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E-commerce AI personalization usually starts with a record of which shopper or pseudonymous user interacted with which product, what they did, and when. Product details and selected customer attributes can add context, but the right data depends on the experience you want to build—such as popular items, related products, or recommendations for a known shopper. Collect and use each field only for a defined purpose, with the required permissions.
What data powers personalized shopping?
For product recommendations, the core is usually item-interaction events: a user identifier, an item identifier, an event type, and a timestamp. The interaction might be a product view or purchase. These records let a recommendation system connect behavior to products and, where relevant, to a particular user.
Interaction events
- User ID: A stable identifier within the recommendation system, which may be pseudonymous if that fits the merchant’s privacy design.
- Item ID: The product identifier that matches the catalog.
- Event type: What happened, such as a view or purchase. Choose events that match the recommendation task.
- Timestamp: When the event happened, so the system can use interaction history in context.
Amazon Web Services (AWS) documents e-commerce recommendation workflows built around item interactions, including views and purchases. Its Amazon Personalize e-commerce use cases specify different event needs for different recommendation goals.
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Catalog metadata can describe products and support item-aware workflows or filtering. Keep product identifiers consistent with the interaction data, and consider which attributes are useful for the chosen experience. Item datasets are optional for the AWS “recommended for you” use case, so catalog fields should not be treated as a universal prerequisite.
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Customer or user attributes
Some systems can use numerical or categorical user metadata, such as age or loyalty membership, as additional context. AWS documents that user metadata may improve relevance and can help supported models make recommendations to users without interaction history. It is an optional aid, not a replacement for choosing suitable events. AWS requires at least one numerical or categorical metadata column when importing a user dataset into Amazon Personalize; that product-specific requirement is not a reason to collect a personal attribute without a defined need. See AWS’s user metadata documentation.
Match the data to the recommendation goal
Different placements need different signals. AWS’s documented examples illustrate how the intended task shapes the event data:
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| Recommendation goal | Relevant data | AWS documentation’s qualification |
|---|---|---|
| Most viewed or popular by views | Product view events | The documented “most viewed” recipe requires view events. |
| Best sellers or frequently bought together | Purchase events | The documented use cases rely on purchase events. |
| “Customers who viewed this also viewed” | View events | AWS describes purchase events as recommended input for this use case. |
| Recommendations for a known shopper | A user identifier and interaction history; views and purchases are recommended event types | For Amazon Personalize’s “recommended for you” use case, item and user datasets are optional. |
These examples describe Amazon Personalize, not universal requirements for every recommender. Check the requirements for the actual model and product you plan to use.
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The answer depends on the recommendation system. AWS’s published Amazon Personalize guidance sets a product-specific minimum and a higher recommendation for quality; neither figure is a general rule for e-commerce AI.
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| Amazon Personalize guidance | Published interaction volume | Scope |
|---|---|---|
| Documented minimum | At least 1,000 item interactions from at least 25 unique users, with two or more interactions per user | AWS’s documented e-commerce use cases. See the use-case requirements. |
| Recommended for quality recommendations | At least 50,000 interactions from at least 1,000 users, with two or more interactions per user | AWS’s readiness guidance for Amazon Personalize, not a universal threshold. See the readiness checklist. |
These thresholds apply to the AWS service guidance cited, not every model type, merchant, or personalization goal. They do not establish how much a recommendation system will improve conversion or revenue.
How should consent and privacy affect collection?
Privacy belongs in the data design, not just in a banner shown after tracking has been planned. Shopify’s Customer Privacy API documents separate permission checks for preferences, analytics, marketing, and data sharing. The result can depend on the shopper’s region, merchant settings, and consent; in regions configured to require consent, non-essential purposes are not allowed by default until consent is given. See Shopify’s Customer Privacy API documentation.
Do not assume that permission for one purpose covers another. For example, a permission to use data for analytics is not automatically permission to use it for marketing. Shopify also cautions that collecting data without consent might not comply with applicable laws and recommends consulting counsel about which rules apply.
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For Shopify apps, the platform’s app privacy requirements call for transparency about what data is collected from Shopify APIs, merchants, and customers; how it is used; how long it is retained; and whether processing takes place outside Europe. These are platform requirements and guidance for Shopify apps, not a complete legal analysis for every merchant. Applicable duties depend on jurisdiction and context, and the cited documentation does not establish a universal retention period.
Build a focused data plan
- Name the experience. Decide whether the goal is popularity, related products, bundles, or recommendations for an identified or pseudonymous shopper.
- Choose the necessary events. Start with matching user and product IDs, an event type, and a timestamp. Include views, purchases, or both according to the task and the selected system’s requirements.
- Assess useful context. Check whether product attributes or selected user metadata improve the chosen experience. Do not collect fields merely because a service accepts them.
- Set permissions by purpose. Check the relevant preferences, analytics, marketing, and sharing permissions in the platform and vendor stack, taking region and configuration into account.
- Explain and govern use. Describe the data collected, its purpose, retention, and processing context. Define retention and a process for data-subject requests in line with applicable platform requirements and law.
How do implementation choices affect the data plan?
Amazon Personalize documents managed, domain-based recommendation models as well as customizable models. It supports real-time recommendation requests and batch recommendations or user segments. AWS recommends starting with an e-commerce domain dataset group for an e-commerce application; a custom dataset group lets the user select a recipe and deploy a configurable model for real-time or batch use. These options are described in AWS’s Amazon Personalize getting-started documentation.
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Compare implementation options by whether they fit the recommendation goal, whether interaction data meets the selected system’s requirements, whether updates are real-time or batch, and how consent and data handling integrate with the merchant’s platform. The official documentation cited here does not establish a comparative vendor ranking, pricing, recommendation lift, integrations, or contractual data protections.
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