, right to your inbox

TL;DR:
Customer education has become a critical factor in converting browsers into buyers. For wellness brands like Cornbread Hemp, where customers need to understand ingredients, dosages, and benefits before making a purchase, education has a direct impact on sales. The challenge is scaling personalized education when support teams are stretched thin, especially during peak sales periods.
Katherine Goodman, Senior Director of Customer Experience, and Stacy Williams, Senior Customer Experience Manager, explain how implementing Gorgias's AI Shopping Assistant transformed their customer education strategy into a conversion powerhouse.
In our second AI in CX episode, we dive into how Cornbread achieved a 30% conversion rate during BFCM, saving their CX team over four days of manual work.
Before diving into tactics, understanding why education matters in the wellness space helps contextualize this approach.
Katherine, Senior Director of Customer Experience at Cornbread Hemp, explains:
"Wellness is a very saturated market right now. Getting to the nitty-gritty and getting to the bottom of what our product actually does for people, making sure they're educated on the differences between products to feel comfortable with what they're putting in their body."
The most common pre-purchase questions Cornbread receives center around three areas: ingredients, dosages, and specific benefits. Customers want to know which product will help with their particular symptoms. They need reassurance that they're making the right choice.
What makes this challenging: These questions require nuanced, personalized responses that consider the customer's specific needs and concerns. Traditionally, this meant every customer had to speak with a human agent, creating a bottleneck that slowed conversions and overwhelmed support teams during peak periods.
Stacy, Senior Customer Experience Manager at Cornbread, identified the game-changing impact of Shopping Assistant:
"It's had a major impact, especially during non-operating hours. Shopping Assistant is able to answer questions when our CX agents aren't available, so it continues the customer order process."
A customer lands on your site at 11 PM, has questions about dosage or ingredients, and instead of abandoning their cart or waiting until morning for a response, they get immediate, accurate answers that move them toward purchase.
The real impact happens in how the tool anticipates customer needs. Cornbread uses suggested product questions that pop up as customers browse product pages. Stacy notes:
"Most of our Shopping Assistant engagement comes from those suggested product features. It almost anticipates what the customer is asking or needing to know."
Actionable takeaway: Don't wait for customers to ask questions. Surface the most common concerns proactively. When you anticipate hesitation and address it immediately, you remove friction from the buying journey.
One of the biggest myths about AI is that implementation is complicated. Stacy explains how Cornbread’s rollout was a straightforward three-step process: audit your knowledge base, flip the switch, then optimize.
"It was literally the flip of a switch and just making sure that our data and information in Gorgias was up to date and accurate."
Here's Cornbread’s three-phase approach:
Actionable takeaway: Block out time for that initial knowledge base audit. Then commit to regular check-ins because your business evolves, and your AI should evolve with it.
Read more: AI in CX Webinar Recap: Turning AI Implementation into Team Alignment
Here's something most brands miss: the way you write your knowledge base articles directly impacts conversion rates.
Before BFCM, Stacy reviewed all of Cornbread's Guidance and rephrased the language to make it easier for AI Agent to understand.
"The language in the Guidance had to be simple, concise, very straightforward so that Shopping Assistant could deliver that information without being confused or getting too complicated," Stacy explains. When your AI can quickly parse and deliver information, customers get faster, more accurate answers. And faster answers mean more conversions.
Katherine adds another crucial element: tone consistency.
"We treat AI as another team member. Making sure that the tone and the language that AI used were very similar to the tone and the language that our human agents use was crucial in creating and maintaining a customer relationship."
As a result, customers often don't realize they're talking to AI. Some even leave reviews saying they loved chatting with "Ally" (Cornbread's AI agent name), not realizing Ally isn't human.
Actionable takeaway: Review your knowledge base with fresh eyes. Can you simplify without losing meaning? Does it sound like your brand? Would a customer be satisfied with this interaction? If not, time for a rewrite.
Read more: How to Write Guidance with the “When, If, Then” Framework
The real test of any CX strategy is how it performs under pressure. For Cornbread, Black Friday Cyber Monday 2025 proved that their conversational commerce strategy wasn't just working, it was thriving.
Over the peak season, Cornbread saw:
Katherine breaks down what made the difference:
"Shopping Assistant popping up, answering those questions with the correct promo information helps customers get from point A to point B before the deal ends."
During high-stakes sales events, customers are in a hurry. They're comparing options, checking out competitors, and making quick decisions. If you can't answer their questions immediately, they're gone. Shopping Assistant kept customers engaged and moving toward purchase, even when human agents were swamped.
Actionable takeaway: Peak periods require a fail-safe CX strategy. The brands that win are the ones that prepare their AI tools in advance.
One of the most transformative impacts of conversational commerce goes beyond conversion rates. What your team can do with their newfound bandwidth matters just as much.
With AI handling straightforward inquiries, Cornbread's CX team has evolved into a strategic problem-solving team. They've expanded into social media support, provided real-time service during a retail pop-up, and have time for the high-value interactions that actually build customer relationships.
Katherine describes phone calls as their highest value touchpoint, where agents can build genuine relationships with customers. “We have an older demographic, especially with CBD. We received a lot of customer calls requesting orders and asking questions. And sometimes we end up just yapping,” Katherine shares. “I was yapping with a customer last week, and we'd been on the call for about 15 minutes. This really helps build those long-term relationships that keep customers coming back."
That's the kind of experience that builds loyalty, and becomes possible only when your team isn't stuck answering repetitive tickets.
Stacy adds that agents now focus on "higher-level tickets or customer issues that they need to resolve. AI handles straightforward things, and our agents now really are more engaged in more complicated, higher-level resolutions."
Actionable takeaway: Stop thinking about AI only as a cost-cutting tool and start seeing it as an impact multiplier. The goal is to free your team to work on conversations that actually move the needle on customer lifetime value.
Cornbread isn't resting on their BFCM success. They're already optimizing for January, traditionally the biggest month for wellness brands as customers commit to New Year's resolutions.
Their focus areas include optimizing their product quiz to provide better data to both AI and human agents, educating customers on realistic expectations with CBD use, and using Shopping Assistant to spotlight new products launching in Q1.
The brands winning at conversational commerce aren't the ones with the biggest budgets or the largest teams. They're the ones who understand that customer education drives conversions, and they've built systems to deliver that education at scale.
Cornbread Hemp's success comes down to three core principles: investing time upfront to train AI properly, maintaining consistent optimization, and treating AI as a team member that deserves the same attention to tone and quality as human agents.
As Katherine puts it:
"The more time that you put into training and optimizing AI, the less time you're going to have to babysit it later. Then, it's actually going to give your customers that really amazing experience."
Watch the replay of the whole conversation with Katherine and Stacy to learn how Gorgias’s Shopping Assistant helps them turn browsers into buyers.
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TL;DR:
Rising customer expectations, shoppers willing to pay a premium for convenience, and a growing lack of trust in social media channels to make purchase decisions are making it more challenging to turn a profit.
In this emerging era, AI’s role is becoming not only more pronounced, but a necessity for brands who want to stay ahead. Tools like Gorgias Shopping Assistant can help drive measurable revenue while reducing support costs.
For example, a brand that specializes in premium outdoor apparel implemented Shopping Assistant and saw a 2.25% uplift in GMV and 29% uplift in average order volume (AOV).
But how, among competing priorities and expenses, do you convince leadership to implement it? We’ll show you.
Shoppers want on-demand help in real time that’s personalized across devices.
Shopping Assistant recalls a shopper’s browsing history, like what they have clicked, viewed, and added to their cart. This allows it to make more relevant suggestions that feel personal to each customer.
The AI ecommerce tools market was valued at $7.25 billion in 2024 and is expected to reach $21.55 billion by 2030.
Your competitors are using conversational AI to support, sell, and retain. Shopping Assistant satisfies that need, providing upsells and recommendations rooted in real shopper behavior.
Conversational AI has real revenue implications, impacting customer retention, average order value (AOV), conversion rates, and gross market value (GMV).
For example, a leading nutrition brand saw a GMV uplift of over 1%, an increase in AOV of over 16%, and a chat conversion rate of over 15% after implementing Shopping Assistant.
Overall, Shopping Assistant drives higher engagement and more revenue per visitor, sometimes surpassing 50% and 20%, respectively.

Shopping Assistant engages, personalizes, recommends, and converts. It provides proactive recommendations, smart upsells, dynamic discounts, and is highly personalized, all helping to guide shoppers to checkout.
After implementing Shopping Assistant, leading ecommerce brands saw real results:
Industry |
Primary Use Case |
GMV Uplift (%) |
AOV Uplift (%) |
Chat CVR (%) |
|---|---|---|---|---|
Home & interior decor 🖼️ |
Help shoppers coordinate furniture with existing pieces and color schemes. |
+1.17 |
+97.15 |
10.30 |
Outdoor apparel 🎿 |
In-depth explanations of technical features and confidence when purchasing premium, performance-driven products. |
+2.25 |
+29.41 |
6.88 |
Nutrition 🍎 |
Personalized guidance on supplement selection based on age, goals, and optimal timing. |
+1.09 |
+16.40 |
15.15 |
Health & wellness 💊 |
Comparing similar products and understanding functional differences to choose the best option. |
+1.08 |
+11.27 |
8.55 |
Home furnishings 🛋️ |
Help choose furniture sizes and styles appropriate for children and safety needs. |
+12.26 |
+10.19 |
1.12 |
Stuffed toys 🧸 |
Clear care instructions and support finding replacements after accidental product damage. |
+4.43 |
+9.87 |
3.62 |
Face & body care 💆♀️ |
Assistance finding the correct shade online, especially when previously purchased products are no longer available. |
+6.55 |
+1.02 |
5.29 |
Shopping Assistant drives uplift in chat conversion rate and makes successful upsell recommendations.
“It’s been awesome to see Shopping Assistant guide customers through our technical product range without any human input. It’s a much smoother journey for the shopper,” says Nathan Larner, Customer Experience Advisor for Arc’teryx.
For Arc’teryx, that smoother customer journey translated into sales. The brand saw a 75% increase in conversion rate (from 4% to 7%) and 3.7% of overall revenue influenced by Shopping Assistant.

Because it follows shoppers’ live journey during each session on your website, Shopping Assistant catches shoppers in the moment. It answers questions or concerns that might normally halt a purchase, gets strategic with discounting (based on rules you set), and upsells.
The overall ROI can be significant. For example, bareMinerals saw an 8.83x return on investment.
"The real-time Shopify integration was essential as we needed to ensure that product recommendations were relevant and displayed accurate inventory,” says Katia Komar, Sr. Manager of Ecommerce and Customer Service Operations, UK at bareMinerals.
“Avoiding customer frustration from out-of-stock recommendations was non-negotiable, especially in beauty, where shade availability is crucial to customer trust and satisfaction. This approach has led to increased CSAT on AI converted tickets."

Shopping Assistant can impact CSAT scores, response times, resolution rates, AOV, and GMV.
For Caitlyn Minimalist, those metrics were an 11.3% uplift in AOV, an 18% click through rate for product recommendations, and a 50% sales lift versus human-only chats.
"Shopping Assistant has become an intuitive extension of our team, offering product guidance that feels personal and intentional,” says Anthony Ponce, its Head of Customer Experience.

Support agents have limited time to assist customers as it is, so taking advantage of sales opportunities can be difficult. Shopping Assistant takes over that role, removing obstacles for purchase or clearing up the right choice among a stacked product catalog.
With a product that’s not yet mainstream in the US, TUSHY leverages Shopping Assistant for product education and clarification.
"Shopping Assistant has been a game-changer for our team, especially with the launch of our latest bidet models,” says Ren Fuller-Wasserman, Sr. Director of Customer Experience at TUSHY.
“Expanding our product catalog has given customers more choices than ever, which can overwhelm first-time buyers. Now, they’re increasingly looking to us for guidance on finding the right fit for their home and personal hygiene needs.”
The bidet brand saw 13x return on investment after implementation, a 15% increase in chat conversion rate, and a 2x higher conversion rate for AI conversations versus human ones.

Customer support metrics include:
Revenue metrics to track include:
Shopping Assistant connects to your ecommerce platform (like Shopify), and streamlines information between your helpdesk and order data. It’s also trained on your catalog and support history.
Allow your agents to focus on support and sell more by tackling questions that are getting in the way of sales.
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TL;DR:
While most ecommerce brands debate whether to implement AI support, customers already rate AI assistance nearly as highly as human support. The future isn't coming. It's being built in real-time by brands paying attention.
As a conversational commerce platform processing millions of support tickets across thousands of brands, we see what's working before it becomes common knowledge. Three major shifts are converging faster than most founders realize, and this article breaks down what's already happening rather than what might happen someday.
By the end of 2026, we predict that the performance gap between ecommerce brands won't be determined by who adopted AI first. It will be determined by who built the content foundation that makes AI actually work.
Right now, we're watching this split happen in real time. AI can only be as good as the knowledge base it draws from. When we analyze why AI escalates tickets to human agents, the pattern is unmistakable.
The five topics triggering the most AI escalations are:
These aren’t complicated questions — they're routine questions every ecommerce brand faces daily. Yet some brands automate these at 60%+ rates while others plateau at 20%. The difference isn't better AI. It's better documentation.
Take SuitShop, a formalwear brand that reached 30% automation with a lean CX team. Their Director of Customer Experience, Katy Eriks, treats AI like a team member who needs coaching, not a plug-and-play tool.
When Katy first turned on AI in August 2023, the results were underwhelming. So she paused during their slow season and rebuilt their Help Center from the ground up. "I went back to the tickets I had to answer myself, checked what people were searching in the Help Center, and filled in the gaps," she explained.
The brands achieving high automation rates share Katie's approach:
AI echoes whatever foundation you provide. Clear documentation becomes instant, accurate support. Vague policies become confused AI that defaults to human escalation.
Read more: Coach AI Agent in one hour a week: SuitShop’s guide
Two distinct groups will emerge next year. Brands that invest in documentation quality now will deliver consistently better experiences at lower costs. Those who try to deploy AI on top of messy operations will hit automation plateaus and rising support costs. Every brand will eventually have access to similar AI technology. The competitive advantage will belong to those who did the unexciting work first.
Something shifted in July 2025. Gorgias’s AI accuracy jumped significantly after the GPT-5 release. For the first time, CX teams stopped second-guessing every AI response. We watched brand confidence in AI-generated responses rise from 57% to 85% in just a few months.
What this means in practice is that AI now outperforms human agents:
For the first time, AI isn't just faster than humans. It's more consistent, more accurate, and even more empathetic at scale.
This isn't about replacing humans. It's about what becomes possible when you free your team from repetitive work. Customer expectations are being reset by whoever responds fastest and most completely, and the brands crossing this threshold first are creating a competitive moat.
At Gorgias, the most telling signal was AI CSAT on chat improved 40% faster than on email this year. In other words, customers are beginning to prefer AI for certain interactions because it's immediate and complete.
Within the next year, we expect the satisfaction gap to hit zero for transactional support. The question isn't whether AI can match humans. It's what you'll do with your human agents once it does.
The brands that have always known support should drive revenue will finally have the infrastructure to make it happen on a bigger scale. AI removes the constraint that's held this strategy back: human bandwidth.
Most ecommerce leaders already understand that support conversations are sales opportunities. Product questions, sizing concerns, and “just browsing” chats are all chances to recommend, upsell, and convert. The problem wasn't awareness but execution at volume.
We analyzed revenue impact across brands using AI-powered product recommendations in support conversations. The results speak for themselves:
It's clear that conversations that weave in product recommendations convert at higher rates and result in larger order values. It’s time to treat support conversations as active buying conversations.
If you're already training support teams on product knowledge and tracking revenue per conversation, keep doing exactly what you're doing. You've been ahead of the curve. Now AI gives you the infrastructure to scale those same practices without the cost increase.
If you've been treating support purely as a cost center, start measuring revenue influence now. Track which conversations lead to purchases, which agents naturally upsell, and where customers ask for product guidance.
We are now past the point where response time is a brand's key differentiator. It is now the use of conversational commerce or systems that share details and context across every touchpoint.
Today, a typical customer journey looks something like this: see product on Instagram, ask a question via DM, complete purchase on mobile, track order via email. At each step, customers expect you to remember everything from the last interaction.
The most successful ecommerce tech stacks treat the helpdesk as the foundation that connects everything else. When your support platform connects to your ecommerce platform, shipping providers, returns portal, and every customer communication channel, context flows automatically.
A modern integration approach looks like this. Your ecommerce platform (like Shopify) feeds order data into a helpdesk like Gorgias, which becomes the hub for all customer conversations across email, chat, SMS, and social DMs. From there, connections branch out to payment providers, shipping carriers, and marketing automation tools.
As Dr. Bronner’s Senior CX Manager noted, “While Salesforce needed heavy development, Gorgias connected to our entire stack with just a few clicks. Our team can now manage workflows without needing custom development — we save $100k/year by switching."
As new channels emerge, brands with flexible tech stacks will adapt quickly while those with static systems will need months of development work to support new touchpoints. The winners will be brands that invest in their tools before adding new channels, not after customer complaints force their hand.
Start auditing your current integrations now. Where does customer data get stuck? Which systems don’t connect to each other? These gaps are costing you more than you realize, and in the future, they'll be the key to scaling or staying stagnant.
Post-purchase support quality will be a stronger predictor of customer lifetime value than any email campaign. Brands that treat support as a retention investment rather than a cost center will outperform in repeat purchase rates.
Returns and exchanges are make-or-break moments for customer lifetime value. How you handle problems, delays, and disappointments determines whether customers come back or shop elsewhere next time. According to Narvar, 96% of customers say they won’t repurchase from a brand after a poor return experience.
What customers expect reflects this reality. They want proactive shipping updates without having to ask, one-click returns with instant label generation, and notifications about problems before they have to reach out. When something goes wrong, they expect you to tell them first, not make them track you down for answers.
The quality of your response when things go wrong matters more than getting everything right the first time. Exchange suggestions during the return flow can keep the sale alive, turning a potential loss into loyalty.
Brands that treat post-purchase as a retention strategy rather than a task to cross off will see much higher repeat purchase rates. Those still relying purely on email marketing for retention will wonder why their customer lifetime value plateaus.
Start measuring post-return CSAT scores and repeat purchase rates by support interaction quality. These metrics will tell you whether your post-purchase experience is building loyalty or quietly eroding it.
After absorbing these predictions about AI accuracy, content infrastructure, revenue-centric support, context, and post-purchase tactics, here's your roadmap for the next 24 months.
Now (in 90 days):
Next (in 6-12 months):
Watch (in 12-24 months):
The patterns we've shared, from AI crossing the accuracy threshold to documentation quality, are happening right now across thousands of brands. Over the next 24 months, teams will be separated by operational maturity.
Book a demo to see how leading brands are already there.
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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.


Aircall is a cloud-based call center software made for support teams. With Aircall, support agents can track everything from A-Z, on any device, with zero hardware to manage. The right tool to increase agent efficiency and customer satisfaction!
After listening to early customer feedback, we quickly realized we needed to find a phone integration that empowered users to manage voice calls as easily as emails or chats.
Traditional helpdesk integrations simply log calls as tickets. We wanted to go one step further and associate the phone call with the right customer. This way, agents can see the full conversation history between the brand and the customer.
By building Aircall’s cloud-based phone into the Gorgias platform, agents can also quickly edit orders while on the phone based on the case history they see. After a call has ended, all notes will be added to the correct customer’s profile along with a link to the full call recording.

Looking back, the partnership has been mutually beneficial and seamlessly implemented.
Aircall has a well-documented API that our dev team could easily use. We were able to build a working and robust phone integration with Aircall in just a few hours. Four days later, after QA testing, the new solutions were fully functional and ready to use.
Since Gorgias and Aircall both seek to provide the best customer experience possible, cross-company visibility has become a valuable source of new leads and sales. Furthermore, we conduct regular catch-up meetings and share a Slack channel to make sure both teams work hand-in-hand to create the best integration and the best results. The partnership with Aircall is super valuable for both our customers and our respective companies and we strongly recommend each others.
If you're already a Gorgias customer, head to your account and go to Integrations to connect Aircall. If not, you can create an account here and get started in a few minutes.


