TL;DR:

• Gorgias eliminated helpdesk outages by implementing multiple database connection pools with PgBouncer, achieving over 99.99% uptime.
• We accelerated data retrieval by organizing 40TB of data into 128 partitions, reducing query times to less than 4ms.
• Our streamlined incident response process with dedicated Slack channels and clear roles now resolves almost all incidents in under an hour.
• Looking ahead, we're strengthening security, doubling our SRE team, implementing production readiness reviews, and enhancing monitoring tools.

When customer service teams are at their busiest, they need a helpdesk that keeps up. That’s exactly why our Site Reliability Engineering (SRE) team has been working behind the scenes to make the Gorgias platform faster than ever.

Over the past year, we've made remarkable improvements to our platform to eliminate bottlenecks, speed up data retrieval, and reduce incidents. For you, this means fewer disruptions, faster load times, and a more reliable helpdesk experience.

Here's how we did it.

Eliminating helpdesk outages by increasing our connection pools

The challenge

Our platform relied on a single, shared database connection pool to manage all queries. Think of it as having just one pipe handling all the water flowing through your house — when too much water rushes in at once, the whole system backs up.

In practice, this meant a single surge in database requests could clog the entire system. When lower-priority background tasks got stuck, they could prevent high-priority operations (like loading tickets or running automations) from working properly. This would cause the entire helpdesk to slow down or, worse, become completely unresponsive.

The solution

Using PgBouncer, a tool that manages database connections and reduces the load on a server, we implemented multiple connection pools. Instead of relying on a single pipeline to stream all requests, we created separate "pipes" for different requests.

Like how road traffic picks up again after an exit, routing our database traffic into separate connection pools makes sure high-priority customer interactions don’t lag behind automated background tasks.

This solution is future-proof. In the event that a lower-priority task is delayed in one connection pool, other functionalities of the helpdesk will continue working because of the remaining connection pools.

The benefits

The results speak for themselves:

• Complete elimination of helpdesk-wide outages caused by connection pooling issues
• Faster response times — 99% of automated rules tasks take less than 800 milliseconds to complete from inception
• Partial degradation instead of full outages if issues do occur — at worst, only a single feature might be affected instead of your entire helpdesk

We've eliminated incidents caused by connection pool issues in the helpdesk completely. This reduced major helpdesk outage incidents by around four per year and maintained an average uptime of over 99.99%.

Speeding up the helpdesk by organizing 40TB of data into 128 partitions

The challenge

As Gorgias grew to over 15,000 customers, so did the volume of data. We’re talking data from tickets, integrations, automations, and many more. The combination of more users and data meant slower searches within the helpdesk. 

However, the amount of data was not the problem — it was how our data was organized. 

Imagine this: An enormous storage room full of file cabinets containing every piece of data. Sure, those file cabinets kept data organized, but you would still need to spend time searching through the entire room, running up and down aisles of cabinets, to find your desired file. This method was cumbersome. 

We needed a more efficient way to keep our data easy to find, especially as more customers used our platform.

The solution

The answer was database partitioning — breaking our large datasets into smaller, more manageable segments. Using Debezium, Kafka, and Kafka-connect JDBC, all managed by Terraform, we migrated over 40TB of data, including 3.5 billion tickets, without a moment of downtime for our merchants.

Instead of a giant room with thousands of file cabinets, we divided that giant room into 128 smaller rooms. So now, instead of looking for a file in one room, you know you just need to go into room number 102, which has a much smaller area to search.

This approach allows our system to quickly pinpoint the location of data, significantly reducing the time it takes to find and deliver information to users. 

Additionally, database maintenance has become more efficient. Some of the partitions can probably sit without needing to be changed at all. We just have to maintain the partitions that are getting new files, which cuts down on maintenance time.

The benefits

Better database partitioning provides several benefits:

• Faster queries — We have an average of 600 lookups or updates per second across these databases, each taking less than 4ms
• More efficient database maintenance — We halved the number of automated maintenance runs and cut each run’s duration in half
• Better scalability as our infrastructure is now equipped to handle continued customer growth

Faster resolutions with a streamlined incident response process

The challenge

When incidents occurred in the past, our response process was inconsistent, leading to delays in resolution. It was sometimes unclear who should take the lead, what immediate actions were required, and how to effectively communicate with affected customers.

Additionally, post-incident reviews varied in quality, making it difficult to prevent similar issues from happening again. We needed a standardized framework to address incidents in a timely fashion.

The solution

To streamline incident management, we introduced a replicable, automated process:

1. Dedicated Slack channels — Every incident gets its own Slack channel, ensuring our team is immediately notified.
2. Clear roles & responsibilities — We defined specific roles so every engineer knows what next steps to take.
3. Retrospectives — After each incident, we conduct thorough post-mortems to analyze root causes, identify improvements, and share learnings across teams.
4. Proactive prevention — By improving our monitoring tools, we catch potential issues earlier, reducing the likelihood of major disruptions.

The benefits

With our improved incident management process:

• Response times have decreased significantly — almost all incidents are mitigated in under an hour
• Customers receive clearer communication during incidents, including our regularly updated Gorgias Status page
• 100+ smarter preventative measures to reduce the overall incident frequency or permanently fix recurring problems

What's next: Four ways we're improving the platform experience

With more brands catching on to how essential a solid CX platform is, our team's got our work cut out for us. Here's what's on the way:

1. Enhanced security measures — We've hired a dedicated security engineer to strengthen our security infrastructure.
2. A bigger SRE team — Our Site Reliability Engineering team has doubled, allowing us to address performance issues rapidly.
3. Production readiness reviews — We're formalizing a process to audit new and existing services, ensuring they meet our reliability standards before deployment.
4. Improved monitoring — We're investing in better monitoring tools to detect and resolve potential issues before they impact customers.

Count on a reliable future with Gorgias

Gorgias will inevitably face new challenges in performance — no system is completely immune to downtime.

But we've built our architecture with the future in mind, and it’s more resilient than ever as more and more brands realize the power of conversational AI CX platforms. 

The result? A platform you can count on to help you deliver exceptional customer service, without technical issues getting in the way.

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TL;DR:

• Use AI across both support and sales. Ecommerce brands are using AI to drive revenue and efficiency by combining automation in chat, email, and customer data with personalized product guidance and upsells.
• Analyze post-purchase surveys with AI to uncover customer insights. AI quickly identifies themes, sentiment, and trends from open-ended feedback to inform product, shipping, and support decisions.
• Predict customer intent with AI before they take action. By analyzing behavior like cart activity or page views, AI can engage high-intent shoppers with personalized nudges in real time.
• Automate QA and proactive support with AI. AI reviews 100% of conversations, flags quality issues, and triggers outreach for known problems — all before customers even ask.

Shoppers aren’t just open to AI — they’re starting to expect it.

According to IBM, 3 in 5 consumers want to use AI as they shop. And a McKinsey study found that 71% expect personalized experiences from the brands they buy from. When they don’t get that? Two-thirds say they’re frustrated.

But while most brands associate AI with support automation, its real power lies in something bigger: scaling personalization across the entire customer journey. 

We’ll show you how to do that in this article.

AI for customer data 

Before AI can personalize emails, recommend products, or answer support tickets, it needs one thing: good data.

That’s why one of the best places to start using AI isn’t in sales or support — but in enriching your customer data. With a deeper understanding of who your customers are, what they want, and how they behave, AI becomes a personalization engine across your entire business.

Enriching surveys with AI

Post-purchase surveys are gold mines for understanding customers — but digging through the data manually? Not so fun.

AI can help by analyzing survey responses at scale, identifying trends, and categorizing open-ended customer feedback into clear, actionable insights. Instead of skimming thousands of answers to spot what customers are saying about your shipping times, AI can surface those insights instantly — along with sentiment and behavior signals you might’ve missed.

Try this prompt when doing this: "Analyze 500 open-ended post-purchase survey responses. Identify the top 5 recurring themes, categorize customer sentiment (positive, neutral, negative), and surface any trends related to product quality, delivery experience, or customer support."

Predicting customer intent before they even say a word

One of AI’s biggest strengths? Spotting intent.

By analyzing things like page views, cart activity, scroll behavior, and previous purchases, AI can identify which shoppers are ready to buy, which ones are likely to churn, and which just need a little nudge to move forward.

This doesn’t just apply to email and retargeting. It also works on live chat, in real time.

Take TUSHY, for example.

To eliminate friction in the buying journey, TUSHY introduced AI Agent for Sales — a virtual assistant designed to guide shoppers toward the right product before they drop off. 

Instead of letting potential customers bounce with unanswered questions, the AI Agent steps in to offer:

• Personalized product recommendations based on shopper questions
• Compatibility guidance (especially for customers unsure which bidet works with their toilet)
• Real-time installation tips and links to helpful how-to articles

With a growing product catalog, TUSHY realized first-time buyers were overwhelmed with options — and needed help choosing what would work best for their home and hygiene preferences.

“What amazed us most is that the AI Agent doesn’t just help customers choose the perfect bidet for their booty — it also provides measurement and fit guidance, high-level installation support, and even recommends all the necessary spare parts for skirted toilet installations. It’s ushering in a new era of customer service — one that’s immediate, informative, and confidence-boosting as people rethink their bathroom habits.”

—Ren Fuller-Wasserman, Sr. Director of Customer Experience at TUSHY

Forecasting revenue by segment

AI also helps you see the road ahead.

Instead of looking at retention and loyalty metrics in isolation, AI can help you forecast what’s likely to happen next and where to focus your attention.

By segmenting customers based on behaviors like average order value, order frequency, and churn risk, AI can identify revenue opportunities and weak spots before they impact your bottom line.

All you need is the right prompt. Here’s an example you can run using your own data in any AI tool:

Prompt: “Analyze my customer data to forecast revenue by segment. Break customers into at least three groups based on behavior patterns like average order value, purchase frequency, and churn risk. 

For each segment, provide:

1. A projected revenue trend for the next quarter
2. A key insight about their behavior
3. One actionable recommendation to either grow or retain revenue from that segment.”

Here’s what a result might look like:

• VIPs (Top 5% by LTV): Predicted 15% growth next quarter based on repeat behavior
• One-time Buyers: 70% churn risk flagged—time to trigger a win-back campaign
• Discount-Only Shoppers: Revenue likely to dip unless incentive strategy changes

Instead of flying blind, you’re making decisions with clarity — and backing them with data that scales.

AI for sales 

When used strategically, AI becomes a proactive sales agent that can identify opportunities in real-time: recommending the right product to the right shopper at the right moment.

Here’s how ecommerce brands are using AI to drive revenue across every part of the funnel.

Dynamic pricing that responds to the market (and the shopper)

Your prices shouldn’t be static — especially when your competitors, inventory, and customer behavior are anything but.

AI-powered pricing tools like AI Agent for Sales help brands automatically adjust pricing based on shopper behavior. The goal is to make the right offer to the right customer.

For example:

• Show a discount to a price-sensitive shopper who’s hesitating at checkout
• Recommend premium add-ons to high-LTV customers who are more likely to spend

With dynamic pricing, you can protect your margins and boost conversions — without relying on blanket sales.

Turning chat into a personal shopper (that never sleeps)

AI-powered chat is no longer just a glorified FAQ. Today, it can act as a real-time shopping assistant — guiding customers, boosting conversions, and helping your team reclaim time.

That’s exactly what Pepper did with “Penelope,” their AI Agent built on Gorgias.

With a rapidly growing product catalog (22 new SKUs in 2024 alone), Pepper knew shoppers needed help discovering the right products. Customers often had questions about styles, materials, or sizing, and if they didn’t get answers right away, they’d abandon carts and move on.

Instead of hiring more agents to keep up, Pepper deployed Penelope to live chat and email.

Her job?

• Instantly answer questions about fit, fabric, or product differences
• Guide shoppers toward the best option for their needs
• Recommend complementary products (like matching panties or bottoms)
• Free up agents to focus on higher-value 1:1 moments, like virtual fit sessions

“With AI Agent, we’re not just putting information in our customer’s hands; we’re putting bras in their hands... We’re turning customer support from a cost center to a revenue generator.”
—Gabrielle McWhirter, CX Operations Lead at Pepper

Let’s look at how Penelope performs on the floor:

Real-time recommendations

A shopper asked about the difference between two wire-free bras. Penelope broke down the styles, support level, and fabric in plain language — then followed up with personalized suggestions based on the shopper’s preferences.

Proactive engagement

Using Gorgias Convert chat campaigns, Pepper triggers targeted messages to shoppers based on behavior. If someone is browsing white bras? Penelope jumps in and offers assistance, often leading to faster decisions and fewer abandoned carts.

Intelligent upsells

If a customer adds a swimsuit top to their cart, Penelope suggests matching bottoms. No full-screen popups, no awkward sales scripts — just thoughtful, helpful guidance.

Support and sales in one

Penelope also handles WISMO tickets and return inquiries. If a shopper is dealing with a sizing issue, Penelope walks them through the return process and links to Pepper’s Fit Guide to make sure the next purchase is spot on.

By implementing AI into chat, Pepper saw a 19% conversion rate from AI-assisted chats, an 18% uplift in AOV, and a 92.1% decrease in resolution time.

With Penelope handling repetitive and revenue-driving tasks, Pepper’s team now has more time to offer truly personalized touches — like virtual fit sessions that have turned refunds into exchanges and even upsells.

Curating bundles with AI-powered sales data

Bundling is a proven tactic for increasing AOV — but most brands still rely on subjective judgment calls or static reports to decide which products to group.

AI can take this a step further.

Instead of just looking at what’s bought together in the same cart, AI can analyze purchase sequences. For example, what people tend to buy as a follow-up 30 days after their first order. This gives you powerful clues into natural buying behavior and bundling opportunities you might’ve missed.

If you’re looking to explore this at scale, you can use anonymized sales data and feed it into AI tools to surface patterns in:

• Frequently bundled items
• Follow-up purchases within a set time frame
• High-value product pairings with repeat potential

Try this prompt:

 "Analyze this spreadsheet of order data and identify product bundle opportunities. Look for: (1) products frequently purchased together in the same order, (2) items commonly bought as a second purchase within 30 days of the first, and (3) patterns in high-value or high-frequency product pairings. Provide insights on the most promising bundles and why they might work well together."

Just make sure you’re keeping customer data anonymous — and always double-check the insights with your team.

Related: Ecommerce product categorization: How to organize your products

AI for support

AI isn’t just here to deflect tickets. From quality assurance to proactive outreach, AI can elevate the entire support experience — on both sides of the conversation.

Quality checks powered by AI

Manual QA is slow, selective, and often feels like it’s chasing the wrong tickets.

That’s where Auto QA comes in. Instead of reviewing just a handful of conversations each week, Auto QA evaluates 100% of private messages, whether they’re handled by a human or an AI agent.

Every message is scored on key metrics like:

• Resolution completeness
• Brand voice
• Empathy and tone
• Accuracy

It gives support leaders a full picture of how their team is performing, so they can coach with clarity, not just gut feeling.

Here’s what brands can do with automated QA:

• Save time by focusing only on the conversations that need attention
• Ensure consistency across agents and AI with a single scoring standard
• Improve agent performance with targeted coaching and feedback
• Deliver higher-quality support that customers actually notice

Let’s walk through a real example.

Customer: “Hi, my device broke, and I bought it less than a month ago.”

Agent: “Hi Kelly, please send us a photo or a video so we can determine the issue with your device.”

Auto QA flags this interaction with:

• Communication Score: 3/5 — The agent was clear, but could have shown more empathy in tone.
• Resolution Score: Complete — The issue was addressed effectively.

Proactive support that reaches out first

Reactive support is table stakes. AI takes it a step further by anticipating issues before they happen — and proactively helping customers.

Let’s say login errors spike after a product update. AI detects the surge and automatically triggers an email to affected customers with a simple fix. No need for them to dig through help docs or wait on chat — support meets them right where they are.

Proactive AI can also be used for:

• Order delay notifications with live tracking updates
• Subscription renewal reminders
• Back-in-stock alerts with support follow-up for next steps

This saves the time of your agents because the AI will spot problems before they turn into tickets.

Understanding sentiment at scale

Your customers are telling you what they think. AI just helps you hear it more clearly.

By analyzing reviews, support tickets, post-purchase surveys, and social comments, AI can spot sentiment trends that might otherwise fly under the radar.

For example:

• Multiple reviews mention “runs small”? AI flags it, so your team can update the product description or add a sizing chart.
• A sudden rise in “frustrated” language in support tickets? Time to check if something’s off with your shipping or product quality.

Related: 12 ways to upgrade your data and trend analysis with Ticket Fields 

Personalization at scale starts with the right AI stack

Whether you’re enriching customer data, making smarter product recommendations, triggering dynamic pricing, or proactively resolving support issues, AI gives your team the power to scale personalization without sacrificing quality.

With Gorgias, you can bring many of these use cases to life — from AI-powered chat that drives conversions to automated support that still feels human. 

And with our app store, you can tap into additional AI tools for data enrichment, direct mail, bundling insights, and more.

Personalized ecommerce doesn’t have to mean more work. With the right AI tools in your corner, it means smarter work — and better results.

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TL;DR:

• AI is reshaping ecommerce, giving early adopters a competitive edge. From visual search to dynamic pricing, these tools meet rising customer expectations and drive growth.
• Conversational AI boosts support efficiency and customer satisfaction. Solutions like Gorgias's AI Agent automatically resolve up to 60% of tickets while personalizing responses across channels.
• Personalization now extends beyond product recommendations. AI is customizing everything from discounts to website layouts in real-time, creating unique experiences that convert.‍
• AI automation streamlines back-end operations for inventory and pricing. By predicting demand and adjusting prices dynamically, brands improve margins while reducing stock issues.

AI is no longer a futuristic concept associated with sci-fi movies and robots. It’s driving real change in ecommerce right now. Currently, 84% of ecommerce businesses list AI as their top priority. And it’s only getting bigger. By 2034, the ecommerce AI market is expected to hit $62.64 billion. 

Brands that use AI to improve personalization, automate customer support, and refine pricing strategies will have a major competitive edge. 

The good news? Most brands are still figuring it out, which means there’s huge potential for early adopters to stand out.

Let’s dive into the key AI trends shaping ecommerce in 2025, and how you can use them to future-proof your business.

1. Visual search

Instead of searching for keywords, shoppers can upload a photo and instantly find similar or matching products. Visual search eliminates the guesswork of finding the right words to describe an item and reduces friction in the search process. 

In 2025, improvements in computer vision and machine learning will make visual search faster. AI will better recognize patterns, colors, and textures, delivering more precise results in real-time. 

For customers, visual search simplifies product discovery while brands benefit from increased average order values. Visual search creates more opportunities to surface related products that customers might miss during manual searches, ultimately boosting conversion and revenue.

Pinterest is already doing it. With Pinterest Lens, users can take a picture on the spot to find similar products or ideas to help them with easier purchases or creative projects.

Pro Tip: Optimize product images and metadata (like color, size, and material) so your products appear accurately in visual search results. Clean, high-quality images and detailed tagging will make your catalog easier for AI to process and match.

2. Conversational AI

Conversational AI, like Gorgias’s AI Agent, already handles 60% of customer conversations. Brands that adopt it often see more than a 25% improvement in customer satisfaction, revenue, or cost reduction.

Soon, advanced natural language processing (NLP) will make it easier for customers to use text, voice, and images to find exactly what they’re looking for. These multimodal capabilities will elevate support conversations, resulting in fewer abandoned carts and support teams that can focus on more complex issues.

For example, Glamnetic uses AI Agent to manage customer inquiries across multiple channels, resolving 40% of requests automatically while maintaining a personalized touch. Their AI can automate responses to common questions, recommend products based on browsing history, and even track orders in real-time. 

Pro Tip: Invest in AI chat tools that integrate with your customer support system and sync with real-time product and order data. Your responses will be accurate and timely, without losing the personal touch.

Read more: The Gorgias & Shopify integration: 8 features your support team will love  

3. Product recommendations 

According to McKinsey, omnichannel personalization strategies, including tailored product recommendations, have a 10-15% uplift potential in revenue and retention. But with only 1 in 10 retailers fully implementing personalization across channels, there’s a massive opportunity for brands to innovate.

In 2025, AI-driven product recommendations will become even more precise by analyzing customer behavior, preferences, and purchase history in real-time. Predictive AI will adjust recommendations on the fly, showing customers the right products at the right moment.

Take Kreyol Essence as an example. They use Gorgias Convert to track customer behavior and recommend products based on past purchases and browsing patterns. When a customer buys a hair mask, AI suggests complementary products like scalp oil or leave-in conditioner — increasing average order value without feeling pushy.

Personalization boosts sales by helping customers discover products they actually want. Plus, it creates a more tailored shopping experience, which encourages customers to return.

Pro Tip: Test different recommendation strategies, like “frequently bought together” or “you may also like,” to see which ones drive the most conversions.

Learn more: Reduce Customer Effort with AI: A Smarter Approach Than Surprise and Delight 

4. Voice commerce 

In 2025, more customers may use smart speakers and voice assistants like Alexa and Google Assistant to shop hands-free. AI will improve voice recognition and contextual understanding, so it’s easier for customers to find products they want.

Instead of fumbling with a keyboard, customers will be able to say, “Order more coffee pods,” and AI will not only recognize the request but also pull up the preferred brand and size based on past orders. Less friction will make the buying process more intuitive, especially for repeat purchases.

Voice commerce expands shopping accessibility and creates a more convenient experience for busy customers. It also opens the door for brands to surface product recommendations and upsell during the conversation.

Pro Tip: Optimize product descriptions and catalog structure for voice search. Clear, simple language and detailed product tags will help AI understand and surface the right products.

5. Dynamic pricing

A recent McKinsey report suggests that investing in real-time customer analytics will continue to be key to adjusting pricing and more effectively targeting customers.

In 2025, machine learning will allow ecommerce brands to adjust product prices instantly based on demand, competitor pricing, and customer behavior. If a competitor drops their price on a popular item, AI can respond immediately, so you stay competitive without sacrificing margins.

Machine learning will also refine pricing models over time, finding the sweet spot between profitability and customer conversion.

For example, AI might detect that customers are more likely to buy a product when it’s priced at $29.99 rather than $30, and adjust accordingly. More competitive pricing means higher revenue and better margins, but it also increases customer trust when prices are consistent with market trends.

Pro Tip: Test different pricing strategies and monitor how they affect sales and customer behavior.

6. Better customer insights

According to McKinsey, AI-driven personalization and customer insights can improve marketing efficiency by 10-30% and cut costs significantly.

In 2025, AI will analyze customer data like purchase history, browsing patterns, and feedback to generate smarter, more actionable next steps. Instead of guessing what customers want, brands will have the data to predict it.

For example, Gorgias’s AI Agent for Sales can identify a shopper’s interest level and purchase intent and then use it to adjust its conversational strategy. It analyzes shopper data like browsing behavior, cart activity, and purchase history.

Here’s how it would behave for different customers:

• A browsing customer: AI Agent will ask clarifying questions
• An interested customer: AI Agent provides tailored recommendations and handles objections
• A customer with an intent to buy: AI Agent assists with checkout, payment, and nudges purchase

7. Personalized shopping 

AI-driven personalization leads to a 5-10% higher customer satisfaction and engagement. Yet, only 15% have fully implemented it across all channels — leaving a huge gap to fill.

In 2025, AI-driven personalization will go beyond product recommendations. Brands will be able to adjust website layouts based on customer preferences, highlight products that align with their style, and even customize customer service interactions.

A higher level of personalization will boost conversion rates and customer satisfaction. When customers feel like a brand “gets” them, they’re more likely to make a purchase and come back for more. 

For example, AI Agent for Sales can adjust discounts and provide smart incentives to drive sales. When adjusting for discounts, AI Agent analyzes shopper behavior, including browsing activity, cart status, and conversation context, to offer a discount based on how engaged and ready the shopper is to buy.

Pro Tip: Use AI to test different personalization strategies and refine them based on performance data. Small adjustments, like changing product order or highlighting specific categories, can have a big impact on sales. 

8. Automated inventory management

Keeping the right products in stock at the right time is about to get a whole lot easier. In 2025, AI will predict demand patterns and automate restocking decisions based on sales trends, seasonality, and customer behavior. Instead of manually tracking inventory, AI will handle it in real time to avoid stock issues.

For example, AI could notice a spike in orders for a specific product right before the holidays. It could then automatically increase stock levels to meet demand or scale back on items that aren’t moving as fast. Real-time tracking means fewer missed sales and less wasted inventory.

Efficient inventory management not only cuts costs but also improves the customer experience. When products are consistently available, customers are more likely to trust and stick with your brand.

Pro Tip: Implement AI-powered inventory management to sync data across all sales channels. This ensures accurate stock levels and seamless fulfillment, whether customers are shopping online or in-store.

Embrace AI trends in your ecommerce store in 2025

AI makes it easier for brands to deliver a personalized and efficient shopping experience. From helping customers find products faster with visual search to automating support with conversational AI, there are plenty of opportunities for personalization.  

The brands that adopt and refine these strategies now will be better positioned to meet customer expectations and stay ahead of the competition. Start by implementing conversational AI and later test some other AI trends like personalized suggestions. 

Ready to see how AI can upgrade your brand? Book a demo to see AI Agent in action.

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