TL;DR

• An AI copilot in customer service (also called agent assist) is an AI that works next to a human agent: it drafts replies, summarizes the thread, and suggests next steps. A person still reviews and sends.
• The major CX copilots do the same core job. Intercom Copilot gives each agent "a personal AI assistant," Kustomer Envoy writes replies reps "review, edit, and send in seconds," and Gladly Team Assist drafts responses but "never sends on your behalf." Zendesk Copilot goes a step further with supervised actions under agent oversight.
• A copilot's ceiling is a faster human. Every drafted reply still reaches an agent, still requires human approval, and still counts as a ticket a person handled.
• Most copilots are a paid add-on on top of your platform: Intercom Copilot is $29 per agent/mo billed annually, $35 monthly; Zendesk Copilot is an add-on on top of a Suite seat.
• Gaia takes a different path. Gaia is the AI that coaches your AI Agent. Instead of making a human faster on a ticket, it finds where automation is missing and drafts the fix, so more tickets resolve with no human at all. Gaia is free during Open Beta, and you approve every change.

What is an AI copilot in customer service?

An AI copilot in customer service is an assistant that works alongside a human support agent inside the helpdesk. It reads the conversation and helps the agent respond faster: it drafts a reply, summarizes a long thread, pulls the right knowledge, and suggests the next step. The agent stays in control and sends the message. The industry term for this is agent assist.

That is the whole category in one line: a copilot points its intelligence at the person. It makes the human on the ticket quicker and more consistent. It does not resolve the ticket on its own, and it does not change how much your automation handles without a human.

Keep that distinction in mind, because it is the difference that matters when we get to Gaia.

What an AI copilot actually does

Strip away the branding and every CX copilot clusters around four jobs:

1. Draft replies. The copilot writes a response grounded in your help center and past conversations. Intercom pitches "instant answers" from "your support materials, wherever they are." Kustomer Envoy "generates on-brand, accurate responses so reps can review, edit, and send in seconds."
2. Summarize and catch up. It condenses a long or reassigned thread so the agent knows where things stand. Gladly Team Assist offers "Catch me up" and "Summarize this customer" out of the box.
3. Surface context and next steps. It brings customer history, order data, and a recommended next action into the ticket. Kustomer Envoy surfaces "complete customer context" and "next best actions."
4. Take supervised actions (some tools). A few copilots act, with the agent watching. Zendesk Copilot can trigger actions like updating fields and processing returns under agent oversight.

Copilots cut onboarding time, lower handle time, and keep tone consistent. If your goal is to help the agents you already staff move faster, a copilot earns its keep.

The copilot ceiling: it makes the human faster, not the AI better

Here is the catch. A copilot removes keystrokes, not tickets.

Every reply a copilot drafts still lands in front of a person who reads it, edits it, and hits send. That ticket still reaches an agent and consumes handle time. You are typing faster, but you are still staffing for the same volume. When your automation rate plateaus, a copilot does nothing to lift it, because a copilot was never pointed at the automation. It was pointed at the human.

That is the ceiling of agent assist: the best case is a faster team, not a smaller queue.

The major CX copilots, compared

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A few call-outs:

• Zendesk does more than draft. Its Copilot takes supervised actions, so if you want the copilot to act inside the ticket, it is ahead of a draft-only tool. The action still happens with a human in the loop.
• Kustomer bundles it in. Envoy now ships inside Kustomer's higher tiers, which takes the "you pay extra for the copilot" objection off the table.
• Gladly is explicit about the boundary. "Team Assist never sends on your behalf" sums up the whole category: the AI drafts, the human sends.

Comparing Gorgias to a specific one? See our head-to-head pages for Zendesk, Intercom, Kustomer, and Gladly.

A different approach: Gaia, the AI that coaches your AI Agent

At Gorgias, we took a different bet. Instead of a copilot to make agents faster, we built Gaia: an AI coaching engine that makes your AI Agent better.

The AI Agent is the part of Gorgias that resolves tickets on its own, in two roles: a Shopping Assistant before the sale and a Support Agent after it. Gaia sits above it with one job: keep raising how much the AI Agent resolves without a human.

How Gaia works, in plain terms:

• It reads your real ticket data. Gaia analyzes your conversations, skills, knowledge, and actions to find where automation breaks down: recurring requests you are not covering, unnecessary escalations, and knowledge gaps.
• It is proactive, not on-demand. A copilot waits until an agent opens a ticket. Gaia surfaces what is missing across your AI Agent setup before it costs another month of handle time, and runs one-click audits.
• It drafts the fix. Gaia does not hand you a to-do list. It recommends or generates new Skills and Actions for you to review.
• You stay in control. Every change Gaia proposes needs your explicit approval before it goes live.

The one-line version: a copilot helps people do the work, while Gaia helps your AI Agent do the work better over time.

Gaia completely transformed how we manage our knowledge base. We had it audit our existing content. It flagged duplications and identified missing information. Within minutes it generated a detailed, ready to use draft. All I had to do was review, test, and click publish.

Nicole Parker, Senior Manager of Customer Service at Bare Necessities and PJ Salvage

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Copilot vs coaching engine: the real difference

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Making a human faster is linear: you save seconds per ticket and still staff for every ticket. Making your AI Agent better compounds: every coaching pass lifts resolution, so the queue shrinks instead of the clock.

Which one is right for you?

Be honest about the job you are hiring for.

• If your priority is helping the agents you already have move faster on the tickets that reach them, a copilot is a reasonable buy. Agent assist has a real place, and it is somewhere Gorgias will go too.
• If your priority is resolving more with no human touch, a copilot will not get you there, because it never touches the automation. Coach the AI that resolves tickets, not the person cleaning up after it.

A copilot helps your team clear the queue. Gaia is built to shrink it.

Try Gaia free in Open Beta or book a Gorgias demo to see AI Agent and Gaia together.

Most ecommerce brands treat customer support as a post-purchase problem. It isn't. Plenty of tickets are written long before checkout the moment a shopper can't find an answer, second-guesses the price, or doesn't trust what they're looking at.

At Blend Commerce, we see the same pattern again and again: when a product page answers buyer questions clearly, conversion goes up and support tickets go down.

This article breaks down seven conversion rate optimization (CRO) A/B tests you can run to head off support tickets before they happen built on real buyer psychology, hands-on testing, and patterns we see across high-performing Shopify stores.

TL;DR

If your product pages answer these six buyer questions:

1. Will it fix my problem?
2. Is it worth the price?
3. Do other people love it?
4. What's my risk if it flops?
5. How long will it take to arrive?
6. Can I get help fast?

You'll get:

• Fewer pre-purchase tickets
• More confident buyers
• Stronger conversion rates
• Less pressure on your Gorgias inbox

The tests below show you how.

Download: The Anatomy of the Perfect Product Page

Why most customer support tickets start on the product page

Before we jump into the tests, its important to understand why users contact support in the first place.

More tickets happen because users:

• Cant find key information
• Want reassurance before buying
• Dont trust pricing or value
• Are unsure about delivery or returns
• Need confirmation that help exists if something goes wron

Every one of these reasons maps directly back to unanswered buyer questions on the PDP. Thats where Conversion Rate Optimisation comes in.

The 6 buyer questions that drive support tickets and conversion

Most Where do I find? or Can you confirm? tickets dont start in support. They start on the product page when something important isnt clear enough.

Customers dont consciously think, I have a buyer question. They feel uncertainty. That hesitation is what slows conversion and drives reassurance tickets.

These six buyer questions represent the friction points your PDP must resolve:

1. Will it fix my problem?

If the outcome isnt obvious within a few seconds, users wont keep digging; they either bounce or ask support to confirm it will work for them. This is especially true when users have edge cases like compatibility, sizing, and will this work for X? Strong benefit-first messaging and visible proof mean customers dont need to double-check with your team before they buy. The page answers the question for them.

2. Is it worth the price?

Price objections rarely show up as this is too expensive in a ticket. They show up as:

• Can I get a discount?
• Is there a cheaper version?
• Why is this priced like this?

When the value isnt clearly framed, users either abandon it or look for a human to justify the price. The right CRO elements make the value feel settled before the user has time to second-guess.

3. Do other people love it?

This is the reassurance question. When social proof is thin, generic, or hard to find, users seek confidence externally, often through support. Reviews, UGC, and people like me proof stop tickets like Which one should I choose? because the crowd does the persuading.

4. Whats my risk if it flops?

Risk is the silent conversion killer and a sneaky support-driver. If returns, exchanges, warranty, or guarantees arent clear, users ask preemptive what if questions to protect themselves. When you remove ambiguity around outcomes, you reduce tickets that exist purely to manage fear.

5. How long will it take to arrive?

Shipping ambiguity drives high-volume WISMO-style (where is my order) questions before purchase, not only after. If users cant quickly find delivery cost, ETA, cutoffs, and tracking expectations, theyll ask (or leave). Clear ETA messaging reduces tickets by removing the need for can you confirm delivery to my area by X date?

6. Can I get help fast?

Even confident users hesitate if they fear being stranded after checkout. Visible support options and expectations (how to reach you, response times, self-serve help) reduce last-mile tickets and increase conversion because users feel protected.

Lets look at the A/B tests that directly reduce support tickets by answering these questions proactively.

7 CRO A/B tests that reduce customer support tickets

1. Add product page accordions to answer key buyer questions

Buyer questions addressed:

• 1. Will it fix my problem?
• 5. How long will it take to arrive?
• 6. Can I get help fast?

Why this CRO test reduces support tickets

Users often contact support because they cannot quickly locate delivery timelines, warranty details, return policies, compatibility information, or usage instructions. When key information is buried within long product descriptions, customers escalate to chat for reassurance before committing to purchase.

Introducing structured accordion sections brings clarity to the PDP, allowing shoppers to self-serve answers instantly without leaving the page or interrupting their buying journey.

Data signals that indicate this test is needed

• Support data signal: A high volume of pre-purchase tickets related to shipping, returns, product details, or compatibility questions.
• Behavioural signal: Repeated scrolling around policy content, frequent FAQ page visits, or heatmap friction near dense information blocks.
• Conversion signal: A strong Add to Cart Rate paired with a noticeable drop-off before checkout progression.
• Qualitative signal: Customer phrases such as I couldnt find or Just checking before I buy appear consistently in chats.
• Action trigger: When clarification-based questions make up 1520% or more of pre-purchase conversations, this test is typically high impact.

When these signals appear together, testing clearly labelled accordion menus on the product page (for example, Shipping, Returns, How It Works, and FAQs) allows users to self-serve the information they need without leaving the PDP.

What to include in your A/B test:

• Accordion layout vs long-form static content
• Shipping and returns information is positioned higher versus lower on the page
• Question-based section titles (e.g. How long will delivery take?) versus generic headings

Expected impact on support and conversion

• Reduction in clarification-based support tickets
• Improved mobile usability and scannability
• Lower PDP bounce rate
• Increased checkout progression

Real-life example & test results

In this case, support data and on-site behaviour revealed that customers were struggling to locate key product information on the PDP. Heatmaps showed repeated scrolling and hesitation around dense product descriptions, while competitor analysis highlighted a clearer accordion-based structure that made answers easier to access.

To test whether clarity was the issue, we introduced a structured accordion menu that grouped critical information such as delivery, returns, and product details into clearly labelled, expandable sections. Instead of forcing users to scan long blocks of text, the updated layout allowed them to quickly self-serve the information they needed.

[Before-and-after mobile product page showing product details. The original page displays a long block of text, while the redesigned version organizes information into expandable accordion sections for description, ingredients, shipping, guarantees, and certifications, making content easier to browse.]

The results were significant. The variant delivered a +15.21% increase in Conversion Rate, a +6% increase in Add to Cart clicks, a +7.33% increase in Checkout visits, and a +22% increase in average session duration compared to the control.

The behavioural insight was clear: when information becomes easier to find, hesitation decreases. Users engaged more confidently with the page and were less reliant on support to clarify basic product details.

See the full A/B test breakdown here.

2. Test per-unit pricing to reduce pricing confusion

Buyer questions addressed:

• 2. Is it worth the price?

Why this CRO test reduces support tickets

Users frequently contact support to request discounts, verify pricing fairness, or clarify the total cost of the product over time. When only a total price is displayed, it can trigger immediate sticker shock, especially for higher-priced or subscription-based products.

Introducing per-unit or per-dose pricing reframes the decision. Instead of focusing on the total outlay, shoppers evaluate affordability in smaller, more digestible increments, such as price per day or per use. This reduces uncertainty and lowers the likelihood of pre-purchase pricing clarification tickets.

Data signals that indicate this test is needed

• Support data signal: Frequent pre-purchase tickets asking about discounts, promo codes, or pricing breakdowns.
• Behavioural signal: High product page engagement paired with a low Add to Cart Rate.
• Conversion signal: Weak subscription uptake, low Revenue per Visitor, or high drop-off after pricing exposure.
• Qualitative signal: Customer phrases such as Do you offer promo codes? or Why is this so expensive? appear consistently in chats.
• Action trigger: A noticeable increase in discount-related queries or subscription selection falling below category benchmarks.

When these patterns appear together, testing alternative pricing communication formats helps shoppers assess affordability instantly and reduces the need for reassurance from support.

What to include in your A/B test:

• Displaying price per dose alongside total price versus total price alone
• Pricing badges near the CTA versus explanatory copy below the Add to Cart button
• Price-per-day framing versus monthly cost framing
• Clear subscription savings callouts versus no explicit savings messaging

Expected impact on support and conversion

• Reduction in pricing clarification tickets
• Increased subscription opt-in
• Lower cart abandonment
• Higher Revenue per Visitor

Real-life example & test results

Support data and on-site behaviour revealed consistent pricing confusion on subscription PDPs. Customers were engaging heavily with pricing sections but hesitating before adding to cart, while support teams were fielding frequent discount and pricing clarification queries. The pattern suggested that shoppers were uncertain about the true cost of committing to a subscription.

To test whether clarity was the issue, we updated the PDP to display price per dose alongside the total price. Rather than presenting a single lump-sum figure, the revised layout introduced a clear $X per dose line directly beneath the headline price, aligning pricing communication with how customers naturally think about product usage and affordability.

[Before-and-after mobile product page showing pricing information. The updated version adds per-serving pricing beneath the main product price and call-to-action button, helping customers quickly understand product value and compare purchase options.]

The results were measurable. The variant delivered a +5.55% increase in Conversion Rate, a +8.53% increase in Add to Cart Rate, and a +6.46% increase in sessions reaching checkout compared to the original layout.

The behavioural insight was clear: when pricing is reframed in smaller, contextual increments, shoppers assess affordability faster and with more confidence. Reducing cognitive friction around cost lowered hesitation and reduced the need for customers to contact support to validate pricing or perceived value.

3. Use reviews to reduce choice paralysis and support questions

Buyer question addressed:

• 3. Do other people love it?

Why this CRO test reduces support tickets

Customers frequently contact support not because something is broken, but because they are unsure which product to choose. When browsing a category with multiple similar SKUs, the absence of visible social proof forces shoppers to seek human reassurance before committing.

Displaying star ratings and review indicators directly on collection pages introduces trust earlier in the journey. Instead of waiting for the PDP to validate popularity or satisfaction, customers can quickly identify which products are favoured by others, accelerating decision-making and reducing comparison-based support tickets. Visible ratings reduce cognitive load by signalling popularity at a glance, preventing shoppers from over-analysing similar options.

Data signals that indicate this test is needed

• Support data signal: Frequent pre-purchase tickets asking Which product is best? or requesting help choosing between similar options.
• Behavioural signal: High comparison behaviour between similar SKUs, including repeated back-and-forth navigation between PDPs.
• Conversion signal: Low collection-to-PDP click-through rate or heavy traffic to Best Sellers and Popular filters.
• Qualitative signal: Customer phrases such as What do most people buy? or Which one do you recommend? appear consistently in chats.
• Action trigger: A broad product range with limited visible differentiation or unclear popularity indicators.

When these signals appear together, testing the visibility of reviews at the collection level, rather than limiting them to PDPs, helps customers make faster, more confident decisions without needing human reassurance.

What to include in your A/B test:

• Displaying star ratings on collection cards versus no visible ratings
• Adding Customer Favourite or Top Rated badges versus a neutral presentation
• Showing review count alone versus including short rating snippets

Expected impact on support and conversion

• Reduction in comparison-based support tickets
• Faster product selection and decision confidence
• Increased collection-to-PDP click-through rate

Real-life example & test results

During a CRO audit, we identified that product reviews were not visible on collection pages, leaving shoppers without immediate confidence signals while browsing. In a category where trust and product credibility heavily influence purchasing decisions, this absence contributed to choice overload and hesitation before users even reached a PDP.

To test whether earlier visibility of social proof would reduce friction, we added star ratings and review counts directly to product cards on the collection page. Instead of requiring users to click into individual products to validate quality, the updated layout made ratings instantly visible at a glance, allowing shoppers to compare options more efficiently.

[Before-and-after collection page displaying multiple products. The original product cards show no review information, while the updated cards include star ratings and review counts beneath each product name to increase trust and confidence.]

The impact was substantial. The experiment delivered a +23.2% increase in overall Conversion Rate and a +73.5% increase in Average Order Value across devices. Mobile performance was particularly strong, with a +69.9% increase in mobile Conversion and a +331.4% increase in mobile Revenue per Visitor compared to the control.

The behavioural insight was clear: surfacing social proof earlier in the browsing journey reduced cognitive friction and strengthened purchase confidence. When users could quickly identify highly rated products, they were more comfortable selecting multiple or higher-value items without seeking additional reassurance from support.

4. Highlight subscription benefits to reduce hesitation and support tickets

Buyer questions addressed:

• 2. Is it worth the price?
• 4. Whats my risk if it flops?

Why this CRO test reduces support tickets

Subscription hesitation is rarely about price alone. It is often driven by uncertainty around cancellation terms, pause or skip flexibility, and the perceived commitment involved. When these concerns are not addressed clearly at the point of selection, customers escalate to support to confirm their risk before proceeding.

Making subscription benefits and flexibility visible near the CTA reduces perceived commitment anxiety. Clear reassurance, such as Cancel anytime or Pause or skip at any time, lowers friction and removes the need for pre-purchase clarification tickets.

Reducing perceived commitment risk is often more impactful than increasing discount incentives, because customers fear being locked in more than they value small savings.

Data signals that indicate this test is needed

• Support data signal: Frequent pre-purchase tickets asking about cancellation policies, pausing options, delivery frequency, or subscription terms.
• Behavioural signal: High interaction with subscription selectors but low subscription uptake.
• Conversion signal: A strong skew toward one-time purchases despite competitive subscription incentives.
• Qualitative signal: Recurring customer phrases such as Can I cancel anytime? or Am I locked in?
• Action trigger: Low recurring revenue growth or subscription churn exceeding internal benchmarks.

When these patterns appear together, testing clearer subscription benefit messaging near the CTA helps remove commitment uncertainty before it becomes a support interaction.

What to include in your A/B test:

• Adding Cancel anytime or Pause or skip anytime reassurance near the subscription selector versus no visible flexibility messaging
• Displaying clear subscription benefit bullet points versus a standard toggle-only layout
• Embedding a short FAQ snippet near the selector versus requiring users to navigate elsewhere for policy details

Expected impact on Ssupport and conversion

• Increased subscription opt-in rate
• Reduction in reassurance-based support tickets
• Higher recurring revenue contribution

Real-life example & test results

In this case, subscription uptake was underperforming despite strong product interest and healthy PDP engagement. Support data revealed recurring pre-purchase questions about cancellation flexibility, pausing options, and perceived commitment risk. Churn levels also suggested that customers were either unclear about the terms at sign-up or hesitant to commit without reassurance.

To test whether clarity and risk framing were limiting adoption, we redesigned the subscription selector and surrounding PDP messaging. The revised layout made flexibility more explicit, highlighting benefits such as convenience and savings alongside clear reassurance copy like Skip, Pause, or Cancel Anytime. Instead of burying subscription details near the pricing block, the variation positioned flexibility messaging directly next to the selector so customers could evaluate value and risk instantly.

[Before-and-after mobile product page for a subscription offer. The updated design adds a bulleted list of subscription benefits, including savings, flexibility, and easy cancellation, helping shoppers better understand the value of subscribing.]

The result was a +32% increase in Subscription Orders per Visitor compared to the control, demonstrating that clearer communication of flexibility significantly improved subscription adoption.

The behavioural insight was consistent with risk psychology: when perceived commitment risk is reduced, customers are more willing to opt into recurring purchases. By addressing uncertainty at the point of decision, the test reduced hesitation and lowered the need for pre-purchase clarification from support, while simultaneously increasing recurring revenue.

5. Improve mobile navigation to reduce support queries

Buyer questions addressed:

• 5. How long will it take to arrive?
• 6. Can I get help fast?

Why this CRO test reduces support tickets

Mobile navigation friction often manifests as support tickets rather than immediate drop-offs. When key areas such as gift cards, shipping policies, returns information, or Help pages are difficult to locate on smaller screens, customers turn to chat as a shortcut.

On mobile, navigation clarity directly influences trust. If users struggle to find policy details or brand information quickly, support becomes the fallback mechanism for reassurance. Improving menu visibility and structure reduces this dependency by making essential information accessible within seconds. On mobile, clarity signals legitimacy. When navigation feels intuitive, brand trust increases before the user even reaches a product page.

Data signals that indicate this test is needed

• Support data signal: Frequent Where can I find? tickets related to policies, gift cards, or Help pages.
• Behavioural signal: High mobile bounce rate or repeated navigation attempts within the menu.
• Conversion signal: A significant mobile versus desktop Conversion Rate gap not explained by traffic quality.
• Qualitative signal: Customer feedback referencing difficulty navigating or locating information on mobile.
• Action trigger: Mobile traffic accounts for the majority of sessions (often 60%+), amplifying the impact of navigation friction.

When these patterns appear together, testing a more visual and accessible mobile navigation structure improves information discovery and reduces reassurance-based support queries during browsing.

What to include in your A/B test:

• Visual category tiles versus text-only navigation
• Increased visibility of Help, About, and policy links within the primary menu
• Reordering categories based on purchase intent and traffic behaviour

Expected impact on support and conversion

• Reduction in navigation-related support tickets
• Higher mobile engagement across the browsing funnel
• Improved mobile Conversion Rate

Real-life example & test results

Analysis of site analytics revealed that mobile users were converting at a significantly lower rate than desktop users, despite comparable traffic quality. Session recordings and heatmaps highlighted repeated menu interactions, back-and-forth navigation, and shallow browsing depth, indicating friction in the mobile experience. Support tickets further reinforced the issue, with customers frequently asking where to find gift cards, return policies, and help resources on mobile.

To address this, we redesigned the mobile navigation into a more intuitive, visually structured menu that prominently surfaced key categories, Help & Policies, and support links. Rather than relying on a text-heavy, deeply nested structure, the revised version introduced a clearer hierarchy with icon-supported navigation and essential links positioned at the top of the menu.

[Before-and-after mobile navigation menu for a coffee brand. The original menu uses text-only category links, while the redesigned menu includes category thumbnails and expanded navigation options, making product discovery faster and more intuitive.]

The redesign delivered meaningful impact, including a +18% increase in Conversion Rate, a +28% increase in Revenue per Visitor, an +8% increase in Average Order Value, and a +6% increase in Subscription Revenue per Visitor compared to the original experience.

The behavioural insight was clear: when navigation becomes frictionless, product discovery expands. The visually led structure helped users find critical information faster while also exposing them to more categories. Improved clarity not only reduced reliance on support but also increased basket depth and subscription engagement, demonstrating that navigation structure directly influences both conversion performance and revenue growth.

6. Highlight reviews that answer common buyer questions

Buyer question addressed:

• 3. Do other people love it?

Why this CRO test reduces support tickets

Even when social proof is present, users often scan reviews for specific reassurance: Does it work? Will it arrive quickly? Is it easy to use? When these answers are buried deep within long review feeds, customers turn to support for direct confirmation before purchasing.

Customers rarely read every review. They scan for proof that someone like them has achieved the outcome they want.

Highlighting reviews that explicitly address common objections brings reassurance closer to the decision point. By surfacing objection-resolving testimonials near the CTA or product title, you reduce uncertainty and limit the need for live clarification.

Data signals that indicate this test is needed

• Support data signal: Frequent reassurance-based chats asking about results, delivery times, or suitability.
• Behavioural signal: High scroll depth within review sections before Add to Cart interaction.
• Conversion signal: Noticeable drop-off after users engage heavily with reviews.
• Qualitative signal: Recurring phrases such as Has this worked for someone like me? or Is it easy to use?
• Action trigger: A strong review base, but low Add to Cart conversion relative to traffic volume.

When these signals appear together, curating and positioning objection-resolving reviews closer to the decision point reduces uncertainty and limits the need for pre-purchase reassurance from support.

What to include in your A/B test:

• Pinning an objection-answering review near the CTA or product title versus leaving reviews in chronological order
• Adding a Most Helpful Review badge versus a neutral presentation
• Displaying a short before-and-after or results-focused snippet above the fold versus relying on full review feeds

Expected impact on support and conversion

• Reduction in reassurance-based support tickets
• Increased Add to Cart Rate
• Higher Revenue per Visitor

Real-life example & test results

Site analytics and session recordings revealed that users were spending significant time within the review section but progressing weakly from reviews to Add to Cart. Heatmaps showed extensive scrolling behaviour, suggesting that shoppers were actively searching for reassurance before committing. The pattern indicated that social proof existed, but it was not positioned effectively within the decision-making flow. Users were not lacking proof; they were lacking proximity to proof.

To test whether placement was the issue, we pinned a highly relevant, objection-resolving review near the CTA. Instead of requiring users to scroll deep into the PDP to validate product effectiveness, the revised layout surfaced a strong use-case testimonial at the point of decision.

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The variant delivered a +11.11% increase in Conversion Rate, a +6.48% increase in Add to Cart Rate, a +6.76% increase in Checkout Visit Rate, and a +2.78% increase in Average Session Duration compared to the control.

The behavioural insight was clear: when relevant social proof is integrated directly into the purchase decision area, hesitation decreases. Highlighting a trusted, outcome-focused review near the CTA transformed passive proof into an active confidence driver, reducing the need for reassurance-based support conversations.

Test homepage value messaging to reduce early-stage support questions

Buyer questions addressed:

• 3. Do other people love it?
• 4. Whats my risk if it flops?
• 5. How long will it take to arrive?

Why this CRO test reduces support tickets

Support tickets often originate before users ever reach a product page. When first-time visitors arrive on a homepage without immediately seeing shipping clarity, return guarantees, or support accessibility, uncertainty forms early in the journey.

If trust signals and risk-reduction messaging are not visible above the fold, customers either abandon or seek reassurance via chat. Addressing these concerns at the homepage level reduces early-stage hesitation and limits reassurance-based support interactions before product exploration even begins.

Early clarity compounds. When risk and legitimacy are established upfront, subsequent pages perform better.

Data signals that indicate this test is needed

• Support data signal: Frequent reassurance tickets from first-time visitors questioning legitimacy, delivery, or return policies.
• Behavioural signal: High homepage bounce rate or shallow session depth from new traffic.
• Conversion signal: Lower Conversion Rate from new visitors compared to returning users.
• Qualitative signal: Customer phrases such as Is this brand legit? or Whats your return policy? appear early in conversations.
• Action trigger: High volume of paid traffic acquisition without strong above-the-fold trust messaging.

When these signals appear together, testing clearer value propositions above the fold, including shipping, returns, and support availability, reduces early-stage doubt and strengthens trust before users navigate deeper into the site.

What to include in your A/B test:

• Displaying trust badges above the fold versus no visible trust reinforcement
• Featuring shipping and returns messaging within the hero section, versus burying it lower on the page
• Including a visible support availability statement versus requiring users to locate contact details elsewhere

Expected impact on support and conversion

• Reduction in early-stage reassurance tickets
• Increased trust among first-time visitors
• Higher first-session Conversion Rate

Real-life example & test results

During a CRO audit, we identified that the homepage lacked clear, above-the-fold value propositions such as shipping benefits, guarantees, and brand differentiators. For first-time visitors, particularly those arriving from paid traffic, this created an immediate trust gap. Users were being asked to explore the site more deeply before understanding what made the brand credible or distinct.

To test the impact of clearer positioning, we introduced two banner variations on the homepage.

Variant 1 emphasised broad, trust-based corporate messaging, while Variant 2 featured a more product-led value proposition strip positioned prominently near the top of the homepage. The goal was to determine whether specific, tangible differentiation would outperform general reassurance messaging.

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Before-and-after homepage comparison for a coffee retailer. The redesigned version introduces clearer, benefit-focused value propositions highlighting freshness, roast timing, and shipping speed, helping new visitors understand the brands key differentiators at a glance.

The results favoured clarity and specificity. Variant 2 delivered a +3% increase in Conversion Rate, a +5% increase in Revenue per Visitor, and a +18% increase in Subscription Revenue per Visitor compared to the original experience.

The behavioural insight was revealing: visitors responded more strongly to concrete product differentiation than to generic trust statements. When the homepage immediately communicated what made the product distinct and valuable, early-stage doubt decreased, and purchase confidence increased, without increasing reliance on support for validation.

Bonus tip: Use intent-based chat triggers to reduce low-quality support tickets

Instead of showing chat to everyone, test triggering Gorgias chat based on intent signals such as high cart value, high page depth, and known friction pages. This ensures the chat supports high-intent users without encouraging unnecessary tickets.

How CRO and Gorgias work together to reduce support tickets

Most teams use CRO to increase conversion and Gorgias to handle tickets. In reality, they are solving the same underlying problem: buyer hesitation. The best-performing brands connect them into one loop:

CRO prevents avoidable customer support tickets

A large chunk of your ticket volume is avoidable because its caused by missing clarity, not genuine support needs (pricing confusion, shipping uncertainty, basic product usage questions, reassurance). Your own A/B test list spells this out clearly: users contact support when they need missing information, trust, promo help, or confirmation that support exists.

When CRO closes those clarity gaps, Gorgias becomes more powerful because your agents spend more time on higher-value conversations rather than repeating the same answers.

Better self-serve content improves resolution time and conversion

Gorgias is basically a live feed of customer friction. The fastest way to build a high-impact CRO roadmap is to pull patterns from top ticket tags, most-used macros, and chat transcripts where users hesitate right before purchase. Those themes map almost perfectly to the 6 buyer questions and tell you which part of the PDP is causing confidence to leak.

How better self-serve content improves resolution time and conversion

When you add clearer answers on-site, like FAQs and policies, youre not only reducing tickets, but also making the tickets that still occur easier to solve because customers arrive in chat already educated. This creates shorter conversations, higher CSAT (Customer Satisfaction Score), and fewer back-and-forth messages.

Intent-based targeting turns support into a conversion tool

When CRO reduces low-intent confusion, you can reserve live chat for moments that genuinely drive conversion. This is where CRO + Gorgias becomes a revenue lever: your chat doesnt become a crutch for unclear pages, it becomes a precision assist for high-intent buyers.

The outcome: fewer tickets, better conversations

When these two work together, you dont just reduce ticket volume. You improve ticket quality and increase revenue efficiency across the funnel.

Fix the questions before they become tickets

Most support tickets do not start inside Gorgias. They start earlier, when shoppers cannot find information, feel uncertain, or need reassurance before they buy. When your site answers the six core buyer questions clearly, conversion improves, pre-purchase tickets decline, support conversations become shorter, and customers move forward with greater confidence.

The fastest way to identify where this is happening on your site is to connect CRO analysis with your support data. Patterns in ticket tags, chat transcripts, and recurring customer questions provide a powerful signal for where clarity gaps exist and which tests will have the greatest impact.

If you want a structured framework to audit your product pages against these buyer questions, we have created a free resource that breaks down the exact elements high-performing PDPs use to reduce hesitation and support volume.

Download: The Anatomy of the Perfect Product Page

TL;DR:

• Customer journeys are collapsing to a single conversation. The traditional browse-and-buy journey is giving way to AI-guided shopping that moves from discovery to purchase in a single exchange.
• 79% of brands say AI-driven conversational commerce has increased their sales and purchase rates.
• AI-only influenced orders grew 63% in a single year, from 2.7 million in Q1 to 4.4 million in Q4.
• Brands treating conversation as a revenue channel. Theyre not just a support function, generating higher AOV, shorter buying cycles, and stronger retention.

The page-based shopping experience dominated for decades. Customers would search, browse, compare, abandon, get retargeted, return, and eventually buy (sometimes).

That journey is no longer the only option.

Shoppers are turning to chat, messaging, and AI-powered tools to find what they need. Instead of clicking through product pages or reading static FAQs, they ask questions, have back-and-forth conversations, and get answers that move them closer to a purchase in real time. The path to checkout has changed, and the brands that recognize this are pulling ahead.

Read our 2026 State of Conversational Commerce Report to learn more about conversation commerce trends from 400 ecommerce decision-makers and 16,000+ ecommerce brands using Gorgias.

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The shopping journey has collapsed into a single thread

The traditional shopping journey was a solo experience. A shopper had a need, searched for options, browsed across sessions, and eventually made a decision often days later, after being retargeted multiple times. Support only entered the picture after the purchase.

[Side-by-side comparison showing traditional page-based shopping with multiple steps and drop-offs versus a streamlined conversation-led journey with AI guidance and fewer friction points.]

The conversation-led journey collapses that timeline:

1. A shopper recognizes a need and starts a conversation via chat, messaging, or a search-triggered prompt
2. An AI agent asks clarifying questions about preferences, budget, and constraints
3. The AI provides personalized product recommendations in real time
4. The shopper validates concerns about fit, compatibility, delivery, and returns, all inside the conversation
5. The shopper completes the purchase directly within or immediately after that exchange
6. The AI picks up the conversation post-purchase for order tracking and proactive support
7. A human agent steps in only when the situation calls for it

What used to take days now takes minutes. Discovery, evaluation, and purchase happen in a single thread.

Conversation is a revenue strategy, not a support upgrade

79% of brands agree that AI-driven conversational commerce has increased sales and purchase rates in their business. When brands were asked to rank the highest-return areas:

• 38% cited improved customer support efficiency
• 23% pointed to higher customer retention and loyalty
• 20% saw improved purchase rates

Those numbers reflect something important: the value of conversation compounds. Faster support reduces friction. Better retention raises lifetime value. More confident shoppers buy more often and spend more per order.

The brands seeing the biggest returns aren't just using AI to deflect tickets. They're using it to create one-to-one shopping experiences at scale.

What the data shows about AI-influenced orders

Looking at AI-only influenced orders across key verticals like Apparel and Accessories, Food and Beverages, Health and Beauty, Home and Garden, and Sporting Goods, the growth across a single year was significant.

[Quarterly bar chart showing conversations linked to orders increasing from about 2.7M in Q1 to 4.4M in Q4, with a small share influenced by AI.]

[Quarterly bar chart showing conversations linked to orders growing from about 753K in Q1 to just over 1M in Q4, with a small AI-driven portion.]

[Quarterly bar chart showing conversations linked to orders growing from about 2.05M in Q1 to 2.82M in Q4, with a small portion influenced by AI.]

[Quarterly bar chart showing conversations linked to orders increasing from about 651K in Q1 to 978K in Q4, with a minor AI contribution.]

[Quarterly bar chart showing conversations linked to orders rising from about 322K in Q1 to 509K in Q4, with minimal AI influence.]

Across industries, ecommerce brands saw AI step into conversations, reduce shopper hesitation, and drive higher QoQ conversion rates.

Learn more about AI-powered revenue generation in the full 2026 Conversational Commerce Report.

Why brands are making this a strategic priority

84% of brands say the strategic importance of conversational commerce is higher than it was a year ago. 82% agree it will be mainstream in their sector within two years.

[Statistics showing 84% of brands increased the strategic importance of conversational commerce and 82% expect AI-driven conversational commerce to become mainstream within two years.]

That shift is registering at the leadership level because of what conversational commerce does to the buying experience. Creating one-to-one touchpoints earlier in the journey drives higher AOV, shorter buying cycles, and stronger purchase rates. Shoppers who get real-time answers to their questions are more confident.

What this looks like in practice: TUSHY

TUSHY, known for eco-friendly bidets and bathroom essentials, is a useful example of what happens when you take conversational commerce seriously.

Bidets aren't an impulse purchase. Shoppers have real questions about fit, compatibility, and installation. Those questions used to go unanswered until the CX team could respond, often after the customer had abandoned the cart.

TUSHY used Gorgias's AI Agent and shopping assistant capabilities to automate pre-sales support. AI Agent engaged shoppers in real-time conversations, addressed their concerns directly, and built confidence at the moment of highest intent.

This resulted in a 190% increase in chat-based purchases, a 13x return on investment, and twice the purchase rate of human agents.

How to apply this to your strategy

You don't need to overhaul your entire operation to start seeing results. The most effective approach is to start where the impact is clearest and expand from there.

A few places to begin:

• Pre-sales chat. Identify your most common pre-purchase questions (sizing, compatibility, shipping timelines) and ensure your AI can answer them confidently and promptly.
• Product page engagement. Use proactive chat prompts triggered by page behavior to start conversations before shoppers leave.
• Post-purchase follow-up. Let AI pick up the conversation after checkout with order updates and proactive support, reducing inbound volume and building trust.
• Human escalation. Define clearly which situations require a human agent complex issues, emotional exchanges, high-stakes decisions.

Want to see the full picture of where conversational commerce is headed in 2026? Read the full report to explore the data, trends, and strategies shaping the next era of ecommerce.

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TL;DR:

• AI adoption is rapidly accelerating. 96% of ecommerce professionals now use AI in their roles, up from 69% in 2024.
• AI has moved beyond support automation. Use cases have evolved into revenue generation, personalization, and logistics.
• Brands are tying AI success to profit-and-loss outcomes. 60% of brands consider AOV a top indicator of AI effectiveness.

A year ago, ecommerce brands were still debating whether AI was worth the investment. That debate is over. Today, nearly every ecommerce professional uses AI to do their job.

The shift isn't just about adoption. It's about what AI is used for and how brands measure its impact. Support automation was the entry point. Now, AI is embedded across the full operation, from product recommendations to inventory control to real-time shopping conversations.

In our 2026 State of Conversational Commerce Report, we break down trends on AI usage among 400 ecommerce decision-makers and 16,000+ ecommerce brands using Gorgias.

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AI adoption has reached a tipping point

If we rewind 12 months ago, the industry was still split on AI. Some ecommerce professionals were excited, but most were still hesitant. In 2024, 69% of ecommerce professionals used AI in their roles. By 2025, that number reached 77%. In 2026, it hit 96%.

[Ecommerce professionals using AI: 69.2% in 2024, 77.2% in 2025, and 96% in 2026.]

The confidence numbers back it up. 71% of brands say they are confident using AI for ecommerce, and 73% are satisfied with its business impact.

In early 2025, only 30% of ecommerce professionals rated their excitement for AI at 10/10. Today, zero percent of respondents describe themselves as hesitant about AI.

[Views on AI among ecommerce professionals: 33% say its transforming their business, 50% see steady improvements, 18% say it hasnt delivered, and 0% remain hesitant.]

AI use cases now span the full ecommerce stack

Using AI in ecommerce is not new. In fact, it dates back to the 1980s with the invention of algorithms and expert systems. And if youve ever leveraged similar product recommendations or chatbots, youve already integrated AI into your ecommerce stack.

Modern AI is far more sophisticated.

With the rise of agentic commerce and conversational AI, brands began leveraging AI agents to automate the processing of repetitive support tickets. Thats still happening today, but the scope has expanded beyond the support queue.

[AI use cases in ecommerce include customer support automation (96%), product recommendations (88%), tracking updates (69%), personalization (64%), inventory control (51%), dynamic pricing (36%), and order fulfillment (18%).]

Ecommerce brands are deploying AI across every layer of their operation:

• Customer support automation: 96%
• Product recommendations: 88%
• Automated tracking and status updates: 69%
• Personalization: 64%
• Inventory control: 51%
• Dynamic pricing and discounting: 36%
• Order fulfillment: 18%

When brands were asked which channels contribute most to their AI success, conversational channels dominated. Social media messaging led at 78%, followed by SMS at 70%, and website live chat at 51%. Shoppers want fast, personal conversations, and AI is the best way to deliver that at scale.

Learn more about AIadoption, perception, and use case trends in the full 2026 ConversationalCommerce Report.

How AI is changing CX success metrics

For decades, customer support success meant fast response times and high satisfaction scores. Those are still important indicators of success, but leading brands are adding revenue-focused metrics to their dashboards.

91% of brands still track CSAT as a measure of AI's impact. But 60% now include AOV as a top indicator, and higher-revenue brands earning $20M+ are focusing on metrics like total operating expenses, cost per resolution, incremental revenue, and one-touch ticket rate.

[AI impact measured by 91% customer satisfaction, 60% average order value, and 43% resolution time.]

AI can now start a conversation, ease customer doubts, sell, upsell, and recover abandoned carts in a single conversation. When youre only measuring CSAT, youre ignoring the real ROI of conversational AI investment.

AI makes every conversational channel a storefront

Virtual shopping assistants now proactively engage shoppers, adapt to their needs in real time, and offer contextual product recommendations and upsells. When the moment calls for it, they can close the deal with a targeted discount.

Gorgias brands using AI Agent's shopping assistant capabilities nearly doubled their purchase rates and converted 2050% better than those using AI Agent for support only.

Orthofeet, the largest provider of orthopedic footwear in the US, is a concrete example of this in practice. Using Gorgias, they achieved:

• 56% of support tickets automated in 2 months
• Email response times down from 24 hours to 35 seconds
• Double-digit revenue growth without adding headcount.

What this means for your AI strategy

The data tells a clear story: AI has evolved beyond a tool for handling tier 1 support tickets. Its a core part of your revenue generation strategy.

57% of brands are already using AI for 2650% of all customer interactions, and 37% expect that share to rise to 5175% within the next two years. The brands building toward that range now are the ones who will have the operational advantage when it matters most.

The practical question isn't whether to invest in AI. It's where to focus first. Based on where brands are seeing the most impact, three priorities stand out:

• Start with high-volume, low-complexity tickets. WISMO (where is my order) inquiries, return policy questions, and order status updates are where AI delivers the fastest return. Automate these first.
• Expand into conversational channels. Social messaging and SMS are where AI is driving the most success right now.
• Connect AI performance to revenue metrics. If you're only measuring CSAT and response time, you're missing half the story. Add AOV, conversion rate, and incremental revenue to your reporting.

Want to go deeper on the full 2026 conversational commerce trends? Read the complete report for data across every major AI use case in ecommerce.

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TL;DR:

• AI is resolving tickets, not just replying. AI now handles 31% of customer interactions for ecommerce brands, and that number is expected to nearly double within two years.
• Every channel is becoming a storefront. Conversations are replacing the traditional browse-and-buy journey, with 79% of brands reporting sales from AI-driven interactions.
• AI is shortening the buying cycle. 93% of AI-influenced purchases happen within the first 48 hours of the conversation.
• CX teams are changing, not shrinking. Ecommerce brands are actively hiring for more technical roles to implement, coach, and maintain AI.
• The winning model is hybrid. AI handles volume and speed, while humans handle complexity and judgment.

The way shoppers buy online has shifted and customers are at the center.

They no longer want to scroll through product pages, dig through FAQs, or wait 24 hours for an email reply. They open a conversation, ask a specific question, and expect a useful answer in seconds. Brands that cant deliver these experiences at scale are seeing customer hesitation turn into abandoned carts and lost revenue.

This shift has a name: conversational commerce. It's the practice of using real-time, two-way conversations as your primary sales channel, through chat, AI agents, messaging apps, and voice.

What started as an experiment for early adopters has become a key growth lever, with 84% of ecommerce brands treating conversational commerce as a strategic pillar this year vs. last year.

[Bar chart showing percentage of customer interactions handled by AI: 31% in 2025 and 47% within the next two years.]

We surveyed 400 ecommerce decision-makers across North America, the U.K., and Europe to understand how conversational commerce and AI are reshaping the ecommerce landscape. These findings are complemented by aggregated and anonymized internal Gorgias platform data from 16,000+ ecommerce brands.

The State of Conversational Commerce in 2026 trends report breaks down all of the findings, including five key trends shaping the ecommerce landscape.

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Trend 1: AI is table stakes for ecommerce and its no longer just about efficiency

A few years ago, adding an AI chatbot to your site that could provide tracking links and Help Center article recommendations was a differentiator. Today, it's table stakes. McKinsey found that 71% of shoppers expect personalized experiences, and 76% get frustrated when they don't get them.

Right now, most ecommerce professionals use AI, with 93% having used it for at least 1 year. Enthusiasm is accelerating quickly, with only 30% of ecommerce professionals rating their excitement for AI at 10/10 in April 2025. Similarly, while AI adoption rose steadily year over year, it reached a clear peak in 2026.

[Bar chart showing ecommerce professionals using AI: 69.2% in 2024, 77.2% in 2025, and 96% in 2026.]

The use cases driving this adoption are practical and high-volume:

• Order tracking and status updates
• Returns, exchanges, and refund requests
• Shipping FAQs and delivery estimates

[Bar chart showing AI use cases across ecommerce: customer support automation (96%), AI product recommendations (88%), automated tracking updates (69%), AI personalization (64%), inventory control (51%), dynamic pricing (36%), and order fulfillment (18%).]

These are the tickets that flood brands inboxes every day. AI agents resolve them instantly, without pulling teams away from conversations that actually require human judgment.

Explore AI adoption and use case data in more depth in the full report.

Trend 2: Conversations are the new path to checkout

The traditional ecommerce funnel, visit site, browse products, add to cart, check out, is losing ground. Shoppers now discover products on Instagram, ask questions via direct message, and complete purchases without ever visiting a website.

[Side-by-side comparison of page-based and conversation-led customer journeys, highlighting AI-driven real-time recommendations, proactive information, and post-purchase support within a single conversation.]

Conversational AI is actively increasing revenue, with 79% of brands reporting that AI-driven interactions have increased sales and conversion in their business.

[Bar chart showing percentage of customer interactions handled by AI: 31% in 2025 and 47% within the next two years.]

The practical implication is that every channel is becoming a storefront. Creating personalized touchpoints with customers earlier in the journey, through proactive engagement, is impacting the bottom line.

Read the full report to explore how AI conversions have increased QoQ by industry.

Trend 3: AI is accelerating the purchase cycle

Pre-purchase hesitation is one of the biggest conversion killers in ecommerce. A shopper lands on your product page, has a question about sizing or compatibility, can't find the answer quickly, and leaves. That's a lost sale that had nothing to do with your product.

Conversational AI changes that dynamic. When a shopper can ask a question and get an accurate, personalized answer in real time, the friction disappears.

Brands using Gorgias saw this play out at scale in 2025. When AI Agent recommended a product, 80% of the resulting purchases happened the same day, and 13% happened the next day.

[AI chat interface recommending apparel items based on cart contents, alongside statistic stating 93% of purchases occur within 48 hours of an AI agents recommendation.]

Brands are further accelerating the buying cycle through proactive engagement. On-site features such as suggested product questions, recommendations triggered by search results, and Ask Anything input bars drove 50% of conversation-driven purchases during BFCM 2025.

Explore how AI is collapsing the purchase cycle in Trend 3 of the report.

Trend 4: AI is making CX teams more technical

There's a persistent narrative that AI is making CX teams redundant. The data tells a different story. 62% of ecommerce brands are planning to grow their teams, not cut them. But the scope of those teams is changing.

[Bar chart of expected headcount changes over 12 months: 21% increase significantly, 41% increase somewhat, 28% stay the same, 9% decrease somewhat, and 1% decrease significantly.]

New roles are emerging around AI configuration and quality assurance. Teams are investing in technical members to write AI Guidance instructions, develop tone-of-voice instructions, and continuously QA results.

CX teams are also bridging the gap between support goals and revenue goals, as the two functions increasingly overlap.

[Donut chart indicating 77% of companies report at least some convergence between support and sales functions due to AI.]

The result is CX teams that are more technical than they were before. Agents who once spent their days answering repetitive tickets are now spending that time on higher-value work: complex escalations, VIP customer relationships, and improving the AI systems and knowledge bases that handle the volume.

Learn more about the evolution of CX roles in Trend #4.

Trend 5: The future is hybrid: AI-first, humans when it counts

Despite increasing AI adoption, data shows that ecommerce brands shouldnt strive for 100% automation. Winning brands are building systems in which AI handles repetitive tier-1 tickets, and humans handle complex, sensitive cases.

[Chart showing which inquiries are handled by AI vs. humans.]

AI handles speed and scale. It resolves order-tracking requests at 2 a.m., processes return-eligibility checks in seconds, and answers the same shipping question for the thousandth time without compromising quality.

Human agents handle conversations that require context, empathy, or decisions that fall outside the standard playbook. There are several topics where shoppers still prefer human support.

[Bar chart showing customers prefer human support for order issues (54%), product advice (35%), and returns or refunds (24%).]

Successful hybrid systems require continuous iteration, meaning reviewing handover topics, Guidance, and reviewing AI tickets on a weekly basis.

Discover how leading brands are balancing human and AI systems in Trend #5.

Where conversational commerce is heading by 2030

The 2026 trends are about expansion and standardization. The 2030 predictions are about what comes next.

[Bar chart showing brand expectations by 2030: 89% expect AI voice purchasing, 29% expect AI multilingual support, and 19% expect proactive AI upsells and cross-sells.]

Voice-based purchasing is the biggest bet on the horizon. Only 7% of brands currently use voice assistants for commerce, but 89% expect it to be standard by 2030. The vision is a customer who can reorder a product, check their subscription status, or manage a return entirely over the phone.

Proactive AI is the other major shift. Rather than waiting for a customer to reach out, AI will anticipate needs based on browsing behavior, purchase history, and where someone is in their relationship with your brand. Think of it as the digital equivalent of a sales associate who remembers what you bought last time and knows what you're likely to need next.

Explore where ecommerce brands are allocating their AI budgets in the full report.

Start building your conversational commerce strategy today

The brands winning in 2026 are creating smart, scalable systems where AIhandles volume and humans handle nuance. Theyre treating every conversational channel as an opportunity to serve and sell.

The data is clear: AI adoption is accelerating, customer expectations are rising, and the revenue impact of getting this right is measurable.

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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 Cornbreads 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 Cornbreads 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 Gorgiass Shopping Assistant helps them turn browsers into buyers.

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TL;DR

• Ecommerce teams are overwhelmed by app overload, with 42% using six tools dailyleading to inefficiencies, rising costs, and poor customer experiences.
• Top brands are consolidating their tech stacks, choosing deeply integrated, ecommerce-specific platforms to simplify operations and lower costs.
• AI adoption and excitement are accelerating, with 77.2% of ecommerce professionals using AI daily and 55.3% rating their excitement as an 8-10 out of 10.
• Gorgias powers AI-driven support and sales in one platform, through a single workspace that helps ecommerce teams resolve faster, sell smarter, and operate more efficiently.

At Gorgias, we work with over 16,000 ecommerce brands and one common challenge emerges over and over:

Ecommerce tools are essential, but too many tools becomes a burden.

With different teams responsible for different functions, brands risk creating a disconnected tech stack that causes inefficiencies, reduces productivity, and ultimately impacts profitability.

Ecommerce teams are shuffling between tabs, copying and pasting order numbers, searching for customer data, and trying to piece it all together. Its not only inefficientits expensive, frustrating, and unsustainable as you scale.

So we dug into that data.

Our 2025 Ecommerce Trends Report surveyed ecommerce professionals across industries and job roles to understand what they really think about tech stacks and AIs role in it.

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Ecommerce professionals are approaching app overload

There is now an ecommerce app for every possible use case a brand could need. But as businesses adopt new technologies for each part of their customer journey, their teams end up working out of dozens of platforms.

The study found that 42.28% of ecommerce pros use at least six apps daily to perform their role. Regardless of the number of apps used, integration and compatibility are a must. When technologies dont talk to each other, you spend time context-switching instead of focusing on customer experience.

[42.28% of users rely on at least six ecommerce apps daily to do their job.]

For Audien Hearing, Gorgiass open API allowed them to create an integration with its warehouse software to manage returns directly in Gorgias rather than a shared Google spreadsheet. This integration helped them reduce returns by 5%, protecting their margins and leading to higher customer satisfaction.

Read more: How Audien Hearing Increased Efficiency for 75 Agents and Reduced Product Returns by 5%

Consolidation is the smartest move a brand can make

The most successful ecommerce brands arent necessarily using more toolstheyre using smarter tools. Leading businesses are opting for platforms that are deeply integrated, AI-compatible, and built specifically for ecommerce needs.

A growing tech stack also comes with a growing tech budget. Each new app has new costs, including subscriptions, set-up, management, and development fees. They quickly add up.

Nearly 40% of ecommerce professionals spend $5,000 to $50,000 annually on their tech stack.

[38.84% of ecommerce brands spend $5,000 to $50,000 annually on their tech stack; 20.33% spend $50,000 to $250,000.]

Different roles have different priorities

We asked ecommerce professionals what they actually value in their tools. Unsurprisingly, the answer changed based on who we were talking to.

Top tool benefits included:

• Revenue growth for support leaders, revenue-focused roles, and founders
• Simplified workflows and time savings for support agents

Theres a clear difference between what ecommerce leaders and agents value in a tool and considering both is key to success.

[30.9% of respondents consider revenue growth the top factor when evaluating a tools usefulness.]

Why brands hesitate to consolidate

Despite the benefits of using fewer, well-integrated tools, there are a few things that hold brands back from consolidating their tech stacks.

We asked respondents:

What, if any, are the biggest deterrents to consolidating your tech stack?

Top concerns are:

1. Compatibility issues with existing software (52.4%)
2. Direct costs (fees for software licenses, subscriptions, or customizations) (47.6%)
3. Time required (40.3%)

[Top deterrents to tech stack consolidation: compatibility issues (52.4%), direct costs (47.6%), and time required (40.3%).]

AI adoption is acceleratingand its driving results

AI is dominating the world of ecommerce. It impacts every aspect of the customer journey, from brand discovery to the post-purchase experience. AI is actively reshaping the way ecommerce professionals work, so we wanted to know how they feel about it.

• 77.2% of ecommerce professionals use AI and automation to perform their role in 2025, compared to 69.3% in 2024.
• 55.3% of respondents rate their excitement for AI as an 8-10, compared to 45.6% in 2024.

Despite growing usage and excitement, teams still have their concerns with AI:

[In 2025, top concerns about AI in ecommerce are: not resolving questions (41.5%), causing frustration (16.3%), lack of personalization (15.4%), and no concerns at all (13.8%).]

Read more: 8 AI Trends in Ecommerce: Whats Changing and How to Prepare

The AI shiftFrom just a support tool to a sales engine

The most impactful use cases weve seen arent just about reducing support ticket volume. AI is now driving revenue, increasing conversion rates, and enabling 24/7 coverage without expanding headcount.

Gorgiass AI Agent is now capable of virtual sales assistance through personalized product recommendations, dynamic discounts to reduce cart abandonment, and cross-sells and upsells.

Top brands are already leveraging these new capabilities and seeing results. For example:

• TUSHY uses a Gorgias AI Agent to accurately answer pre-sale questions, leading to a 15% conversion rate, with AI Agent driving 2x more sales than human agents.
• Trove Brands enabled an AI Agent named Wally that cut misshipments by 70%, meaning reducing unwanted charges, avoidable fulfillment costs, high return rates, and customer frustration.
• Caitlyn Minimalist uses AI to reduce response times by 99% and saw a 150% increase in ticket conversions.

Gorgias: A one-tab tool for Conversational AI

We asked one final question to make ecommerce folks really reflect on how they work:

How many tabs do you currently have open?

The average ecommerce professional works with 22 open tabs. Were not here to judge, but if youre looking to close a few of those tabs, Gorgias might be what youre missing.

Gorgias replaces all that complexity with a single workspace. From support to sales, order management to automation, it all happens inside one platform.

Ecommerce businesses can now leverage Gorgiass Advanced AI for both support and sales. Within the same AI Agent, ecommerce brands can

• Fully resolve customer inquiries, not just respond
• Provide order tracking details
• Cancel, edit, and manage orders in Shopify and integrated apps
• Process returns and exchanges
• Provide tailored product recommendations
• Drive conversions
• And much more!

Get your copy of the 2025 Ecommerce Trends Report

This blog just skims the surface of what we uncover in our 2025 Ecommerce Trends report.

Want the full story?

Download the complete 2025 Ecommerce Trends: AI Adoption & Smarter Tech Stacks report to access:

• Data-backed insights and trends from ecommerce professionals
• The biggest barriers to consolidationand how to overcome them
• Real-world case studies from leading brands like TUSHY, Caitlyn Minimalist, and July that are winning with AI
• Tactical recommendations to future-proof your ecommerce stack through AI-powered consolidation

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Not sure where to go once the Black Friday and Cyber Monday excitement settles down? Don't worryyou're not alone. Many ecommerce brands will celebrate the uplift in sales from the four-day shopping frenzy without realizing there's a huge opportunity to keep the momentum going.

The holiday shopping season is your final chance to drive sales, delight customers, and end the year strong. These five proven campaigns will help you capture last-minute shoppers, increase repeat purchases, and maximize results.

1. Gift finder campaign: make gifting easy

Help shoppers find the perfect gift or gift bundle by linking existing resources in the campaign or offering pre-sales assistance through a conversational campaign (recommended).

Shoppers love personalized recommendations. A gift finder campaign highlights curated suggestions that simplify their decision-making.

• How it works: Trigger a message when shoppers spend over 15 seconds on a product page.
• Example message: Looking for the perfect gift? This [product] is a fan favorite and pairs perfectly with [accessory]. Complete your gift with just one click! OR;
• Example message #2: Not sure what to get? We can help! Click here to chat with us about finding the perfect gift.
• Why it works: Gift bundles increase average order value (AOV) by 20%.

Pro tip: Highlight top-rated products or seasonal bestsellers to build trust.

[A campaign message that says, "Need help finding the perfect gift? Chat with us to find the ideal match."]

2. Last-minute shopper campaign: create urgency

Encourage quick action as deadlines approach.

Last-minute shoppers are in a rush, so combining urgency with a discount is the perfect motivator.

• How it works: Use an exit-intent popup during the final shopping days.
• Example message: Still need gifts? Use code HOLIDAY10 for 10% off and get delivery by [date]. Hurrytime is running out!
• Why it works: Exit-intent campaigns double engagement and reduce bounce rates by 37%.

Pro tip: You can also set a cart value threshold, ensuring that tailored offers are only provided to shoppers once theyve added the minimum to their cart. You can also use a unique discount code rather than a generic code to drive a higher CTR.

[A campaign message that says, "Special for you! Use your unique code at checkout to enjoy an exclusive discount."]

3. Post-BFCM re-engagement campaign: boost loyalty

Turn Black Friday shoppers into repeat customers.

Reconnect with returning buyers by rewarding them for their loyalty.

• How it works: Display a personalized offer when previous shoppers return to your site.
• Example message: Thanks for shopping with us on Black Friday! Heres an exclusive 15% loyalty discount for your next holiday purchase.
• Why it works: Repeat customers typically spend more, with a 10% boost in gross merchandise value (GMV).

Pro tip: Use the customers previous purchase to recommend complementary products.

[A campaign message that says, "Welcome back! Enjoy 15% off your next holiday purchase with code HOLIDAY15. Thanks for shopping with us!"]

4. AI-powered product recommendation: upsell effortlessly

Suggest relevant items to increase cart value.

Use AI to offer personalized product recommendations based on what shoppers are browsing or have in their cart.

• How it works: Show a message at checkout or when shoppers add an item to their cart, utilizing our tagging conditions to create highly targeted audience segments.
• Example message:Love [product]? Add [related item] to complete the set! Customers who bought this also loved [product 2].
• Why it works: Personalized upsell recommendations increase AOV by up to 15%.

Pro tip: Highlight frequently bought together items or exclusive bundles for the holidays.

[A GIF of an on-site campaign recommending curated products. There is a rotating carousel of related skincare products. ]

You can easily set up product recommendations shown on your cart page with Gorgias Convert:

[A screenshot of Gorgias's UI showing how to set up an engagement campaign on the cart page. It shows a user drafting copy and configuring which products to display in the on-site campaign.]

5. Holiday new arrivals campaign: spotlight seasonal exclusives

Build excitement with fresh, holiday-themed products.

Shoppers love discovering new arrivals, especially during the holiday season.

• How it works: Target homepage visitors or holiday collection browsers.
• Example message: Just in time for the holidays! Explore our exclusive [product line] and find the perfect gift.
• Why it works: Announcements for new products increase conversions by 6%.

Pro tip: Use festive visuals and emphasize limited availability to drive urgency.

[An upsell campaign that prompts customers to buy a related item by displaying the product with a link to add it to cart. ]

Dont forget to A/B test your campaigns

Testing helps you discover what works best for your audience. Experiment with different offers, visuals, or CTAs to optimize results.

• Example: Test two versions of a free shipping upsell campaign.
  • Version A: Spend $15 more to get free shipping!
  • Version B: Youre $15 away from free shipping. Add [product] to your cart now!
  • Result: Version B may outperform because it includes a clear product recommendation, reducing customer effort.

Pro tip: Track key metrics like click-through rate (CTR) and AOV to measure success.

[A graphic showing a sample A/B test of two campaigns offering discounts. There is different messaging and images in each campaign. ]

These holiday campaigns are proven to boost sales and customer engagement during the busiest time of year. Use tools like Gorgias Convert to launch, personalize, and optimize your strategies seamlessly.

Dont waitend the year strong with campaigns that deliver results!

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