

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.
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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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:
Most shoppers arrive with questions. Is this the right size? Will this match my skin tone? What’s the difference between these models? The faster you can guide them, the faster they decide.
As CX teams take on a bigger role in driving revenue, these moments of hesitation are now some of the most important parts of the buying journey.
That’s why more brands are leaning on conversational AI to support these high-intent questions and remove the friction that slows shoppers down. The impact speaks for itself. Brands can expect higher AOV, stronger chat conversion rates, and smoother paths to purchase, all without adding extra work to your team.
Below, we’re sharing real use cases from 11 ecommerce brands across beauty, apparel, home, body care, and more, along with the exact results they saw after introducing guided shopping experiences.
When you’re shopping for shoes similar to an old but discontinued favorite, every detail counts, down to the color of the bottom of the shoe. But legacy brands with large catalogs can be overwhelming to browse.
For shoppers, it’s a double-edged sword: they want to feel confident that they checked your entire collection, but they also don’t want to spend time looking for it.
How Shopping Assistant helps:
Shopping Assistant accelerates the process, turning hazy details into clear, friendly guidance.
It describes shoe details, from colorways to logo placement, compares products side by side, and recommends the best option based on the shopper’s preferences and conditions.
The result is shoppers who feel satisfied and more connected with your brand.

Results:
Big events call for great outfits, but putting one together online isn’t always easy. With thousands of options to scroll through, shoppers often want a bit of styling direction.
How Shopping Assistant helps:
Shoppers get to chat with a virtual stylist who recommends full outfits based on the occasion, suggests accessories to complete the look, and removes the guesswork of pairing pieces together.
The result is a fun, confidence-building shopping experience that feels like getting advice from a stylist who actually understands their plans.

Results:
Shade matching is hard enough in-store, but doing it online can feel impossible. Plus, when a longtime favorite gets discontinued, shoppers are left guessing which new shade will come closest. That uncertainty often leads to hesitation, abandoned carts, or ordering multiple shades “just in case.”
How Shopping Assistant helps:
Shoppers find their perfect match without any of the guesswork. The assistant asks a few quick questions, recommends the closest shade or formula, and offers smart alternatives when a product is unavailable.
The experience feels like chatting with a knowledgeable beauty advisor — someone who makes the decision easy and leaves shoppers feeling confident in what they’re buying.
Katia Komar, Sr. Manager of Ecommerce and Customer Service Operations at bareMinerals UK says, “What impressed me the most is the AI’s ability to upsell with a conversational tone that feels genuinely helpful and doesn't sound too pushy or transactional. It sounds remarkably human, identifying correct follow-up questions to determine the correct product recommendation, resulting in improved AOV. It’s exactly how I train our human agents and BPO partners.”

Results:
When shoppers are buying gifts, especially for someone else, they often know who they’re shopping for but not what to buy. A vague product name or a half-remembered scent can quickly make the experience feel overwhelming without someone to guide them.
How Shopping Assistant helps:
Thoughtful guidance goes a long way. By asking clarifying questions and recognizing likely mix-ups, Shopping Assistant helps shoppers figure out what the recipient was probably referring to, then recommends the right product along with complementary gift options that make the choice feel intentional.
It brings the reassurance of an in-store associate to the online experience, helping shoppers move forward with confidence.

Results:
Finding the right bra size online is notoriously tricky. Shoppers often second-guess their band or cup size, and even small uncertainties can lead to returns — or abandoning the purchase altogether.
Many customers just want someone to walk them through what a proper fit should actually feel like.
How Shopping Assistant helps:
Searching for products is no longer a time-consuming process. Shopping Assistant detects a shopper’s search terms and sends relevant products in chat. Like an in-store associate, it uses context to deliver what shoppers are looking for, so they can skip the search and head right to checkout.

Results:
For shoppers buying personalized jewelry, the details directly affect the final result. That’s why customization questions come up constantly, and why uncertainty can quickly stall the path to purchase.
How Shopping Assistant helps:
Shopping Assistant asks about the shopper’s style preferences and customization needs, then recommends the right product and options so they can feel confident the final piece is exactly their style. The experience feels quick, helpful, and designed to guide shoppers toward a high investment purchase.

Results:
Decorating a home is personal, and shoppers often want reassurance that a new piece will blend with what they already own. Questions about color palettes, textures, and proportions come up constantly. And without guidance, it’s easy for shoppers to feel unsure about hitting “add to cart.”
How Shopping Assistant helps:
Giving shoppers personalized styling support helps them visualize how pieces will work in their home.
Shoppers receive styling suggestions based on their existing space as well as recommendations on pieces that complement their color palette.
It even guides them toward a 60-minute virtual styling consultation when they need deeper help. The experience feels thoughtful and high-touch, which is why shoppers often spend more once they feel confident in their choices.

Results:
When shoppers discover a new drink mix, they’re bound to have questions before committing. How strong will it taste? How much should they use? Will it work with their preferred drink or routine? Uncertainty at this stage can stall the purchase or lead to disappointment later.
How Shopping Assistant helps:
Clear, friendly guidance in chat helps shoppers understand exactly how to use the product. Shopping Assistant answers questions about serving size, flavor strength, and pairing options, and suggests the best way to prepare the mix based on the shopper’s preferences.

Results:
Shopping for health supplements can feel confusing fast. Customers often have questions about which formulas fit their age, health goals, or daily routine. Without clear guidance, most will hesitate or pick the wrong product.
How Shopping Assistant helps:
Shopping Assistant detects hesitation when shoppers linger on a search results page. It proactively asks a few clarifying questions, narrows down product options, and points shoppers to the best product or bundle for their needs.
The entire experience feels supportive and gives shoppers confidence they’ve picked the right option.

Results:
Shopping for kids’ furniture comes with a lot of “Is this the right one?” moments. Parents want something safe, sturdy, and sized correctly for their child’s age. With so many options, it’s easy to feel unsure about what will actually work in their space.
How Shopping Assistant helps:
Shopping Assistant guides parents toward the best fit right away. It asks about their child’s age, room layout, and safety considerations, then recommends the most appropriate bed or furniture setup. The experience feels like chatting with a knowledgeable salesperson who understands what families actually need as kids grow.

Results:
Even something as simple as choosing a toothbrush can feel complicated when multiple models come with different speeds, materials, and features. Shoppers want to understand what matters so they can pick the one that fits their routine and budget.
How Shopping Assistant helps:
Choosing between toothbrush models shouldn’t feel like decoding tech specs. When shoppers can see the key differences in plain language, including what’s unique, how each model works, and who it’s best for, they can make a decision with ease.
Suddenly, the whole process feels simple instead of overwhelming.

Results:
Across all 11 brands, one theme is clear. When shoppers get the guidance they need at the right moment, they convert more confidently and often spend more.
Here’s what stands out:
What this means for you:
Look closely at your most common pre-purchase questions. Anywhere shoppers hesitate from fit, shade, technical specs, styling, bundles is a place where Shopping Assistant can step in, boost confidence, and unlock more sales.
If you notice the same patterns in your own store, such as shoppers hesitating over sizing, shade matching, product comparisons, or technical details, guided shopping can make an immediate impact. These moments are often your biggest opportunities to increase revenue and improve the buying experience.
Many of the brands in this post started by identifying their most common pre-purchase questions and letting AI handle them at scale. You can do the same.
If you want to boost conversions, lift AOV, and create a smoother path to purchase, now is a great time to explore guided shopping for your team.
Book a demo or activate Shopping Assistant to get started.
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Like any major topic in your company, your compensation policy should reflect your organizational values.
At Gorgias, we created a compensation calculator that reflected ours, setting salaries across the organization based on 3 key principles:
Since the beginning, we applied the first two: Each of our employees was granted data-driven stock options that beat the market average.
However, we were challenged internally: Our team members asked how much they would make if they switched teams or if they got promoted.
This led to the implementation of our third key principle, as we shared the compensation calculator with everyone at Gorgias and beyond: See the calculator here.
This was not a small challenge. We’re sharing our process in hopes that we can help other companies arrive at equitable, transparent compensation practices.
First, let’s get back to how we built the tool. We had to decide which criteria we wanted to take into account. Based on research articles and benchmarks on what other companies did before, we decided that our compensation model would be based on 4 factors: position, level, location, and strategic orientation.
If we had to sum it up all briefly, our formula looks like this:
Average of Data (for the position at defined percentile & Level) x Location index

This is the job title someone has in the company. It looks simple, but it can be challenging to define! Even if the titles don’t really vary from one company to another, people might have different duties, deal with much bigger clients or have more technical responsibilities. Sometimes your job title or position doesn’t match the existing databases.
For some of these roles, when we thought that our team members were doing more than average in the market, we crossed some databases to get something closer to fairness.
To assess a level we defined specific criteria in our growth plan for each job position. It is, of course, linked to seniority, but that is not the primary factor. When we hire someone, we evaluate their skills using specific challenges and case studies during our interview processes.
Depending on the databases you’ll find beginner, intermediate, expert, which we represent as L1, L2, L3, etc.We decided to go with six levels from L1 to L6 for individual contributors and six levels in management from team lead to C-level executive.
Our location index is based on the cost of living in a specific city (we rely on Numbeo for instance) and on the average salary for a position we hire (we use Glassdoor). Some cities are better providers of specific talents. By combining them, we get a more accurate location index.
When we are missing data for a specific city, we use the nearest one where we have data available.
Our reference is San Francisco, where the location index equals 1, meaning it’s basically the most expensive city in terms of hiring. For others, we have an index that can vary from 0.29 (Belgrade, Serbia) to 0.56 (Paris, France) to 0.65 (Toronto, Canada) etc. We now have 50+ locations in our salary calculator — a necessary consideration for our quickly growing, global team of full-time employees and contractors.

We rely on our strategic orientation to select which percentile we want to use in our databases. When we started Gorgias we were using the 50th percentile. As we grew (and raised funds), we wanted to be 100% sure that we were hiring the best people to build the best possible company.
High quality talent can be expensive (but not as expensive as making the wrong hires)! Obviously, we can’t pay everyone at the top of the market and align with big players like Google, but we can do our best to get close.
Since having the best product is our priority we pay our engineering and product team at the 90th percentile, meaning their pay is in the top 10% of the industry. We pay other teams at the 60th percentile.
Some other companies take into account additional criteria, such as company seniority. We believe seniority should reflect in equity, rather than in salary. If you apply seniority in the company index on salaries, eventually some of your team members will be inconsistent with the market. Those employees may stay in your company only because they won’t be able to find the same salary elsewhere.
Data is at the heart of our company DNA.
Where should you find your data? Data is everywhere! What matters most is the quality.
We look for the most relevant data on the market. If the database is not robust enough, we look elsewhere. So far we have managed to rely on several of them: Opencomp, Optionimpact, Figures.hr, and Pave are some major datasets we use for compensation. We’re curious and always looking for more. We’ll soon dig into Carta, Eon, and Levels. The more data we get, the more confident we are about the offers we make to our team.

Once we have the data, we apply our location index. It applies to both salaries and equity.
To build our equity package, we use the compensation and we then apply a “team” multiplier and a “level” multiplier. Those multipliers rely on data, of course. We’re using the same databases mentioned above and also on Rewarding Talent documentation for Europe.
As we mentioned above, once our tool was robust enough, we shared it internally.
To be honest, checking and checking again took longer than expected. But we all agreed that we’d rather release it to good reactions than rush it and create fear. We postponed the release for one month to check and double-check the results..
For the most effective release, we decided to do two things:
Overall, the reactions have been great. People loved the transparency and we got solid feedback.
We released the new calculator in September 2021, and overall we’re really happy with the response. We also had positive feedback from the update this month.
Let’s see how it goes with time.
Let’s be humble here: It’s only the beginning. It’s a Google Sheet. Of course, we’ll need to iterate on it.
In the meantime, you can check out the calculator here.
So far we’ve made plans to review the whole grid every year. However, now that it’s public within the teams, we can collect feedback and potentially make some changes. Everyone can add comments as they notice potential issues.
The next step for us is to share it online with everyone, on our website, so that candidates can have a vision of what we offer. We hope we’ll attract more talent thanks to this level of transparency and the value of our compensation packages.

I come from the world of physical retail where building a bond was more straightforward. We often celebrated wins with breakfast and champagne (yes, I’m French!) or by simply clapping our hands and making noise of joy.
We would also have lunch together every day, engaging in many informal discussions.
Of course, it bonded us! I knew my colleagues’ dog names and their plumber problems, and I felt really close to many of them.
Employee engagement is one of the primary drivers of productivity, work quality, and talent retention. When I joined Gorgias, where we have a globally distributed team, I wondered how you create the sense of belonging that drives that engagement
Like many companies now, our workforce is distributed. But at Gorgias, it’s a truly global affair: Our team lives in 17 countries, four continents, and many different time zones, which can be challenging.
And yet, I believe Gorgias culture is truly amazing and even better than the one I used to know.
I realize that we achieved that by relying on the critical ingredients of a strong relationship

By repeating these strong moments, you can make the connection between people stronger as well. The stronger the connection, the stronger the engagement.
Speaking of a strong engagement, Gorgias’s eNPS (employee Net Promoter Score) is 50. How is this possible? Well, what’s always quoted as one of our main strengths is the company culture, and how it connects our employees.
Let’s take it further by exploring five actionable steps we have taken to make that happen.
While some would push back against events like these falling under the purview of the People team, they are important for building strong culture, team cohesion, and employee happiness — all areas that are definitely part of our directive.
Here’s what you need to know to bring these summits to your organization.

As the name states, it’s a virtual event where the whole company connects.
It’s not mandatory, but it is highly recommended to attend because it’s fun and you learn many things.
It’s a mix of company updates, fun moments, and inspiring sessions. Each session is short, to let everyone the opportunity to breathe.
Typically we have three kinds of sessions:
Due to timezones, some sessions don’t include every country.


Our last virtual summit cost us roughly $13,000, which means $65 per head. Here’s the breakdown:
The first thing you might already have in mind is: It takes time! And you’re right.
The more we grow, the more challenging it becomes to organize these events.
I believe we’ll eventually need to have a dedicated event manager for all of our physical and virtual events. I want to have them within my team, and I 100% believe it’s worth it.
Another challenge can be technical difficulties with your event software choice, so make sure that you find a reliable platform that suits your needs.
Our team is a mix of hybrid and full-remote workers.
Since we don’t want the full-remote people to become disconnected, we highly encourage them to join the nearest hub once a quarter.
And when they do, we organize some happy hours, games or movie nights. Those face-to-face activities help create bonds between employees. It’s simple and doesn’t require a lot of organization, but it creates an incredible moment every time the remote teams join. We call them Gorgias Weeks.
We were fortunate to be able to organize our company offsite and gather a massive part of the crew together in October 2021.
The pandemic created doubt and additional points of stress, but looking back I’m so glad we were able to create an opportunity for everyone to meet in person.
We asked everyone to bring a health pass — full vaccination or PCR test — and we picked a location that allowed for a lot of outdoor activities.
We made sure the agenda for the two days was not too busy. As with our virtual summit, it was a balance of company alignment, learning, and fun. We made sure people had enough free time to relax, talk to each other, play games, or play sports.
This company offsite is surely an essential and strong moment for us and it helps create strong bonds and great memories.

We encourage every team to organize their own offsite for team-building purposes. Since people don’t meet a lot physically, having these once a year is great!
We let each team lead own it. They pick up the location and the agenda. Then, we provide guidelines with the budget.
Needless to say, it helps build stronger bonds and great memories.
In my experience, it was quite tough to create those moments internally with the team. That’s why we decided to start our team meeting with a fun activity of 10-15 minutes, where we are able to share more than just work.
Every week, there is a different meeting owner who has to come up with new fun activities and games. Starting the meeting with this kind of ice-breaking activity brings powerful energy, and people are more engaged and effective in the sessions. I would recommend it to everyone, especially to those who think, “We already have so many things to review in those weekly meetings, we don’t have time for that.” Try it once, you’ll see how the energy and productivity are different afterward.
On top of that, I also believe tools that encourage colleagues to randomly meet together are great. On our side we use Donut. It gives a weekly reminder that encourages employees to make it to their meeting with a colleague.
Overall, we’ve organized six virtual summits, four company retreats, three Gorgias weeks, and hundreds of virtual coffee and fun meetings.
At the beginning there were only 30 people in the company — now there are 200 of them. As I mentioned, it’s becoming more and more challenging to organize these meetups, but it’s also the most exciting part: making sure the next summit is better than the previous one!
Of course, I’m aware that employee fulfillment and connection are not the only ingredients for retention. But they are key ingredients and shouldn’t be forgotten, especially as we all become more remote.
It’s a worthy investment to organize these events and allocate resources to them, because it makes everyone at Gorgias feel included and connected. And I have no doubt, now, that it’s part of our responsibilities in People Ops.

When a customer's problem goes unanswered on Twitter, you lose that customer and possibly the audience of people who watched it happen.
It’s hard to come back from that, which is why customer care is so important on social media platforms. In fact, Shopify found 57% of North American consumers are less likely to buy if they can’t reach customer support in the channel of their choice.
Your customers want to talk to you — and you should want the same, before they head to a competitor. But first, you need to build a customer support presence on Twitter that lives up to your broader customer experience.
We've helped over 8,000 brands upgrade their customer support and seen the best and worst of social media interactions. Here are our top 10 battle-tested best practices for providing exceptional Twitter support.
Prompt response time is one of the most important pillars of great customer service, and according to data from a survey conducted by Twitter, 75% of customers on Twitter expect fast responses to their direct messages.
Of course, responding with accurate and helpful information is ultimately even more important than responding in real time, so be sure that you don't end up providing inaccurate information in a rush to reduce your response times.
Promptly and accurately responding to customer service issues that are sent to your company's Twitter account is often easier said than done. To do both, you need an efficient system and a well-trained customer support team.
This is where a helpdesk is critical, to bring your Twitter conversations into a central feed with all your other tickets.

If you’re trying to manage Twitter natively in a browser, or through copy-paste discussions with your social media manager, you’re not going to see the first-response times you need to succeed.
As data from Twitter's survey shows, speed is a necessity in order to meet customer expectations and provide a positive experience.
There may be instances where customers contact your Twitter support account via a mention in a tweet as opposed to a direct message. In fact, one in every four customers on Twitter will tweet publicly at brands in the hopes of getting a faster response according to data from Twitter. In these instances, it is important to move the conversation out of the public space as soon as possible by moving the conversation to the DMs.
There are a couple of reasons you would want to avoid resolving customer service issues on a public forum. For one, keeping customer service conversations private allows you to maintain better control over your brand voice and image since customer service conversations can often get a little messy and may not be something you want to broadcast to your entire audience.
Moving conversations out of the public space also enables you to collect more personal data from the customer such as their phone number or other contact information, details about their order and their credit card information without having to worry about privacy concerns.
In Gorgias, you can set up an auto-reply rule that responds to public support questions and directs them to send a DM for further help. This can ensure that people feel heard immediately, even if it takes a while for your team to get to their DM.
Regardless of whether you are discussing an issue with a customer via your Twitter account or any other medium, it is never a good idea for your reps to get into arguments with the customer.
Social media platforms such as Twitter tend to have a much more informal feel than other contact methods, and they also tend to sometimes bring out the worst in the people who hide behind the anonymity that they provide. You may end up finding that customers who contact you via Twitter are sometimes a little more argumentative than customers who contact you via more formal channels.
Nevertheless, it is essential for your Twitter support reps to maintain professionalism and avoid engaging in emotional arguments with customers. It may even help to establish guidelines for your team, to help deal with this type of customer tweet. You can include rules on emoji use, helpful quick-response scripts, and whatever other priorities you have.
Recommended reading: How to respond to angry customers
It is certainly possible to use Twitter alone when providing customer support via the platform. However, this isn't always the most efficient way to go about it.
Keep in mind that, like other social networks, Twitter wasn't necessarily designed to be a customer support channel. There aren't a lot of Twitter features beyond basic notifications that will be able to help your team organize support tickets.
Thankfully, there are third-party solutions that you can use that allow your support agents to respond to tweets and Twitter direct messages from your company website in a way that is much more organized and efficient. At Gorgias, for example, we offer a Twitter integration that will automatically create support tickets anytime someone mentions your brand, replies to your brand's tweets, or direct messages your brand. (By the way, we also offer integrations for Facebook Messenger and WhatsApp.)
Agents can then respond to these messages and mentions directly from the Gorgias platform, where they will show up in the same dashboard as the tickets from your other support channels.
This integration makes Twitter customer support far more efficient for your team and is one of the most effective ways to take your Twitter customer support services to the next level.

It is always important to respond to all questions and feedback that customers provide via Twitter, even if that feedback is negative. This is an important part of relationship marketing.
Many brands shy away from responding to negative feedback on public forums for fear of drawing more attention to the issue. However, this doesn't usually have the desired effect. Failing to respond to negative feedback can make it seem to anyone who happens to see the tweet in question that your brand is dodging the issue.
While you may wish to move the conversation out of the public space as soon as possible, you should always provide a public response to public feedback — negative or not.
For examples of brands effectively responding to negative tweets, check out this article.
According to data from Forbes, 86% of customers say that they would rather speak with a real human being than a chatbot. Even if you don't rely on chatbots for providing customer support, though, your customers may not be able to tell the difference unless you train your reps to be as personable as possible.
When your reps tailor their responses and connect on a personal level, it provides a much more positive support experience that provides a halo effect to your brand. Customers will remember that the next time they arrive at the checkout button, and they might even be open to upsell opportunities at that very moment.
Small businesses may not struggle to keep up with brand mentions, given that there are less to track. For larger companies, though, keeping up with brand mentions can often be a difficult task. This is especially true when some users tag brands with hashtags instead of handles.
This makes it important to create an effective strategy for tracking brand mentions in an efficient and organized manner. One of the best ways to go about this is to utilize integrations that will create a support ticket anytime a customer mentions your brand in a tweet. You can even create custom views in Gorgias to centralize all of these mentions.
By tracking these brand mentions, you can also retweet positive posts for brand awareness.

Not every customer service issue can be handled via Twitter. If there are certain types of issues that fall into that category for your brand, it's a good idea to keep your customers in the loop by providing concise FAQ guidelines that explain which issues you do and don't support via Twitter.
These guidelines can come in the form of a pinned Tweet at the top of your Twitter support account or an off-Twitter link that you provide to customers when they contact you on Twitter with an issue that requires a different medium for resolution. You could even have a visual you add when you respond to questions that don’t fit your guidelines.
Simply responding to customers and requesting that they direct message you for further assistance is another option for addressing issues that you don't want to handle on Twitter. If you set up the auto-reply we mentioned in the second tip, above, it could even include a link to these guidelines.
Check out what this brand did when contacted on Twitter with a problem that needed to be taken off-platform in order to be resolved.
If it makes sense for your brand, it may be a good idea to create multiple Twitter handles that are designated for sales, marketing, and customer support. Creating multiple Twitter handles that serve different purposes allows you to better organize your direct messages and mentions by breaking them down into different categories.
Having a designated customer support Twitter account can also better encourage customers to contact you via Twitter with their customer support issues since it reassures them that this is the purpose that the account serves.
But even then, some customers will still tweet at your main account with issues. When this happens, you can use intent and sentiment analysis in Gorgias to automatically route those issues to the correct agent or team.

When a customer takes the time to reach out to you on Twitter, whether it’s via direct message or a mention, it’s likely not the first time that customer has interacted with your brand.
If you respond on Twitter, you can see the direct message history on that platform, but that’s where the context ends. With Gorgias’s Twitter integration, you can see the full customer journey, including all social media engagement, support tickets across all of your channels and even past orders.
This context is crucial to understanding the conversation you’re walking into, so you can deal with the situation appropriately. If the person is a long-time customer who engages frequently, you’re going to treat that conversation differently than that of a customer who bashes you on social networks and returns products frequently.
Any customer support you provide through Twitter will make things more convenient and accessible for your audience.
But to make the experience faster and more pleasant on both sides of the conversation, you should consider handling all of your social media customer support in one platform, alongside all your other tickets.
Gorgias ties social handles to customer profiles from your Shopify, BigCommerce or Magento store, uniting relevant conversations from across all of your support channels. All of that info is automatically pulled into your response scripts, and you can even automate the process for no-touch ticket resolution.
Check out our social media features to learn more.

Strong website performance is no longer a “nice to have” metric — it’s a critical part of your user experience.
Slow loading times and laggy pages tank conversion rates. They serve up a negative first impression of your brand and can push even your most loyal customers to greener pastures.
When we found out our chat widget had started negatively impacting our customers’ Google Lighthouse scores — an important performance metric — we immediately started searching for a solution.
Live chat is a notoriously resource-intensive category, but we were able to cut our entry point bundle in half using the process I lay out in this article. As a result, we reduced the Lighthouse score impact to just one point, compared with a control.
Here’s what we’ll cover:
Chat widgets are small apps that allow visitors to get quicker results without leaving the webpage they’re on. The chat window usually sits in the bottom corner of the screen, when open.
Here is an example:

Live chat is especially helpful on ecommerce websites, because retail shoppers expect quicker responses. Repetitive questions involving order status, return policies, and similar situations are easily resolved in chat, and it can also provide a starting point for more complex inquiries.
Because merchants make up the bulk of our customers at Gorgias, our live chat feature is a major part of our product offering.
Our live chat feature is a regular React Redux application rendered in an iframe. It may appear simple and limited, but its features extend beyond simple chat to include campaigns, a self-service portal and widget API.
We implemented code-splitting from the beginning to reduce bundle size, leaving us with the following chunks:
Unfortunately, that initial action wasn’t enough to prevent performance issues.
We started hearing from merchants that the chat widget was impacting their Google Lighthouse scores, essentially decreasing page performance. As I previously mentioned, chat widgets generally have a bad reputation in this regard. But we were seeing unacceptable drops of 15 points or more.
To put those 15 points in context, here are the Google Lighthouse ranges:
So if you had a website with 95 performance points, it was considered to be “good” by Lighthouse, but the chat could take it down to “needs improvement”.
Of course, we immediately set out to find and fix the issue.
There were several potential causes for these performance issues. To diagnose them and test potential solutions, we prioritized the possible problem areas and worked our way down the list. We also kept an open mind and looked in other areas, which allowed us to find some fixes we didn’t initially expect.
The initial entrypoint file was 195kB gzipped and the entire bundle was 343kB gzipped. By the end, we had reduced those numbers to 109kB and 308kB respectively.
Here’s what we found.
First, we opened a test shop with chat installed and tried to find something unusual.
It didn’t take long: The chat window chunk was loaded and the corresponding component was rendered, even if you didn't interact with the chat. It wasn't visible, because the main iframe element had a display: none property set.

Then, we moved to the Profiler tab, where we found that the browser was using a lot of CPU, as reported:

Here's what happens if you defer rendering of this component, as originally intended:

However, this deferral introduced another issue. After clicking the button to open the chat, this window starts to appear with some delay. It's easy to explain: Previously, the JS chunk with this component was downloaded and executed immediately, while these changes caused the chunk to load only after interaction.
This problem is easily fixable by using resource hints. These special HTML tags tell your browser to proactively make connections or download content before the browser normally would. We needed a resource hint called prefetch, which asks the browser to download and cache a resource with a low priority.
It looks like this:
There's a similar resource hint called preload which basically does the same thing, but with higher priority. We chose prefetch, because chat assets are not as important as the resources of the main site.
Since we're using webpack to bundle the app, it's very easy to add this tag dynamically. We just added a special comment inside dynamic import, so it looked like this:
Though this solution didn’t affect bundle size, it significantly increased the performance score by only loading the chat when necessary.
Once the rendering was working as intended, we started to search for opportunities to reduce the bundle size.
Bundle size doesn’t always affect performance. For example, here you can see almost the same amount of JS, although execution times are very different:

In most cases, however, there is a correlation between bundle size and the performance. It takes the browser longer to parse and execute the additional lines of code in larger bundle sizes.
This is especially true if the app is bundled via webpack, which wraps each module with a function to execute. This isn’t a problem with just a couple of modules, but it can add up — especially once you start getting up into the hundreds.
We used a few tools to find opportunities to reduce bundle size.
The webpack-bundle-analyzer plugin created an interactive treemap, visualizing the content in all bundles

The Coverage tab inside Google Chrome DevTools helped us see which lines were loaded, but not used. The minified code made it more difficult to use, but it was still insightful.

Next, we discovered the client bundle included the yup validation library, which was unexpected. We use this library on the backend, but it’s not a part of the widget.
It turns out the intended tree-shaking didn't work in this situation — we had a shared file which was used by the JS client and backend. It contained a type declaration and validation object, and for some reason webpack didn't eliminate the second one.
After moving type declaration to its own file, bundle size was reduced dramatically - 48kB gzipped
We also discovered the Segment analytics SDK took 37.8 kB gzipped.
Since we don't use this SDK on initial load, we created a separate chunk for this library and started to load it only when it's needed.
By looking into the chart from webpack-bundle-analyzer, we realized that it was possible to move React Router's code from the main chunk to the chunk with the chat window component. It reduced entrypoint size by 3.7kB and removed unnecessary render cycles, according to React Profiler.
We also found that the Day.js library was included in the entrypoint chunk, which we found odd. We actively use this library inside the Chat Window component, so we expected to see this library only inside the chunk related to this component.
In one of the initialization methods, we found usage of utc() and isBefore() from this library, functionality that is already present in native Date API. To parse date string in ISO format you can run new Date() and for comparison just add the < sign. By rewriting this code, we were able to reduce entrypoint size by 6.67kB gzipped. Not a lot, but it’s all starting to add up.
Another offender was the official client of Sentry (23.4kB gzip). It is a known issue which has not been resolved yet.
One option is to lazy load this SDK. But in this case, there was a risk that we could miss errors occurring before the SDK fully loaded. We followed another approach, using an alternative called micro-sentry. It’s only 2kB and covered all functionality that we needed.
We also tried to replace React with Preact, which worked really well and decreased the bundle size by 33kB in gzip. However, we couldn't find a big difference in the final performance score.
After further discussion with the team, we decided not to use it for now. We think the React team could introduce some interesting features in new versions (for example, concurrent mode looks very promising), while it would take some time for the Preact team to adopt it there. It happened before with hooks: The stable Preact version of the React feature followed a full year later.
From further inspection, we found the mp3 file used for the notification sound could be compressed using FFmpeg without a noticeable difference in sound, saving 17.5kB gzipped.
We also found that we used a TTF format for font files, which is not a compression format. We converted them to WOFF2 and WOFF formats, which reduced size by 23kb in gzip for each font file — 115kB in total.
We didn't notice any differences in performance score after these changes, but it was not a redundant exercise. With these changes, we transfer less information, using less network resources. This could be beneficial for customers with bad network connection.
We already used a content delivery network (CDN) to improve the loading time, but we were able to reconfigure its cache policies to make it more efficient. Instead of downloading chat every time user visits the page, chat is downloaded via network only on a first visit, while all subsequent requests will use a version from the browser cache.
A CDN is a very good way to deliver assets to clients, because CDN providers store a cached version of chat application assets in multiple geographical locations. Then, these assets are served based on visitor's location. For example, when someone in London accesses the website with our chat, chat assets are downloaded from a server in the United Kingdom.
Below, you can see how the bundle composition changed after applying the fixes we’ve mentioned. The entrypoint file was halved in size, and the total amount of JS was reduced by 35kB gzipped.

And here’s the full chart inclusive of all chat assets, including the static assets.

To see the impact of these reductions, we performed Google Lighthouse audits on our Shopify test store using three configurations:
We also used the mobile preset to tighten up the conditions. In this mode Lighthouse simulates mobile network and applies CPU throttling.
Here are the results:

Not only did we improve on the original penalties, but we were able to get the performance score almost to the same level as when there is no chat enabled at all.
This is either in line with, or outperforming most other chat widgets we have analyzed.
To maintain the current levels of performance and impact, we added a size-limit check to our continuous integration pipeline. When you open a pull request, our CI server builds the bundle, measures its size and raises an error if it exceeds the defined limit.
When you import a function, it’s not always obvious what kind of code would be added under the hood — sometimes it's just a few bytes of code, but other times it could import a large library.
This new step makes it possible to detect these regressions in a timely manner.

It's also possible to define a time limit using this tool. In this case, the tool runs a headless version of Chrome to track the time a browser takes to compile and execute your JS.
While it sounds nice, in theory, we found results from this method very unstable. There's an open issue with a suggestion on how to make measurements more stable, so hopefully we can take advantage of the time limit functionality in the future.
It turns out there is a lot of low-hanging fruit when it comes to performance optimization.
Just by using built-in developer tools in the browser and a plugin to generate a visual representation of the bundle, you might find a lot of opportunities to optimize performance without refactoring the whole codebase. In our case, we reduced entrypoint file size by 49% and reduced impact on the client's website significantly.
If you work on a new project, we strongly advise you to think about performance before it's too late. You can prevent the accumulation of technical debt by taking simple steps like checking bundlephobia before installing a library, adding size-limit to your build pipeline and running Lighthouse audits from time to time.

As ecommerce grew this year, we continued to work toward a decentralized vision of commerce — a model where merchants take back their customer relationships from colossal marketplaces and connect one-to-one with the people who buy their products.
Our merchants had a record-breaking number of these personal interactions in 2021 and that’s worth celebrating. So we’ve collected all the firsts, upgrades and proudest moments to share with you.
Since January 2021 feels like 10 years ago (and also 10 minutes ago, somehow), let’s take a walk down memory lane.
This year, we helped 8,000 brands support over 290 million shoppers, bringing in customers like Bidabo, Biketart, Lillie's Q and Livinguard.
All together, our customers generated $1.1 billion from their customer support functions in 2021.
Those companies varied in size, from single entrepreneurs still proving their products to enterprise companies scaling beyond their wildest dreams. Differences aside, they united in prioritizing customer experience to grow their businesses.
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Some industries came up again and again on our roster, including:
And because Gorgias powered growth across 110 industries, our customers’ customers were purchasing everything from medical supplies to maritime essentials.
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Every minute of 2021, Gorgias customers closed out an average of 179 tickets. In more relatable terms, they helped more than 10,000 shoppers in the time it took to watch a new episode of Shark Tank.
At the peak of support volume — the five-day period from Thanksgiving and Black Friday through Cyber Monday (BFCM) — our merchants answered 2.5 million tickets. Their support teams drove $25.6 million in sales during that time.
With tools made for that moment, they were able to stay on top of the ticket pile and turn the holiday rush into a gold rush.
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The impact didn’t stop there. On average, our merchants received a 4/5 satisfaction rating from their customers in 2021. The 75 million tickets they answered reinforced their brands, one loyal customer at a time.
After all, when your team has a million fires to extinguish, the only flames in customer support should be the emoji reactions to your five-star ratings.
And that’s exactly what you’ll be chasing as your performance metrics approach those from our top quartile of merchants. The top-performing teams clocked first-response times under two hours and resolution times under 8 hours, on average.
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As ecommerce becomes more decentralized, so do the channels that provide your customer feedback.
Still, it’s no surprise that email remains the most popular support channel, used by 92% of our brands. Together, they answered 64 million emails in 2021 (85% of all tickets).
This next stat may be more of a revelation: 78% of our brands have brought Facebook, Instagram, and/or Twitter interactions into their Gorgias workspace. They answered 3.7 million comments across those three channels, with almost two-thirds coming from Facebook.
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These social channels were used even more than our live chat, phone, and SMS integrations. And Gorgias helped merchants meet their customers in all of the above, without ever leaving their dashboard.
2021 also saw the launch of our long-awaited Gorgias App Store. This hub features 75 apps to extend the power of our helpdesk and centralize the information support agents rely on.
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62% of our merchants are using at least one of our partner apps, and we’re exploring new partnerships all the time to continue streamlining the customer support process.
This allows us, and all of our partners, to stay focused on being the absolute best at what we do.
Some of our merchants’ favorite integrations include:
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So go ahead and close those 20 tabs out — you won’t need them where we’re headed.
We released 91 features this year, 42 of which were led by your requests on our public roadmap.
Our most requested features (that are all available today!) were:
The quick adoption of our 2021 social media updates made it clear these channels were critical to our merchants’ success this year. We expect that to continue into 2022. (TikTok, anyone? Give it an upvote here!)
And while voice support didn’t see the same volume of requests as the social channels, we knew it was essential for certain brands. To better serve these merchants, we built a native phone integration that’s easily set up for new and existing numbers.
Merchants responded by taking more than 4,000 calls from shoppers this year. As a result, resolution times were up to 34% faster than others who left phone service out of their strategies.
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And while we want to give our merchants a variety of tools to provide help, sometimes it's best to empower shoppers to help themselves.
Our new Help Center feature provides FAQ hubs on merchant websites, to work toward this goal. The first 100 Help Centers that went live attracted over 100,000 views, answering inquiries before they could turn into tickets.
Another contribution is perhaps our most exciting release: Our Automate product allows for customization of self-service flows and deflects even more tickets to boost team efficiency.
Hundreds of merchants used the add-on in 2021 to automate their tickets, increasing efficiency across their support teams.
Our self-service portal alone deflected up to another 33% of tickets specific to shoppers (like order status). This freed up agent time to provide a more personal touch to important conversations.
We tripled the size of our team in 2021 to continue building the best possible helpdesk for the specific needs of ecommerce brands. There are now 185 employees who work in 16 countries around the globe and speak 18 different languages.
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That means there’s more Gorgians building out integrations, furthering the product roadmap, and contributing to our merchants’ success.
And our customers have let us know how much these improvements impacted their businesses. We currently hold top marks among the helpdesk categories on G2, Capterra and the Shopify app store.
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2021 was a year to remember for the Gorgias team and our customers, but 2022 is shaping up to be even better. It might even be the year people learn to pronounce our name. (470 people asked how during this year’s demos; think “gorgeous.”)
Fingers crossed.
Either way, we have some key new features on the roadmap and several surprises up our sleeves. We’ll continue building and optimizing channels so you can meet your customers where they are (including a much-requested Whatsapp integration). We’re also going to renew our focus on automation tools to increase efficiency across your team.
Make sure you subscribe to our newsletter, below, to beam all of our updates directly to your inbox.
As for the rest of the ecommerce industry, we have high hopes for 2022 (and plenty of predictions). We’re expecting continued shift of support tickets to social channels, a bigger emphasis on self-service options and a sharper focus on app integrations across the ecommerce ecosystem.
Until then, thanks for a great year!

Building an incredible customer support team at your company starts with finding the right people. But once you attract a pool of applicants, distinguishing between an excellent candidate and a so-so one isn’t always simple.
Why does this matter? Research from Harvard Business Review concluded that positive customer experiences can create as much as a 140% spread in how much customers spend at transactional businesses. And a massive part of that customer experience rides on your customer service team.
And for subscription-based businesses, that same study found a 31% spread in churn: quality of the experience was a significant driver of recurring membership and revenue.
This adds up over time, as well. Forbes found a cumulative loss of $75 billion yearly across all businesses pegged to a single source: poor customer service.
So, in the big picture, a great hire in customer service or support makes you more money. A bad one hurts the bottom line, and so much more.
As a hiring manager, it’s never been more important to get the most out of your interviews. The needs of customer service teams are more technically complex than before, with numerous channels for meeting customer needs. Working with an ecommerce helpdesk support partner like Gorgias can ease some of the pressure, but the interview itself remains highly strategic.
Before we get into specific questions, it’s worth noting that not every candidate has the right disposition for customer service work. Many customer service skills are crucial, and these traits and characteristics rise to the top of the list. As you work through the selection and interview process, look for these elements.
This isn’t an all-out dealbreaker, but interviewees that are already familiar with your brand and tone of voice will assimilate much more quickly into your organization. This is true whether this familiarity is natural (because the candidate is already a fan of your brand) or learned (because the candidate took the time to prepare before the interview).
Your customer service representatives spend all day communicating directly with customers, so you want to hire people with great communication skills for customer service roles. Your customer service team forms the face of your company for many customers, so the ability to communicate clearly about a customer’s problem is essential
Customer service reps will inevitably deal with angry customers as well as difficult customers, so hiring managers should look for candidates that can keep those communication skills up even under pressure.
The best candidates for customer service roles will pair strong communication skills with superior problem-solving ability, as well.
The days of single-channel customer service departments that existed solely as phone-based call centers are long gone. Most businesses rely on a multichannel customer service strategy that could involve face-to-face assistance, phone communication, or any of a wide range of web-based platforms. Today's agents need to apply customer service best practices to many channels.
A customer service candidate that can speak empathetically and clearly but cannot navigate social media or a customer service platform may not be a good fit for your current needs.
Conversely, you may find candidates that struggle with over-the-phone communication but can tirelessly plow through online tickets with superior skill.
If your team is large enough to differentiate, both types of candidates could succeed in clearly defined roles.
Still, it’s a good idea to get a sense of which service channels a candidate is likely to succeed in. Do this during the interview if you don’t do it earlier in the process.
If you’re a hiring manager in customer service or customer support, you already know that crafting the perfect customer service job interview questions is difficult. And if you’re an aspiring or current customer service representative, you may be looking for advice on how to answer customer service interview questions.
No matter your role, this list of 17 customer service interview questions and answers will give you some new approaches to the customer service job interview. Consider adding several of these questions to your interview process so you can hone your interview process and get better results.
(And if you’re a job seeker, these sample answers should give you insight into what interviewers might be expecting to hear. Adapt them to fit your situation, of course! You might also benefit from 5 Tips to Find Your Next Job in Support.)
You don’t have to hire someone with prior knowledge of your brand, but it sure helps. The more the prospect knows about you, the less you have to teach them. So it’s worth asking how familiar the prospective hire is with your brand and what they think about it.
If your interviewee is familiar with your brand, go ahead and ask if they’ve ever interacted with your customer service team. If they have, their answers could be illuminating — about the candidate and about the customer service experience.
Listen for an honest, enthusiastic response. It’s OK if a candidate doesn’t know much about the brand, but finding someone with the skills AND who is a big fan of your brand can lead to the ultimate customer service prospect.
Pro Tip: Watch out for a response in which a candidate wants to change too much: some constructive criticism is healthy, but customer service is not the place from which to spearhead major company-wide change.
“I’ve been using your [specific product/service] for some time now. I use it to [use case], and my appreciation for your [brand/culture/products] is a big part of why I applied for this job!”
Or
“I hadn’t heard of your company before I saw this job post, but as I began to research the company, I resonated with [product or aspect of mission]. I believe it’s something I can get behind and contribute to!”
Customer service is more software-oriented than ever before, and this question does double (maybe triple) duty: first, it tells you the obvious (whether the interviewee is familiar with your software). Second, it also often reveals a candidate’s overall comfort level with software. You’ll usually get a sense in their answer of whether they’re worried about the prospect of learning new software.
Third, assuming your job listing indicated which software solutions you use, this question will reveal how closely the applicant studied the job listing. If they seem confused by the question or don’t know which software solution you’re asking about, that might be a red flag.
As you listen to the applicant’s answer, don’t settle for a blunt “yes.” Follow up with a question or two that will reveal whether the person has actual knowledge of the software. And if the applicant isn’t familiar with your chosen software, listen for confidence about the ability to learn.
“At my previous company, we used [competitor software solution], and I can tell based on my own research that the two are pretty similar. I’m sure there may be some slight gaps, but I’m eager to learn those differences and get up to speed in [your software solution].”
Behavioral interview questions can be powerful because they give you insight into an interviewee’s thought processes and ways of engaging with the world. Length of employment at the previous company is merely factual, but the “why did you leave” portion is deeply behavioral.
Tenure at a previous employer isn’t always important, though a resume filled with a series of three- to six-month gigs could be a red flag. More important is the stated reason for leaving. Did the candidate struggle with a previous manager? Did they leave over scheduling issues (that are likely to be an issue at your company as well)?
To be clear, leaving a previous company isn’t always a bad thing. But the reasons why — and the way the candidate explains those reasons — can teach you a lot about the person’s approach to working on a team.
“I’m still employed at [current employer], but I see a better future for myself with your company. I’m happy enough at [current employer], but I’m more passionate about your company for [give a reason or two].”
Or
“I worked at [previous employer] for [time period] but had to leave due to [reason]. That said, I know [reason] won’t be an issue here because [explanation].”

This question helps you keep developing a profile of the candidate’s experience. At the most basic level, you want to learn whether they are familiar with the most common customer service questions that your team deals with.
While the key to delivering great customer service is the ability to use problem-solving skills to navigate especially difficult situations, those everyday types of questions make up the bulk of the actual work. Finding an employee that’s already well-versed in your most common customer issues (and who already has good responses to those issues) makes your job a lot easier.
Also, this is another behavioral interview question that can give you deeper insight into what makes a candidate tick. Listen carefully to how the person describes their activity helping various types of customers. You’ll learn at least as much about how the person thinks as how they solved a specific problem.
“Issues with account logins made up around 15 percent of my customer service interactions at my last job. We had a script to follow for this kind of issue, and I used it when I could. But over time I noticed that many users were getting tripped up on the same problem that wasn’t covered by the script. I helped them resolve it by [x] and recommended we add this step to the script.”
This is the first of several classic behavior-based questions. You’re listening for soft skills here, those intangibles that differentiate truly excellent customer service reps from the rest.
With this question, you’re looking at conflict resolution skills surrounding customer issues like public complaints or angry customer emails. Does the candidate have a handy example of being able to
You’re also looking at the ability to follow instructions and think outside the box. You don’t want renegades and mavericks, but you do want folks that can think beyond provided customer support scripts.
Push the interviewee to be specific with their answer to questions like these.
“Once I dealt with a customer with [stated problem]. I could tell the customer was upset before we ever started talking because of [verbal/written cues]. I kept my cool, sidestepped that anger, and determined that the customer’s core problem was [actual problem]. Once I identified that I made sure to empathize with the customer as I guided them to a solution to [actual problem.”

[Image source: Me.me]
Hiring for customer service is a delicate balance. You don’t want a cadre of people constantly trying to reinvent the wheel (or, worse, the company itself). And you also don’t want mindless followers. This question helps you gauge a candidate’s ownership mindset.
If they don’t have an answer at all, they might not be thinking enough about the big picture. Conversely, if their answer sounds a little too revolutionary, you’ll be aware that this candidate might need guidance in what’s appropriate.
As the candidate responds, listen for actionable ideas and methods that seem genuinely useful. Vague feedback with no clear outcomes isn’t what you’re looking for here.
“I noticed we were getting tons of customer support calls about one of the company’s products. The product was fine, but the included instructions left out a crucial step that was leading to the calls. I was able to point this out and escalate it to the proper team, who corrected the instructions for the next printing. In the meantime, I created an email template to help agents respond to customer questions about the issue faster. By getting the instructions fixed, I reduced these calls so the team could focus on more important customer issues.”
Sometimes, the best agents have experience from other roles with complementary skillsets. For example, wait staff at restaurants have a ton of insights about human interactions and communication since they serve people in person for hours on end.
Or, if the company products are highly technical or industry-specific, you’ll benefit from finding customer service reps with relevant tech or industry backgrounds as well.
Find out if your prospect has other experience they can bring to the table. Maybe they can even teach your team a thing or two.
“I’ve worked for ‘x’ years in foodservice, including ‘y’ years as a server. In those years I developed the ability to read verbal and nonverbal cues. And have found creative ways to meet customer needs. My time in restaurants has prepared me to excel in customer service by giving me a keen sense of customers’ needs, proactive tactics to keep them happy, and multiple strategies for resolving their complaints.”
You already know a candidate’s formal education as it is listed on their resume, but this question gives them a chance to expand upon that. Perhaps they took a specific course that’s relevant to this job or additional training certifications that didn’t make it on the resume.
Give them a chance to explain how some of their formal education enhances their abilities for this job.
This is also a great place to explore whether a candidate has experience in the systems you use, such as these:
Of course, reliance on formal training varies from company to company. Some brands focus more exclusively on skills and traits. Use your judgment with this question (but make sure you don’t imply that a degree is required unless it is).
“My degree was in [field], and as a part of my coursework, I took several courses in communication as well as a technology course. In these courses, I learned [two or three high-level lessons], which will help me in this role [explain how].”

“My top three core values in the workplace are [list three]. These core values permeate every aspect of my work: how I interact with customers, how I work with other team members, and more. If you ask [reference at previous employer] about this, I believe you’ll hear that I lived this out there, and I’ll do the same here.”
You want customer service agents that can, in most cases, get your customer what they want. But sometimes customers are wrong, demanding things that can’t be done. An experienced customer service rep will certainly have run into this scenario and learning how they handled it will give you great insight into their abilities.
Were they able to salvage a customer relationship? Show the customer a better way? Or did they just blow up the situation and provide no alternatives?
“I always do my best to meet customer requests, but of course this isn’t always possible. One time, a customer [describe illegitimate request scenario]. He was convinced I could do this for him, but it was out of scope. However, instead of just flat-out denying him, I was able to guide him to an alternative that was in scope. He didn’t get everything he wanted, but I did keep him as a customer.”
For interviewees with previous customer service experience, this question gives you insight into how far they’ve been stretched — as well as their emotional intelligence after the fact.
Look for how serious or difficult the described situation is (compared to what’s typical in your organization), and pay attention to how calmly — or not — the candidate can recount the scenario.
“In my current/previous position, I’ve had a few encounters in a class all their own. Probably the most challenging one was [describe the scenario]. It was challenging for sure, but I’m glad I went through it because I learned [lesson/insight]. I also really appreciated the support I got from my leadership team throughout the situation.”
If a candidate has a quality answer to this question, it will likely reveal the sorts of situations that motivate the individual. You should look for excitement, interest, and perhaps even joy as the individual answers this question. And knowing the kinds of situations that motivate an individual can give insight into whether they’ll be a good fit for your team.
“I once had a customer call in who was incredibly angry, but it was an issue that I knew I could solve. As I worked with the customer to unpack the layers of the issue, I heard her tone gradually soften. By the end of the encounter, I’d not only solved her problem, but I’d also managed to upsell her to a higher tier of service — and she was happy about it!”

The stock answer here is “contact my supervisor,” of course, but see if you can get a little more. What avenues (official and unofficial) would the candidate pursue before escalating to a manager? Will this prospect solve problems independently, or will the individual create an unending cascade of manager escalations?
“When I didn’t know an answer, I’d quickly search our internal knowledge base/wiki. If I didn’t find the answer there, I might search Google, our company Slack, or a more knowledgeable peer. I tried to minimize the number of escalations since I know that solving the problem myself is always the ideal outcome. But of course, I escalated issues to my manager when needed.”
“The customer is always right” only goes so far. Sometimes the customer is quite wrong, and your customer support teams know this. The real question is what a prospective team member will do when this happens.
This is another question that explores soft skills. There are countless ways to say “you’re wrong” without coming out and saying it. Can the prospect guide a customer to a better understanding without insulting them along the way? That’s the kind of customer service rep you need.
“I never take a confrontational approach when this happens. Instead, I assume that the customer isn’t willfully wrong, and I try to find a gentle way to guide them to a better understanding.
If there’s documentation or fine print that the customer missed, I’ll guide them to that information. I might also ask questions to get a better sense of where the customer got the incorrect understanding.”
Every one of us brings experiences from our personal lives into our professional work, and great customer service reps are no exception. We’ve all been the customer in need of service at some point, and there are great lessons to learn from the good experiences.
A prospect’s answer to this question should demonstrate their insightfulness and awareness. It will also likely reveal more about a person’s priorities in customer service encounters.
“I had an experience with [company] that impressed me as a customer and gave me some great ideas for how to solve customer challenges in my work. [Describe scenario and lessons learned.]”
Asking the opposite question gives you similar insights: whatever got under the skin of your interviewee is an aspect of customer service that they’re passionate about. And, of course, having been a frustrated consumer can build empathy when working with other frustrated consumers.
“I had an encounter with one company where the agents were working from scripts, and it didn’t seem like they took the time to process what I’d said in my complaint. It was deeply frustrating, but I learned from the experience that scripts can only get you so far and that I need to make sure I always understand the customer’s concern before I start trying to solve it.”
If you’ve gotten to this point and expect that you’ll offer the candidate a job, it’s time to get this crucial information. If your only needs are second shift and the candidate can’t or won’t work it, you need to know now.
Simply be honest. “I’m looking for full-time work, and normal business hours are my preference. I could work the second shift if necessary, but no overnights. That said, let me know what you’re looking for, and let’s talk about it.”

[Image source: IMC]
Getting the right team in place is a crucial component of your customer service strategy and so is giving your new team members the best in ecommerce customer service technology.
Find out why Gorgias is the #1 rated helpdesk for ecommerce merchants. See how Gorgias integrates with Shopify, Magento, and BigCommerce.
And, if you need help with the technology portion of your customer service strategy, schedule a Gorgias demo today.

Do you switch multiple screens and views to understand what’s going on with your team? If you do, we’re happy to report that there is now a shortcut. 🪄 Live Statistics on Gorgias is your destination to get a quick overview of ticket volume, agent activity and active channels in real-time.
It’s up top! Once you navigate to Statistics, you will see Live Statistics conveniently placed at the top. Click on Overview to see a snapshot of all customer support activity over all channels and agents.

In Live Overview, you will see the number of Agents Online and offline. Next to these, you will find the numbers of Open Tickets in two sections (to indicate whether they’re assigned or unassigned).

Pro tip: Hover over the tooltip to see a quick list of the actual agents who are online and offline. ⬇️

Say it’s late afternoon and you’re seeing a spike in open tickets. In Live Overview, you read 60 Open Tickets, and only 2 Agents Online. → 30 tickets per person 😱
With this information, you can immediately make decisions and take action on how to handle the higher volume your team is experiencing. For example, you may consider:
• Going to Live Agents Statistics to see the number of Chat tickets
• Creating a macro and a rule for your Chat customers who are waiting for longer than 1 minute ⌛️
• Creating a macro and a rule to set expectations around the delay
• Check internally to see if there is a problem with your delivery ops
• Stepping in to answer tickets with the Urgent tag
Knowing exactly what’s going on as it happens live, later observing fluctuations in First Response Time or Resolution Time won’t catch you by surprise. You can make a better assessment on your team’s performance by being fully aware of the circumstances around your metrics.
In Live Statistics, we simplify and organize information so you can be fully aware without needing to contact your team personally or be physically there.
In Live Overview, you will see a nice graph to inform you on the hourly Support Volume. Use this graph to see how your team is responding to inquiries as they emerge.

Looking at this graph, you can quickly grasp the volume you’re getting by the number of Tickets Created, Tickets Replied and Tickets Closed separately, but on the same timeline so you can compare.
When do you receive the bulk of inquiries? Does it happen before business hours?

You can see above an example where a lot of customers decided to contact the support team throughout the night. As a manager, you can monitor to see if this is a consistent pattern over time, and develop strategies on the support and operations side to improve experience. Looking at this graph, and seeing this type of pattern, you may want to ask:
• Are these tickets urgent? Are we properly auto-tagging to identify urgency?
• Are my agents ready to tackle this ticket volume at the beginning of their shift?
• Are these tickets auto-assigned?
• If these inquiries are urgent, should we set autoresponders?
• Is it worth getting additional staffing or how can I leverage self-service?
Monitoring the Support Volume graph, you can start to detect patterns that are connected to the entire customer experience. Anticipating problems or delays before they can occur, you can take measures to improve CSAT despite the predicaments due to international shipping challenges or a mix-up on a batch of orders etc. You can use the visibility and insights from Live Statistics to inform your overall operations.
Live Statistics is designed to inform you on an hourly basis. It gathers the right metrics and combines them strategically so you can get the right information and react quickly without being there.
All metrics in Live Statistics reflect your current day from 12:00 am to 11:59 pm in your time zone. If you need to change the time zone, you can easily do this in Business Hours under Settings.
In Live Agents, you will see whether each team member is currently online or offline, indicated by a green dot if they’re online - 💪 working away, or an orange dot indicating that 💤 they’re offline.

Pro Tip: Hover over these dots to see when they signed in (on the green dot), and when they were last seen that day (on the orange dot).

Live Agents Statistics will show you the exact amount of time each team member has been online in hours and minutes (e.g. 2h 6m). On this table, you will also see the number of Tickets Closed and Messages Sent. The ability to see this exact combination of data per agent will really help you monitor your team’s efficiency and know which agents to coach to improve performance.

Have all of your team use this, so everyone can self-check 🩺 and have their sense of achievement for the day based on data - 😉 not just feelings. Reviewing Live Statistics as a team, you can collaborate to come up with effective strategies to reduce the rate of messages sent per ticket closed.
Located in the far right column, Open Tickets 🤩 feature makes Live Statistics whole. This is where you can clearly see who’s working on what. You can see the total number of open tickets currently being helped by each team member. Placed immediately next to this number is the breakdown by channel.

You can read above how Jenny has 15 open tickets, with 3 from Chat, 7 from Email, 3 from Facebook and 2 from Instagram.
Click on this number to see just exactly what Jenny’s working on. Each of the numbers provided under Open Tickets, the total and by channel, is clickable and it will bring you to a readily filtered view where you can take a closer look at what your agent has at hand.
This experience will help you review your team’s activity in microscopic detail as you desire, without needing to interfere or navigate to different Views.
Check out the full combination of Online Time, Tickets Closed, Messages Sent and Open Tickets. Let’s bring back Jenny and look closer to see that she has 3 Tickets Closed, 12 Messages Sent and 15 Open Tickets. Under Online Time, it reads 6h 12m so she is approaching the end of her shift.

Right away, we can notice that she’s approaching the end of her shift with a lot (15) more tickets to go. There’s no need to get alarmed, but it’s better to take a quick look at what’s going on. Clicking on the number listed under Open Tickets, we can easily review the tickets she’s working on. Taking a quick look at her tickets, we notice a shipping problem on multiple orders - all large items shipping internationally with a third party. Knowing Jenny’s not familiar with third party shipping, we can remove these tickets from Jenny’s queue and make sure that we mark our calendar to train her on this topic before BFCM hits. ✅
All of these features in Live Statistics, Overview and Agents are designed to give you full visibility and control so you can take timely action to remove stress from work and from your team. 😅
📞 Let’s get on a call to have a quick chat.
✏️ We love hearing your insights, please drop us a note here.

Gorgias connects to over 70 leading ecommerce applications, giving you the power to centralize customer data in your helpdesk, perform support actions from a single place, and streamline your store’s toolkit.
This month, we launched 7 new integrations:
Read on to learn how you can use these tools to help manage your store, and visit the Gorgias App Store to activate them today!
LoyaltyLion is a digital loyalty framework that gives ecommerce stores innovative ways to engage and retain customers. If you're using LoyaltyLion for your loyalty program, you can connect it to Gorgias to display information next to support tickets, and reward loyalty points using Macros.

Note: Gorgias no longer supports Twitter. You can still use Facebook, Instagram, and WhatsApp in Gorgias.
Give your support team the power to provide customer service to shoppers on Twitter, without having to log into another platform or share credentials with your social media manager. View past Twitter conversations, gain cross-channel message context, and customize your replies to provide exceptional customer support.
Note: This integration is currently only available for Enterprise plans. View pricing here.

CallHippo allows startups and businesses to buy instant local support numbers from over 50+ countries around the world. With this integration, you can create tickets in Gorgias for phone calls and SMS conversations via Call Hippo.

Shipup follows your packages in real-time to create a seamless, transparent, and branded delivery experience. With the Gorgias integration, you can easily share shipping information with your support team, immediately notify them with a ticket in Gorgias when an incident occurs, and find customer information right next to conversations.

Tolstoy is an interactive video platform, helping users create meaningful and personal conversations at scale. With this integration, Gorgias users can sync their Tolstoy videos and monitor every viewer interaction as a ticket, empowering support agents to engage without ever leaving the help desk.

Autopilot is a data and customer journey marketing platform designed for businesses who sell online. With this integration, you can now combine your Shopify and Gorgias data together seamlessly in Autopilot. You’ll not only have a single view of your customer, but you’ll be able to deliver a more personalized marketing experience and get glowing reviews from satisfied customers.

SentiSum is an automated ticket tagging engine powered by natural language processing technology. With this integration, SentiSum tags can auto-fill form fields directly in Gorgias. From there, you can implement additional automation that saves agent time and improves customer outcomes.

You can now receive Yotpo product reviews right in Gorgias and reply to them as tickets! This gives your agents visibility into how shoppers feel about your product and allows them to address concerns without ever leaving the helpdesk. Each ticket will include the review details (like score and product) and allow you to either reply publicly or privately, so you can customize the support experience.

To add these integrations and discover more, go to the Gorgias App Store.


