30 Smart AI in Ecommerce Examples from Top Brands Winning Online
August 3rd, 2025
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Shopping online can feel overwhelming. With countless products, brands, and ecommerce sites to choose from, consumers are left to sift through endless options to find what they’re looking for. This is where AI in ecommerce comes in. Imagine this: A shopper visits your online store. They’re interested in a few products, but they don’t find exactly what they want. Instead of sticking around to browse, they leave your site for a competitor. The chances of making a sale just increased significantly. These AI in ecommerce examples illustrate how the technology can help businesses boost sales by creating personalized experiences that delight customers.
This blog will provide valuable insights on AI in ecommerce examples to help you learn how successful ecommerce brands are using AI to boost sales, personalize experiences, and scale operations, so you can apply similar strategies in your own business. Ground's ecommerce personalization platform can help you achieve this goal.
Table of Content
How is AI Personalization Transforming E‑Commerce?
A lot of issues faced by ecommerce companies can be traced back to an original sin committed by the industry, it expected customers to do the work. When buyers flocked online to find products, they were met with vague or overly technical descriptions. It was up to them to learn what separated one laptop from another or what RAM meant.
It was a big change from the highly personal and interactive experience of shopping in-store. Fast forward 25 years, and not much has changed. I recently went searching for a DSLR camera. I looked at 12 options and encountered terms like ‘24.2 megapixel DX-format CMOS sensor,’ ‘EXPEED 4 processor,’ and ‘low-light sensitivity to ISO 25600.’ All I wanted to know was if this camera would take great quality pictures for family events. But I had to do an hour of research to narrow my choices and understand a handful of terms.
How Generative AI Can Fix Ecommerce’s Low Conversion Rate
No wonder the average conversion rate in ecommerce is only 2.5% compared to 30% in-store. How do you solve a problem so pervasive and embedded in ecommerce? That’s where generative AI comes in. While AI has been used in ecommerce since day one, generative AI is a newer concept. It’s free from the flaws and biases built into past iterations of AI.
This is an era when ecommerce companies can build something better, from scratch. And because generative AI has the power to create, modify, optimize, and automate at scale, it’s a perfect candidate to atone for the original sin of ecommerce.
What is Generative AI?
Generative AI is an algorithm that can create something. As the name suggests, it generates content and information. You can think of it as a digital artist, writer, and problem solver rolled into one. Ask the algorithm a question, and it will give you an answer. Tell it to write a song and it will. Request an image of a basketball game on the moon, and it will draw it.
How Does Generative AI Work?
Generative AI operates on two elements:
Data
Data fuel every generative AI program. When given a task, the AI will scan the available data set to get an answer. That might be a relatively small amount of content on a specific website or billions of pieces of content from millions of websites.
It learns about the information it finds and how it can be put together to provide a complete, correct, and coherent answer.
Human Interaction
The program also requires human interaction. It relies on prompts to know what information to scan and what information to provide. For example, you could ask a generative AI program, “How many SKUs does the average ecommerce company sell?”
How Generative AI Learns: Data Sources and Human Feedback
The AI might use a mix of licensed data, data created by human trainers, and publicly available data to generate an answer. Generative AI algorithms also rely on humans to point out mistakes, inaccuracies, and unintelligible answers to learn and improve over time.
AI is Reshaping Ecommerce Today
AI tools can be used to speed up and automate processes, significantly lowering costs and efficiently handling tasks that humans once managed. As a result, the integration of artificial intelligence is transforming many ecommerce businesses.
Here are the main ways AI is impacting the eCommerce landscape for retail.
Customer Interaction
The way consumers interact with retailers has been influenced by AI tools, with intelligent technology that’s now able to have conversations using machine learning. A tool that many are familiar with is chatbots, which communicate with consumers and answer questions using a database of pre-programmed responses.
While a chatbot may not fully replace customer service reps, they do reduce the need for direct human interaction. AI voice recognition software also enhances customer interaction through listening and dictation.
Operational Efficiency and Personalization with Generative AI
This also provides retailers with an opportunity to speed up processes, boost user experience, and reduce the time needed for an employee to service a customer query. The list goes on, from personalized content creation to product recommendation and discovery, all of which we explore later on using examples.
Data Analysis
Another way in which AI has massively impacted the retail sector is through data analysis. AI algorithms can analyze enormous amounts of data quickly and accurately, much quicker than a human.
Data-Driven Decision Making in Ecommerce with AI
The main benefits this has on eCommerce are streamlined reporting and predictive sales forecasting, a better understanding of different customer segments, and using data on customer behavior to shape strategy.
Through data analysis, ecommerce businesses can uncover valuable insights to help optimize their business. AI tools help inform strategic decision-making, whether it’s:
Stock management
Marketing budgets
Expanding into new areas
Content Creation
No matter what industry you are in or the type of content you need support with, AI can assist you in writing it. While straightforward and well-thought-out prompts are necessary, and human oversight is still required to check and refine AI-generated content, AI offers a fast and effective solution for almost all content needs.
Some of the most popular ways retailers use AI content tools are for:
Writing SEO content
Product titles and descriptions
Advertising copy
Organic social content
General Operations
The use of AI in retail can support many other business processes and operations as well. This could include robots in warehouses that help build, sort, and pack products. Or use visual recognition tools to tackle counterfeiting.
Even dynamic pricing tools consider competitor prices, manufacturing costs, and product demand to alter prices in real-time. AI integration in retail continues to evolve with new tools that are constantly being developed, offering useful solutions for eCommerce. Retailers must grow with this changing landscape to find solutions that are suitable for them.
Related Reading
• Personalized Shopping Experience
• Best eCommerce Tools
• eCommerce Personalization Examples
30 Real AI in Ecommerce Examples That Drive Growth
1. Personalized Product Recommendations
Generative AI can accelerate the online shopping experience by creating personalized product recommendations. Instead of browsing through pages of search results to find what you need, you can ask for product recommendations and get specific answers tailored to your needs. For example, say you’re looking for a vacuum.
You could ask an AI chatbot something like, “Which vacuum is best for my home? I have a dog and live in a two-story house.” The AI will take your input and provide targeted recommendations, perhaps even listing products, their features, and pros and cons.
Driving Revenue Through AI‑Powered Personalisation in eCommerce
When it comes to eCommerce, Ground AI is the leading AI revenue driver, helping brands grow by personalizing the shopping experience. Brands using Ground grow, on average, 20% more a year. That’s $200K in extra revenue if you’re making $1M today!
With Ground, you can automatically acquire more first-time customers, ensure you're converting as many bounced sales, and increase repeat purchase revenue (more cross-selling, replenishment, and subscriptions). Book a call for a free action plan and get an ROI guarantee or your money back.
2. Tailored Recommendations for Customers
AI-powered product recommendations allow store owners to personalize customer interactions, offering more relevant online shopping experiences. This leads to higher conversion rates, larger average order values (AOV), and increased customer loyalty.
How Netflix does it: Netflix’s recommendation engine (NRE) generates $1 billion in value per year by delivering personalized content to millions of subscribers. The AI-powered engine uses algorithms to filter over 3,000 titles at once, organizing them into 1,300 clusters based on user preferences.
From Streaming to Shopping: Applying Hyper-Personalised Recommendations in E‑Commerce
By analyzing each customer’s viewing history and behavior, NRE creates hyper-personalized recommendations. This technology works seamlessly, no matter how many users are on the platform, instantly matching each individual with content they’re most likely to enjoy. This same approach can be applied to e-commerce, helping stores offer personalized product suggestions to enhance customer experience and boost sales.
3. Flexible Pricing Based on Market Conditions
Pricing your products correctly involves several factors, like competitor prices, manufacturing costs, and customer demand. Gathering and analyzing this information can be time-consuming, especially if you have a lot of products. That’s where AI becomes invaluable. AI tools help you set optimal prices in real-time by analyzing big data, including your costs and competitor pricing.
These systems can also predict when to raise prices or launch a sale based on market trends. AI can automatically adjust prices across hundreds or even thousands of products, saving time on manual updates.
Dynamic Pricing Strategies Driven by Real-Time Market Data
For example, you could increase prices when your competitors are out of stock. Customers who want a product immediately are more likely to pay a higher price if they know they can get it from your store.
How Amazon does it: Amazon uses a dynamic pricing strategy, adjusting its prices by up to 20% when competitors offer promotions or discounts. By doing so gradually and using sales forecasts, Amazon maximizes profitability while maintaining competitive prices and controlling its margins.
4. Chatbots for Enhancing Customer Service
Handling customer requests can overwhelm support teams, especially when they need to respond quickly to questions about:
Products
Checkout issues
Returns
And with customer expectations higher than ever, 90% of customers now consider a response within ten minutes essential or significant.
Using AI-powered chatbots on your website can improve customer service while taking pressure off your support team. These virtual assistants can offer support even on weekends or holidays, handling simple tasks like answering common questions, directing customers to the right resources, or allowing them to leave messages and schedule callbacks.
Enhancing Customer Support and Sales with AI‑Powered Chatbots
While chatbots aren’t entirely autonomous, they can handle up to 80% of routine inquiries, letting human agents focus on more complex issues. Chatbots can also automatically gather customer feedback by prompting users to fill out short surveys.
How Zalando does it: Zalando uses chatbots to enhance the customer experience and drive sales. Their chatbot acts as a virtual shopping assistant, helping users find fashion items, offering style advice, recommending outfits, and even assisting with ordering. This integration improves customer experience, streamlines support, and boosts sales by offering personalized guidance throughout the shopping journey.
5. Enhanced Customer Segmentation and Marketing Promotions
Effective audience segmentation is crucial for delivering personalized marketing communications. 80% of consumers prefer brands that personalize their user experiences. Segmenting your audience can have a significant effect on your marketing campaigns. Campaign Monitor found that segmented campaigns can lead to a 760% increase in revenue.
These revenue gains come from personalized marketing promotions, targeted sales strategies designed to convert each segment, and developing products that meet the specific needs of different customer groups. However, manually creating customer segments can be time-consuming. This is where AI can help. By analyzing data, AI tools can quickly create customer segments based on shared characteristics.
Eliminating Bias with AI: Discovering Hidden Audience Segments
These tools can also analyze data without preconceived assumptions, such as the belief that young males are the primary audience for video games or that women are more interested in fitness products. As a result, AI can provide a more accurate understanding of your audience. Many AI tools can even identify entirely new segments by highlighting overlooked parts of your audience.
How Spotify does it: Spotify uses AI to gain a deep understanding of each user’s musical preferences, continually adapting and refining its recommendations without relying on biases. The AI algorithms consider various factors, including listening history, favorite genres, preferred artists, and even the time of day music is played. This approach enhances customer satisfaction and demonstrates how AI can create a more accurate and dynamic picture of audience segments.
6. Efficient Production with Smart Logistics Systems
Logistics can greatly benefit from AI-powered or “smart” devices that automate repetitive tasks. Smart logistics use real-time data collected through sensors, RFID tags, and similar technologies to optimize inventory management and predict demand. For example, smart warehouse shelves can sense weight and pressure, sharing this information with warehouse management systems.
Streamlining Inventory and Supply Chain Management with AI and Smart Tagging
By using RFID tags or barcodes, companies can keep their inventories up-to-date by tracking when items are received, stored, and shipped. This approach increases production efficiency and helps prevent overspending, as it eliminates the risk of ordering excess inventory due to disorganization.
AI also allows businesses to monitor products throughout the entire supply chain, from the manufacturer to the store, not just those currently in stock.
AI-Driven Logistics: Enhancing Visibility and Reliability in Supply Chains
By implementing a tracking system with integrated RFID and GPS technology, you can locate your products or supplies and monitor their storage and transport conditions. This capability is handy when managing multiple suppliers and vendors, as it minimizes the chances of late deliveries or accepting damaged products.
How Amazon does it: Amazon has heavily invested in intelligent logistics to streamline its operations and ensure efficient order fulfillment. The company utilizes advanced technologies to manage a massive inventory, fulfill orders quickly, and optimize warehouse operations. The integration of technologies like RFID, barcodes, and smart shelves contributes to a highly efficient and automated logistics ecosystem.
7. Sales and Demand Predictions for Improved Planning
E-commerce companies frequently rely on forecasting to manage inventory, plan logistics and warehouse space, and develop pricing strategies. However, accurately predicting demand is becoming increasingly challenging, as historical sales data alone, even when combined with seasonal trends, is often insufficient. To enhance the accuracy and reliability of demand forecasts, many brands are turning to AI.
Unlike traditional methods that rely solely on historical data, AI incorporates real-time information such as demographics, weather patterns, the performance of similar products, and online reviews or social media activity. As machine learning algorithms process more data over time, they continuously improve their forecasts.
Improving Forecast Accuracy and Cross-Departmental Planning Through Machine Learning
How Danone does it: Danone has implemented a machine learning system to enhance its demand forecasting. This system not only generates more accurate estimates for short-life products but also facilitates better planning across departments such as sales, supply chain, finance, and marketing. As a result, Danone has improved its operational efficiency and inventory balance, successfully meeting its targeted service levels at both the channel and store levels.
8. Enhanced Experience with Voice Search
Voice search is becoming a popular way for consumers to interact with their devices, and this shift is influencing how people shop online. According to a forecast by eMarketer, 162.7 million Americans will use voice assistants by 2027, from 149.1 million in 2024.
And by 2033, the global smart speaker market is expected to reach $110 billion. To take advantage of this, e-commerce businesses need to optimize their websites and product listings for voice search.
Optimizing E‑Commerce for Voice Search and Conversational Shopping
This includes using natural language, focusing on long-tail keywords, and providing clear, concise answers to common questions. As voice technology continues to evolve, there may be opportunities for deeper integration of voice assistants into the shopping experience. This could include offering personalized recommendations or even enabling customers to place orders with their voice.
How Sephora Uses Voice Assistants to Enhance Omnichannel Customer Engagement
How Sephora does it: Sephora is a great example of leveraging voice search. In 2017, it became one of the first brands to launch a Google Assistant action, allowing users to book beauty services, play quiz games, and listen to beauty podcasts.
Today, Google Assistant users can shop directly from Sephora, while those with Google Home can access the Skincare Advisor tool to find nearby stores, get skincare tips, and determine their skin type. Additionally, users can ask Google Assistant to play Sephora makeup tutorials through Google Home.
9. Visual Search for Finding Relevant Products
Visual search is an innovative tool that e-commerce platforms are adopting to enhance the shopping experience. Instead of relying on text or keywords, shoppers can use images to search for products. This is especially helpful for people who aren’t sure what they’re looking for or struggle to find the right search terms.
How Pinterest does it: Pinterest’s “Lens” feature lets users take a photo or upload one from their camera roll to find similar items online. In 2020, Pinterest’s visual search engine attracted 459 million monthly active users who preferred visual search over traditional methods for discovering new products or brands.
10. Fighting Against Fake Reviews
Customer reviews are important for building trust. Since shoppers can’t physically inspect products before buying, they rely heavily on reviews and ratings from other buyers. 93% of shoppers read reviews before making a purchase, and 91% of those aged 18-34 trust online reviews as much as personal recommendations.
The Scale of Fake Reviews and How AI Is Fighting Back
Unfortunately, not all reviews are genuine. Fakespot, which develops AI technology to detect fraudulent reviews, analyzed data from over two million online stores on platforms like Shopify, Amazon, Walmart, Best Buy, eBay, and Sephora, and found that around 31% of online reviews are fake.
Fortunately, AI is getting better at identifying fake reviews. These systems can quickly analyze text patterns, writing styles, and formatting to flag suspicious reviews. By comparing reviews in real time, AI could be the solution to help the e-commerce industry combat fake reviews more effectively.
How Amazon Uses AI to Detect and Prevent Fake Reviews at Scale
How Amazon does it: Amazon uses advanced technology and proprietary data to detect and prevent hundreds of millions of fake reviews, manipulated ratings, fake accounts, and other forms of abuse before they reach customers.
Machine learning analyzes various data points, such as:
Whether a seller has invested in ads (which may drive more reviews)
Customer reports of abuse,
Risky behavior patterns, review histories, and more.
Advanced language models and NLP techniques are used to identify data irregularities. These irregularities can indicate fake reviews or suggest that incentives, such as gift cards, free products, or other rewards, may have influenced a review.
11. Increased Security with Fraud Detection
According to the UK’s Office for National Statistics (ONS), nearly 63,000 consumer frauds were reported in the first half of 2020, of which around 41,000 were related to online shopping and auction fraud. What’s even more worrying is that during the first half of 2019 and the first half of 2020, online shopping and auction fraud increased by 37%.
As customers slowly shift from in-store to online purchasing, e‑commerce businesses are particularly susceptible to these attacks due to the increasing volume of transactions, orders, and deliveries.
AI in Fraud Detection: Spotting Suspicious Buyer Behaviour in Real Time
AI-based fraud detection and prevention tools can help merchants gain the upper hand. After analyzing all available data and understanding the context, AI tools are effective at spotting and blocking suspicious transactions. For example, a user might place many orders quickly, enter an unrecognized address in the relevant field, or skip over basic information required for delivery.
AI can detect all such cases and alert business owners to potential fraud attempts. Using AI to combat fakes, these engines can also analyze multiple ID or credit card parameters. This technology can also prevent abuse of promo codes and loyalty programs by catching users with multiple accounts or using proxy servers to make various purchases.
Multi-Layered Fraud Detection: PayPal’s AI Strategy Explained
How does PayPal do it: PayPal employs AI-driven fraud detection and prevention tools to secure financial transactions. AI algorithms analyze user behavior and transaction patterns, multiple parameters associated with user identification and credit card information, and address verification.
PayPal also analyzes patterns, leading to the identification of users with multiple accounts or individuals trying to leverage proxy servers for diverse purchases. ML ensures adaptation and continuously improves its fraud detection capabilities.
12. Accurate Product Descriptions
Even for experienced copywriters, writing product descriptions that are both persuasive and SEO-friendly is not an easy task. The more products you have to sell, the longer it might take to write unique descriptions for each.
How about reaching out to AI‑powered tools for those? Of course, they are not on the level of professional copywriters, but when it comes to simple product descriptions, AI can quickly create engaging, unique, and optimized content based on the specifications.
Automating Product Descriptions with AI: Enhancing E‑Commerce Content at Scale
Most AI tools can now use copywriting principles like AIDA (attention, interest, desire, action) to write like a human and add keywords to fit the text naturally.
How does Walmart do it: Walmart employs AI-driven robots to scan shelves, generating product descriptions seamlessly for its online store. This streamlined process enables swift and precise updates to Walmart’s online inventory, ensuring informative and consistent descriptions.
This initiative aims to enhance the overall customer experience and boost sales by providing up-to-date and comprehensive product information.
13. Product Discovery
Product discovery can be enhanced by AI tools, helping to connect online shoppers with relevant products. This is why it’s a great example of how eCommerce can leverage the power of AI. Rather than using consumer behavior to personalized recommendations, product discovery is about making the browsing experience intuitive and engaging.
This is so consumers can easily explore and uncover products they might not have initially considered. Lily AI is a tool that facilitates this function, using artificial intelligence to boost product discovery and conversions for eCommerce. The tool is specifically designed for fashion, home, and beauty retailers, and Lily AI claims to be the “first vertical AI platform purpose-built for retail”.
AI Attribute Tagging: Matching Product Details with Shopper Intent
The sophisticated technology works by using product attributes and matching them with consumer needs. For example, for fashion retailers, this could include analyzing attributes such as fabric, style, detailing, and even neckline, to surface relevant products.
So if a user is browsing white T-shirts that have a crewneck, Lily AI can show other white crewneck t-shirts that are similar. For homeware, attributes like product type, color, color family, wood tone, and home style can be used.
14. Image Recognition
Number 13 on this list of AI in eCommerce examples is image recognition and tagging. AI tools can be used to analyze images supplied by consumers, to provide product recommendations that resemble the supplied image. A great example of this is the social networking site Pinterest, one of the more well-known platforms that offers image recognition.
It’s possible to select an item that’s shown within an image and use image recognition software to search for similar items. This is particularly handy for consumers because it helps them discover and purchase items they are interested in, but don't necessarily know where to find them.
Visual Search in eCommerce: Letting Images Replace Keywords
For example, a consumer could use a photo of a cream lamp in a living room to find similar lamps from a selection of retailers, purchasing the one they like most. Google Lens is another tool that uses image recognition to help users search using images rather than text.
It’s possible to search by uploading an image or by taking a new picture with Google Lens. In the same way Pinterest does it, Google Lens will show similar images to the ones you've uploaded.
15. AI Product Images
Another great example of AI-powered tools in eCommerce is AI product images. Although AI tools are not yet advanced enough to produce final product image designs that are ready to go, they help accelerate the design process.
They can give designers a new perspective, the ability to make large edits and small touch-ups to perfect them. Specifically for retailers, AI can be used to change up product backgrounds, place products in a new setting, or even create lifestyle images to accompany standard product photography.
Using Generative AI to Create Custom Product Images for Marketing and Testing
One AI product image tool to consider testing out is Dall-E, which can be used to generate images from text prompts. Image outputs may need adjusting and refining, and they can be abstract at times; however, the responsiveness of text prompts is highly effective.
16. Custom Marketing Messages
Another example of how ecommerce businesses can benefit from the power of AI is through customized marketing messages. There are numerous channels in which brands and consumers communicate via message, and consumers have grown to expect a personalized experience.
This could include standard marketing communications, such as tailoring email newsletters and SMS updates to individual interests. It could also include more sophisticated communications like customizing ad experiences and adding conversational ads into the mix.
AI-Enhanced SMS Campaigns: Personalisation and Performance Tracking in Retail Messaging
A smart tool that allows retailers to customize SMS messages is Emotive. As well as customizing SMS messaging to consumers, Emotive uses AI to track the engagement of the messages and to improve them over time. Emotive claims that its SMS messaging technology can boost conversion rates and ROI, making it worthwhile for retailers looking to nurture and engage their customers.
17. Pricing Optimization
AI-powered tools can be used to optimize product pricing, a strategy that ecommerce businesses are increasingly incorporating into their pricing strategy. Pricing tools are designed to automatically adjust product pricing based on several real-time factors, such as competitor prices, changes to manufacturing costs, and consumer demand.
Dynamic Pricing & Promotion Triggers: How AI Adjusts in Real Time to Competitor Moves and Shopper Intent
For example, suppose a competitor selling the same product suddenly drops prices. In that case, AI tools recognize this and react to it by automatically lowering your prices to prevent missing out on a sale. There are also AI tools that facilitate limited-time promotions, which can pop up on the screen of consumers who show high intent.
This type of tool uses statistics and analyzes online behavior to determine who should receive a promotion and when. Pricing optimization is another great example of how AI helps to improve the responsiveness of retailers to boost sales.
18. Counterfeit Product Detection
Counterfeit products are standard in the retail world, which can be highly damaging to a brand. Not only does it result in the loss of sales, but it can also impact reputation since the quality of counterfeit products can be poor.
Tackling this by identifying counterfeit products and then removing them has been made easier thanks to AI.
Brand Protection through AI: How Counterfeit Detection Safeguards Revenue and Consumer Trust
This has earned Counterfeit product detection a spot on this list of the best AI in eCommerce examples. One tool in particular is Corsearch, which retailers can adopt to detect and remove counterfeits across all online channels using AI, along with their industry expertise.
Corseach can quickly find counterfeit products, faster than manual searches, and automatically remove counterfeit goods. This is a great example of how AI in eCommerce can help retailers safeguard revenue, uphold reputation, and maintain the trust of consumers.
19. Retarget Potential Customers
According to Conversica, at least 33% of marketing leads are not followed up by the sales team. This means pre-qualified potential buyers interested in your product or service fall through the inevitable cracks. Looking deeper into the retail industry, facial recognition is already used to capture shoplifters by scanning their faces on CCTV cameras.
Facial Recognition and Behavioural Data: Predictive Personalisation in Physical Retail
How can AI be used to enhance a customer’s shopping experience? Some businesses now use facial recognition to capture customer dwell times in the physical store. This means that if a customer spends a notable amount of time next to a specific product, e.g., an iPod, this information will be stored for use upon their next visit.
The face of sales is changing, with businesses responding directly to the customer. It is as if businesses are reading customers' minds thanks to the data used with AI.
20. Demand Forecasting
If you want to predict demand but don’t have enough historical sales data to use, AI can help. Many brands have used artificial intelligence in eCommerce to make demand forecasts. Instead of using historical data alone, AI uses real-time consumer data, including demographics, social media, and online reviews, to make sales and demand predictions more accurate and reliable.
21. Starbucks: Voice Shopping
Gone are the days of spending countless hours browsing through product catalogs. Today, we demand fast, convenient, and efficient online shopping experiences. Conversational commerce has become a significant trend, thanks to advanced technologies like Artificial Intelligence (AI) and Natural Language Processing (NLP). Modern chatbots can now understand the human voice, grasp the context, and respond accurately in voice format.
This means you can express what you’re looking for, and the chatbots will present products that match your preferences. In voice shopping, platforms like Amazon Alexa or Google Assistant use voice recognition technology to help users shop online through voice commands. Starbucks, the renowned global coffeehouse chain, has integrated this technology into its app with a feature called ‘Voice Order.’ This enables customers to place orders online using just their voice.
22. Product Intelligence
Gone are the days of spending hours manually researching industry trends, monitoring competitors, and surveying customers to get insight into buying behavior. Instead, data analytics platforms can do this for you, using artificial intelligence to aggregate and analyze data, providing you with the important insights.
This is great news for quickly getting product intelligence, for example, knowing what the latest trend is, what influencers are promoting, competitor products and pricing, and so much more.
AI-Powered Product Intelligence: Predicting Inventory Needs Through Search, Social, and SKU Analysis
Trendalytics offers a product intelligence solution for ecommerce businesses, describing themselves as a personal data scientist helping with strategic decision-making. Trendalytics scrapes 4 million SKUs every week, monitors 20 thousand social profiles and 2.4 million search terms, all of which help create its prediction engine.
All of this insight can be used to help retailers decide what products to invest in and when. It can help streamline inventory and the entire operation.
23. Fake Reviews Identification
There are many scenarios in whereby fake reviews can get mixed up with real ones, whether it’s ill-natured competitors, unhappy customers, or consumers who get brands mixed up. The AI-powered solution to this is fake review identification software.
Fake review software can be used to identify fake reviews through AI technology that analyzes and scrutinizes text patterns, writing styles, and formatting. They are an excellent way for ecommerce businesses to maintain the integrity of customer feedback, minimizing the harm caused by fake reviews.
24. AI Retargeting
The final example of some of the best AI tools used in eCommerce is AI retargeting. Retargeting is an age-old advertising strategy that works by reaching past customers, website visitors, or those who have interacted with the brand in some capacity. The aim is to show ads to this warm audience to encourage them to revisit the site and purchase.
AI retargeting takes things one step further. Rather than just using one large segment of users, AI retargeting can build and maintain audiences that are narrower and more defined using customer behavior, browsing history, and other data points. This enables retailers to personalize retargeting ads, which are then more likely to lead to a conversion.
AI Retargeting in Action: How Carrefour Taiwan Boosted Conversions with Behaviour-Based Coupons
An example of a retailer that uses AI-powered retargeting is Carrefour Taiwan, onboarding Appier’s AI retargeting solution, which ended up increasing conversion rate by +20%. One of the tactics Carrefour Taiwan used was tailoring coupons based on what a user was browsing, which helped encourage them to convert.
25. Bundling and Upselling
Product recommendations are usually best guesses of what customers want. But generative AI can make better connections between products, pick up on signals from shoppers, and recommend items that are genuinely complementary to the customer.
For example, let’s say a customer is searching for a watch that’s good for a frequent traveler. Not only can a generative AI program recommend a watch, but it can also recommend a travel case.
26. Virtual Fittings/Trials
In the recent survey by Reputation Leaders and Zoovu, 29% of consumers say they struggle to find products because they can’t physically touch them. While generative AI can’t transport your customers to a store, it can do the next best thing, digitally showcase how a product would look on them or in their space.
For example, if a customer is shopping for a winter jacket, it can create an image of the customer wearing that item. Or if a customer is looking for the perfect couch, gen AI can be used with visual product configurators to show them what the products would look like in their living room or kitchen.
27. Write Support and How-To Articles
The number of ways a customer can use your products (and the problems they might face while doing them) will almost always outpace your ability to create content addressing these use cases.
But with generative AI, good product data, and a few dozen articles to get you started, you can build a vast library to cater to diverse and niche customer questions for every product.
28. Targeted Emails and Ads
It would take an entire team of copywriters to design emails and ads for the dozens or hundreds of specific shopper segments. And that’s not counting the time spent putting together those segments based on mountains of behavioral data.
Hyper-Personalized Content Creation with Generative AI
This is work that a generative AI system can do in a few minutes, spinning up ads and emails tailored to small groups of customers based on their needs, preferences, and lifestyle. While Jack will get an ad for a particular vacuum worded a certain way because he has children and a dog, and lives in a house, Jill will get an email, worded a different way, for a different vacuum because she’s single, lives in an apartment, and likes to bake.
29. Translation
Generative AI can translate entire sections of an ecommerce website into any language in less than a day. The translations aren’t 100% perfect, and they often don’t pick up regional nuances, such as tone and formality. Having a native speaker review and make minor tweaks is a lot less expensive and time-consuming than contracting a completely manual translation project.
30. Mondelez International: Product Research and Development
With advanced technologies like NLP and AI, modern chatbots are now instrumental in research and product development. These bots gather a wealth of information, including customer behavior, preferences, and choices. With such info, companies can customize their product offerings to meet customer needs.
Mondelez International, a global food giant known for our beloved brands like Oreo, Toblerone, Clif Bar, and Tate’s Bake Shop, has effectively leveraged this advantage. They utilize bots in their research and development efforts, tapping into the efficiency and accuracy of AI that surpass human capabilities.
Related Reading
• How to Improve eCommerce Customer Experience
• Ai Tools for Ecommerce
• B2B eCommerce Personalization
How Do You Implement AI in Your Business?
Define Your Business Goals
Start with a clear understanding of what you aim to achieve. Are you looking to enhance customer experience, boost sales, or streamline operations? Pinpointing these objectives will help you evaluate the potential return on investment.
Collaborate with your team to set these goals, as they will be your guiding star throughout this journey.
Understand Implementation Requirements
Bringing new tech on board isn’t always straightforward. Consider the costs, time, and potential regulatory hurdles. If your AI tool will handle customer data, you might need to update your privacy policy. Being aware of these requirements early on will save headaches down the road.
Focus on Tools That Improve Efficiency and Experience
The AI environment is vast. There's no shortage of options from supply chain optimization to personalized marketing. Prioritize solutions that enhance customer service and streamline your workflows. This will not only improve the shopping experience but also reduce operational costs.
Avoid Shiny Object Syndrome
It’s easy to get swayed by the latest tech trends, but not every new tool will be beneficial. Before committing to a new AI solution, make sure it addresses a specific need or challenge. Ask yourself: Will this tool solve a problem or add complexity?
Monitor and Audit AI Workflows
After implementing AI, keep a close eye on its performance. Is it meeting your goals? Can it be adjusted to better serve your needs? Regular audits ensure the AI delivers value and adapts as your business evolves.
Benefits of AI in eCommerce
For your eCommerce business, AI helps to provide the best experiences to your customers, better engagement, and ensure they are happy and satisfied with your brand. These are the three key objectives every eCommerce business works hard to achieve, and AI makes it easier to reach these goals.
Keep in mind: if your customers aren’t happy, your business won’t survive. AI not only helps you boost customer satisfaction but also paves the way for increased sales.
Let’s explore the benefits AI offers for your business:
Automated Customer Service
AI bots can replicate human conversations and efficiently handle customer queries 24/7. When your customer service agents are busy with other tasks or face a high volume of inquiries, it can be difficult for them to manage everything at once. Hiring extra staff to handle the load can be costly, but advanced chatbots offer a cost-effective solution.
These bots fully automate customer service, allowing you to assist multiple customers simultaneously and provide instant support whenever it’s needed.
Related Reading
• Klaviyo Alternatives
• eCommerce Customer Journey
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