Artificial intelligence (AI) is sweeping the ecommerce industry.
And it’s complicated.
AI isn’t one type of technology — it’s a range of models that use algorithms to automate workflows, predict patterns, and personalize experiences.
Over the next 12 months, 60% of retail directors plan to deploy AI and machine learning.
If you don’t know what it can do, you’ll get left behind.
Knowing how each AI technology works will help you reshape your online shopping experience and stay ahead of your competitors.
Take Microsoft, for example.
Not only has Microsoft made sizable investments in ChatGPT. It’s also used AI to build omnichannel product delivery experiences, using these experiences toincrease engagement by 90%.
But with a world of AI advancements optimizing all areas of ecommerce, how do you choose which tools to use and how to use them?
In this article, we’ll explore exactly how AI is transforming ecommerce. We’ll explain how ecommerce AI models work and how to apply them to propel business growth and streamline the customer journey.
Let’s get started!
In 2022, the AI-enabled ecommerce solutions market was worth $5.5 billion. Predictions estimate that it will grow to $16.8 billion by 2030.
But AI isn’t a new technology for the ecommerce industry.
AI was already being used by ecommerce companies in the early 2000s with a few rudimentary use cases for fraud detection.
The machine learning boom hit in the 2010s. AI-powered personalized marketing, dynamic pricing, and inventory management began to take off.
- In 2011, Netflix introduced its personalized recommendation algorithm
- In 2015, Google launched its search ranking algorithm
- In 2017, Walmart started using warehouse robots to pick and pack orders
But despite steady advancements over the past two decades, there has been a significant acceleration in the growth and use of AI in the last five years.
Today, AI is being used in all aspects of ecommerce — from product discovery and customer service to supply chains and security.
Here are some of the main AI trends transforming the ecommerce market now:
- Personalized guided shopping experiences that harness data-driven recommendations and dynamic pricing
- Conversational AI for customer service, such as chatbots and personal assistants
- Product discovery using AI-powered text, visual, and voice search engines
- Fraud prevention to identify fraudulent activity, anomalies, and fake accounts
- Logistics and supply chain management, such as delivery route optimization and demand forecasting
- Generative AI to analyze data, predict patterns, and generate content
- Nike now uses AI to offer product recommendations and life-like virtual assistants.
- Warby Parker uses AI to enable users to try on products before purchasing.
- Amazon, eBay, and Shopify are rolling out generative AI tools to help craft product descriptions.
Ecommerce companies aim to focus AI on three main outcomes: Business transformation, better decision-making, and systems modernization.
Plus, 40% of AI leaders say they see value from AI in enhancing customer experience, while 40% say they see value in innovating products and services.
It’s clear businesses realize AI improves every part of ecommerce, from customer systems to back-end operations.
As Amazon’s founder, Jeff Bezos, puts it, “Machine learning and AI are a horizontal enabling layer. They will empower and improve every business, every government organization, every philanthropy — basically, there’s no institution in the world that cannot be improved with machine learning.”
Artificial intelligence isn’t just reshaping the way retailers deliver services. It’s completely revolutionizing the way people shop.
As Andrew Ng, Co-founder of Google Brain, puts it, “We’re making this analogy, that AI is the new electricity. Electricity transformed industries: Agriculture, transportation, communication, manufacturing.”
But artificial intelligence isn’t one type of technology — it’s an umbrella term. AI technologies have diverse capabilities, each best suited for certain tasks.
Let’s explore this further.
Machine learning (ML) is a type of AI that allows computers to learn without explicit programming.
The algorithms train on large amounts of data, learning to identify patterns and make predictions.
Ecommerce companies use ML to personalize the customer experience.
For example, Etsy uses ML to recommend relevant products to customers based on their past purchases and browsing history.
Companies also create dynamic pricing systems with ML. These systems adjust prices in real-time based on variables like demand and inventory levels.
Take the ethical clothing store, Everlane. It uses dynamic pricing for overstocked goods.
Customers have three payment options:
- Cover costs
- Contribute to overheads
- Fund research
No matter which price they choose, the customer gets a bargain.
For Everlane, this decreases storage overheads of unwanted stock. And, at the very least, the sale still covers costs.
As Everlane CEO Michael Preysman explains, “There’s a lot of fat in the supply chain that existed. We’re cutting it all out, really slimming it down, and keeping it as simple as possible.”
Natural language processing (NLP) helps computers understand and generate human language.
Ecommerce uses NLP algorithms in a whole host of ways, like:
Take SurferSEO, Clearscope, and Jasper. All three use NLP models to inform SEO text content for ecommerce stores and other business types.
Computer vision allows computers to process and interpret images and video.
The ecommerce industry uses these algorithms in a variety of ways, such as:
- Image recognition
- Augmented reality shopping
- Visual search
For instance, Sephora’s Virtual Artist is an AI-powered makeup try-on tool.
Customers can virtually test makeup products and receive suggestions on what to try. The tool uses computer vision to track facial features and apply virtual makeup.
Semantic analysis helps computers understand the meaning of text and language.
The retail industry uses these algorithms to:
- Enrich semantic data
- Recommend products
- Understand customer reviews
- Conduct market research
- Improve SEO
Take Zoovu’s Semantic Studio for AI content enrichment.
It uses semantic analysis to enrich ecommerce content. It does this by first cleaning the data automatically, so it’s 100% machine-readable.
Using Zoovu’s world-class semantic library, it then interprets context, intent, and meaning. It uses this information to enrich data, insert missing attributes, and remove inaccuracies.
Anomaly detection identifies unusual or unexpected data patterns.
Ecommerce uses these algorithms most often for fraud detection and prevention.
For instance, Stripe Radar uses anomaly detection to analyze transactional data for potential illicit activity.
This helps protect merchants from fraudulent transactions.
Chatbots and virtual assistants are AI-powered tools that use NLP to simulate human conversation.
Ecommerce retailers use chatbots and virtual assistants to enhance customer service.
An ecommerce chatbot might:
- Help customers navigate your site
- Answer customer queries
- Assist with returns
- Track orders and update customers
- Conduct feedback surveys
- Suggest relevant products
Freshworks live chat is a good example. It’s an AI-powered customer service chatbot for ecommerce. It answers customer questions, resolves issues, and helps customers discover products.
AI is transforming the online shopping experience. From customer interactions to automation, AI systems craft innovative ways to reach potential customers and deliver personalized experiences on your ecommerce websites and apps.
Here are some of the main ways AI can streamline your operations to boost ecommerce sales.
More than 70% of online shoppers expect personalized customer engagement.
From tailored marketing campaigns to customized checkout experiences, your touchpoints need to be interactive and customer-focused.
“Customer-first search” uses AI to build and improve the search function on ecommerce sites so it’s as user-friendly and relevant as possible.
AI processes the intent behind the customer’s query — even if it’s incomplete or misspelled — and delivers the most relevant search results.
69% of customers visiting your online store will head straight to the search bar. But, 80% will leave your site if the search feature isn’t very good.
Since the search function is the first point of contact for most people, it needs to be well-designed.
AI refines search algorithms for greater relevance and efficiency.
For example, it can:
- Identify related search terms to expand the range of results returned for each query
- Understand a query’s context and return hyper-relevant results
- Personalize search results based on customer behavior and purchase history
Not only does this increase customer satisfaction, it boosts conversion rates.
If customers can’t find what they’re looking for, they definitely won’t buy it. If you take them to what they need immediately, there’s a much better chance of a sale.
For example, Zoovu’s intelligent search helps ecommerce businesses optimize their search functionality with AI. It uses NLP to help your site deliver accurate search results while personalizing the search experience to each customer’s preferences.
It even offers real-time search suggestions to make it easier for the customer to explain their query.
The sales process is chock-full of data. Data is the fuel that powers AI engines.
When ecommerce companies harness data effectively, they quickly adapt to customer preferences and capitalize on trends.
As Brian Krzanich, Former CEO of Intel, explains, “Data is the new oil. It’s going to change most industries across the board. AI is the new tool that will help us process it.”
There are lots of ways that AI improves the sales process.
Here are a few…
- Guided selling to recommend the ideal products based on a customer’s needs and preferences
- Automating sales tasks like lead qualification, product recommendations, and sales proposal generation
- Personalizing the experience to speed up conversions and improve customer loyalty
- Providing insights into customer behavior and sales trends
Let’s look at guided selling.
Instead of clunky “Other Products You Might Like” recommendations, you can use guided selling platforms to direct customers straight to their perfect product.
Traditional sales tactics try to convince customers to buy a product or service for its attributes and benefits. Guided selling is a more needs-based approach.
The goal of guided selling is to match customers to the right products for them, even if that means selling a lower-ticket item.
For example, Zoovu uses a series of needs-based questions to clarify a customer’s needs and preferences. Once it has a good understanding of these, it recommends products that are likely to be a good fit.
Zero-party data is data that customers provide voluntarily and explicitly. Customers agree for companies to use this data, usually when signing up for something or buying a product.
This is the most valuable data type for ecommerce companies because it’s accurate and reliable.
To collect zero-party data, though, businesses need explicit user consent. Otherwise, they’re breaching privacy regulations.
But collecting it impacts the user experience.
Let’s say a customer tries to check out quickly, and the checkout form is asking for mountains of personal information. They’ll likely get frustrated and buy somewhere else.
One way to overcome this hurdle is to collect zero-party data through AI-driven hyper-personalized experiences. This way, you get consent to collect valuable data, and you supercharge the customer buying experience.
Let’s look at the cycling brand Trek, for example.
To match buyers to their ideal bike, it created a hyper-personalized product discovery experience with an AI-driven question-and-answer flow.
Customers answer the questions so they can find a bike more easily, while Trek collects the zero-party data.
Product detail pages or PDPs are the sales pitches of ecommerce pages.
Product detail page optimization enhances the buyer journey. Customers want PDPs that are responsive, load quickly, and contain the right key information.
Imagine you think you’ve found the right part to fix your washing machine. Only its PDP has missing product specs and only one product image.
Since you can’t be sure it’s the exact product you need, you buy elsewhere.
But it’s not just that. It’s also a searchability issue.
In terms of general SEO, strong PDPs help Google rank your products for relevant search terms. The higher you rank on Google, the easier it is for buyers to find you.
As Sarah Blocksidge at Sixth City Marketing explains, “When we work with new ecommerce clients, we often see a lack of keywords in the product name, metadata, photo title, alt tags, and product copy. So while the product’s use might be evident to people searching your site, those nuances don’t translate to search engine bots when they crawl the pages of your website.”
Beyond that, well-crafted PDPs make it easier for customers to search for products on your site.
With AI-powered search and top-quality PDPs, it’s easy for your site to match customers to the right products.
But there’s another layer of complexity.
Customers cross-reference PDPs when comparing prices across sites. If your PDP information is inaccurate or missing, they’re likely to go elsewhere.
For example, imagine you’re comparing a drill on Amazon with the same product on an online hardware store.
You spot a price mismatch or feature inconsistency on the hardware store’s PDP.
You’re not likely to trust their competence.
To overcome this, AI can help you syndicate the experience so that data is consistent across all channels. This reduces customer confusion and presents a seamless, scalable brand narrative, no matter where customers experience your brand.
Generative AI models like ChatGPT and Google Bard use NLP to produce high-quality content automatically.
This is a game-changer for sales and marketing teams as it significantly decreases their time spent on creating content.
Here are a few examples of how ecommerce retailers use this technology to create content:
- Dynamic product descriptions: AI analyzes product specifications, user reviews, and related products to create unique, up-to-date, searchable product descriptions.
- Ad copy: Not only does AI generate ad copy, but it also tests multiple iterations and optimizes future content based on real-time performance metrics.
- Personalized email campaigns: You can use AI to create personalized emails for different customer segments based on their preferences, behavioral data, and browsing patterns.
- Trend-based marketing content: AI analyzes market trends to predict upcoming popular products and styles and automatically creates content to stay ahead of the curve.
Imagine a clothing company introducing a new line of summer dresses.
AI analyzes the dress features and creates engaging product descriptions that make it easy for customers to find these products, both through search and recommendations.
AI also automatically generates personalized email campaigns that target customers who buy similar items in the summer.
AI algorithms use deep learning to understand big data and return insights to make predictions.
Here are some of the top ways online retailers use in-store data.
Half of all consumers say they’re likely to return to a brand after a personalized shopping experience.
Personalized product recommendations are an effective way to tailor the buyer’s journey. Customers feel like you understand their needs, increasing your chance of conversion and future sales.
While “people who bought this also bought…” kind of suggestions are fine, they’re still pretty generic. They’re not always great at providing contextual suggestions.
For example, a customer buys a paintbrush to redecorate a piece of furniture. The suggestions include a paint roller and wall paint.
While customers often buy these items together, it’s not what the customer needs for their project.
You lose an opportunity to make a sale and the customer doesn’t feel you understand their needs.
AI-powered recommendations use algorithms to understand individual preferences and purchasing behaviors.
Not only do you point customers to the products that fit their needs, but you also upsell and cross-sell to boost the average order value.
Take Zoovu’s product bundling capabilities, for example.
No need to settle for static recommendation bundles when AI tailors each suggestion to the buyer’s needs and behavior.
This dynamic approach to product recommendations speaks directly to the customer. It establishes trust and builds a dialogue while providing relevant opportunities to increase revenue.
Ecommerce used to be local. The butcher, the baker, and the candlestick maker — all catering to the community in which they live.
But the digital age has upended this.
Ecommerce businesses now cater to a global audience.
While one service style worked for the local salesperson, global ecommerce brands now sell to a diverse clientele with unique cultural nuances.
It’s not enough to simply translate text anymore. You need to convey meaning, emotion, and intent in a culturally appropriate way to appeal to each market.
AI’s ability to not only auto-translate but to interpret messaging, enables it to tailor regional-specific content. This way, a shopper in Tokyo feels just as valued as one in Toronto.
For example, Zoovu doesn’t just translate across 88 different languages. It also returns culturally optimized shopping experiences with the right messaging and relevant product recommendations.
The ecommerce horizon is expanding. Customers expect personalization and they demand streamlined experiences.
AI is at the heart of this transition.
Ecommerce businesses power hyper-personalized retail with tailored product recommendations, automated workflows, and guided shopping experiences.
Not only does this encourage brand loyalty, it helps you stay ahead of competitors and drive conversions.
But it’s not just about using AI to analyze data anymore. Thanks to the introduction of generative AI, retailers can now create content, designs, and products automatically.
By combining computation and creativity, generative AI helps you meet user needs while adapting to market demands at scale.
Ready to transform your buyer journeys for personalized shopping experiences that speak directly to your customers?
Check out Zoovu’s demo to synchronize your narratives and match buyers with exactly what they need.