TL;DR:

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

Conversational commerce finally has a scoreboard.

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

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

Fast forward to today, and everything has changed.

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

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

Why measuring conversational commerce matters now

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

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

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

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

These metrics let you track impact with clarity and confidence.

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

The 4 metric categories that define conversational commerce success

So, what exactly should CX teams be measuring?

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

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

Let’s dive into each.

Automation performance metrics

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

The two most foundational metrics?

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

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

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

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

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

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

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

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

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

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

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

That accuracy paid off. 

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

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

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

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

This metric is a direct lens into:

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

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

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

Brands with strong deflection rates typically see:

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

Conversion and revenue impact metrics

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

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

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

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

A strong CVR tells you that conversations are:

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

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

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

Arc’teryx saw this firsthand. 

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

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

2. GMV influenced: The revenue ripple effect of conversations

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

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

It’s especially powerful for:

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

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

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

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

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

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

Examples of AOV-lifting conversations include:

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

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

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

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

Strong ROI shows that your AI:

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

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

Related: The hidden power and ROI of automated customer support

Engagement metrics that indicate purchase intent

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

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

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

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

If CTR is high, it’s a sign that:

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

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

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

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

That level of engagement translated directly into better outcomes:

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

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

Discounting behavior metrics

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

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

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

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

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

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

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

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

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

A high “discounts applied” rate suggests:

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

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

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

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

How CX teams use these metrics to make better decisions

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

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

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

Here’s what a powerful dashboard unlocks:

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

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

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

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

When you track these consistently, you can:

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

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

2. You uncover what shoppers actually need to convert

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

With these insights, CX teams can:

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

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

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

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

3. You prove that conversations directly drive revenue

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

A clear set of metrics shows how conversations tie to:

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

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

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

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

4. You identify where shoppers are dropping off or hesitating

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

Metrics make friction obvious:

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

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

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

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

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

A clear analytics dashboard gives teams visibility into:

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

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

CX drives revenue when you measure what matters

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

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

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

{{lead-magnet-2}}

TL;DR: 

• AI hallucinations happen when AI models create false information that sounds real. 
• They occur in customer support when chatbots lack proper training data or face unclear questions.
• Main causes include bad training data, overfitting, and confusing prompts. You can prevent them by grounding responses in real data, setting clear limits, and keeping humans in the loop. 
• When managed right, AI reduces support tickets while staying accurate and on-brand.

Your AI chatbot just told a customer their order ships in two days when you actually need five. That's an AI hallucination, or false information delivered with complete confidence.

These fake-but-convincing responses can wreck customer trust and create headaches for your support team. Understanding what triggers these AI mistakes and how to stop them matters for any brand using AI in customer service. 

This guide covers everything support teams need to know about AI hallucinations, from spotting them to building systems that keep your AI honest and helpful.

{{lead-magnet-1}}

What are AI hallucinations?

AI hallucinations are false or made-up responses from AI models that sound completely believable. This means the AI creates information that isn't based on real data but presents it like it's a fact.

The term “hallucination” comes from psychology, but AI hallucinations work differently than human ones. When an AI hallucinates, it's not seeing things that aren't there. Instead, it's filling gaps in its knowledge with creative guesswork.

Large language models (LLMs) power most modern chatbots. These models predict what word comes next based on patterns they learned during training. Sometimes they predict wrong and create entirely made-up information.

The real problem isn't that AI makes mistakes — it's how confidently it delivers wrong answers. Your AI won't say “I think” or “maybe.” It states false information with authority, making errors hard to catch.

For your ecommerce brand, this could mean your chatbot invents return policies or confirms products are in stock when they're not. These confident lies break customer trust and force your human agents to clean up the mess.

What causes AI hallucinations

AI hallucinations are baked into how current models function, caused by training data limitations, missing context, and statistical prediction errors that lead AI to invent convincing but false answers. Knowing why they happen helps you prevent them in your customer support.

Insufficient training data creates the biggest problems. When you ask AI about topics it hasn't learned, it tries to fill knowledge gaps with educated guesses. These guesses often sound reasonable but are completely wrong.

Overfitting happens when AI memorizes training examples instead of learning patterns. The model becomes an expert at repeating what it's seen before but terrible at handling new situations.

Ambiguous prompts confuse AI models. When customers ask vague questions that could mean several things, AI picks one interpretation and runs with it.

Outdated information causes problems because most AI models have knowledge cutoffs. They don't know about new promotions, products, or policy changes after their training ended. Your AI might confidently share old shipping times or discontinued product details.

Pattern misapplication occurs when AI applies learned patterns to wrong situations. The model recognizes linguistic patterns and uses them inappropriately, creating responses that sound right but make no sense in context.

Examples of AI hallucinations in customer support

AI hallucinations create real problems for support teams. When chatbots hallucinate, human agents spend time fixing the damage instead of helping customers with genuine needs.

Here are common ways AI hallucinates in ecommerce support:

• Product feature invention: Customer asks if a jacket is waterproof. AI confidently says "Yes, it's fully waterproof and tested to 10 feet underwater" when it's only water-resistant.
• Fake discount codes: Shopper requests a discount. AI creates "SAVE20NOW" — a realistic-sounding but non-existent code that fails at checkout.
• Wrong return windows: AI tells customers they have 60 days to return items because it hasn’t been fed your return policy, creating expectations you can't meet.
• Fabricated shipping promises: AI promises next-day delivery for standard shipping orders, setting impossible expectations.
• Invented company policies: Customer asks about price matching. AI creates a detailed policy you've never offered, forcing agents to disappoint customers.

Each hallucination creates a cascade of problems. Customers get frustrated when promises don't match reality. Your support team wastes time explaining why the AI was wrong. Your brand reputation takes a hit when customers can't trust your automated responses.

The worst part? These hallucinations often sound more helpful than honest answers. An AI that says “I don't know” seems less useful than one that confidently provides detailed (but wrong) information.

How to prevent AI hallucinations in customer support

You can't eliminate AI hallucinations completely, but you can reduce them. The key is moving from pure text generation to grounded, controlled responses based on your real data.

Limit possible outcomes

Instead of letting AI create any response it wants, give it specific options to choose from. For common questions about returns or shipping, create approved response categories. This prevents AI from inventing policies on the spot.

Decision trees work well for this approach. When customers ask about returns, your AI follows a predetermined path: check order date, verify product type, provide appropriate response. No creativity required.

You can also set response templates for frequent questions. Templates include placeholders for customer names or order numbers while keeping core information consistent and accurate.

Related: How to write Guidance with the “when, if, then” framework

Train models on relevant data

Generic AI training creates generic problems. Train your AI specifically on your business data — past support tickets, verified Help Center articles, and current product information.

Your training data should include:

• Real customer conversations: Use actual support tickets to teach AI how customers ask questions and what answers work
• Current product catalogs: Keep AI updated on what you actually sell, not what it thinks you might sell
• Verified policies: Only include official company policies, not industry standards or competitor information
• Brand voice examples: Show AI how your team actually talks to customers
• Your public Help Center: Use the resources you already provide to customers to teach it your most-asked questions

Quality matters more than quantity. It’s better to train on 1,000 accurate examples than 10,000 low-quality ones.

Create response templates

Templates give AI structure while allowing personalization. Instead of generating responses from scratch, AI fills in blanks within approved frameworks.

A shipping template might look like: 

• "Hi [customer name], your order [order number] shipped on [ship date] and will arrive by [delivery date]. Track it here: [tracking link]."

Templates ensure consistency across all AI responses. Customers get the same quality information whether they chat at 2 pm or 2 am. Your brand voice stays consistent even when AI handles the conversation.

Tell the model what to include and exclude

Set explicit boundaries for your AI through system prompts and instructions. Create lists of topics that always need human agents, like product safety complaints or legal threats.

Specify which information sources AI can use. Point it toward your Help Center and product database while blocking access to general internet information that might be outdated or irrelevant.

You can also create escalation triggers. When AI encounters certain keywords or question types, it automatically passes the conversation to a human agent instead of guessing.

Test and refine the system

AI isn't a set-it-and-forget-it tool. Regular testing catches problems before customers do.

Set up quality checks on AI responses. Review a sample of conversations weekly to spot inaccuracies or areas for improvement. Look for patterns in escalated tickets — they often reveal gaps in AI training.

A/B testing helps optimize AI performance. Try different prompt configurations to see which produces more accurate responses. Monitor customer satisfaction scores for AI-handled tickets compared to human-handled ones.

Customer feedback provides valuable insights. When customers report AI errors, use those examples to improve training data and refine response templates.

Add human oversight and escalation

Build safety nets into your AI system. Set confidence thresholds so the AI only responds when it's confident in its answer. Uncertain responses get escalated to human agents automatically.

Create clear escalation paths for complex issues. AI should recognize when questions go beyond its capabilities and smoothly transfer customers to appropriate team members.

Human oversight doesn't mean micromanaging every AI response. Instead, focus on monitoring patterns and intervening when AI consistently struggles with specific questions.

Ground answers in Shopify and help center data

This strategy delivers the biggest impact for ecommerce brands. Grounding means forcing AI to base responses on verified, real-time data from your tech stack.

When customers ask about order status, grounded AI checks live Shopify data instead of guessing. When they ask about policies, it pulls exact text from your Help Center instead of paraphrasing from memory.

Grounding transforms AI from a creative writer into a reliable assistant. It can only share information that exists in your approved systems, dramatically reducing hallucination risk.

Integration with your ecommerce platform ensures AI always has current information about inventory, shipping, and customer orders. No more promising products you don't have or delivery dates you can't meet.

Turn AI accuracy into competitive advantage

The brands winning with AI have put in the hours to train it. They've built systems that combine AI efficiency with human oversight, creating customer experiences that feel both fast and trustworthy.

Gorgias AI Agent demonstrates this approach in action. It grounds responses in your Shopify data and Help Center content, ensuring every automated interaction reflects your actual business information. Customizable guardrails let you set boundaries while maintaining the speed customers expect.

Ready to see how accurate AI can transform your support operations? Book a demo and discover how to automate customer service without sacrificing quality or control.

{{lead-magnet-2}}

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

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

Key takeaways:

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

Top learnings from Rhoback’s AI rollout  

1. You can start before you “feel ready”

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

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

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

Why it worked:

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

2. Audit your knowledge sources before you automate

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

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

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

Rhoback’s pre-implementation audit checklist:

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

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

3. Train your AI Agent in small, clear steps

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

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

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

Practical tips:

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

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

4. Prioritize Tone of Voice to make AI feel natural

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

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

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

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

5. Use AI to surface knowledge gaps or inconsistencies

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

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

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

Tips to improve internal alignment:

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

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

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

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

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

How Rhoback built AI confidence:

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

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

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

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

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

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

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

‍{{lead-magnet-1}}

TL;DR:

• AI in ecommerce spans customer support, personalization, operations, and fraud prevention
• Top use cases include conversational AI assistants, personalized recommendations, and demand forecasting
• CX leaders see the highest ROI from AI that automates repetitive support and drives sales
• Start with one high-impact use case and measure conversion, resolution time, and revenue lift
• Successful implementation requires clear metrics, pilot testing, and gradual scaling

AI has moved from experimental technology to essential infrastructure for ecommerce brands. The smartest CX leaders are using it to handle repetitive support tickets, personalize shopping experiences, and predict customer needs before they arise. 

This guide breaks down the AI use cases that actually move the needle for ecommerce teams. You'll learn which applications deliver immediate ROI, how to implement them without disrupting operations, and how to measure success.

Types of AI use in ecommerce

Different AI technologies handle different parts of your ecommerce business. Understanding what each type does helps you pick the right tool for each job.

Generative AI

Generative AI creates new content from existing information — whether that's product descriptions, email responses, or marketing copy. It writes in a natural tone that matches your brand voice instead of sounding robotic.

Where it’s used: AI agents in chat, automated content creation for product pages, and personalized email campaigns

Large language models (LLMs)

LLMs are the engines behind conversational AI. They understand context, handle complex customer questions, and generate human-like responses at scale.

Where it’s used: Customer support chatbots, content generation tools, automated response systems

Predictive analytics

Predictive analytics uses your historical data to forecast future outcomes. It tells you which customers are likely to churn, what products will be in demand next month, and when to reorder inventory.

Where it’s used: Customer lifetime value forecasts, at-risk customers detection, and seasonal inventory planning

Machine learning

Machine learning algorithms spot patterns in your data that humans would miss. These systems learn and improve over time, making smarter recommendations and decisions with each interaction.

Where it’s used: Product recommendations, dynamic pricing, fraud detection, and customer segmentation

Computer vision

Computer vision teaches machines to understand images and videos. It analyzes visual content to identify products, detect quality issues, and recognize patterns.

Where it’s used: Automated product tagging, quality control checks, counterfeit detection, and visual inventory management

Visual search

Visual search lets customers upload photos to find similar products in your catalog. Instead of describing what they want, they show you.

Where it’s used: Reverse image search, style matching, and "shop the look" features 

Benefits of AI for ecommerce brands

AI delivers real improvements you can measure across revenue, costs, and customer satisfaction. Here's what happens when you implement AI the right way.

Increases sales with personalization

AI analyzes how customers browse, what they've bought before, and their preferences to create personalized experiences at scale. Product recommendations become spot-on. Marketing messages hit the right tone. Prices adjust based on what customers are willing to pay.

You get higher conversion rates, bigger average order values, and more repeat purchases. Personalization that would take your team hours happens instantly.

Reduces operational costs with automation

AI handles the repetitive tasks that eat up your team's time. Support tickets get answered immediately. Product descriptions write themselves. Inventory levels adjust based on predicted demand.

This cuts operational costs while freeing your team to focus on strategic work. Instead of answering "where's my order" hundreds of times, your agents handle complex issues that actually need human judgment.

Improve decision-making with faster data processing

AI analyzes massive amounts of data in seconds that would take your team days or weeks to process manually. It connects patterns across millions of customer interactions, inventory movements, and sales transactions instantly.

Instead of digging through spreadsheets, you get clear answers about what's working, what's not, and what to do next. Your team moves faster and makes more informed decisions across marketing, inventory, and customer experience.

AI use cases in ecommerce that move the needle

These AI applications deliver immediate impact for ecommerce brands. Each use case solves a specific problem while driving measurable results.

Power conversational commerce and AI assistants

AI agents handle customer conversations across chat, email, and social channels. They answer product questions, process returns, and guide shoppers to the right items. Unlike basic chatbots, modern AI assistants understand context and keep conversations flowing naturally.

Key capabilities your AI assistant should have:

• Instant FAQ answers: Resolves common questions without human help
• Order management: Processes modifications and cancellations automatically
• Smart recommendations: Suggests products based on customer needs
• Seamless escalation: Hands off complex issues to human agents

The best AI assistants learn from every conversation, getting better at helping customers over time.

Personalize product recommendations

Recommendation engines analyze customer behavior to suggest products they actually want. AI considers browsing history, past purchases, what similar customers bought, and store inventory to deliver suggestions.

Effective recommendations show up throughout the shopping experience:

• Homepage personalization: Customized based on past visits
• Product page suggestions: "Customers also bought" recommendations
• Post-purchase upsells: Relevant add-ons in order confirmations
• Cart recovery: Alternative products for abandoned items

Forecast demand and plan inventory

AI predicts future demand by analyzing historical sales, seasonal trends, market conditions, and external factors. This prevents stockouts during busy periods and reduces excess inventory when sales slow down.

Your demand forecasting should consider:

• Sales patterns: Historical data and seasonal trends
• Marketing impact: Upcoming campaigns and promotions
• Market conditions: Competitor pricing and economic factors
• External events: Weather patterns and local happenings

Create and localize product content

Generative AI writes product descriptions, creates marketing copy, and translates content for international markets. This scales your content production without sacrificing quality or losing your brand voice.

AI content generation handles:

• Product descriptions: Written from basic specifications
• SEO content: Optimized category and landing pages
• Localization: Content adapted for different markets
• Testing variations: Multiple versions for A/B testing

Enhance search and product discovery

AI-powered search understands natural language queries and shopping intent. Instead of just matching keywords, it figures out what customers actually want. Visual search lets customers find products by uploading photos.

Modern search AI includes:

• Natural language understanding: Handles complex, conversational queries
• Smart corrections: Recognizes synonyms and fixes typos
• Visual search: Finds products from uploaded images
• Personalized results: Tailored based on browsing history

Optimize prices and revenue in real time

Dynamic pricing AI adjusts your prices based on demand, competition, inventory levels, and customer segments. This maximizes revenue while keeping you competitive in the market.

Your pricing optimization should monitor:

• Competitor movements: Real-time price tracking across competitors
• Inventory costs: Stock levels and holding expenses
• Customer sensitivity: How price changes affect different segments
• Demand patterns: Seasonal and promotional impacts

Detect and prevent payment and account fraud

AI identifies fraudulent transactions before they go through. Machine learning models analyze transaction patterns, user behavior, and device information to flag suspicious activity.

Fraud detection systems watch for:

• Unusual patterns: Purchases that don't match normal behavior
• Account takeovers: Signs someone else is using the account
• Payment mismatches: Credit cards that don't match shipping addresses
• Address anomalies: Shipping locations that seem suspicious

How leaders implement AI without the chaos

Successful AI implementation needs strategy, not just technology. Follow this approach to avoid common mistakes and deliver results you can measure.

Define outcomes and success metrics

Start with clear business goals. What specific problem will AI solve for you? How will you know if it's working? Set baseline measurements before you implement anything so you can track real improvement.

Track these essential metrics:

• Customer metrics: How fast you resolve issues, satisfaction scores, effort scores
• Operational metrics: How many tickets AI deflects, automation rate, cost per ticket
• Revenue metrics: Conversion improvements, average order value increases, customer lifetime value

Document your current performance for each metric. This becomes your starting point for measuring AI impact.

Pilot one use case and A/B test impact

Pick one high-impact use case for your pilot program. Run it alongside your existing processes to compare performance. This controlled approach proves ROI before you roll out AI everywhere.

Follow these pilot best practices:

• Clear success criteria: Choose a use case with obvious win conditions
• Sufficient data collection: Run for at least 30 days to gather meaningful results
• Direct comparison: A/B test AI against your current process
• Complete monitoring: Track both numbers and customer feedback
• Document learnings: Record what works for your broader rollout

How to measure ROI of AI use 

Measuring AI ROI means tracking both quick wins and long-term value. Focus on metrics that directly connect to business outcomes.

Track conversion, resolution time, and revenue lift

Monitor three core categories to understand your AI impact.

Conversion improvements:

• Recommendation performance: How much AI suggestions boost conversion rates
• Cart abandonment reduction: Fewer customers leaving with AI assistance
• Upsell success: Higher rates of additional purchases

Efficiency gains:

• Resolution speed: How much faster AI resolves customer issues
• First contact resolution: More problems solved in one interaction
• Automation rate: Percentage of tickets handled without human help

Revenue impact:

• Revenue per visitor: Increased earnings from each site visitor
• Customer lifetime value: Long-term value improvements
• Cost savings: Money saved through automation

Calculate ROI by comparing these metrics before and after AI implementation. Include both direct revenue gains and cost savings in your calculations.

Getting started checklist for ecommerce teams

Use this checklist to launch your first AI use case successfully.

Assessment:

• Identify your biggest customer pain points from support data
• Calculate current cost per ticket and resolution time
• Check if your tech stack can integrate with AI tools

Planning:

• Select one use case with clear ROI potential
• Define success metrics and improvement targets
• Create a rollout timeline with specific milestones

Implementation:

• Configure AI with your brand voice and policies
• Train your team on new AI tools and workflows
• Launch pilot with a small group of customers

Optimization:

• Review performance against your success metrics
• Collect feedback from both agents and customers
• Scale successful use cases gradually across your business

Start transforming your customer experience with AI

Your next step is simple. Pick one use case that addresses your biggest pain point. Measure the impact. Then expand from there. 

Book a demo to see how Gorgias helps ecommerce brands implement AI that drives real results.

{{lead-magnet-2}}

TL;DR:

• AI crossed the trust threshold in 2025. Customer satisfaction with AI support now nearly matches human agents, with brands reporting 85% confidence in AI-generated responses.
• Documentation quality separates high performers from everyone else. Brands with clear help center content automate 60%+ of tickets, while those with vague policies plateau at 20-30%.
• Support is becoming a scalable revenue channel. AI-powered product recommendations are driving 10-97% AOV lifts across brands by making every conversation a sales opportunity.
• Connected context matters more than response speed. Customers expect you to remember them across every channel, and systems that share data seamlessly will define premium CX by 2026.
• Post-purchase experience predicts repeat purchases better than marketing. 96% of customers will repurchase after an easy return experience. How you handle returns, delays, and problems will determine customer lifetime value.

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.

Documentation quality separates high performers from the rest

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:

• Order status, 12.4%
• Return requests, 7.9%
• Order cancellations, 6.1%
• Product quality issues, 5.9%
• Missing items, 4.6%

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.

What the leading brands are doing

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:

• Help Center articles written in customer language, not internal jargon
• Policies with explicit if/then logic instead of “contact us for details”
• Regular content audits based on which questions trigger escalations
• Deep integration between their helpdesk and ecommerce platform, so AI can access real-time data

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

What happens next

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.

Thoroughness matters more than speed in customer support 

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:

• Language proficiency: AI scores 4.77/5 versus humans at 4.4/5
• Empathy and communication: AI at 4.48/5 versus humans at 4.27/5
• Resolution completeness: AI at a perfect 1.0 versus humans at 0.99

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.

What happens next

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.

AI finally makes support-as-revenue scalable

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.

What the data shows

We analyzed revenue impact across brands using AI-powered product recommendations in support conversations. The results speak for themselves:

• An outdoor apparel brand saw 29.41% AOV uplift and 6.88% chat conversion rate by helping customers understand technical product details before purchase
• A furniture brand achieved 12.26% GMV uplift by guiding parents to age-appropriate furniture for their children
• A lingerie brand reached 16.78% chat conversion rate by helping customers find the right size through conversational guidance
• A home decor brand saw 97.15% AOV uplift by recommending complementary pieces based on customers' existing furniture and color palettes

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.

What happens next

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.

Connected customer data matters more than quick replies

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.

What the leading brands are doing

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."

What happens next

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 experience determines repeat purchase rate

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.

What the data shows

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.

What happens next

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.

The roadmap to get ahead of the competition

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):

• Audit your top 10 ticket types using your helpdesk data
• Build or improve Help Center documentation using actual customer language
• Set up basic automation for order tracking and return eligibility
• Implement proactive shipping notifications

Next (in 6-12 months):

• Use AI support on your highest-volume channel
• Measure support metrics tied to revenue influence
• Launch a self-service return portal with exchange suggestions
• Expand conversational commerce to social channels (Instagram, WhatsApp)
• Train support team on product knowledge and consultative selling

Watch (in 12-24 months):

• Voice commerce integration is maturing
• AI reaching zero satisfaction gap with humans for transactional support
• Social commerce shifting from experimental to primary
• Support conversations becoming the main retention driver over email marketing

Tomorrow's ecommerce leaders are investing in foundations today

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.

{{lead-magnet-2}}

TL;DR:

• AI chatbots give ecommerce stores instant, 24/7 support without growing your team. They handle common tasks like order tracking, returns, and product questions using conversational AI.
• Unlike scripted bots, modern AI chatbots understand context and nuance. They recognize intent even when customers don’t use exact keywords and can escalate complex issues to humans when needed.
• Combining AI with live agents creates a seamless support experience. Bots handle routine tickets while humans take over when empathy or deeper problem-solving is required.
• AI chatbots reduce support costs and drive more revenue. They free up agents, improve response times, boost conversions, and increase customer satisfaction.
• To choose the right chatbot, focus on ecommerce-specific features. Prioritize deep integrations, brand tone training, clear escalation paths, and performance analytics.

Your customers expect answers now, not in hours, not tomorrow, but the instant they ask. An AI chatbot handles order tracking, returns, and product questions around the clock without hiring more support agents. 

For ecommerce brands buried in repetitive tickets while trying to keep service personal, AI chatbots turn support costs into actual revenue. Here's everything you need to know about choosing and implementing the right solution for your store.

What is an AI chatbot?

An AI chatbot is conversational software that uses large language models (LLMs) to chat with customers. This means it can hold natural conversations with your shoppers, answer their questions, and help them resolve tasks without human intervention.

Unlike older chatbots that followed pre-set scripts, AI chatbots understand context and nuance. They can interpret what a customer really means, even when they don't use exact keywords. For example, if someone asks, "Can I get my money back?" the chatbot understands they're asking about returns, not requesting a literal cash withdrawal.

Modern AI chatbots use techniques like retrieval-augmented generation to pull information from verified sources — like your Help Center or product catalog — ensuring accurate answers. When they encounter issues beyond their capabilities, they know to escalate to human agents.

Related: What is conversational AI? The ecommerce guide

AI chatbots vs live chat 

While these chat tools both facilitate conversations, they serve different purposes and have unique strengths.

Live chat excels at solving complex or sensitive issues that require human empathy and judgment. AI chatbots provide instant, 24/7 answers to common questions.

The most effective approach combines AI chatbots with seamless human handoff. The chatbot handles initial inquiries, and if it can't resolve the issue, it escalates the conversation — along with all context — to a live agent. Modern platforms blend these capabilities into unified helpdesk solutions.

How an AI chatbot works for ecommerce

When asks a question in your website’s chat tool, your AI chatbot follows a sophisticated process to deliver accurate answers in seconds:

• Natural language processing (NLP): Breaks down the customer's message to understand their core request
• Intent recognition: Detects whether they're tracking an order, asking about returns, or seeking product information
• Vector search: Converts questions into mathematical representations to find the closest match in your knowledge base, understanding questions asked in different ways
• Context window: Maintains conversation history to reference earlier messages and hold natural, back-and-forth dialogue
• API integrations: Connects to your Shopify store and other tools for real-time access to order details, inventory, and customer data
• Grounding and confidence thresholds: Anchors responses to verified information and escalates to human agents when unsure

This combination allows AI chatbots to handle routine inquiries while knowing when to bring in your support team for complex issues.

Benefits of AI chatbots for ecommerce brands

AI chatbots deliver measurable improvements to both customer experience and business outcomes. They transform your support operation from a cost center into a revenue driver.

Better customer engagement and loyalty

Customers expect instant, personalized answers no matter the time — and AI chatbots do these at scale. Using your brand’s knowledge base, AI chatbots maintain your brand voice and guidelines while giving unique responses to customers. This means better customer education, engagement, and a higher likelihood of conversion.

Lower operational costs and higher efficiency

AI interactions cost significantly less than human support. By automating repetitive tickets, you scale support without adding headcount — a crucial move during peak seasons. Tedious work is dramatically reduced, giving agents time to strategize, address complex tickets, and build deeper customer relationships.

Increased revenue and conversions

An AI chatbot’s ability to detect customer intent means it knows when to upsell your products. Whether it is dealing with a new customer or a returning one, AI keeps conversations proactive by providing personalized recommendations, 

What to use an AI chatbot for in your ecommerce store

Start by automating your highest-volume, repetitive inquiries. This delivers the fastest ROI and lets your team focus on conversations that actually need human expertise.

Answer "Where is my order?" instantly

WISMO tickets likely dominate your inbox. Connect your chatbot to shipping carriers via API for real-time tracking, split shipment explanations, and delay notifications. Set up proactive shipping updates to prevent these tickets entirely. The bot escalates only when packages are missing.

Process returns and exchanges without agent involvement

Your chatbot checks return eligibility, generates labels, and communicates refund timelines. Integrate with Loop or ReturnGO for self-service. It suggests exchanges over refunds to preserve revenue — swapping a wrong size instead of losing the sale. Complex cases like damaged goods get escalated with full context.

Guide shoppers to the right products

Turn your chatbot into a sales associate that recommends products based on browsing history, answers sizing questions, suggests gifts, and bundles complementary items. Instantly addressing purchase-blocking questions about materials or stock availability removes friction and increases conversions.

Related: Guide more shoppers to checkout with conversation-led AI

AI chatbot risks and limitations for ecommerce

While powerful, AI chatbots have limitations you need to understand and plan for. Being aware of these risks helps you implement safeguards and set appropriate expectations:

• AI chatbots can sometimes generate plausible but incorrect answers, a phenomenon called “hallucination.” Using grounding techniques that anchor responses to verified information from your knowledge base helps prevent this. Regular monitoring and quality assurance are essential for maintaining accuracy.
• Data privacy and security require careful attention. Your chatbot must comply with regulations like GDPR and PCI standards when handling customer information. Look for platforms with built-in safety filters and data redaction features to protect sensitive information.
• Brand voice can drift over time as the AI learns from interactions. Regular audits ensure responses stay consistent with your intended tone and messaging. Complex emotional situations requiring human empathy should always escalate to human agents — AI cannot replace genuine human connection in sensitive circumstances.

How to choose an AI chatbot for your ecommerce store

Selecting the right AI chatbot requires evaluating platforms based on ecommerce-specific needs, not generic chatbot features. Focus on solutions built specifically for online retail.

Define priority intents

Analyze your support ticket data to identify the most common customer questions. These become your priority intents that the chatbot must handle excellently. Differentiate between must-have intents like order tracking and returns versus nice-to-have intents like detailed product education.

Calculate potential deflection rates for each intent category to understand the business impact. Focus on intents that represent high volume and clear resolution paths.

Map required integrations

Create a comprehensive list of your essential tools and platforms:

• Shopify: Core ecommerce platform integration
• Shipping carriers: Real-time tracking and delivery updates
• Returns platforms: Automated returns processing
• Review systems: Customer feedback management
• Subscription tools: Recurring order management
• Loyalty programs: Customer tier and rewards information

Look for platforms with deep, native integrations rather than basic API connections. Native integrations provide richer data access and more reliable performance.

Set guardrails and escalation

Define clear boundaries for AI capabilities and establish escalation triggers:

• Sentiment detection: Route frustrated customers to human agents
• Keyword triggers: Escalate conversations mentioning legal issues or health concerns
• Repeated failures: Hand off when AI cannot resolve after multiple attempts
• VIP customers: Provide premium support routing for high-value customers

Ensure the handoff process preserves conversation context so human agents can continue seamlessly where AI left off.

Validate brand tone

Your chatbot represents your brand in every interaction. The platform should allow you to train the AI on your specific brand guidelines, approved language, and desired tone of voice.

Test responses across different scenarios and customer types to ensure consistency. Look for platforms that provide ongoing monitoring tools to prevent tone drift over time.

Plan analytics and QA

Choose a platform with robust analytics and quality assurance capabilities:

• Performance dashboards: Real-time metrics on key performance indicators
• Conversation reviews: Tools for auditing AI interactions
• Feedback loops: Systems for continuous training and improvement
• A/B testing: Capabilities to optimize response strategies

Core performance metrics:

• CSAT scores: Compare customer satisfaction for AI versus human interactions
• Deflection rate: Percentage of tickets resolved without human intervention
• Containment rate: Conversations completed entirely by AI
• Average handle time: Speed of resolution for different inquiry types
• First contact resolution: Issues solved in a single interaction

Business impact metrics:

• Revenue attribution: Sales directly influenced or generated by the chatbot
• Cost per resolution: AI versus human agent cost comparison
• Self-service adoption: Customers successfully using AI for resolution
• Abandonment rate: Conversations left unfinished by customers

Set realistic benchmarks based on your industry and business model. Use these metrics to identify improvement opportunities and demonstrate return on investment to stakeholders.

Transform your customer experience with Gorgias AI Agent

Ready to join thousands of ecommerce brands using AI to delight customers and drive revenue? Gorgias AI Agent integrates seamlessly with Shopify to deliver instant, accurate support that sounds just like your brand.

Book a demo to see how AI Agent can handle your specific use cases and start automating within days, not months.

{{lead-magnet-2}}

TL;DR:

• AI customer support tools go beyond speed and actually reduce workload. The right platform automates repetitive tickets so your team can focus on high-value conversations.
• AI works by using natural language processing (NLP) to generate real-time, context-aware responses. NLP helps the AI understand customer intent and reply in a way that matches your knowledge base and brand tone.
• Choosing the right platform depends on your business type, budget, and goals. Gorgias is ideal for ecommerce, Shopify Inbox offers a solid free option, and Zendesk or Intercom fit larger or multichannel teams.
• Take a phased approach when implementing AI. Start small by automating common inquiries, train the AI on your brand voice, and expand based on performance.
• Top brands are already seeing ROI from AI. Companies like Psycho Bunny, Osea Malibu, and Dr. Bronner’s use AI to cut costs, boost efficiency, and improve customer satisfaction.

If you lead a support team today, you’re probably evaluating AI tools with a different lens than you were a year ago. The question isn’t only “How fast is it?” It’s “What work will this actually take off my team’s plate?”

By 2026, Forrester predicts 30% of enterprises will build parallel AI functions, including hiring managers to train AI agents, ops teams to tune their performance, and specialists to step in when things go wrong.

That means choosing the right AI platform isn’t optional — it’s a step into the future of support work.

In this list, we cover what AI for customer support is, how it helps customer experience teams hit their goals, the top platforms to consider, how to evaluate and implement them, and the brands already seeing results.

Jump ahead:

• Best for ecommerce brands: Gorgias
• Best for large multichannel teams: Zendesk
• Best for limited budgets: Shopify Inbox
• Best for email-only support: Help Scout 
• Best for in-app messaging: Intercom
• Best for small and mid-sized businesses: Tidio
• Best for Freshworks ecosystems: Freshdesk 
• Best for automation at scale: Ada ‍
• Best for compliance-sensitive brands: Level AI

What is AI for customer support?

AI for customer support is software that uses artificial intelligence to manage and automate customer interactions. It can respond to customers on channels like chat, email, and social messaging — even before a human agent needs to step in.

It works by using natural language processing (NLP) to understand intent and generate contextually relevant replies. Instead of following rigid scripts like traditional chatbots, AI produces responses in real time based on your policies, data, and brand voice.

Because of this, AI can handle a significant share of repetitive tickets while giving agents the space to focus on more complex and relationship-driving issues.

Read more: What is conversational AI? The ecommerce guide

How AI helps customer support teams hit their goals

By automating repetitive tasks, AI frees up human agents to focus on complex problems that require empathy and creative thinking.

Here's how AI improves your support metrics:

• 24/7 availability: AI provides instant responses around the clock, even when your team is offline
• Deflection rate: AI resolves common questions without human intervention, reducing overall ticket volume
• First contact resolution: AI delivers consistent answers from your knowledge base, solving more issues in one interaction
• Average handle time: Automation speeds up resolutions by giving agents context and suggested replies
• Customer satisfaction: Faster, more accurate support leads to happier customers

AI also helps you scale during peak seasons like Black Friday without hiring temporary staff. This efficiency translates into lower costs and a more strategic support operation.

The best AI platforms for customer support

Choosing the right AI platform depends on your industry, team size, and specific challenges. We evaluated solutions based on AI capabilities, ease of use, integrations, and business fit.

Best for ecommerce brands: Gorgias

Pricing: $40/month

Gorgias is a conversational AI platform built specifically for ecommerce. Its deep integration with Shopify lets it automate up to 60% of support tickets with direct access to Shopify actions right in the platform.

The AI Agent can edit orders, issue refunds, and apply discount codes directly in your helpdesk. This means customers get instant help with common requests like order changes or returns. The platform also powers personalized product recommendations and proactive chat campaigns, turning your support team into a revenue driver.

Gorgias offers tiered pricing starting with a Starter plan for small brands and scaling to enterprise solutions.

Best for large multichannel teams: Zendesk

Pricing: $55/month

Zendesk is an enterprise-grade platform with mature AI features. Its Answer Bot and intelligence tools help manage high volumes across multiple channels. Zendesk is known for scalability and extensive integrations.

The AI analyzes intent and sentiment to route tickets effectively and provide agents with helpful context. You can automate responses to common questions while ensuring complex issues reach the right specialists.

However, Zendesk's complexity and higher price point can overwhelm smaller teams. It's best for businesses that need its full suite of enterprise features.

Best for limited budgets: Shopify Inbox

Pricing: Free

Shopify Inbox is a free live chat tool built specifically for Shopify brands, making it an easy entry point for teams that want to experiment with AI support. The AI suggests replies based on customer messages, helping agents respond quickly without needing a full helpdesk.

Because it’s tied directly to Shopify, agents can see customer details, past orders, and cart activity right inside the chat. This gives small teams enough context to answer common questions fast and keep shoppers moving toward checkout.

That said, Shopify Inbox’s automation capabilities are limited. It’s best for smaller brands testing live chat or those who need a no-cost solution. This means teams that want deeper automation will likely outgrow it.

Best for email-only support: Help Scout 

Pricing: $25/month

Help Scout focuses on simplicity and human-centric customer service. Its AI features, including Beacon and AI Assist, are straightforward and easy to implement. The AI suggests replies to agents and pulls relevant articles into conversations.

This platform is ideal for teams that want a clean interface and simple AI augmentation. While user-friendly, its AI capabilities aren't as advanced as platforms like Gorgias or Intercom.

Best for in-app messaging: Intercom 

Pricing: $0.99 per resolution with your current helpdesk

Intercom excels at conversational support, particularly for product-led and SaaS companies. Its AI chatbot, Fin, uses advanced language models to provide natural, human-like conversations within your app or website.

Intercom's AI can qualify leads, onboard new users, and resolve support questions by referencing your knowledge base. It's excellent for engaging users during their product experience.

The pricing model is usage-based, which can become expensive as you scale and add more advanced AI capabilities.

Best for small and mid-sized businesses: Tidio

Pricing: $24.17/month

Tidio combines live chat and basic chatbot features, making it popular with small businesses. It features a visual flow builder for creating simple chatbots without coding.

Tidio offers a free plan with limited features, with paid plans unlocking more capabilities. While it's a great starting point for chat automation, it lacks sophisticated NLP and deep integrations needed for complex operations.

Best for Freshworks ecosystems: Freshdesk 

Pricing: $49/month

Freshdesk offers Freddy AI, which provides omnichannel support capabilities. It's a strong choice for businesses already using other Freshworks products. Freddy AI automates responses, suggests solutions to agents, and predicts customer needs.

The platform includes workflow automation and predictive contact scoring to help prioritize tickets. Freshdesk offers several pricing tiers, but the most powerful AI features are on higher-priced plans.

Best for automation at scale: Ada 

Pricing: $499/month

Ada is a pure-play conversational AI platform designed for enterprise automation. It offers a powerful, no-code bot builder for creating sophisticated automation flows for complex use cases.

Ada handles massive scale and integrates with existing helpdesks. Because it focuses solely on automation, it can achieve very high deflection rates. The downside is that you need a separate system for human agents and enterprise-level pricing.

Best for compliance-sensitive brands: Level AI

Pricing: $35 per agent + $1500+ per integration + platform fees

Level AI specializes in quality assurance and agent performance. Instead of focusing on ticket deflection, it analyzes customer conversations to provide real-time coaching and feedback to agents.

The platform uses sentiment analysis, topic detection, and agent screen recording to identify coaching opportunities. It's excellent for large teams focused on improving agent quality and consistency. However, it's a specialized solution that requires a separate helpdesk.

How to evaluate and implement AI for customer support

Adopting AI requires a strategic approach, not just a technical one. Successful implementation starts with clear planning and phased rollout. Instead of automating everything at once, focus on early wins and expand from there.

1. Define goals and KPIs for automation

Before starting, determine what you want to achieve. Are you trying to reduce response times, lower cost-per-ticket, or improve customer satisfaction scores? Set specific, measurable goals like "achieve 30% ticket deflection for order inquiries within 60 days."

Establish baseline metrics before implementing AI. This lets you accurately track progress and demonstrate return on investment.

2. Select channels and intents to automate first

Start with low-hanging fruit, or basic, repetitive customer inquiries. For most ecommerce brands, this means questions like "Where is my order?", "What is your return policy?", and basic product questions.

Prioritize channels where you receive the most inquiries, whether email, live chat, or social media. By tackling your most frequent questions first, you'll see the biggest impact on your team's workload.

3. Train AI on brand voice and policies

Your AI is only as smart as the information you provide. A comprehensive and current knowledge base is critical for success. The AI uses these articles to learn your policies, product details, and brand voice.

Set up clear guardrails and escalation rules. Define which topics the AI shouldn't handle — like angry customers or complex technical issues — and create seamless handoff processes to human agents. Getting your AI brand voice right ensures consistent, on-brand interactions across all automated responses.

Which companies use AI for customer support?

Today’s leading brands are fully leveraging AI to help deliver high-quality support. Take a look at how AI helps these four brands win:

Psycho Bunny uses AI to double revenue without adding headcount

What they use AI for: Automating 25–30% of repetitive tickets across email and chat on Gorgias after switching from Zendesk.

Results: Faster responses (1-minute email first response time), reduced seasonal hiring, and 10% YoY savings in operational costs.

Read Psycho Bunny’s story ->

Osea Malibu uses AI to cut QA time by 75%

What they use AI for: Automatically reviewing 100% of tickets daily with Auto QA to surface tone, adherence, and macro-usage issues.

Results: 15 minutes of weekly QA versus over 1 hour, and faster coaching cycles that improve agent performance and customer experience.

Read Osea Malibu’s story -> 

Dr. Bronner’s uses AI to save $100k/year

What they use AI for: Automating routine support questions to improve efficiency and reduce reliance on Salesforce.

Results: Automated 45% of inquiries in two months, saved $100k per year, and improved CSAT by 11%.

Read Dr. Bronner’s story ->

Ekster uses AI to do the work of four agents

What they use AI for: Automating high-volume, repetitive questions to offset a leaner support team and manage peak-season spikes.

Results: Automated 27% of customer support tickets and kept service levels high despite losing almost half of their support team.

Read Ekster’s story -> 

Stay reliable with the right AI platform

The strongest platforms aren’t just chatbots. They’re systems that make your agents’ jobs easier, automate the repetitive work they’re tired of, and help you bring in more revenue.

If you’re still hesitant, you’re not alone. Most CX leaders worry about where to start. The safest path is to focus on the problems that slow your team down today, roll out AI in phases, and refine as you go.

When you do that, AI stops being a risky bet and becomes one of the most dependable parts of your operation.

Book a demo to see how the right platform can make that shift a whole lot easier.

{{lead-magnet-2}}

TL;DR:

• Customer experience (CX) encompasses every interaction a customer has with your brand, from first discovery through post-purchase support
• CX differs from customer service by including the entire journey, not just support touchpoints
• Great ecommerce experiences drive higher retention rates, increased lifetime value, and stronger word-of-mouth marketing
• Measuring CX requires tracking both quantitative metrics (CSAT, NPS) and qualitative feedback across all channels
• Modern CX relies on omnichannel tools, AI automation, and self-service options to meet customer expectations

Customer experience shapes how shoppers perceive your brand at every touchpoint. From the moment they discover your store through ads or social media to their post-purchase support interactions, each moment contributes to their overall impression. 

For ecommerce brands, this means coordinating everything from your website design to your shipping notifications to your return process. The brands that excel at CX turn one-time buyers into loyal customers who spend more and recommend your products to others.

What is customer experience?

Customer experience is the overall perception a shopper has of your brand based on every interaction they have with you. This means everything from seeing your Instagram account to unboxing their order and getting help from your support team shapes how they feel about your business.

CX includes three types of responses from your customers. Cognitive responses are what they think about your brand. Emotional responses are how your brand makes them feel. Behavioral responses are the actions they take, like making a purchase or leaving a review.

Your customer experience spans multiple touchpoints and stages:

• Discovery touchpoints: Social media ads, search results, influencer mentions, word-of-mouth recommendations
• Shopping touchpoints: Website browsing, product pages, checkout process, payment options
• Fulfillment touchpoints: Order confirmation emails, shipping notifications, delivery experience, packaging
• Support touchpoints: Live chat conversations, email responses, return processes, help center articles

Each touchpoint either builds trust or creates friction. When you nail the experience across all these moments, customers come back for more.

How is customer experience different from customer service?

Customer service is reactive support when problems arise. Customer experience is proactive engagement across your customer's entire journey with your brand.

Think of customer service as one piece of a much larger puzzle. Customer service focuses on solving problems after they happen, while customer experience shapes the entire journey that a customer goes through — from their welcome email, all the way to their conversation with an agent after purchase.

Why customer experience matters in ecommerce

Customer experience becomes your advantage when products and prices look similar across brands. A better experience makes shoppers choose you, come back again, and recommend you to others.

These are the main benefits of investing in customer experience as an ecommerce business.

Good first impression for new customers

A strong first experience builds confidence. When shoppers understand your product, know what to expect, and can get quick answers, buying feels easy instead of risky. Clear details remove second thoughts. Helpful support fills in any gaps. A checkout that “just works” keeps people moving forward rather than leaving you for a competitor.

Lower operating costs

When customers can find answers on their own, your team spends less time on repetitive questions. Good CX practices like communicating before issues pop up help your team avoid a wave of preventable tickets. And when your product info is accurate and helpful? You’ll notice fewer returns and disappointed reviews. All of this reduces workload and saves money as you grow.

Related: The hidden power and ROI of automated customer support

Stronger brand reputation

People love to talk about brands that make their lives easier, and that starts with the customer experience. A well-thought-out customer experience becomes strong enough to inspire positive word-of-mouth reviews, viral social shares, and a better reputation.

What makes a great customer experience in ecommerce

A great customer experience is the one shoppers barely notice because nothing gets in their way. The path from browsing to buying feels simple, and customers never have to wonder what to do next. When the experience feels this easy, it builds trust — and trust becomes the reason they come back.

Here are the core components that lead to that kind of experience.

Accuracy

As AI becomes essential to customer experience, accuracy is the new standard customers judge you by. Speed matters, but it's worthless if the answer is wrong. Shoppers want one-touch resolutions, not back-and-forth conversations or unnecessary escalations.

Related: AI Agent keeps getting smarter (here’s the data to prove it)

Speed

Speed still matters because most shoppers want to get in, get what they need, and get out. When they have a question about items already in their cart, a quick answer can be the difference between a completed order and an abandoned one. Slow support creates doubt, while fast responses and reliable shipping options keep momentum going and help customers finish their purchase with confidence.

Read more: Why faster isn’t always better: The pitfalls of fast-only customer support

Personalization

A 2024 survey found that about 80% of consumers expect personalized interactions from the brands they shop with personalization expectations. When recommendations feel relevant, customers feel understood and are more likely to come back.

Transparency

All your customers want is honesty. Showing accurate inventory, reliable shipping estimates, and clear return policies all build trust from the very start. Make your expectations clear, and you're less likely to face returns, complaints, and frustrated customers.

Accessibility

The best customer experiences feel intuitive. Give shoppers a clear path to the details they need, whether they’re checking sizing or reviewing return policies. Nothing should feel tucked away. Visible support options and intuitive navigation help customers move toward checkout without second-guessing the process.

How to measure customer experience (metrics and KPIs)

You need both numbers and stories to understand your customer experience performance. Quantitative metrics show you what's happening. Qualitative feedback explains why it's happening.

CSAT

Customer Satisfaction (CSAT) measures immediate happiness with specific interactions. Ask customers to rate their experience after support conversations or purchases. This gives you real-time feedback on individual touchpoints.

NPS

Net Promoter Score (NPS) measures overall loyalty by asking how likely customers are to recommend your brand. Scores range from zero to 10. Promoters (9-10) drive growth through referrals. Detractors (0-6) may damage your reputation through negative word-of-mouth.

Customer effort

Customer Effort Score (CES) measures how much work customers put in to get help. Lower effort scores predict higher loyalty. Customers remember when you make things easy for them.

Handle times

Average handle time (AHT) and first contact resolution (FCR) measure your support team's efficiency. While not direct customer experience metrics, they impact how customers perceive your responsiveness and competence.

Churn rate

Churn rate shows the percentage of customers who stop buying from you. High churn often signals experience problems that need attention. Track churn by customer segments to identify patterns.

Customer lifetime value

Customer lifetime value (CLV) predicts total revenue from each customer relationship. Improving experience is one of the most effective ways to increase CLV. Happy customers buy more often and spend more over time.

What you need to run your first customer experience function

A customer experience strategy is the plan for how your brand treats customers from the moment they discover you to the moment they buy again. The easiest way to think about it is in layers.

1. Customer-facing interactions

This is the top layer and the part customers notice first. Clear product pages, helpful support, fast shipping updates, and easy returns all belong here. These touchpoints affect how customers feel about buying from you. A strong strategy starts with deciding what “a great experience” looks like at each of these moments.

Quick Tip: Start small. Pick one or two touchpoints that cause the most friction, like a product page or the returns process, and improve them first. Early wins give you the confidence to keep expanding your CX foundation without getting overwhelmed.

2. Customer research

To deliver an unforgettable experience, you need to know what customers actually want. This layer focuses on gathering real feedback from reviews, surveys, and customer conversations. You don’t need a complex process for this — just a consistent way to spot patterns and record what customers love and don’t love.

Read more: How to use CX data to improve marketing, messaging & conversions

3. Journey planning

Once you understand your customers, map out their relationship with your brand from first click to repeat purchase. It can be a simple outline that shows the main steps customers take and where friction typically occurs. This layer helps you prioritize the improvements that will have the greatest impact.

4. Roles and responsibilities

It’s time to get in the weeds: decide who owns which part of the customer journey. Who will handle product info? Respond to support tickets? Oversee shipping and logistics? Clear ownership ensures a consistent experience even as the business grows.

Here are some guiding questions to help decide who should own what:

• Which parts of the customer journey should the CX team own right now? This might include support responses, FAQs, returns communication, and post-purchase messaging. It typically wouldn’t own inventory, shipping operations, or product page content.
• Which tasks take the most time or create the most friction for customers? These become your first areas to delegate or hire for.
• If you could hire one person next, what CX work would they take over immediately? This helps you prioritize whether you need a support specialist, a CX operations role, or someone focused on retention.

5. Tools and systems behind the scenes

This is the foundation layer that supports your entire CX function. You need tools that bring customer data together, help your team communicate with shoppers, automate repeat questions, and show how you’re performing. A good CX platform becomes the backbone of your operation.

We recommend using an ecommerce-specific helpdesk with the following features:

• Omnichannel: Your helpdesk should integrate all your support channels — from email and chat to SMS and social media — and funnel them into a single inbox for quick responding. 
• AI-powered chat features: Customers ask questions even when your team is offline. Ensure you can resolve their issues with an AI chat trained on your policies and can deliver accurate answers 24/7.
• In-depth analytics: Improvement is key to meeting customer expectations. It’s imperative that your tool comes with analytics on agent performance, automation opportunities, customer satisfaction, and product insights.

Read more: Best AI helpdesk tools: 10 platforms compared

Put your customer experience strategy into motion

You now have the building blocks of what makes a strong customer experience. The next step is to put those elements into practice by improving the touchpoints customers feel most strongly about and tightening the systems that support them.

AI-powered support helps you do this at scale by resolving repeat questions instantly and giving your team more time for work that moves the business forward.

Book a demo to explore how leading ecommerce brands use Gorgias to automate up to 60% of support inquiries.

{{lead-magnet-2}}