20 Ways eCommerce Brands Are Using AI (Real Examples)

AI is all the buzz there is, and will be for some time to come. 

But when it comes to AI powered eCommerce, what we’re hearing isn’t a fad—know how to implement and engage AI in your day to day operations and you will have created a marked difference in revenue. 

At Convertcart, we’ve been powering up our own AI-based functionalities and helping clients weave through their own—which brings us to this list of top AI use cases that seem to be creating the most relevant impact across eCommerce businesses. 

20 AI Use Cases for eComm Stores

1. Early identification of high value customers

Unlike the traditional Recency, Frequency, Monetary model used for detecting high value customers, AI in eCommerce helps brands go beyond the basics. For one, the initial purchase value isn’t treated as the holy grail for long-term predictions. 

What’s also taken into view are actions like time spent reviewing product details, number of pages viewed before making a purchase and levels of responsiveness to post-purchase communications & updates. Similarly, cross-category exploration is something that AI can detect without fuss: for example, shoppers who come back to explore more categories within two weeks of their first visit, typically represent a higher lifetime value. 

What you can do with AI: 

👉 Identify those who make a second purchase quickly—Which product categories drive the fastest repeat purchases? How do AOV of repeaters compare to non-repeaters?

👉 Look at high-worth pre-purchase signals closely—Which visitors return to high-ticket product pages & how often? Which shoppers add high-value products to cart but don’t buy?

AI powered eCommerce example: Dollar Shave Club

Men’s grooming brand uses AI in eCommerce to ace predictive Customer Lifetime Value (pCLV) through a variety of approaches including using Retina AI to identify the most high-value customers. This also includes anticipating customer needs, offering relevant recommendations so that repeat orders go up and curating offers that’ll lead to conversions:

2. Right timing for marketing outreach 

With AI, the idea is to really get into the heart of individual browsing patterns and not stay restricted to segment-level behavior that indicates interest. Getting send, device and channel time right for marketing outreach is one of the most relevant AI in eCommerce use cases we’ve been noticing. 

Getting the timing right involves analyzing various behaviors with the help of AI, including when shoppers would be most receptive to marketing messages (for example, right after they’ve completed a first purchase) and when they would be most likely to reorder or repurchase. 

What you can do with AI: 

👉 Identify “quiet periods” to avoid outreach—Consider time preferences around geographical location, consider most active hours preferred to shop, consider preferred timing around favorite channels.

👉 Assess browse-to-buy timeframes—How many hours / days does a browse take to turn into a sale? Do ads on certain channels convert faster than those on others? How long do shoppers spend on product pages before buying?

👉 Adjust outreach frequency based on engagement—Suggest more personalized content to repeat shoppers & high spenders. Detect which channels idle shoppers engage with more. Assess engagement based on multiple factors like clicks, opens and interactions.

AI eCommerce use case example: Sephora 

In terms of triggering the right marketing material at the right time as an AI use case, Sephora aces with their Beauty Insider program. With the user behavior data that’s collected, AI tailors recommendations based on skin type, color preferences and even seasons! For example, the brand picks the right free gifts based on available data and shoots marketing messages to shoppers on key milestone dates:

Further Reading: 32 Founders Predict eCommerce Marketing Trends (2025)

3. Omnichannel data collection 

Since CX plays such a huge role in conversions, what you’d want to target first through AI is how you resolve customer data discrepancies. 

With omnichannel data collection, the idea is to aggregate all data about a customer effectively, whether they use their login info consistently or not. In this eCommerce AI use case, a brand can come up with probabilistic matching algorithms to create a single customer view across channels.

The data that reveals patterns include browsing, purchase and engagement behavior, device information and transaction data.

What you can do with AI: 

👉 Identify key touchpoints—look at higher engagement junctures like the website, live chat, funnel quiz and app to look at behavioral engagement more closely.

👉 Identify key actions—this will include assessing click-through rates on recommendations, banners and hello bars, looking at cart additions and abandonment, tracking time spent on discussing a product over live chat, mentions of response time preferences etc.

AI use case example: Nordstrom

While Nordstrom has always been at the forefront of eCommerce innovation, in more recent days, it’s their use of Generative AI that has got the world talking. In addition to a fresh homepage feel, the brand’s app now boasts of the AI powered Style Swipes feature. This helps shoppers access recommendations based on the data that’s been collected around their unique habits and preferences. 

4. Real-time dynamic pricing 

Almost 55% retailers plan to pilot some form of real-time AI-based dynamic pricing system in 2025. 

This is great news because this is one of the AI use cases in eCommerce that more brands are comprehensively looking into—because a whole lot of different & evolving data like historical sales numbers, competitor prices and demand trends can at once be assessed. 

Further Reading: 15 Critical Steps In eCommerce Competitor Analysis

What you can do with AI: 

👉  Monitor prices of rival brands—Look into web scraping tools like Octoparse or Beautiful Soup to regularly collect pricing data from competitor websites, also consider API tools like PriSync to track pricing across multiple retail brands

👉  Suggest prices based on loyalty—Apart from creating loyalty segments, define loyalty metrics like purchase frequency, purchase recency, total lifetime value etc. to apply the right % discount to the right tier

AI in eCommerce use case example: Chubbies

The shorts brand Chubbies assesses purchase patterns and accordingly prices seasonal items. They also take help in dynamic product bundling and adjusting prices based on loyalty. AI also identifies returning customers and declares more attractive pricing points for them:

5. Better customer segmentation

Did you know that campaigns sent to well-segmented audiences result in a 200% conversion boost?

And that brands enabling AI powered eCommerce are 23 times more likely to acquire new customers and 19 times more likely to achieve above-average profitability?

To bring this as one of the AI eCommerce use cases into your store, look into critical areas like search query analysis, product view duration patterns, micro-conversion patterns, cross-device behavior tracking and social media engagement. 

What you can do with AI: 

👉 Unlock answers to more complex segmentation questions—Which buyers go for full-priced items across certain categories but look for only deals in others? 

👉 Find not-so-obvious patterns between browsing & purchasing behaviors—Which buyers browse during Wednesday lunch hour but buy on Sunday evening? 

👉 Identify when shopping behavior changes—At what point does a buyer who was shopping with deals convert to a full-price payer?

👉 Recognize links between microconversions & conversions—Which buyers read educational content but never buy the products mentioned in them?

Use case example of AI in eCommerce: Allbirds

Sustainable shoe and apparel brand Allbirds uses AI tools for eCommerce to assess first purchases and predict future purchases. AI also helps them create style-specific segments, from which they again sift audiences that prioritize sustainability and those that are focused more on performance. It’s also through AI that this brand is able to generate recommendations that match weather and seasons. 

6. High quality product descriptions

AI tools for eCommerce ensure brands are able to create the most compelling product descriptions. They take into account customer reviews, the product category in question and the customer segment that will see it (in case of dynamic content). 

In fact some AI tools like Narrative AI are also able to infuse brand-consistent storytelling into the descriptions. 

What you can do with AI: 

👉 Adapt the description to local preferences & search behavior—To do this, look for tools that integrate local market data and regional search & purchase trends.

👉 Bring seasonal relevance into product benefits—Make your prompts as specific as possible to ensure the description actually focuses on what audiences place importance on.

AI use case example from eCommerce: 

Home appliance brand Dyson leverages AI to create precise yet targeted product descriptions that leave no room for doubt, even if it’s similar products that a shopper is viewing, one after another:

Further Reading: 23 Key Elements Every Product Description Page Must Have (eCommerce)

7. Targeted repeat purchase campaigns

This happens to be one of the most compelling use cases for AI in eCommerce, because while repeat purchasers make up only 8% of the total customer base, they bring in 40% of the revenue. 

Creating repeat purchase campaigns with AI can be significant also because the latter can predict optimal replenishment intervals and also fetch more specific recommendations around complementary purchases. 

What you can do with AI: 

👉 Show higher prices when browsing intent is high—And base this on patterns that AI reveals on site visits, previous purchase history and even time to purchase.

👉 Identify the most powerful purchase triggers—This includes price drops, stock level changes, cart abandonment offers, email interaction patterns and wishlist additions.

AI powered eCommerce example: Ritual

The supplement brand’s AI system tracks supplement consumption patterns and correlates them with wellness goals. The brand is able to identify when customers might experience "supplement fatigue", and the system sends personalized content about benefits already being experienced when wavering is detected. Alongside, they also conduct review sentiment analysis to identify satisfaction drivers for different customer segments:

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8. Wider recommendations with visual search

It is projected that eCommerce sites that leverage visual search could potentially improve their digital revenue by 30%. 

On the other hand, combining advanced filtering within the search alongside visual search can further improve conversions.

What you can do with AI: 

👉 Integrate Google Lens as an additional site capability—This enables instant product discovery, irrespective of whether the shopper knows what your brand is about, for all they need to do is click a picture and search for similar products.

👉 Show “the most popular” visual results when someone clicks on search—AI tools can scan and tag products based on color, style, patterns etc. - in fact a tool like Google Vision AI auto-tags products for better search relevance.

eCommerce AI use case example: Etsy

Global eCommerce marketplace engages with Vertex AI to optimize its search recommendations as well as ad models to offer listings that are both precise and visually more appealing for various customer segments. To make this more compelling, they even tag the top results as “Etsy’s Picks”:

Further Reading: eCommerce visual search: 9 smart optimization tips (+ 4 tools to use)

9. Voice-first product discovery

In the US alone, 49% of shoppers use voice search for shopping and 60% shop with smart home assisted devices. 

This is a huge playing field if you’re an eCommerce brand that wants to make this one of your compelling AI use cases. Natural Language learning ensures that AI tools are able to understand nuances behind search queries and can even take on comparison queries effectively. 

What you can do with AI: 

👉 Help discover intent across queries—AI tools look at tone, context and language patterns in addition to past behavior to fetch the most relevant results.

👉 Use fuzzy logic for misspelt words—With AI, shoppers are able to access the closest phonetic matches to what they misspelt, and this often becomes an indirect method of product discovery.

👉 Sequence similar products for comparison—This is especially helpful when your store doesn’t have an immediate comparison chart on display, or when shoppers want to buy on-the-go.

eCommerce AI use case example: Walmart

eCommerce aggregator giant Walmart has been making huge strides in the AI space, and now have their own in-house open-source AI tool that reads Walmart-specific queries more deeply. In effect, if shoppers earlier had to separately call out product preferences, now they can simply offer a themed request like “Help me plan a football watch party” for the right recommendations to flow in:

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Further Reading: 33 Scientific Ways To Improve eCommerce Product Discovery

10. Deeper email personalization

Personalized email content has 29% more unique open rates and 41% more unique click rates. 

On the other hand, personalized AND segmented email content show 46% higher open rates. 

The eCommerce world has moved from optimizing send times per segment to optimizing send times per user with AI tools. Similarly, some eCommerce brands power their emails to adjust according to live shopper behavior with the help of artificial intelligence.

What you can do with AI: 

👉 Send social proof on heavily browsed products/categories—This creates an additional impetus to buy, sometimes even more than a discount would create.

👉 Send restock alerts to those who sign up for multiple waitlists—Such emails can be compelling reminders as well as smart conversion drivers, especially during peak shopping season.

👉 Send staff picks to those who consume more educational content on-site—Serving up curated authority-backed recommendations to shoppers across the funnel can improve conversions drastically.

AI use case example in eCommerce: Allbirds

To move beyond passive email flows that depend only on shopper actions, sneaker brand Allbirds used AI to automate repurchase emails and even feature highly personalized messages in them. 

11. Automating customer support

On one hand, 73% of shoppers believe AI would improve their customer experience. 

And on the other, support agents using AI can handle almost 14% more inquiries per hour. 

AI eCommerce tools have moved way beyond offering automated answers to queries—now it’s about assessing the urgency of a request or question, looking at the sentiment of the customer and also pulling more insights from the topics being asked. 

What you can do with AI: 

👉 Show instant answers for product specifications—Tools like Tidio and Zendesk AI have the capability to learn entire catalogs so that customer questions can trigger the right answers.

👉 Personalize Whatsapp recommendations based on purchase history—AI chatbot tools like WATI and Zoko can send such personalized recommendations, which are also complementary to past buys.

👉 Generate alternative suggestions to avoid refunds—here are the steps to follow:

Step 1 → Collect data across interactions including purchases, wishlist additions, product pages visited 

Step 2 → Identify key information like product attributes & preferred categories

Step 3 → Recognize important relationships between products

Step 4 → Analyze similar products based on features, ratings & purchase patterns

AI powered eCommerce example: Casper

Mattress brand Casper has taken AI powered eCommerce to another level with their customer support chatbot Luna 2.0. This AI tool offers support beyond the first mattress purchase to include help choosing pillows, bed-frames and other products as well.

12. Predicting stock levels

While 42% of small businesses have to negotiate the problem of overstocking, real-time tracking can reduce stockouts by at least 30%. 

The most common yet compelling areas that eCommerce brands are using AI tools for stock prediction are around seasonal variations, flash sale preparation and product launch initiatives.

What you can do with AI: 

👉 Identify sudden spikes in demand—And look at factors like increasing search volume, cart additions & social media mentions.

👉 Predict when a limited edition product is about to run out—What is the real-time sales velocity like? How is social media engagement impacting conversions?

👉 Track real-time shopper behavior that shows intent—Look closely at telling behavior like repeated search queries, wishlist additions, cart activity and engagement around discounts & BOGO offers.

eCommerce AI use case example: 

eCommerce jewelry brand Pura Vida Bracelets has an extensive SKU catalog for which they take AI’s support to especially forecast demand for seasonal collections and limited edition items. The system tracks multiple touchpoints of data including historical purchases and social media engagement:

13. Customer review and sentiment analysis

4 in 10 shoppers use reviews as the main channel of online research. 

So when it comes to sentiment analysis as an eCommerce AI use case, the possibilities are endless.

When passed through an AI tool, reviews can be assessed as a whole for common complaints, preferences and requests for improvement.

Resolutions on this basis can lead to more conversions in the short run, and better customer retention in the long run. 

What you can do with AI: 

👉 Extract most-loved product features from reviews—in this case, AI builds a bridge between positive words and features / aspects that are most generally complimented - you can even use these as design elements on associated product pages.

👉 Generate a comfort / fit rating analysis—in this case, sentiment extraction in terms of words take place first, after which AI can put this into structured data for sub-themes like fit, comfort, size etc.

👉 Arrive at product improvements based on commonly flagged concerns—here are the most common areas that’ll give you the most relevant data:

- Product reviews on pages & platforms

- Chat logs & support ticket themes

- Return reason data

- Survey & feedback responses

- Q&A sections on product pages

AI use case example for eCommerce: Gymshark

eCommerce athleisure brand Gymshark uses a mix of AI and natural language processing to run sentiment analysis across social media engagement as well as website reviews. Alongside, they also integrate data from their live chat function to make the assessment more comprehensive:

Further Reading: The Founder's Guide to Customer Journey Map (eCommerce)

14. Product identification for social media promotions

The idea here is to leverage a market where 82% of shoppers use social media to discover and research about products. 

Using AI tools in eCommerce, brands can have the most engagement with posts identified, and the products featured on them brought to the forefront. AI can also come in handy to narrow down UGC that features the said products for better spotlighting by the brand, or even picking out micro-influencers. 

What you can do with AI: 

👉 Analyze search trends & social media chatter—Detect search volume fluctuations across platforms and notice trends picking up early enough to announce offers or product drops.

👉 Track micro-trends at the category level—This includes picking up interest in visual micro-trends like color, pattern etc., emotional responses to products across categories and shifts in language.

👉 Match products with the right audiences—AI can predict which categories will best appeal to which segments based on past behavior as well as which new products will fly with existing customer segments.

AI eCommerce use case example: Rothy’s

Shoe & accessory brand Rothy’s uses AI in retail to be able to instantly recognize which product features generate the highest engagement. Additionally, they also use AI to assess real-time performance data and accordingly adjust ad targeting across social media channels:

Further Reading: 15 Underutilized Social Media Ideas For eCommerce Brands

15. Smarter cart recovery strategies

In eCommerce, industry leaders are able to retrieve 10% to 14% of abandoned carts. 

And if numbers are to be believed, 45% of cart recovery emails are opened, 21% of them are clicked and 50% of the clicked ones lead to conversions. 

Which only makes smarter cart recovery one of the best AI use cases in eCommerce. And AI powered strategies go beyond “You forgot your cart”. They predict what emotional nudge will fly with which customer and at what time a shopper is not only likely to open the email but act on it. 

What you can do with AI: 

👉 Send SMSs through smart timing—This includes waiting for the right time to send SMSs segment-wise - for example, high intent buyers would act if an SMS reached them within half an hour of cart abandonment while deal hunters would like it only if there was a live discount.

👉 Fire a chatbot conversation on a favorite platform—Preferably, do this on the platform the visit right after abandoning their cart - and make sure the SMS carries invitational language, “Hey need help on that product you just left behind?”

👉 Pair cart recovery emails with UGC based on personalized preference—here are a few crucial aspects AI can track:

✔ Browsing & Cart History → What products they viewed/added to cart.

✔ Past Purchases → Have they bought something similar before?

✔ Engagement Behavior → Did they ever click on UGC-heavy emails before?

✔ Demographic & Psychographic Data → What type of content resonates the most?

AI powered use case example: Bombas

Sock brand Bombas employs AI to segment abandoned carts by customer type and purchase intent. Analyzing back-to-cart motivation is a priority for them. For first-time visitors, they might offer a welcome discount, while for returning customers, they focus on emphasizing their charitable mission (one pair donated for each pair purchased):

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16. Predicting returns based on customer data

By 2025, brands could lose anywhere between $20 and $30 billion in revenue because of returns going up as high as 40%. 

And stores miss out on 8% of repeat revenue potential every year due to poor return experience. 

Good reasons why you need AI tools for eCommerce to predict returns more effectively. 

What you can do with AI: 

👉 Detect patterns in customer behavior—Multiple forms of behavior fall into this category including frequent returns, impulsive cart additions that later generate regret, shoppers who habitually leave negative reviews etc.

👉 Monitor fit / size / ingredient complaints in reviews—Tools like Yotpo AI Sentiment Analysis and MonkeyLearn can identify common negative themes across reviews and categorize the latter into return-risk segments.

👉 Track category-specific return rates—Which customer segments seem to be making maximum returns within these categories? What is the cost impact (both direct & indirect) because of returns across these categories?

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AI powered eCommerce example: Everlane

Fashion brand Everlane’s AI system flags orders with a high probability of return based on factors like unusual size selections compared to previous purchases or combinations of items that historically lead to returns. This allows them to proactively reach out with sizing advice or product information before shipping:

17. Identifying serial returners

Slow and serial returners combined generate more than 45% of the total returns in eCommerce. 

What’s worse, over 24% of serial returners are opportunistic, repurchasing the returned items when there’s a sale.

These factors combined make this one of the most compelling AI use cases for eCommerce. 

What you can do with AI: 

👉 Identify suspicious patterns in return behavior—Following data collection around purchase history and return patterns, AI performs analysis on risk scoring and setting tags on return patterns - post this, brands can make return & shipping policy adjustments 

👉 Identify common return reasons from similar customers—What are the most common root causes for these returns?Which customers are most likely to make returns based on these causes cited in the past?

AI in retail eCommerce example: Outdoor Voices

The athleisure brand’s approach includes using AI to distinguish between legitimate returns and potentially abusive return behavior. They use this information to adjust their marketing strategies, focusing less investment on customers with consistently high return rates.

18. Improved virtual try-on experiences

Virtual try-ons lead to sales growing by almost 30%. 

In fact, with virtual try-ons, eComm brands like AVON have seen a 33% increase in AOV. 

Right from suggesting complementary products based on what has already been tried on to creating accurate digital avatars to assess proper fit, AI use cases in VR are many. 

What you can do with AI: 

👉 Improve visual rendering—AI helps in creating realistic layering when items overlap or interact with the user's body and enhances small elements like buttons, zippers, and stitching that might otherwise be lost.

👉 Make multiple relevant recommendations for trying on—AI tools can easily pick up patterns in color, texture and style and even create a fingerprint on what will sit based on previous try-ons & successful purchases.

👉 Offer before / after context for BoFu buyers—AI can generate realistic before/after comparisons showing how products transform the customer's appearance over time, while also creating contextual environments in some cases.

AI powered eCommerce example: Warby Parker

Warby Parker's Virtual Try-On uses augmented reality to show glasses on the customer's face in real-time. The AI adjusts the rendering based on lighting conditions and head movements. This creates a realistic representation of how the frames will look from different angles:

19. Behavior-based ad copy generation

AI tools can help save 30% to 50% of the initial time taken to draft ad copy. 

AI can also optimize ad copy by analyzing vast amounts of data & improving CTR by up to 50%. 
Combined, these make for a great reason why more brands ought to see ad copy generation as an excellent eCommerce AI use case. 

What you can do with AI: 

👉 Adapt ad copy to platform-specific behaviors—If Instagram users prefer straight-up facts on discounts, FB users would much rather enjoy a li’l storytelling.

👉 Analyze which product descriptions drive maximum engagement—It’s also a good idea to take a look at product descriptions from competitor brands.

👉 Identify patterns between emotional language & conversions—Mapping this back to different customer segments can be valuable.

eCommerce use case with AI example: 

Men’s shirt brand Bonobos uses AI in retail tools to analyze how customers interact with their website and which items they view, then generates personalized ad copy that highlights the specific styles or features that caught the customer's attention. Their system also adapts messaging based on whether customers are typically discount-driven or quality-focused.

20. Adjust loyalty program rewards

More than 40% of loyalty programs associate higher redemption with tailored rewards. 

On the other hand, 58% of brands see repeat purchases due to improvements in loyalty initiatives. 

With the use of AI tools in eCommerce, brands are better able to tell which reward structures would drive maximum action from individual segments and adjust rewards based on lifetime value projections.

What you can do with AI: 

👉 Identify patterns in how each segment engages with the brand—Which segment is likely to avail early access to sales and which would appreciate exclusive launches?

👉 Forecast future purchasing behavior—What emerges when internal factors like purchasing behavior meet external factors like market trends?

👉 Analyze redemption rates—this is how it might look like:

Further Reading: 14 eCommerce Loyalty Programs Backed By Science (Examples)

Before you go...

98% of visitors who visit an eCommerce site—drop off without buying anything.

Why: user experience issues that cause friction for visitors, even if the brand has been catching up on AI improvements.

And this is the problem Convertcart solves.

We've helped 500+ eCommerce stores (in the US) improve user experience—and 2X their conversions.

How we can help you:

Our conversion experts can audit your site—identify UX issues, and suggest changes to improve conversions.