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1. Define the personalization job before comparing platforms
Start with the shopper outcome, not a vendor feature list. “Personalization” may mean suggesting products to an individual, finding items similar to one a shopper is viewing, showing popular or trending products, re-ranking a product list, applying a promotion, or changing content or pricing according to storefront context.
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Write down where the experience appears, what decision it should influence, and what business result would count as improvement. Then ask each candidate to show how its documented capabilities map to those requirements. For example, Amazon Personalize documents several recommendation strategies and business controls; Adobe Commerce Product Recommendations documents recommendation types and storefront options; Salesforce’s cited Shopper Context guide describes contextual storefront changes in a particular PWA Kit implementation.
2. Check whether your events and catalog are ready
Recommendation quality depends on usable interaction history and a coherent catalog. Before a demo or implementation, inventory the events you can provide, how current they are, and whether product identifiers and categories remain consistent across analytics, catalog, and storefront systems.
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Audit interaction data
- List the events you collect, such as product views, cart additions, purchases, and any other relevant interactions.
- Check event volume, unique shopper counts, event freshness, and whether you can stream new events or import historical data.
- Identify gaps in identifiers or event coverage that could make behavior difficult to connect to catalog items.
Amazon Web Services gives service-specific guidance for Amazon Personalize: its ecommerce documentation specifies at least 1,000 events for the documented “Recommended for you” use case and recommends view and purchase event types. Its broader readiness documentation sets a minimum of 1,000 item interactions across 25 unique users, with at least two interactions per user. For quality recommendations, AWS recommends 50,000 item interactions from at least 1,000 users, each with two or more interactions. These are AWS’s thresholds, not universal requirements for personalization systems. Amazon Personalize readiness guidance and the AWS ecommerce use cases explain the requirements for that service.
Audit the catalog
- Confirm that item IDs are stable and match the IDs used in interaction events.
- Review missing values and consistency in names, categories, and other fields used to describe products.
- Check whether product availability and price data can stay current in the recommendation experience.
AWS specifically recommends checking missing values and consistency in naming, IDs, and categories. Adobe Commerce Product Recommendations documents use of catalog information such as product names, prices, and availability, alongside aggregated behavioral data such as product views, cart additions, and purchases. Confirm the exact fields and data flow required for the candidate product and your implementation.
3. Match the integration to your storefront architecture
Implementation effort depends on whether a product plugs into your commerce stack or requires a custom connection to the storefront. Record your storefront type, commerce platform, deployment model, and the engineering work your team can maintain—not just the initial integration path.
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- Amazon Personalize: AWS describes a managed machine-learning service that accepts data through S3 or real-time integrations, inspects data, selects algorithms, trains models, provides metrics, and generates recommendations. Verify how your systems would supply data and consume recommendations.
- Adobe Commerce Product Recommendations: Adobe documents storefront options that include PWA Studio, custom headless frontends, and Commerce Edge Delivery Services. Its workflow includes deploying data collection, configuring templates, testing recommendation types and placements, and deploying recommendation units.
- Salesforce B2C Commerce Shopper Context: The cited developer guide is an implementation example for PWA Kit. It requires configuration, authorization, and custom code to fit a merchant’s strategy; Salesforce says the described functionality is not supported in a hybrid storefront. Do not treat this narrower context implementation as proof of feature parity with a recommendation engine.
Ask the implementation team to identify required APIs or modules, custom code, authorization, data ownership, and ongoing operational work. A product that supports your general commerce platform may still not support your specific storefront pattern or the way your team deploys changes.
4. Verify privacy and governance product by product
Ask what information is collected, how shopper identifiers are handled, whether consent signals are respected, how long data is retained, and whether the service is suitable for the information your business processes. Check current vendor documentation and terms for the exact product and deployment; a statement about one product does not establish another product’s privacy or compliance posture.
Adobe says its Product Recommendations collection excludes personally identifiable information (PII) and anonymizes identifiers such as cookie IDs and IP addresses. Adobe also explicitly warns that Product Recommendations is not a HIPAA-ready service and must not be used in implementations that process protected health information (PHI). This is a product-specific limitation, not a general compliance assessment. Adobe’s Product Recommendations overview describes the service and its stated limitations.
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5. Plan a controlled test with useful business measures
Do not judge recommendations only by whether they render or receive clicks. Define the business goal, establish a baseline, and test the experience in the relevant placements with a way to compare outcomes. Account for merchandising rules, promotions, fallback content, and the way new products are handled, since those controls can affect what shoppers see and how results should be interpreted.
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Use the candidate’s documented test path
AWS recommends A/B testing recommendation strategies against business goals. Adobe’s documented workflow is to deploy collection, customize a recommendation template, test recommendation types and placements in a non-production environment using production behavioral data, and then deploy recommendation units to production. Salesforce’s Shopper Context guide recommends testing its PWA Kit implementation outside production.
Before rollout, agree on the comparison, success measures, test duration and decision rule with the teams responsible for analytics and merchandising. Also confirm that the candidate exposes the metrics and deployment controls you need to understand results and revert a change if it does not meet the agreed goal.
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6. Compare commercial terms with a full-cost view
The cited documentation does not establish comparable pricing, contract terms, service-level commitments, or total cost across these products. Request current, product-specific commercial terms rather than inferring cost from feature descriptions.
Compare usage charges and any other costs relevant to your deployment, along with contract length, support, service-level commitments, and the engineering and operational effort needed to maintain data feeds and storefront integrations. A low quoted service price is not necessarily the lowest-cost implementation if it requires substantial custom work.
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Use a shortlist that reflects your use case and technical context rather than naming a universal winner. The available official documentation supports a practical first division:
- Consider a managed recommendation service when you need a range of recommendation strategies, can supply suitable interaction and catalog data, and have a clear route to integrate its outputs into your storefront. Amazon Personalize is an example of this service model.
- Consider a commerce-native recommendation feature when you use the relevant commerce platform and want its documented collection, recommendation types, templates, and supported storefront paths. Adobe Commerce Product Recommendations is an example.
- Consider a storefront-context implementation when your need is to personalize promotions, pricing, recommendations, or content through the specific supported storefront implementation. Salesforce’s cited Shopper Context guide is a PWA Kit example, not evidence of a general recommendation platform.
For each candidate, record evidence for the same questions: use cases, data prerequisites, storefront fit, privacy and governance, business controls, testability, and current commercial terms. Keep only products that meet the hard requirements, then validate the remaining options with a limited implementation and a measured test.
Adobe test-environment details to account for
Teams maintaining a long-lived Adobe Commerce Product Recommendations non-production environment should account for its documented inactivity rules: a non-production catalog can become inactive after 90 consecutive days without a query, and an empty catalog can become inactive 45 days after environment creation. These thresholds may affect test environments that are not queried regularly.
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