Conversational AI: The game changer for e-commerce

The e-commerce industry is undergoing a seismic shift. Shoppers no longer want to click through endless menus, read static FAQs, or struggle with broken search results.

They expect instant, conversational, and personalized experiences, much like those they’re accustomed to with messaging apps and voice assistants.

This is where conversational AI comes in. Powered by platforms like Skara by Salesmate, conversational AI is transforming the way customers discover products, make purchase decisions, and resolve post-purchase issues.

Seven surprising facts about conversational AI

From semantic AI search to checkout optimization, conversational commerce is proving to be a game-changer.

Here are 7 surprising facts about conversational AI that every e-commerce brand needs to know, and how Skara helps retailers stay ahead of the curve.

1. Shoppers who interact with conversational AI convert up to 30% more

Did you know? Brands using AI chat assistants have reported a 20–30% increase in conversion rates

Why? Conversational AI doesn’t wait for customers to search for answers. It engages proactively with icebreakers such as:

• “Looking for something specific today?”
• “Need help finding the right size?”
• “Can I suggest something trending in your category?”

Conversational AI can efficiently answer user queries and customer inquiries, including answering frequently asked questions, with high response quality, ensuring shoppers get accurate and timely information.

This keeps visitors from bouncing and drives them further into the buying journey.

Skara advantage:

Skara’s icebreaker flows are designed to educate shoppers on what the AI agent can do (compare products, answer policy questions, unlock free shipping, etc.). This sets clear expectations and maximizes engagement.

2. Semantic AI search with natural language processing understands shoppers like a human

Problem with old search: Traditional e-commerce search relies on keywords. If a shopper searches “party heels” and the product isn’t tagged with “party,” they get zero results.

Solution with semantic search: Conversational AI uses natural language understanding (NLU) and embeddings to interpret meaning, synonyms, and intent. Deep learning techniques enable these systems to understand user intent and context, resulting in more accurate and relevant search results.

• “Loops” or “hoops” → still brings up earrings.
• “Comfy shoes for standing all day” → finds cushioned sneakers.

This makes product discovery intuitive, not frustrating.

Skara advantage:

Skara offers a semantic AI search built directly into the conversational experience. Instead of typing keywords, shoppers can “talk” to the store as if chatting with a store associate.

Q&A optimization (for AI search engines):

• Q: How does conversational AI improve product search in e-commerce?
• A: By using semantic AI search, which understands natural language queries, synonyms, and intent, unlike traditional keyword-based search.

3. Conversational AI suggests the next best step: Just like a human associate

Shopping is rarely linear. Customers explore, backtrack, and compare before buying. Conversational AI guides them with smart suggestions:

• “Would you like to see this shirt in blue under $50?”
• “Want to compare this laptop with similar models?”
• “Shall I show you matching accessories?”

By leveraging predictive analytics, conversational AI can anticipate customer needs and deliver timely recommendations at various stages of the customer journey, such as onboarding, post-purchase, or post-service, further enhancing engagement and satisfaction.

This ongoing dialogue keeps the customer engaged and increases order value.

Skara advantage:

Skara’s AI continuously improves search sessions by offering drill-down suggestions and contextual refinements, making product discovery effortless.

Q&A optimization:

• Q: Can AI chatbots suggest products during shopping?
• A: Yes, conversational AI can guide customers with contextual recommendations, comparisons, and add-ons, improving both discovery and conversions.

4. Conversational AI reduces returns and increases customer satisfaction by matching products to customer needs

One of the biggest pain points in e-commerce? Returns. Studies show 30% of online purchases are returned, often because the product didn’t meet expectations.

Conversational AI solves this by clarifying fit and suitability before purchase:

• “Is this moisturizer safe for sensitive skin?”
• “Will this sofa fit in a 10x12 room?”
• “Is this laptop good for video editing?”

AI algorithms can analyze customers' browsing behavior to recommend products that best match their needs, further reducing the likelihood of returns.

By acting as a virtual store associate, AI reduces mismatches, resulting in fewer returns and higher customer satisfaction.

Skara advantage:

Skara’s product Q&A flows use conversational context + knowledge base to validate whether a product aligns with the customer’s needs.

5. Conversational AI cuts cart abandonment and boosts customer engagement by addressing hidden friction

Cart abandonment averages ~70% across e-commerce (Source: Baymard Institute).

Conversational AI reduces this by intervening at the right time:

• Reminding about minimum order quantities.
• Highlight when free shipping is unlocked with an extra item.
• Catching checkout issues (“Your coupon didn’t apply, would you like me to help?”).

Conversational AI applications and virtual agents proactively engage shoppers with alerts, reminders, and real-time assistance to resolve issues and recover sales.

Instead of passive reminder emails, conversational AI solves barriers in real time; before the shopper leaves.

Skara advantage:

Skara’s checkout intelligence nudges shoppers conversationally, turning abandoned carts into recovered sales.

Q&A Optimization:

• Q: How does conversational AI reduce cart abandonment?
• A: By proactively solving barriers like shipping thresholds, coupon issues, or checkout confusion in real time.

6. 60% of e-commerce support queries can be automated with AI

Most customer support queries are repetitive:

• “Where’s my order?”
• “What’s your return policy?”
• “How do I track my shipment?”

Traditionally, these queries would be handled by call centers, but now conversational AI software automates responses using advanced natural language processing and machine learning, improving efficiency and reducing costs.

Studies show 60–70% of e-commerce support can be automated. Conversational AI resolves these instantly, freeing agents for complex issues.

Skara advantage:

Skara connects directly with ecommerce tools (order tracking, returns, policies), enabling customers to self-serve without waiting in a queue.

7. Conversational AI works everywhere customers shop: Not just on websites

Shoppers expect support where they are: on WhatsApp, Instagram, SMS, or even email. Conversational AI enables true omnichannel commerce.

• Same assistant across channels.
• Context carried over (no need to repeat details).
• Unified brand voice and faster resolutions.

Conversational AI technologies enable consistent support and engagement across all customer touchpoints, ensuring seamless experiences regardless of the channel.

Skara advantage:

Skara delivers omnichannel conversational AI so brands can meet customers on their preferred channels: web, social, or messaging.

The numbers don’t lie; conversational AI is changing e-commerce forever.

From reducing cart abandonment to driving conversions and creating seamless omnichannel experiences, conversational shopping assistants are no longer optional - they’re a competitive necessity.

Platforms like Skara by Salesmate are leading this transformation by combining semantic search, proactive engagement, checkout optimization, and support automation into a single conversational layer.

The future of e-commerce is conversational. The only question is: Will your store keep up?

How to grow your e-commerce margins with conversational AI

Margins are what truly determine the health of an ecommerce business. You might celebrate record-breaking revenue months, but if your costs climb at the same pace, your bottom line stays stagnant.

In fact, ecommerce businesses today face one of the toughest balancing acts in recent history.

Customer acquisition costs are climbing, shipping fees continue to rise, returns eat away at profits, and customer service teams demand more resources as shoppers expect instant, 24/7 responses.

That’s why more brands are turning to conversational AI; not as a gimmick, but as a strategic lever to improve margins.

Implementing conversational AI is crucial for driving user engagement and operational efficiency, as it enables personalized interactions and proactive assistance that can boost conversions and sales.

Unlike traditional customer service, which is often treated as a cost center, AI-powered agents transform everyday conversations into opportunities for sales growth, operational efficiency, and customer loyalty.

With Skara by Salesmate, ecommerce brands are discovering that AI isn’t just a support tool; it’s a margin growth engine. Ongoing efforts to enhance conversational AI; by leveraging large language models and generative AI, can further improve margins and elevate customer satisfaction.

The margin squeeze in e-commerce

Margins in e-commerce are thinner than most outsiders realize. Research shows that net profit margins often hover between 4 and 10 percent, depending on the category.

That means a business earning $10 million in revenue may only walk away with $400,000 to $1 million in profit. Any fluctuation in shipping costs, return rates, or ad spending can wipe out hard-earned gains.

Consider returns alone: across e-commerce, the average return rate is 16.9%, and in apparel and footwear, it climbs past 30%.

Each return isn’t just a lost sale; it’s also reverse logistics, restocking, potential discounting, and in some cases, wasted inventory.

Add to that an average cart abandonment rate of around 70%, and you can see why even brands with strong sales volumes struggle to see meaningful profit growth.

For years, retailers tried the standard tactics: raising prices, cutting discounts, outsourcing customer service, or launching loyalty programs.

While these can help, they usually deliver only incremental results. Each extra 1% of margin becomes harder to gain, and meanwhile, costs keep creeping up.

Also read: 13 Ecommerce CRM strategies to build a profitable business.

Modern conversational AI solutions can now be deployed without requiring deep technical expertise, making them accessible to a wider range of businesses.

This is why companies are beginning to look at conversational AI differently, as a lever that impacts multiple points of the margin equation simultaneously.

Conversational AI as a margin driver

Conversational AI shifts the way brands think about digital engagement. Instead of seeing service as a cost to manage, AI turns it into a channel that directly influences profit.

Platforms like Skara do this by blending sales enablement, customer support, and intelligent search into one seamless experience.

The result? Customers get help faster, buy with more confidence, and spend more; while businesses spend less on manual labor and inefficiencies.

Take customer support. Traditionally, a brand might spend millions annually staffing agents to answer repetitive questions about shipping, order status, or returns.

With conversational AI, these routine tickets are resolved instantly. That frees human agents to focus on complex issues, while the business slashes its average cost per ticket.

At the same time, AI agents can guide customers mid-shopping, reducing abandonment and increasing average order value.

Conversational AI agents can proactively engage users by initiating conversations, providing timely updates, and assisting without waiting for prompts, which helps foster stronger customer relationships through more personalized and human-like interactions.

These combined effects directly flow into margins.

From cost center to profit lever: How Skara impacts margins

With AI-driven automation, Skara helps eCommerce brands turn every customer interaction into an opportunity to save costs, boost satisfaction, and protect profit margins.

1. Reducing customer service costs

One of the largest operational costs in e-commerce is customer service. Shoppers expect immediate answers, and without automation, businesses must scale agents in proportion to customer volume.

With AI, that equation changes. Skara automates common queries like “Where’s my order?” or “How do I return this product?” in real time, across web, WhatsApp, Instagram, and SMS.

Instead of paying $5–6 per ticket, brands see costs drop dramatically. Over thousands of interactions, those savings add up to hundreds of thousands of dollars annually.

2. Recovering abandoned carts

Cart abandonment is one of ecommerce’s biggest revenue leaks. With seven out of ten carts left behind, recovering even a small fraction creates substantial upside.

Skara intervenes before the cart is lost by spotting friction points: minimum order requirements, shipping thresholds, or coupon errors.

For example, if a shopper is $12 away from free shipping, Skara nudges them with a conversational prompt. This simple interaction not only saves the sale but also raises order value.

Unlike post-abandonment emails that often go unopened, real-time chat keeps the customer engaged when it matters most.

3. Increasing average order value

Upselling and cross-selling have always been part of retail, but most e-commerce stores rely on static “you may also like” widgets that feel impersonal.

Skara makes upselling conversational. If someone is browsing a phone, the AI can suggest a case, charger, or an upgraded model based on their preferences.

According to McKinsey, personalization can lift digital revenue by 10–15%, and conversational AI makes that personalization dynamic. More items in the cart mean better margins without increasing customer acquisition costs.

4. Reducing returns

Every return chips away at profit margins, and in categories like fashion, they can devastate profitability. Many returns stem from misaligned expectations: a product didn’t fit, didn’t match the description, or wasn’t suitable for the buyer’s needs.

Conversational AI helps by answering contextual questions before purchase: “Will this sofa fit in a 10x12 room?” or “Is this moisturizer safe for sensitive skin?”

Skara taps into product data and policies to provide clear, confident answers. The result is fewer mismatched purchases, fewer returns, and stronger margins.

5. Scaling without scaling costs

Conversational AI enables scale without linear cost growth. During peak seasons, many retailers scramble to hire and train temporary support staff.

Skara eliminates that need by absorbing query spikes automatically. It supports multiple languages and channels, making it easier to expand into new regions without hiring an entirely new team.

The ability to grow customer interactions without proportional costs means margins improve even as the business expands.

The bottom-line impact: A worked example

Let’s put the numbers together. Imagine an apparel retailer with $20M in revenue, a 70% abandonment rate, an 18% return rate, and $2M in annual support costs. With Skara’s conversational AI:

• Recovering just 5% of abandoned carts adds $1M in revenue.
• Reducing returns by 3% saves $600k in margin.
• Cutting support costs by 40% frees up $800k.
• Boosting average order value by 10% contributes an extra $2M.

That’s $4.4M in margin improvement without spending more on ads or raising prices. For a company operating at 5% net margin, that’s nearly doubling profits with one strategic shift.

Best practices for adopting conversational AI

Adopting conversational AI isn’t about flipping a switch. Successful retailers follow a phased approach:

• Start with quick wins by automating repetitive queries like tracking and policies.
• Integrate deeply into e-commerce platforms and CRMs for seamless data flow.
• Design natural interactions that feel like talking to a store associate, not a bot.
• Measure impact with metrics like AOV, ticket deflection, and return rates.
• Iterate continuously as AI learns from real customer conversations.

Retailers who treat conversational AI as a living system, not a one-time deployment, see the greatest gains.

E-commerce margins are fragile, but they don’t have to be. Conversational AI represents a step change, not an incremental fix.

By transforming customer conversations into profit drivers, brands can protect their bottom line while delivering the fast, personalized experiences customers expect.

Skara by Salesmate offers a complete conversational platform built for e-commerce: semantic search, intelligent upselling, cart recovery, product Q&A, and omnichannel support.

For retailers facing margin pressure, Skara is more than just a tool; it’s a new way to run a profitable e-commerce business.

Overcoming challenges of conversational AI technologies

While the benefits of conversational AI are substantial, implementing these systems comes with its own set of challenges.

One of the biggest hurdles is ensuring that conversational AI systems can accurately understand and respond to a wide variety of user queries, including those that are complex or ambiguous.

Achieving this level of sophistication requires high-quality training data and advanced natural language processing and machine learning algorithms.

Another challenge is managing complex queries and multi-turn conversations, where customers may ask follow-up questions or change topics mid-conversation.

Conversational AI systems must be robust enough to handle these scenarios without losing context or providing irrelevant answers.

Security and compliance are also critical considerations. Conversational AI systems must be designed to protect sensitive customer data and adhere to regulations such as GDPR and HIPAA, especially when handling personal or financial information.

By investing in advanced AI technologies, ongoing training, and rigorous compliance measures, businesses can overcome these challenges and unlock the full benefits of conversational AI, delivering efficient, accurate, and satisfying customer experiences at scale.

Developer operations

Conversational AI isn’t just transforming customer-facing interactions; it’s also revolutionizing developer operations behind the scenes.

By automating routine tasks such as code reviews, debugging, and testing, conversational AI tools help development teams work more efficiently and reduce the risk of human error.

These AI systems can provide real-time support and guidance, answering technical questions, suggesting best practices, and even flagging potential issues before they become problems.

Automating routine tasks frees up developers to focus on more strategic and creative work, accelerating innovation and improving the overall quality of software development.

Conversational AI can also enhance collaboration by facilitating seamless communication among team members, ensuring that everyone stays aligned and informed throughout the development process.

By leveraging conversational AI to streamline developer operations, businesses can boost productivity, reduce time-to-market, and maintain a competitive edge in today’s fast-paced digital landscape.

Where would e-commerce brands begin their AI agent journey?

When ecommerce brands commit to conversational AI, the first question is often: where should we begin?

Executives see the potential, smarter customer service, higher conversion rates, and scalable operations, but execution rarely starts cleanly. One team runs a chatbot pilot on the website. Another experiment with a generative campaign tool. Meanwhile, IT is quietly testing integrations behind the scenes.

The result? Isolated pilots, fragmented customer experiences, and no clear strategy.

To turn AI into real business value, ecommerce brands need clarity. The first step is to decide which department should lead the AI Agent journey.

Also read: How AI is transforming eCommerce: A new era of possibilities

The problem: Scattered AI pilots, no strategy

In today’s e-commerce landscape, AI experiments are happening everywhere:

• Product managers test conversational flows to help shoppers explore catalogs.
• Marketing teams trial AI-generated campaigns or personalized offers.
• IT investigates platforms to plug into Shopify, Magento, or BigCommerce.
• Customer service scrambles to keep up with order queries, returns, and shipping delays.
• This energy is natural. But without coordination, it creates confusion instead of transformation. Customers encounter different bots with different answers.

Teams duplicate efforts with multiple vendors. Infrastructure becomes bloated.

That’s why leadership must answer the key question: where should adoption start?

Why customer service is the best starting point

The most effective starting place is customer service.

Here’s why:

1. It’s already conversational. Customers expect to chat, email, or call when they need help. AI can enhance this without changing behavior.
2. It exposes pain points. Order delays, return issues, and unclear product details; all surface in support tickets.
3. It holds data gold. Support teams know what questions repeat, where shoppers drop off, and what frustrates them most.
4. Unlike marketing or product, support doesn’t need a new roadmap or reorganization to begin. The problems are visible, and the use cases are clear.

Skara advantage:

Skara was designed for these high-volume support use cases. From answering “Where’s my order?” instantly to handling returns or policy questions, Skara turns support conversations into faster resolutions and higher margins.

Setting the right expectations

Starting in support is powerful, but it only works if framed as a strategic shift, not a cost-cutting project.

• Support leaders should surface insights: top contact drivers, recurring inquiries, and automatable use cases.
• Executives must give them ownership, tools, and authority, not just a mandate.

This isn’t about replacing agents. It’s about redefining how the brand engages with shoppers, using AI to resolve friction points while freeing humans for complex issues.

Why support can’t do it alone

Support can’t carry AI adoption without engineering. To move from helpful conversations to resolved problems, AI Agents must integrate deeply.

That means:

• Pulling order status from Shopify or Magento.
• Applying refund rules automatically.
• Checking shipping thresholds for free delivery.
• Updating customer records in the CRM.

Skara advantage:

Skara connects with ecommerce platforms, logistics providers, and CRMs to give AI Agents the power not just to answer questions, but to take action.

Support defines the “what” and “why,” while engineering enables the “how.” Together, they create customer experiences that feel instant, intelligent, and trustworthy.

Check out: AI agents in customer experience: The next CX revolution.

How AI spreads across the e-commerce business

Once service and engineering align, early wins appear quickly: faster resolutions, happier customers, lower ticket costs. This success creates momentum. Other teams take notice:

• Product teams explore agents to handle sizing/compatibility questions or gather feedback.
• Logistics considers automating shipping updates, delay alerts, and inventory checks.
• Marketing sees the potential for conversational campaigns that guide shoppers to products in real time.

The difference this time?

Adoption spreads on a shared foundation, not in silos. Every department builds for its domain, but they build on the same orchestration layer. That’s how AI moves from pilot projects to an organization-wide capability.

Why platform ownership matters

As AI adoption expands, the risk shifts from “too little activity” to too much fragmentation. Without central ownership, you end up with:

• One bot is giving outdated return policies.
• Another set of contradicting product specs.
• A third introduces compliance risks.

That’s why leadership. Usually, the CTO or Head of Technology must own the platform strategy. Their role isn’t to slow things down, but to unify:

• One orchestration platform.
• One data foundation.
• One standard for experience.

Skara advantage:

Skara provides this orchestration layer, allowing multiple teams to build and deploy AI Agents consistently, with governance built in.

The future of e-commerce: Fully conversational

As AI matures, customer service will no longer be the sole department answering every question. Knowledge and tools will decentralize.

• A logistics AI agent will manage delivery queries.
• A product AI agent will handle specifications and fit.
• A marketing AI agent will run interactive campaigns.

Support is the launchpad, but the future is a fully conversational business. Customers won’t need to navigate menus or forms. They’ll simply ask: “Does this qualify for free shipping?” or “Where’s my order?” and Skara will resolve it instantly, across channels.

Conclusion

E-commerce leaders don’t fail at AI because of technology. They fail because they start in the wrong place.

By beginning with customer service, the department closest to friction, richest in data, and most used to conversations, brands can build momentum and prove value fast. With Skara as the foundation, this success then scales to other teams, creating a unified conversational layer across the business.

The question isn’t if you’ll start your AI Agent journey. It’s where. And for e-commerce, the answer is clear: start with customer service and scale with Skara.