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AI can personalize online shopping by matching product discovery, page content, and guidance to a shopper’s interests and current intent. Ecommerce teams can use it for recommendations, conversational product finders, tailored homepages, and relevant offers—but useful results depend on reliable product and customer data, clear privacy practices, and controlled testing.
Where AI can personalize the shopping journey
Personalization is broader than placing a recommendation carousel on a product page. It can help shoppers discover items, understand choices, and see content that fits what they are trying to do.
Product recommendations
Recommendation systems can use signals such as previous purchases, browsing activity, stated preferences, and product attributes to suggest products. Suggestions may be based on what a shopper is likely to interact with, or on similarities such as color, style, scent, or category. AWS describes MonAmie using Amazon Personalize to recommend products and identify attribute-based segments, including connecting perfume scent profiles with similar-smelling products in other categories (AWS’s MonAmie case study).
Conversational discovery and guided shopping
A conversational assistant can let a shopper describe an occasion, need, or style in ordinary language, then surface suitable products and explain choices. Omoda’s AI stylist is described as helping customers find coordinated clothing, shoes, and accessories through conversation, informed by purchase history, trends, and brand affinity. The vendor-published case study reports that users of the stylist had 2.5 times the average conversion rate; this is an initial result for that implementation, not evidence that the tool caused the difference or a forecast for other retailers (Google Cloud’s Omoda case study).
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AI can also support decisions rather than simply suggest products. Best Buy’s Gift Finder uses customer preferences to guide gift selection; the case study also describes summarizing thousands of reviews and answering product questions using manuals and vendor-provided information (Google Cloud’s Best Buy case study).
Homepage and product-page content
A homepage can vary layouts, copy, and campaigns according to a shopper’s current intent, while product pages can show complementary or otherwise relevant items. In a 2025 Mastercard case study, Saks began its homepage personalization test with 5% of traffic, testing purchase-intent groupings and associated experiences. It later expanded the experience to homepage traffic across desktop, mobile, and app. Saks also uses Dynamic Yield for some product-page recommendations (Mastercard’s Saks case study).
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Offers and targeted content
Personalization can select which campaign, editorial content, or offer a shopper sees, using relevant signals and the retailer’s business rules. The goal should be to improve relevance, not to show every visitor a different discount. Keep eligibility, exclusions, and offer economics explicit so that personalization does not create confusing or inconsistent promotions.
What data does AI personalization use?
Most implementations combine information about shoppers with structured information about the catalog. Shopify’s guide describes first-party inputs such as direct interactions, accounts, loyalty programs, browsing, and purchases; Mastercard’s Saks case study emphasizes product and content attributes as part of the work required to personalize experiences (Shopify’s personalization guide; Mastercard’s Saks case study).
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- Customer and interaction signals: preferences a customer shares, account or loyalty information, relevant browsing and purchase events, and responses to previous recommendations.
- Product and content attributes: accurate category, size, color, brand, material, style, scent, availability, and other metadata that helps a system distinguish products.
- Context: the page or channel a shopper is using and their current shopping intent, where those signals are available and appropriately collected.
Data quality matters as much as volume. Missing or inconsistent product attributes can make similar items hard to identify; incomplete event data can leave the system with a weak picture of shopper interests. Before deployment, check that product records and events are accurate, timely, and usable across the relevant commerce systems.
How to implement personalization responsibly
- Choose a specific customer problem. Decide whether the first use case is helping shoppers find relevant products, assemble an outfit, choose a gift, or see a homepage that reflects current intent. A narrow goal makes it easier to design and evaluate the experience.
- Audit data and product metadata. Confirm that the needed customer profiles, appropriately collected first-party events, and product attributes exist and are reliable. Identify gaps before choosing a system.
- Set outcome measures and a control. Choose a small set of relevant measures before launch, such as conversion, revenue per visitor, average order value, recommendation engagement, or customer engagement. Compare the personalized experience with an appropriate control, and record the audience and test period.
- Start with limited traffic. Roll out to a defined portion of eligible shoppers, monitor results and experience quality, and expand only when the evidence supports doing so. Saks’s case study provides an example of an initial 5% traffic test followed by a broader rollout, rather than starting with every visitor.
- Review results and failure modes. Check whether the experience is relevant and usable, not only whether a business metric moves. Watch for poor matches, unavailable products, inconsistent offers, or differences between customer groups.
In the 2025 Mastercard case study, Saks reported a 9.5% conversion-rate improvement, a 7% increase in revenue per visitor, and an 18.4% decrease in bounce rate during its described test. These are reported results from that retailer’s implementation; they are not independent benchmarks, and they do not establish that personalization alone caused every change (Mastercard’s Saks case study). AWS reports that MonAmie saw ecommerce revenue and average order value each increase 14%, and users engaging with its recommendation section increase 200%; these are provider-published outcomes for MonAmie, not expected results for other businesses (AWS’s MonAmie case study).
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How to choose an approach or provider
There is no universally superior option established by the available case studies. Compare systems against your data readiness, catalog, current commerce stack, measurement plan, and capacity to operate the experience.
- Data inputs and customer controls: Identify what the system uses, how those inputs are updated, and what choices customers can exercise.
- Catalog fit: Verify that the system can use the attributes that distinguish your products and handle the size and structure of your catalog.
- Relevance and explainability: Determine whether your team can understand why products or content are being shown and correct unsuitable results.
- Integration, latency, and scale: Check how the system connects to your commerce stack and whether it can serve the required experience at the speed and volume you need.
- Experimentation and measurement: Make sure you can create a control, define audiences, and evaluate outcomes without confusing the results with unrelated changes.
- Skills and support: Consider the expertise needed to configure, monitor, and improve the system. MonAmie’s case study describes using an implementation partner because the retailer lacked internal machine-learning expertise (AWS’s MonAmie case study).
Privacy, bias, and system limitations
Personalization relies on signals about people and their behavior, so collect only information that is appropriate for the use case, explain its use clearly, and respect applicable customer choices. Shopify’s guide discusses shoppers’ concerns about company-held data and first-party data collection, but it is commercial guidance rather than a jurisdiction-specific compliance standard (Shopify’s personalization guide). Consult qualified privacy counsel for obligations that apply to your business and customers.
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Recommendation systems can also encounter bias, scalability constraints, and cold-start problems: a new shopper or new product may have too little history to support confident recommendations. A 2024 arXiv preprint identifies these as challenges in ecommerce recommendation systems; it is a summary of potential issues, not proof that a particular deployed system has them or a regulator’s requirements (2024 arXiv preprint).
Quick Recap
- Cold start: Decide how to provide useful discovery when a shopper or item has little interaction history, for example by relying on product attributes or broad contextual relevance.
- Bias and uneven relevance: Monitor results across relevant shopper and product groups, and provide ways to correct poor matches.
- Scale and operational risk: Test data freshness, system response, and fallback behavior so pages remain useful when recommendations are delayed or unavailable.
- Over-personalization: Preserve clear navigation and useful general discovery options; a shopper should not be trapped in a narrow inferred profile.
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