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How to Pitch Gorgias Shopping Assistant to Leadership
Want to show leadership how AI can boost revenue and cut support costs? Learn how to pitch Gorgias Shopping Assistant with data that makes the case.
TL;DR:
- Position Shopping Assistant as a revenue-driving tool. It boosts AOV, GMV, and chat conversion rates, with some brands seeing up to 97% higher AOV and 13x ROI.
- Highlight its role as a proactive sales agent, not just a support bot. It recommends products, applies discounts, and guides shoppers to checkout in real time.
- Use cross-industry case studies to make your case. Show leadership success stories from brands like Arc’teryx, bareMinerals, and TUSHY to prove impact.
- Focus on the KPIs it improves. Track AOV, GMV, chat conversion, CSAT, and resolution rate to demonstrate clear ROI.
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.
Why conversational AI matters for modern ecommerce
1) Meet high consumer expectations
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.
2) Keep up with market momentum
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.
3) Raise AOV and GMV
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.
How to show the business impact & ROI of Shopping Assistant
1) Pitch its core capabilities
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.
Success spotlight
After implementing Shopping Assistant, leading ecommerce brands saw real results:
2) Position it as a revenue driver
Shopping Assistant drives uplift in chat conversion rate and makes successful upsell recommendations.
Success spotlight
“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.
3) Show its efficiency and cost savings
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.
Success spotlight
"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."
4) Present the metrics it can impact
Shopping Assistant can impact CSAT scores, response times, resolution rates, AOV, and GMV.
Success spotlight
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.
5) Highlight its helpfulness as a sales agent
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.
Success spotlight
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.
6) Provide the KPIs you’ll track
Customer support metrics include:
- Resolution rate
- CSAT score
Revenue metrics to track include:
- Average order value (AOV)
- Gross market value (GMV)
- Chat conversion rate
Shopping Assistant: AI that understands your brand
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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11 Real Ways Ecommerce Brands Use Gorgias Shopping Assistant to Drive Sales
Here are 11 ways to use Gorgias Shopping Assistant to make the shopping experience more valuable.
TL;DR:
- Shoppers often hesitate around sizing, shade matching, styling, and product comparisons, and those moments are key revenue opportunities for CX teams.
- Guided shopping removes that friction by giving shoppers quick, personalized recommendations that build confidence in their choices.
- Across 11 brands, guided shopping led to measurable lifts in AOV, conversion rate, and overall revenue.
- Your biggest upsell opportunities likely sit in the same places your shoppers pause, so start by automating your most common pre-purchase questions.
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.
1. Recommend similar shoes when an old classic disappears
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:
- AOV uplift: +6.5%
2. Suggest complete outfits for special occasions
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:
- Chat CVR: 13.02%
3. Match shoppers to the right makeup shade when the formula changes
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:
- GMV uplift: +6.55%
4. Help find the perfect gift when shoppers don’t know what to buy
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:
- Chat CVR: 8.39%
5. Remove the guesswork from bra sizing online
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:
- GMV uplift: +6.22%
- Chat CVR: 16.78%
6. Guide shoppers through jewelry personalization step by step
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:
- GMV uplift: +22.59%
7. Recommend furniture that works well together
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:
- AOV uplift: +97.15%
- Chat CVR: 10.3%
8. Reassure shoppers about flavor before purchase
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:
- Chat CVR: 12.75%
9. Match supplements to age, lifestyle, and health goals
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:
- AOV uplift: +16.4%
- Chat CVR: 15.15%
10. Align products with safety needs in kids’ rooms
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:
- GMV uplift: +12.26%
- AOV uplift: +10.19%
11. Clarify technical specs that create hesitation
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:
- AOV uplift: +11.27%
- Chat CVR: 8.55%
What these results tell us
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:
- AOV jumps when products are technical or high in consideration. Home decor, supplements, and outdoor gear see the biggest lifts because shoppers feel more confident committing to higher-priced items once the details are explained.
- CVR surges in categories with complex decisions. Lingerie, apparel, and personal styling all showed strong conversion rates because shoppers finally get clarity on fit, shade, or style.
- GMV rises when AI removes friction from the buying journey. Furniture and beauty saw meaningful gains thanks to personalized recommendations that reduce uncertainty and push shoppers toward the right product faster.
- The use cases reveal clear upsell opportunities. If your team sees recurring questions about sizing, shade matching, product differences, or how items work together, that’s a strong signal that guided selling can drive more revenue.
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.
Want Shopping Assistant results like these?
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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Your Support Team Drives More Revenue Than You Think: Conversational Commerce Metrics
Your chat might be closing more sales than your checkout page. Here’s how to measure it.
TL;DR:
- Support chats can now be directly tied to revenue. Brands are measuring conversations by conversion rate, average order value (AOV), and GMV influenced.
- AI resolution rate is only valuable if the answers are accurate and helpful. A high resolution rate doesn’t matter if it leads to poor recommendations — the best AI both deflects volume and drives confident purchases.
- Chat conversion rates often outperform traditional channels. Brands like Arc’teryx saw a 75% lift in conversions (from 4% to 7%) when AI handled high-intent product questions.
- Shoppers who chat often spend more. Conversations lead to higher AOVs by helping customers understand products, explore upgrades, and discover add-ons — not just through upselling, but smarter guidance.
Conversational commerce finally has a scoreboard.
For years, CX leaders knew support conversations mattered, they just couldn’t prove how much. Conversations lived in that gray area of ecommerce where shoppers got answers, agents did their best, and everyone agreed the channel was “important”…
But tying those interactions back to actual revenue? Nearly impossible.
Fast forward to today, and everything has changed.
Real-time conversations — whether handled by a human agent or powered by AI — now leave a measurable footprint across the entire customer journey. You can see how many conversations directly influenced a purchase.
In other words, conversational commerce is finally something CX teams can measure, optimize, and scale with confidence.
Why measuring conversational commerce matters now
If you want to prove the value of your CX strategy to your CFO, your marketing team, or your CEO, you need data, not anecdotes.
Leadership isn’t swayed by “We think conversations help shoppers.” They want to see the receipts. They want to know exactly how interactions influence revenue, which conversations drive conversion, and where AI meaningfully reduces workload without sacrificing quality.
That’s why conversational commerce metrics matter now more than ever. This gives CX leaders a way to:
- Quantify the revenue influence of conversations
- Understand where AI improves efficiency — and where humans add the most value
- Make informed decisions on staffing, automation, and channel investment
- Turn CX into a profit center instead of a cost center
These metrics let you track impact with clarity and confidence.
And once you can measure it, you can build a stronger case for deeper investment in conversational tools and strategy.
The 4 metric categories that define conversational commerce success
So, what exactly should CX teams be measuring?
While conversational commerce touches every part of the customer journey, the most meaningful insights fall into four core categories:
- Automation performance
- Conversion & revenue impact
- Engagement quality
- Discounting behavior
Let’s dive into each.
Automation performance metrics
If you want to understand how well your conversational commerce strategy is working, automation performance is the first place to look. These metrics reveal how effectively AI is resolving shopper needs, reducing ticket volume, and stepping into revenue-driving conversations at scale.
The two most foundational metrics?
1. Resolution rate: Are AI-led conversations actually helpful?
Resolution rate measures how many conversations your AI handles from start to finish without needing a human to take over. On paper, high resolution rates sound like a guaranteed win. It suggests your AI is handling product questions, sizing concerns, shade matching, order guidance, and more — all without adding to your team’s workload.
But a high resolution rate doesn’t automatically mean your AI is performing well.
Yes, the ticket was “resolved,” but was the customer actually helped? Was the answer accurate? Did the shopper leave satisfied or frustrated?
This is where quality assurance becomes essential. Your AI should be resolving tickets accurately and helpfully, not simply checking boxes.
At its best, a strong resolution rate signals that your AI is:
- Confidently answering product questions
- Guiding shoppers to the right SKU, variant, shade, size, or style
- Reducing cart abandonment caused by confusion
- Helping pre-sale shoppers convert faster
When resolution rate quality goes up, so does revenue influence.
You can see this clearly with beauty brands, where accuracy matters enormously. bareMinerals, for example, used to receive a flood of shade-matching questions. Everything from “Which concealer matches my undertone?” to “This foundation shade was discontinued; what’s the closest match?”
Before AI, these questions required well-trained agents and often created inconsistencies depending on who answered.
Once they introduced Shopping Assistant, resolution rate suddenly became more meaningful. AI wasn’t just closing tickets; it was giving smarter, more confident recommendations than many agents could deliver at scale, especially after hours.
That accuracy paid off.
AI-influenced purchases at bareMinerals had zero returns in the first 30 days because customers were finally getting the right shade the first time.
That’s the difference between “resolved” and resolved well.
2. Zero-touch tickets: How many tickets never reach a human?
The zero-touch ticket rate measures something slightly different: the percentage of conversations AI manages entirely on its own, without ever being escalated to an agent.
This metric is a direct lens into:
- Workload reduction
- Team efficiency
- Cost savings
- AI’s ability to own high-volume question types
More importantly, deflection widens the funnel for more revenue-driven conversations.
When AI deflects more inbound questions, your support team can focus on conversations that truly require human expertise, including returns exceptions, escalations, VIP shoppers, and emotionally sensitive interactions.
Brands with strong deflection rates typically see:
- Shorter wait times
- Higher CSAT
- Lower support costs
- More AI-influenced revenue
Conversion and revenue impact metrics
If automation metrics tell you how well your AI is working, conversion and revenue metrics tell you how well it’s selling.
This category is where conversational commerce really proves its value because it shows the direct financial impact of every human- or AI-led interaction.
1. Chat Conversion Rate (CVR): How often do conversations turn into purchases?
Chat conversion rate measures the percentage of conversations that end in a purchase, and it’s one of the clearest indicators of whether your conversational strategy is influencing shopper decisions.
A strong CVR tells you that conversations are:
- Building confidence
- Removing hesitation
- Guiding shoppers toward the right product
You see this clearly with brands selling technical or performance-driven products.
Outdoor apparel shoppers, for example, don’t just need “a jacket” — they need to know which jacket will hold up in specific temperatures, conditions, or terrains. A well-trained AI can step into that moment and convert uncertainty into action.
Arc’teryx saw this firsthand.
Once Shopping Assistant started handling their high-intent pre-purchase questions, their chat conversion rate jumped dramatically — from 4% to 7%. A 75% lift.
That’s what happens when shoppers finally get the expert guidance they’ve been searching for.
2. GMV influenced: The revenue ripple effect of conversations
Not every shopper buys the moment they finish a chat. Some take a few hours. Some need a day or two. Some want to compare specs or read reviews before committing.
GMV influenced captures this “tail effect” by tracking revenue within 1–3 days of a conversation.
It’s especially powerful for:
- High-consideration purchases (like outdoor gear, home furniture, equipment)
- Products with many options, specs, or configurations
- Shoppers who need reassurance before buying
In Arc’teryx’s case, shoppers often take time to confirm they’re choosing the right technical gear.
Yet even with that natural pause in behavior, Shopping Assistant still influenced 3.7% of all revenue, not by forcing instant decisions, but by providing the clarity people needed to make the right one.
3. AOV from conversational commerce: Do conversations lead to bigger carts?
This metric looks at the average order value of shoppers who engage in a conversation versus those who don’t.
If the conversational AOV is higher, it means your AI or agents are educating customers in ways that naturally expand the cart.
Examples of AOV-lifting conversations include:
- Recommending complementary gear, tools, or accessories
- Suggesting upgraded options based on needs
- Helping shoppers understand the difference between product tiers
- Explaining why a specific product is worth the investment
When conversations are done well, AOV increases not because shoppers are being upsold, but because they’re being guided.
4. ROI of AI-powered conversations: The metric your leadership cares most about
ROI compares the revenue generated by conversational AI to the cost of the tool itself — in short, this is the number that turns heads in boardrooms.
Strong ROI shows that your AI:
- Does the work of multiple agents
- Drives new revenue, not just ticket deflection
- Provides accurate answers consistently, at any time
- Delivers a high-quality experience without expanding headcount
When ROI looks like that, AI stops being a “tool” and starts being an undeniable growth lever.
Related: The hidden power and ROI of automated customer support
Engagement metrics that indicate purchase intent
Not every metric in conversational commerce is a final outcome. Some are early signals that show whether shoppers are interested, paying attention, and moving closer to a purchase.
These engagement metrics are especially valuable because they reveal why conversations convert, not just whether they do. When engagement goes up, conversion usually follows.
1. Click-Through Rate (CTR): Are shoppers acting on the products your AI recommends?
CTR measures the percentage of shoppers who click the product links shared during a conversation. It’s one of the cleanest leading indicators of buyer intent because it reflects a moment where curiosity turns into action.
If CTR is high, it’s a sign that:
- Your recommendations are relevant
- The conversation is persuasive
- The shopper trusts the guidance they’re getting
- The AI is surfacing the right product at the right time
In other words, CTR tells you which conversations are influencing shopping behavior.
And the connection between CTR and revenue is often tighter than teams expect.
Just look at what happened with Caitlyn Minimalist. When they began comparing the results of human-led conversations versus AI-assisted ones over a 90-day period, CTR became one of the clearest predictors of success. Their Shopping Assistant consistently drove meaningful engagement with its recommendations — an 18% click-through rate on the products it suggested.
That level of engagement translated directly into better outcomes:
- AI-driven conversations converted at 20%, compared to just 8% for human agents
- Many of those clicks led to multi-item purchases
- Overall, the brand experienced a 50% lift in sales from AI-assisted chats compared to human-only ones
When shoppers click, they’re moving deeper into the buying cycle. Strong CTR makes it easier to forecast conversion and understand how well your conversational flows are guiding shoppers toward the right products.
Discounting behavior metrics
Discounting can be one of the fastest ways to nudge a shopper toward checkout, but it’s also one of the fastest ways to erode margins.
That’s why discount-related metrics matter so much in conversational commerce.
They show not just whether AI is using discounts, but how effectively those discounts are driving conversions.
1. Discounts offered: Are incentives being used strategically or too often?
This metric tracks how many discount codes or promotional offers your AI is sharing during conversations.
Ideally, discounts should be purposeful — timed to moments when a shopper hesitates or needs an extra nudge — not rolled out as a one-size-fits-all script. When you monitor “discounts offered,” you can ensure that incentives are being used as conversion tools, not crutches.
This visibility becomes particularly important at high-intent touchpoints, such as exit intent or cart recovery interactions, where a small incentive can meaningfully increase conversion if used correctly.
2. Discounts applied: Are those discounts actually influencing the purchase?
Offering a discount is one thing. Seeing whether customers use it is another.
A high “discounts applied” rate suggests:
- The offer was compelling
- The timing was right
- The shopper truly needed that incentive to convert
A low usage rate tells a different story: Your team (or your AI) is discounting unnecessarily.
This metric alone often surprises brands. More often than not, CX teams discover they can discount less without hurting conversion, or that a non-discount incentive (like a relevant product recommendation) performs just as well.
Understanding this relationship helps teams tighten their promotional strategy, protect margins, and use discounts only where they actually drive incremental revenue.
How CX teams use these metrics to make better decisions
Once you know which metrics matter, the next step is building a system that brings them together in one place.
Think of your conversational commerce scorecard as a decision-making engine — something that helps you understand performance at a glance, spot bottlenecks, optimize AI, and guide shoppers more effectively.
In Gorgias, you can customize your analytics dashboard to watch the metrics that matter most to your brand. This becomes the single source of truth for understanding how conversations influence revenue.
Here’s what a powerful dashboard unlocks:
1. You learn where AI performs best (and where humans outperform)
Some parts of the customer journey are perfect for AI: repetitive questions, product education, sizing guidance, shade matching, order status checks.
Others still benefit from human support, like emotional conversations, complex troubleshooting, multi-item styling, or high-value VIP concerns.
Metrics like resolution rate, zero-touch ticket rate, and chat conversion rate show you exactly which is which.
When you track these consistently, you can:
- Identify conversation types AI should fully own
- Spot where AI needs more training
- Allocate human agents to higher-value conversations
- Decide when humans should step in to drive stronger outcomes
For example, if AI handles 80% of sizing questions successfully but struggles with multi-item styling advice, that tells you where to invest in improving AI, and where human expertise should remain the default.
2. You uncover what shoppers actually need to convert
Metrics like CTR, CVR, and conversational AOV reveal the inner workings of shopper decision-making. They show which recommendations resonate, which don’t, and which messaging actually moves someone to purchase.
With these insights, CX teams can:
- Refine product recommendations
- Improve conversation flows that stall out
- Adjust the tone or structure of AI messaging
- Draft stronger scripts for human agents
- Identify recurring questions that indicate missing PDP information
For instance, if shoppers repeatedly ask clarifying questions about a product’s material or fit, that’s a signal for merchandising or product teams.
If recommendations with social proof get high engagement, marketing can integrate that insight into on-site messaging.
Conversations reveal what customers really care about — often before analytics do.
3. You prove that conversations directly drive revenue
This is the moment when the scorecard stops being a CX tool and becomes a business tool.
A clear set of metrics shows how conversations tie to:
- GMV influenced
- AOV lift
- Revenue generated by AI
- ROI of conversational commerce tools
When a CX leader walks into a meeting and says, “Our AI Assistant influenced 5% of last month’s revenue” or “Conversational shoppers have a 20% higher AOV,” the perception of CX changes instantly.
You’re no longer a support cost. You’re a revenue channel.
And once you have numbers like ROI or revenue influence in hand, it becomes nearly impossible for anyone to argue against further investment in CX automation.
4. You identify where shoppers are dropping off or hesitating
A scorecard doesn’t just show what’s working, it surfaces what’s not.
Metrics make friction obvious:
Once you identify these patterns, you can run targeted experiments:
- Test new scripts or flows
- Adjust product recommendations
- Add social proof or benefit framing
- Reassess discounting strategies
- Rework messaging on key PDPs
Compounded over time, these moments create major lifts in conversion and revenue.
5. You create a feedback loop across marketing, merchandising, and product
One of the biggest hidden values of conversational data is how it strengthens cross-functional decision-making.
A clear analytics dashboard gives teams visibility into:
- Unclear or missing product information (from repeated questions)
- Merchandising opportunities (from your most popular products)
- Landing page or PDP improvements (from drop-off points)
- Messaging that resonates with real customers (from AI messages)
Suddenly, CX isn’t just answering questions — it’s informing strategy across the business.
CX drives revenue when you measure what matters
With the right metrics in place, CX leaders can finally quantify the impact of every interaction, and use that data to shape smarter, more profitable customer journeys.
If you're ready to measure — and scale — the impact of your conversations, tools like Gorgias AI Agent and Shopping Assistant give CX teams the visibility, accuracy, and performance needed to turn every interaction into revenue.
Want to see it in action? Book a demo and discover what conversational commerce can do for your bottom line.
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Further reading
Why Your Strategy Needs Customer Service Quality Assurance
TL;DR:
- Manual QA is time-consuming and inconsistent. Reviewing conversations manually makes it difficult to ensure uniform quality across agents and touchpoints.
- Automating QA saves time and improves accuracy. Automation ensures all tickets are reviewed with the same quality, freeing up agent time to create stronger customer connections.
- QA helps teams continuously improve. It enables better agent training and brings forth actionable feedback to exceed customer expectations.
- Implement QA one step at a time. Begin by setting KPIs, introducing small changes, and investing in automation tools to streamline and measure success effectively.
Forrester’s 2024 Customer Experience Index reports that 39% of brands’ customer experience (CX) quality has declined over the past year.
It can be challenging to get a full understanding of how your team—and AI, if you use it—are truly performing.
This is true even with metrics like CSAT.
“A 5-point scale only tells you and your agents so much, and relying on consumers providing feedback further limits what you’re able to look at and learn from,” says Kayla Oberlin, Senior Manager of Customer Experience at amika.
Quality Assurance (QA) is becoming a more crucial component of a customer experience strategy, especially one that prioritizes customer happiness.
We’ll cover the importance of customer service QA, best practices, tools, and tips to implement QA effectively.
🗺️ This article at-a-glance
- What is QA (Quality Assurance) in CX?
- Why QA is critical for customer experience
- Best practices for implementing QA in CX
- Challenges
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What is QA (Quality Assurance) in CX?
In the CX context, QA (Quality Assurance) refers to reviewing customer conversations to improve your support team’s performance and enhance customer satisfaction. QA ensures a consistent and satisfying customer journey across touchpoints, including your website, support channels, and social media.
Common QA pain points for CX teams
Aside from accuracy issues, a manual quality assurance process is:
- Time-consuming: Manual conversation reviews are slow and labor-intensive.
- Limited visibility: It’s difficult to get a clear, scalable view of team and AI performance.
- Inconsistent: Maintaining uniform quality across customer service teams can be tough.
- Resource allocation: Difficulty in ensuring the right skills, training, and resources are in place.
- CSAT limitations: Negative scores often reflect policies, not agent performance.
The solution isn’t for CX teams to skip the QA process altogether but to automate it.
According to research from McKinsey, “A largely automated QA process could achieve more than 90 percent accuracy—compared to 70 to 80 percent accuracy through manual scoring—and savings of more than 50 percent in QA costs.”
With an automated QA process, brands can:
- Save time: Automated quality checks help support agents to focus on the most critical tickets.
- Ensure consistency: Both human agents and AI agents are evaluated with a unified, comprehensive QA score.
- Boost performance: Agents receive targeted coaching to provide more consistent customer experiences.
- Meet customer expectations: Customers benefit from higher-quality support with quicker resolutions and accurate responses.
Why QA is critical for customer experience
According to Statista, 94% of customers are more likely to purchase again after receiving top-notch support. Quality assurance ensures that every customer gets the same experience, and provides agents with the feedback to learn and stay on-brand with each resolution.
At its core, QA:
- Prevents errors
- Ensures consistency
- Builds trust with customers
- Helps with personalization
- Aids in better coaching and training
- Offers opportunities for continuous improvement
Prevent errors
Addressing errors early is important, as even small mistakes can harm customer trust and create lasting negative impressions. QA tools can prevent mistakes because of better coaching and training. This can stop misinformation in its tracks –– and from escalating into bigger problems down the line.
Ensure consistency
QA makes sure that all customer touchpoints, like calls, emails, live chat, and even AI responses, are handled with the same level of care. This is especially helpful when training new team members, introducing new products or policies, or during high-traffic periods.
Build trust
Consistent and reliable experiences build customer trust and loyalty. If you were to reach out to a brand and have an amazing experience the first time but a bad experience the next, you’d probably question which experience was the norm.
Top-notch experiences that happen time and time again tell your customers that you’ll always be there to help. This can boost repeat sales and even referrals: According to Statista, 82% of customers recommend a brand after a great experience.
Personalize experiences
Aside from increasing happiness and making customers feel heard and appreciated, personalized support also affects your bottom line. Statista notes that 80% of businesses found that providing personalized customer experiences led to increased spending for consumers.
Aids in better coaching and training
With QA, teams are able to rate and review all tickets instead of spot-checking. This provides them with a:
- Quicker turnaround on coaching opportunities
- Wider volume of tickets they can review, learn from, and use for training
- Better understanding of when a Macro or a process is leading to incomplete or unhelpful conversations
- Bigger opportunity for constructive feedback and flow improvements that are based on real responses and not frustrations with brand policies
Continuously improve
Whether it’s lowering resolution times, introducing a knowledge base, or adding AI Agent to your team, making continuous improvements will help you stay ahead of the competition.
Implementing a QA program (especially if you can automate it) is one of those additions that provides you with the refinements you need on a resolution-to-resolution level.
Best practices for implementing QA in CX
QA best practices include:
- Establish a baseline for metrics and KPIs
- Monitor and evaluate regularly
- Implement automation tools
- Collect customer feedback
Establish a baseline for metrics and KPIs
As you set out to integrate a Quality Assurance process into your CX program, first establish benchmarks for various metrics and KPIs. These benchmarks help track and evaluate the performance of QA as you implement it.
If you don’t already track customer support metrics, CSAT, first response time (FRT), resolution time, and net promoter score (NPS) are great ones to start with.
💡Tip: If you use Gorgias, you’ll find your current support performance statistics in the Statistics menu. Make sure that you can see back at least six months. Then, compare an equal time frame for post-QA implementation.
Monitor and evaluate regularly
While it might sound a bit “meta” to monitor your quality assurance (which is already monitoring your support responses), it’s still worth noting.
Ensure that your QA process works smoothly, helps your metrics rather than hurts them, and provides actual helpful feedback to your agents.
Implement automation tools
The simplest way to maintain your support quality standards is to use an automated QA tool. Automating the QA process lets CX teams get deeper insights into agent strengths and areas for improvement, and captures deeper insights than a CSAT score could.
Collect customer feedback
Understanding how customers feel will allow you to fine-tune your processes and ensure you’re delivering a consistent and high-quality experience. Here are a few ways to collect feedback:
- Surveys and reviews - Post-interaction surveys or direct reviews provide real-time feedback on what customers think of their experience.
- Social listening and real-time feedback - Monitoring online reviews, social media mentions, and customer comments offers insight into how your customers are feeling that might not be captured through formal surveys.
Challenges of adding QA
Lack of resources, ineffective training, poor communication between team members, not having the right tools, and doing everything manually are some of the challenges you can encounter when adding a QA process.
Here are a couple of solutions we recommend:
- Start with phased rollouts. Rather than rolling out a QA process across your whole team, let more seasoned agents experiment with it first to give you feedback and make tweaks.
- Make incremental improvements. Changing an entire CX process at once to include QA can be overwhelming. We recommend making small changes (like starting to send CSAT surveys if you don’t already) one at a time. These changes will allow you to better measure what’s really working.
- Invest in better technology. A manual QA process can be more time-consuming than helpful. Look for an automated QA tool that’s already integrated into your helpdesk. It will allow you to measure AI and agent responses equally, while also measuring results from a handy dashboard.
Ensure customer experience meets quality standards
By prioritizing QA, your team can identify potential problems early, reduce errors, and improve overall performance, leading to a smoother, more reliable experience for customers –– and your CX team.
In the long run, brands that focus on QA can gain a competitive edge, building stronger relationships with customers and driving sustainable growth. Book a demo now.
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Building Customer Loyalty Through Effective Post-Purchase Support and Automation in Ecommerce
Let's talk about something that often gets overlooked in ecommerce: what happens after someone hits that "Place Order" button. You might think the hard part's over once you've made the sale, but here's the thing the post-purchase experience can make or break your relationship with customers.
In today's competitive online marketplace, those relationships are everything — especially considering that loyal customers spend an average of 67% more per purchase than new customers.
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The importance of post-purchase support and automation in ecommerce
Providing an excellent post-purchase customer experience can turn one-time customers into loyal advocates who are more likely to make repeat purchases and recommend your brand to others.
It's all about the customer experience
When someone buys from your store, they're not just getting a product — they're starting a relationship with your brand.
A great post-purchase experience shows customers you actually care about their satisfaction beyond just making the sale. 90% of U.S. customers say that an immediate customer service response is "important" or "very important.”
When you nail this part, something magical happens: one-time shoppers transform into passionate advocates who not only come back for more but can't help telling others about their amazing experience with your brand.
Having accessible support and an efficient and easy returns process may make the difference between a happy customer and an unsatisfied one.
Building trust that lasts
Trust is everything in online shopping. When customers feel supported after making a purchase, they're much more likely to give you the benefit of the doubt if something goes wrong down the line.
It's like building a friendship: every positive interaction adds another layer of trust. And that trust translates directly into repeat business and glowing recommendations.
The post-purchase support experience makes a huge difference in building that trust. In fact, 96% of customers say excellent customer service builds trust.
Keeping your return rates down
Great post-purchase support can actually help reduce your return rates. By addressing concerns quickly and providing clear information upfront, you can prevent many returns before they happen.
This can save you money on shipping and restocking and create a smoother experience that keeps customers happy and your business healthy.
Making processes more efficient
Automation eliminates manual tasks, freeing up your team to focus on more strategic initiatives. By automating repetitive tasks, you can improve efficiency and productivity, allowing your team to focus on more value-added activities.
You can automate everything from customer support to returns and exchanges to your order tracking and more. Besides meeting customers' straightforward needs, automation allows you to focus your team's energy on solving bigger problems and strengthening customer relationships.
Accuracy, guaranteed
Automation helps ensure consistency across all your post-purchase processes.
When customers know they can count on a reliable experience every time they shop with you, it builds confidence in your brand.
Plus, fewer mistakes mean happier customers and less time spent fixing problems.
Creating better customer experiences
Speed matters in today's world, and automation helps you deliver faster, more personalized responses to customer needs.
Whether it's instant order updates or quick responses to questions, automation helps you meet and exceed customer expectations. The result? More satisfied customers who feel valued and understood.
How to automate the post-purchase experience for better loyalty
Here are some ways to automate the post-purchase experience:
Automate your returns and exchanges process
Streamline the returns process with automated return labels, tracking, and updates. Use ReturnGO to automate this process, saving time and reducing manual errors. With automated returns, you can provide a hassle-free experience for customers, encouraging them to return to your store in the future.
Automated returns can help to improve the customer experience by making the returns process easier and more convenient. 65% of customers say the speed and ease of refunds affect where they choose to shop.
By automating tasks such as generating return labels and tracking packages, you can reduce the time and effort required for customers to return items.
Think about it from their perspective — if returning an item is hassle-free, they'll feel more confident buying from you in the future. It's like having a safety net that makes customers more comfortable taking chances on new products.
Centralize customer support
In today's fast-paced world, customers expect quick and efficient support. Using a customer experience platform like Gorgias, you can manage all your customer support tickets in one place, making it easier to provide fast, accurate help when people need it.
By centralizing your post-purchase support, you can manage support tickets more efficiently, respond to customer inquiries quickly, and provide the most up-to-date information. This centralized approach can hugely improve response times.
Keep customers in the loop
Nobody likes being left in the dark about their order. Automated post-purchase notifications keep your customers informed every step of the way - from order confirmation to delivery and returns. Using tools like ReturnGO, you can send personalized updates that make customers feel looked after. This is essential for building customer loyalty.
Keeping customers informed about their orders can help reduce customer anxiety. When customers know what to expect, they’re less likely to worry about their purchase and are more likely to keep buying from you again and again.
Create an integrated workflow
To truly streamline your post-purchase customer service, if you connect your returns management system with your customer support system, you really bring all of the pieces of a puzzle together.
When these two systems are in sync, you can create a smooth workflow that makes things easier for both your team and your customers.
By automating tasks like creating support tickets and processing returns, you can save time and create a more reliable, efficient system that helps you serve customers better. No more jumping back and forth between systems to check on a return when a customer reaches out about it.
The ReturnGO-Gorgias integration makes this happen seamlessly, with features like:
- Automatic ticket generation: When a customer requests a return, a support ticket is automatically created on Gorgias, saving you time and preventing errors.
- Real-time updates: Return request information is automatically updated from ReturnGO to Gorgias, so your team always has the latest details right there.
- Centralized system: No more digging through multiple systems. This means your support agents always have access to the most up-to-date information and respond quickly and efficiently to customers.
- Smart widget: The ReturnGO-Gorgias integration includes a widget embedded in your Gorgias dashboard, for managing RMAs directly from within Gorgias. This widget enables your team to:
- View RMA information: See all the relevant details about a return, including the customer's information, the items being returned, and the reason for the return.
- Take actions on the RMA: Easily approve or reject a return request directly from Gorgias.
The ReturnGO-Gorgias integration makes it easy for your team to manage returns and communicate with customers without having to jump between systems to hunt for information.
The path to lasting customer loyalty
So, there you have it! In the world of online shopping, how you handle the after-purchase experience can be just as important as making the sale in the first place.
By automating your post-purchase process, you can create a seamless and satisfying customer experience.
Tools like ReturnGO and Gorgias can help you create the kind of experience that builds customer loyalty.
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How to Customize AI Agent with 7 Brand Voice Examples
TL;DR:
- AI Agent adapts to your brand's unique tone of voice. Choose from three default voice options (Friendly, Professional, and Sophisticated), or create countless types of tone with the Custom option.
- Aligning AI with your brand voice builds consistency. A consistent tone in customer interactions helps build trust and brand loyalty.
- Specify what AI Agent can and can’t say. Like your human agents, tell AI Agent your brand do’s and don’ts. From going all out with fun and emoji-filled replies to avoiding certain words, use custom instructions to make AI Agent sound distinctly on-brand.
People are only able to identify AI-generated content 46.9% of the time. That’s less than half the time!
In the ecommerce customer service industry, this is just one reason teams are getting more comfortable with using AI.
Better language processing abilities mean AI can be a better extension of CX teams, relieving agents of repetitive questions, like where is my order?, while speaking in a way that’s familiar and delightful to customers.
Upholding a strong brand voice should be one of your top priorities in CX. With Gorgias AI Agent, you can choose AI Agent’s exact tone of voice, from sophisticated to fun. Below, check out seven AI Agent brand voice examples from real customer conversations.
“We’ve had customers respond to the AI thinking they were speaking to a real person. That’s how elevated the response was from AI.”
—Emily McEnany, Senior CX Manager at Dr. Bronner’s
What is Tone of Voice?
Tone of Voice refers to how AI Agent communicates with your customers. In Gorgias, you can select from three pre-built tone options:
- Friendly
- Professional
- Sophisticated
Or, you can create a custom tone, keeping your brand guidelines, style guide, and target audience in mind.
Note: AI Agent and Tone of Voice are only available to Gorgias Automate subscribers.
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7 Tone of Voice Examples for AI Agent to Match Your Brand's Style
Explore how effectively AI Agent adapts to seven distinct tones in the examples below. First, we’ll show you what a preset AI Agent tone option sounds like, then we’ll move on to six examples using custom instructions.
Feel free to copy and paste our provided instructions to set up your AI Agent with the custom tone of your choice, or, even better, take some inspiration to create your own.
1. Friendly
A friendly AI Agent is the go-to for most CX teams. A Friendly tone of voice is outgoing and welcomes inquiries with enthusiasm. If you were to imagine the model support agent, they would speak like this.
The Friendly tone of voice is available by default in AI Agent’s settings.
How it looks in action
Here’s how an AI Agent with a Friendly tone of voice responds to a customer asking for samples and coupons:
2. Direct and brief
Now, we move away from AI Agent’s default Tone of Voice options and toward the vast possibilities of the Custom option.
If you prefer your AI Agent get to the point in as few words as possible, create a Custom tone of voice that breaks up text into separate lines, limits paragraphs to two to three sentences, and keeps responses short.
💡 Tip: Access a custom tone of voice by going to Automate > AI Agent > Settings > Tone of Voice > Custom. A text field will appear where you can write your instructions.
Tone of voice instructions:
Acknowledge the customer's feelings by briefly repeating their initial concern(s). Break text up, don’t send entire paragraphs, and keep responses short and easy to read. Keep interactions brief but filled with empathy. We are not long-winded. Keep an informative tone while remaining professional, clear, and easy for customers to follow. Insert links where needed. Don't use too many adjectives when expressing empathy. Never tell the customer to email support or contact our customer service team.
How it looks in action
Here’s how an AI Agent with a direct and brief tone of voice responds to a customer who wants to cancel their order:
3. Fun (with lots of emojis! 🤗)
Who says support agents can’t have personality? Bring some fun into your conversations by creating a custom tone of voice that allows your AI Agent to use emojis and exclamation points.
Tone of voice instructions:
Greet with first name only. Acknowledge the customer's feelings by repeating their initial concern(s). Be concise and provide shorter responses, try to keep your responses to a few sentences. Use a warm, positive, and engaging—like chatting with a helpful, considerate friend. Sign off with "Best Regards". Avoid jokes or comments related to sensitive topics. Make the customer feel like a friend. You can include approved emojis for a personal touch and exclamation points. Approved emojis to use: 💞🫶✨🥰💖🎀💓💘🥳💗💕💯 You should recognize and celebrate personal milestones mentioned by customers, making the interaction feel more personal. After the customer's initial message, there's no need to restate their issue in follow-up responses.
How it looks in action
Here’s how an AI Agent with a fun tone of voice responds to a customer asking about exchanging their damaged product:
4. Comforting
Customer support often gets a bad rep. Customers anticipate long response times and unpleasant interactions. Flip customer expectations by giving your AI Agent a calming and comforting voice that can instantly fix negative experiences.
💡 Tip: Brands in the wellness and baby industry would do well to use a comforting tone of voice for their AI Agent.
Tone of voice instructions:
Our brand embodies the role of a nurturing parent, promoting happiness, growth, and well-being while creating moments of joy and inspiration. Stay genuine and reflect childlike wonder without being overly sentimental. We maintain a positive and supportive tone, offering a safe, comforting space. Avoid admitting fault or apologizing. Be shorter in replies. Do not offer replacements. Do not give out phone numbers.
How it looks in action
Here’s how an AI Agent with a comforting tone of voice responds to a customer asking about exchanging their damaged product:
5. Bro-y
Give your AI Agent a laid-back, “we’ve got your back” vibe that feels like chatting with a buddy. This tone keeps things casual, approachable, and like you’re ready to tackle any issue together.
Tone of voice instructions:
Sound like a gym bro. Speak casually and friendly. Be eager to help. However, do not go overboard with puns or stereotypical phrases. You may use the following emojis: 🤙💪🏋️ End responses with "Stay awesome,"
How it looks in action
Here’s how an AI Agent with a bro-y tone of voice responds to a customer asking about glove sizing:
6. Punny
If your brand isn’t afraid to lean into humor and puns, this tone will definitely connect with your audience. Let your AI Agent use wit and clever wordplay to keep conversations lighthearted and customers smiling at their screens.
Tone of voice instructions:
Speak in bee and honey puns and use colorful emojis. Use at least one emoji per message. Keep your messages brief. Sign off with a different pun in every conversation. If a customer is upset or needs urgent help, avoid puns.
How it looks in action
Here’s how an AI Agent with a punny tone of voice responds to a customer asking about suit sizes:
7. Bonus: Robotic
In all of our examples, AI Agent responses can easily be mistaken for one of your human agents. But if, for any reason, you want to change that by making your AI Agent sound robotic — it’s possible.
Tone of voice instructions:
Sound like a robot. Make robot sounds and puns. Use short, direct, and easy-to-read sentences.
How it looks in action
Here’s how an AI Agent with a robotic tone of voice responds to a customer asking about exchanging their damaged product:
Say it how you want with AI Agent
Like a chameleon, AI Agent adapts to your brand voice. Whether it’s friendly, professional, or a custom tone, you can be sure that every interaction aligns with your brand’s identity.
With AI Agent on your side, you have the power to make each conversation feel authentic. Take it from Psycho Bunny’s Senior Customer Experience Manager Tosha Moyer who says, “The overall tone is good, and its responses are really excellent.”
Ready to see AI Agent’s excellence for yourself? Book a demo and discover how AI Agent can be a permanent part of your team.
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