TL;DR:

• Implement quickly and optimize continuously. Cornbread's rollout was three phases: audit knowledge base, launch, then refine. Stacy conducts biweekly audits and provides daily AI feedback to ensure responses are accurate and on-brand.
• Simplify your knowledge base language. Before BFCM, Stacy rephrased all guidance documentation to be concise and straightforward so Shopping Assistant could deliver information quickly without confusion.
• Use proactive suggested questions. Most of Cornbread's Shopping Assistant engagement comes from Suggested Product Questions that anticipate customer needs before they even ask.
• Treat AI as another team member. Make sure the tone and language AI uses match what human agents would say to maintain consistent customer relationships.
• Free up agents for high-value work. With AI handling straightforward inquiries, Cornbread's CX team expanded into social media support, launched a retail pop-up shop, and has more time for relationship-building phone calls.

Customer education has become a critical factor in converting browsers into buyers. For wellness brands like Cornbread Hemp, where customers need to understand ingredients, dosages, and benefits before making a purchase, education has a direct impact on sales. The challenge is scaling personalized education when support teams are stretched thin, especially during peak sales periods.

Katherine Goodman, Senior Director of Customer Experience, and Stacy Williams, Senior Customer Experience Manager, explain how implementing Gorgias's AI Shopping Assistant transformed their customer education strategy into a conversion powerhouse. 

In our second AI in CX episode, we dive into how Cornbread achieved a 30% conversion rate during BFCM, saving their CX team over four days of manual work.

Top learnings from Cornbread's conversational commerce strategy

1. Customer education drives conversions in wellness

Before diving into tactics, understanding why education matters in the wellness space helps contextualize this approach.

Katherine, Senior Director of Customer Experience at Cornbread Hemp, explains:

"Wellness is a very saturated market right now. Getting to the nitty-gritty and getting to the bottom of what our product actually does for people, making sure they're educated on the differences between products to feel comfortable with what they're putting in their body."

The most common pre-purchase questions Cornbread receives center around three areas: ingredients, dosages, and specific benefits. Customers want to know which product will help with their particular symptoms. They need reassurance that they're making the right choice.

What makes this challenging: These questions require nuanced, personalized responses that consider the customer's specific needs and concerns. Traditionally, this meant every customer had to speak with a human agent, creating a bottleneck that slowed conversions and overwhelmed support teams during peak periods.

2. Shopping Assistant provides education that never sleeps

Stacy, Senior Customer Experience Manager at Cornbread, identified the game-changing impact of Shopping Assistant:

"It's had a major impact, especially during non-operating hours. Shopping Assistant is able to answer questions when our CX agents aren't available, so it continues the customer order process."

A customer lands on your site at 11 PM, has questions about dosage or ingredients, and instead of abandoning their cart or waiting until morning for a response, they get immediate, accurate answers that move them toward purchase.

The real impact happens in how the tool anticipates customer needs. Cornbread uses suggested product questions that pop up as customers browse product pages. Stacy notes:

"Most of our Shopping Assistant engagement comes from those suggested product features. It almost anticipates what the customer is asking or needing to know."

Actionable takeaway: Don't wait for customers to ask questions. Surface the most common concerns proactively. When you anticipate hesitation and address it immediately, you remove friction from the buying journey.

3. Implementation follows a clear three-phase approach

One of the biggest myths about AI is that implementation is complicated. Stacy explains how Cornbread’s rollout was a straightforward three-step process: audit your knowledge base, flip the switch, then optimize.

"It was literally the flip of a switch and just making sure that our data and information in Gorgias was up to date and accurate." 

Here's Cornbread’s three-phase approach:

1. Preparation. Before launching, Cornbread conducted a comprehensive audit of their knowledge base to ensure accuracy and completeness. This groundwork is critical because your AI is only as good as the information it has access to.
2. Launch and training. After going live, the team met weekly with their Gorgias representative for three to four weeks. They analyzed engagements, reviewed tickets, and provided extensive AI feedback to teach Shopping Assistant which responses were appropriate and how to pull from the knowledge base effectively.
3. Ongoing optimization. Now, Stacy conducts audits biweekly and continuously updates the knowledge base with new products, promotions, and internal changes. She also provides daily AI feedback, ensuring responses stay accurate and on-brand.

Actionable takeaway: Block out time for that initial knowledge base audit. Then commit to regular check-ins because your business evolves, and your AI should evolve with it.

Read more: AI in CX Webinar Recap: Turning AI Implementation into Team Alignment

4. Simple, concise language converts better

Here's something most brands miss: the way you write your knowledge base articles directly impacts conversion rates.

Before BFCM, Stacy reviewed all of Cornbread's Guidance and rephrased the language to make it easier for AI Agent to understand. 

"The language in the Guidance had to be simple, concise, very straightforward so that Shopping Assistant could deliver that information without being confused or getting too complicated," Stacy explains. When your AI can quickly parse and deliver information, customers get faster, more accurate answers. And faster answers mean more conversions.

Katherine adds another crucial element: tone consistency.

"We treat AI as another team member. Making sure that the tone and the language that AI used were very similar to the tone and the language that our human agents use was crucial in creating and maintaining a customer relationship."

As a result, customers often don't realize they're talking to AI. Some even leave reviews saying they loved chatting with "Ally" (Cornbread's AI agent name), not realizing Ally isn't human.

Actionable takeaway: Review your knowledge base with fresh eyes. Can you simplify without losing meaning? Does it sound like your brand? Would a customer be satisfied with this interaction? If not, time for a rewrite.

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

5. Black Friday results proved the strategy works under pressure

The real test of any CX strategy is how it performs under pressure. For Cornbread, Black Friday Cyber Monday 2025 proved that their conversational commerce strategy wasn't just working, it was thriving.

Over the peak season, Cornbread saw: 

• Shopping Assistant conversion rate jumped from a 20% baseline to 30% during BFCM
• First response time dropped from over two minutes in 2024 to just 21 seconds in 2025
• Attributed revenue grew by 75%
• Tickets doubled, but AI handled 400% more tickets compared to the previous year
• CSAT scores stayed exactly in line with the previous year, despite the massive volume increase

Katherine breaks down what made the difference:

"Shopping Assistant popping up, answering those questions with the correct promo information helps customers get from point A to point B before the deal ends."

During high-stakes sales events, customers are in a hurry. They're comparing options, checking out competitors, and making quick decisions. If you can't answer their questions immediately, they're gone. Shopping Assistant kept customers engaged and moving toward purchase, even when human agents were swamped.

Actionable takeaway: Peak periods require a fail-safe CX strategy. The brands that win are the ones that prepare their AI tools in advance.

6. Strategic work replaces reactive tasks

One of the most transformative impacts of conversational commerce goes beyond conversion rates. What your team can do with their newfound bandwidth matters just as much.

With AI handling straightforward inquiries, Cornbread's CX team has evolved into a strategic problem-solving team. They've expanded into social media support, provided real-time service during a retail pop-up, and have time for the high-value interactions that actually build customer relationships.

Katherine describes phone calls as their highest value touchpoint, where agents can build genuine relationships with customers. “We have an older demographic, especially with CBD. We received a lot of customer calls requesting orders and asking questions. And sometimes we end up just yapping,” Katherine shares. “I was yapping with a customer last week, and we'd been on the call for about 15 minutes. This really helps build those long-term relationships that keep customers coming back."

That's the kind of experience that builds loyalty, and becomes possible only when your team isn't stuck answering repetitive tickets.

Stacy adds that agents now focus on "higher-level tickets or customer issues that they need to resolve. AI handles straightforward things, and our agents now really are more engaged in more complicated, higher-level resolutions."

Actionable takeaway: Stop thinking about AI only as a cost-cutting tool and start seeing it as an impact multiplier. The goal is to free your team to work on conversations that actually move the needle on customer lifetime value.

7. Continuous optimization for January and beyond

Cornbread isn't resting on their BFCM success. They're already optimizing for January, traditionally the biggest month for wellness brands as customers commit to New Year's resolutions.

Their focus areas include optimizing their product quiz to provide better data to both AI and human agents, educating customers on realistic expectations with CBD use, and using Shopping Assistant to spotlight new products launching in Q1.

Build your conversational commerce strategy now

The brands winning at conversational commerce aren't the ones with the biggest budgets or the largest teams. They're the ones who understand that customer education drives conversions, and they've built systems to deliver that education at scale.

Cornbread Hemp's success comes down to three core principles: investing time upfront to train AI properly, maintaining consistent optimization, and treating AI as a team member that deserves the same attention to tone and quality as human agents.

As Katherine puts it:

"The more time that you put into training and optimizing AI, the less time you're going to have to babysit it later. Then, it's actually going to give your customers that really amazing experience."

Watch the replay of the whole conversation with Katherine and Stacy to learn how Gorgias’s Shopping Assistant helps them turn browsers into buyers. 

{{lead-magnet-1}}

TL;DR:

• Position Shopping Assistant as a revenue-driving tool. It boosts AOV, GMV, and chat conversion rates, with some brands seeing up to 97% higher AOV and 13x ROI.
• Highlight its role as a proactive sales agent, not just a support bot. It recommends products, applies discounts, and guides shoppers to checkout in real time.
• Use cross-industry case studies to make your case. Show leadership success stories from brands like Arc’teryx, bareMinerals, and TUSHY to prove impact.
• Focus on the KPIs it improves. Track AOV, GMV, chat conversion, CSAT, and resolution rate to demonstrate clear ROI.

Rising customer expectations, shoppers willing to pay a premium for convenience, and a growing lack of trust in social media channels to make purchase decisions are making it more challenging to turn a profit.  

In this emerging era, AI’s role is becoming not only more pronounced, but a necessity for brands who want to stay ahead. Tools like Gorgias Shopping Assistant can help drive measurable revenue while reducing support costs. 

For example, a brand that specializes in premium outdoor apparel implemented Shopping Assistant and saw a 2.25% uplift in GMV and 29% uplift in average order volume (AOV).

But how, among competing priorities and expenses, do you convince leadership to implement it? We’ll show you.

Why conversational AI matters for modern ecommerce

1) Meet high consumer expectations

Shoppers want on-demand help in real time that’s personalized across devices. 

Shopping Assistant recalls a shopper’s browsing history, like what they have clicked, viewed, and added to their cart. This allows it to make more relevant suggestions that feel personal to each customer. 

2) Keep up with market momentum

The AI ecommerce tools market was valued at $7.25 billion in 2024 and is expected to reach $21.55 billion by 2030. 

Your competitors are using conversational AI to support, sell, and retain. Shopping Assistant satisfies that need, providing upsells and recommendations rooted in real shopper behavior. 

3) Raise AOV and GMV

Conversational AI has real revenue implications, impacting customer retention, average order value (AOV), conversion rates, and gross market value (GMV). 

For example, a leading nutrition brand saw a GMV uplift of over 1%, an increase in AOV of over 16%, and a chat conversion rate of over 15% after implementing Shopping Assistant.

Overall, Shopping Assistant drives higher engagement and more revenue per visitor, sometimes surpassing 50% and 20%, respectively.

How to show the business impact & ROI of Shopping Assistant

1) Pitch its core capabilities

Shopping Assistant engages, personalizes, recommends, and converts. It provides proactive recommendations, smart upsells, dynamic discounts, and is highly personalized, all helping to guide shoppers to checkout. 

Success spotlight

After implementing Shopping Assistant, leading ecommerce brands saw real results:

2) Position it as a revenue driver

Shopping Assistant drives uplift in chat conversion rate and makes successful upsell recommendations.  

Success spotlight

“It’s been awesome to see Shopping Assistant guide customers through our technical product range without any human input. It’s a much smoother journey for the shopper,” says Nathan Larner, Customer Experience Advisor for Arc’teryx. 

For Arc’teryx, that smoother customer journey translated into sales. The brand saw a 75% increase in conversion rate (from 4% to 7%) and 3.7% of overall revenue influenced by Shopping Assistant. 

3) Show its efficiency and cost savings

Because it follows shoppers’ live journey during each session on your website, Shopping Assistant catches shoppers in the moment. It answers questions or concerns that might normally halt a purchase, gets strategic with discounting (based on rules you set), and upsells. 

The overall ROI can be significant. For example, bareMinerals saw an 8.83x return on investment.  

Success spotlight

"The real-time Shopify integration was essential as we needed to ensure that product recommendations were relevant and displayed accurate inventory,” says Katia Komar, Sr. Manager of Ecommerce and Customer Service Operations, UK at bareMinerals. 

“Avoiding customer frustration from out-of-stock recommendations was non-negotiable, especially in beauty, where shade availability is crucial to customer trust and satisfaction. This approach has led to increased CSAT on AI converted tickets."

4) Present the metrics it can impact

Shopping Assistant can impact CSAT scores, response times, resolution rates, AOV, and GMV.  

Success spotlight

For Caitlyn Minimalist, those metrics were an 11.3% uplift in AOV, an 18% click through rate for product recommendations, and a 50% sales lift versus human-only chats. 

"Shopping Assistant has become an intuitive extension of our team, offering product guidance that feels personal and intentional,” says Anthony Ponce, its Head of Customer Experience.

5) Highlight its helpfulness as a sales agent 

Support agents have limited time to assist customers as it is, so taking advantage of sales opportunities can be difficult. Shopping Assistant takes over that role, removing obstacles for purchase or clearing up the right choice among a stacked product catalog.

Success spotlight

With a product that’s not yet mainstream in the US, TUSHY leverages Shopping Assistant for product education and clarification. 

"Shopping Assistant has been a game-changer for our team, especially with the launch of our latest bidet models,” says Ren Fuller-Wasserman, Sr. Director of Customer Experience at TUSHY. 

“Expanding our product catalog has given customers more choices than ever, which can overwhelm first-time buyers. Now, they’re increasingly looking to us for guidance on finding the right fit for their home and personal hygiene needs.”

The bidet brand saw 13x return on investment after implementation, a 15% increase in chat conversion rate, and a 2x higher conversion rate for AI conversations versus human ones. 

6) Provide the KPIs you’ll track 

Customer support metrics include: 

• Resolution rate 
• CSAT score 

Revenue metrics to track include: 

• Average order value (AOV) 
• Gross market value (GMV) 
• Chat conversion rate 

Shopping Assistant: AI that understands your brand 

Shopping Assistant connects to your ecommerce platform (like Shopify), and streamlines information between your helpdesk and order data. It’s also trained on your catalog and support history. 

Allow your agents to focus on support and sell more by tackling questions that are getting in the way of sales. 

{{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}}