AI Personalization in Ecommerce: How to Use It to Drive Growth

AI personalization in ecommerce uses machine learning and artificial intelligence to tailor each shopper’s experience based on their behavior and preferences—and it’s a major competitive advantage. Brands that lead in personalization grow 10 percentage points faster than those that lag behind, according to the BCG Personalization Index. 

Here’s what AI personalization looks like in practice, how it compares to the rules-based tools you may already use, and five ways you can put it to work in your store.

What is AI personalization in ecommerce?

AI personalization in ecommerce tailors each shopper’s experience using machine learning algorithms that analyze customer data and adapt in real time. AI systems draw on behavioral data—browsing history, past purchases, clicks, time on page, cart activity, and social media interactions—to decide what each shopper sees next. 

Traditional personalization is rules-based, built on logic that marketers write manually, like “if the customer is in segment X, then show them banner Y.” AI personalization learns from historical data, real-time data, and customer behavior, then updates its predictions as new data comes in. It can then surface patterns more efficiently than a human team could manually. 

Say the model identifies that shoppers who view three or more products on mobile in the evening place larger orders. For a shopper matching that pattern, it might promote a bundle with free shipping at a threshold those customers are likely to hit.

These kinds of personalization efforts once required a dedicated team and a serious development budget, says Maryam Haghighi, director of data science at the Bank of Canada, on the Shopify Masters podcast. “It’s becoming less and less expensive to access that kind of disruptive tech,” she says. 

The AI personalization workflow

Most personalization tech stacks span more than one tool, since data, modeling, and execution often live in different systems. A setup might include an ecommerce platform that captures and stores customer data, an email platform, plus various specialized apps for recommendations, on-site personalization, customer reviews, loyalty programs, and ad platforms with their own AI delivery layers.

Regardless of the precise tools in your software stack, AI personalization typically happens in three stages:

1. Data collection. Customer data flows in from connected sources that capture browsing history, past purchases, user behavior, and other shopper signals.

2. Analysis. Algorithms process that data to build customer profiles, user segments, and predictions about what each shopper is most likely to want next.

3. Execution. Those predictions deliver personalized interactions across product pages, search results, email campaigns, push notifications, ads, and in some cases, dynamic pricing.

How to use AI personalization in ecommerce

1. AI-powered product recommendations
2. Homepage and on-site personalization
3. Personalized emails
4. AI-generated advertising at scale
5. AI shopping assistants and agents

Here are five ways to put AI personalization to work in your online store:

1. AI-powered product recommendations

AI-powered product recommendations suggest items personalized to each shopper’s browsing and purchase history, similar customers’ previous purchases, cart contents, and other customer data signals. Machine learning algorithms pick the products most likely to convert for that shopper at that moment. 

What sets this apart from static recommendation widgets is that no one writes the pairing rules. A rules-based system pairs a cutting board with a chef’s knife because a merchandiser wrote that logic; an AI model generates pairings based on behavioral data.

Recommendation engines are now standard infrastructure in ecommerce. Shopify’s Search & Discovery app, for example, automatically generates complementary and related product recommendations that you can customize further—by pinning complementary products to a given SKU, for example. 

2. Homepage and on-site personalization

Homepage personalization applies machine learning to surface relevant content for each shopper based on past behavior. Two returning shoppers at the same athleisure store might see two different homepages: one leading with new yoga attire, the other with running gear. Each version reflects what the AI predicts each shopper wants next. 

Fully dynamic homepage personalization—where banners, content blocks, and sort order change for each shopper—typically requires a dedicated app. Apps like Intellimize, available on Shopify’s App Store, let you swap banners, hero images, and on-page content based on a shopper’s traffic source or behavior. 

A practical entry point for small businesses is a single personalized surface or a single customer segment, like returning visitors. Return visitors come with behavioral signals the model can act on—such as what they’ve viewed, what they’ve purchased, and when they last visited, allowing you to personalize content such as a “recommended for you” row or a personalized hero image. 

3. AI-driven email segmentation

Customer segmentation isn’t new—marketers have been using behavioral data to send tailored messages to lapsed customers for years—but the segments themselves used to be slow to build and update, limited to whatever rules an analyst could write by hand. 

AI is making personalized segmentation more accessible. Thanks to advances in natural language processing (NLP), you can describe a segment in plain English. Shopify’s Sidekick supports prompts like “customers who bought running shoes but not socks,” then keeps that segment updated as new orders come in. The email each shopper receives is therefore targeted to a more granular slice of their behavior, with the data refreshing automatically rather than requiring manual rebuilds.

Built-in segmentation tools can generate these automatically. Sidekick, for example, can build a segment that plugs directly into email campaigns, with each message pulling in content based on the individual shopper’s data—name, past purchases, and recently browsed items. 

If you’re starting from scratch, build segments around moments a shopper is closest to a decision: They’ve left something behind in the cart, their last order is nearly used up, or they bought once and haven’t come back. Pair each segment with a dynamic email that references their real order history, and run it for a month against your baseline.

4. AI-generated advertising at scale

AI-powered personalization also shows up in paid media. Ad platforms like Meta are built around creative volume and variation, automatically matching each ad to the user most likely to engage. Meta’s ad delivery system narrows tens of millions of eligible ads down to a few thousand candidates per impression. The more creative variants feed it, the more the system has to draw on when matching an ad with a specific user.

Wallet and accessories brand Ridge built an AI workflow to scale up the volume of ad variants they could create. The team trained a custom AI model on the company’s best-performing ads and connected it to n8n, an open-source workflow automation tool that connects different apps and services. The system now generates roughly 500 static ads a day. Most never run, but the best 10% score well enough on his team’s internal rubric to earn a test budget. 

While CEO Sean Frank says his design team still produces the strongest single ads, the AI-generated creative gives Meta more options to test. 

“The future of advertising is just shots on goal,” Sean says on Shopify Masters. “It’s going to be more personalized advertising.” 

You don’t need Ridge’s setup to start accessing AI personalization benefits. Start by feeding your ad account three to five creative concepts per campaign instead of one, then use a generative AI tool to produce copy variations for each.

5. AI shopping assistants and agents

AI shopping assistants are chat-style tools that live on your storefront by helping shoppers navigate your catalog, compare products, and complete a purchase. Unlike scripted chatbots that follow fixed decision trees, these assistants reference specific SKUs, handle questions you didn’t anticipate, and build on the context from earlier messages in the same conversation. Large language models are what make that flexibility possible.

Intimate apparel brand Underoutfit added an AI shopping assistant from Rep AI to handle questions from shoppers, mostly about sizing and fit. The assistant answers in real time using the store’s product data, flags disengaged shoppers and prompts them to re-engage, and routes complex queries to a human agent via the help desk platform Gorgias. After launching Underoutfit saw an 8% lift in conversion rate and a 7% lift in average order value (AOV).

If you’re considering a shopping assistant for your store, start with three questions:

• What are shoppers already asking your support team? Product specifics, sizing, and return policies are straightforward questions AI assistants can answer. 

• Do you have clean product data for the assistant to pull from? An assistant is only as good as the catalog data behind it. Detailed product descriptions, size charts, and attribute tags give it the foundation it needs to answer accurately.

• Where does escalation happen? Pick a tool that integrates with your existing help desk so the assistant can route complex questions to a human.