Commerce Search Customization

Commerce Search Customization

This skill activates when a practitioner needs to configure, tune, or troubleshoot search behavior in a B2B Commerce or D2C Commerce storefront — including attribute-level search and facet configuration, result ranking, search index rebuilds, Einstein product recommendations, or silent product disappearance caused by BuyerGroup entitlement policy. It does not cover SOSL, Experience Cloud federated search, or Einstein Search outside the Commerce context.

Before Starting

Gather this context before working on anything in this domain:

• Confirm whether the storefront is B2B Commerce or D2C Commerce — both use the same Connect REST Search Indexes API surface, but recommender behavior differs (B2B recommenders auto-resolve anchor type; D2C requires explicit anchor configuration).
• Identify the WebStore ID for the target storefront; all Search Indexes API calls are scoped to /commerce/webstores/{webstoreId}/search/indexes.
• Check whether Einstein Activity Tracking is already instrumented on the storefront. Einstein product recommendations will return empty or low-quality results until buyer interaction data has been collected through the Activity Tracking API.
• Confirm the number of index rebuilds already triggered in the current hour. The platform enforces a hard cap of 60 search index rebuilds per hour per store. Hitting this cap blocks all further rebuilds until the window rolls over.
• Audit the BuyerGroup and entitlement policy configuration. Products without a matching entitlement policy for the buyer's BuyerGroup are silently excluded from search results at query time — they do not generate an error or warning.

Core Concepts

Three Attribute Sets That Control Search Behavior

B2B and D2C Commerce search is governed by three independent attribute sets that must be explicitly configured via the Connect REST API. They are not derived automatically from product field metadata.

Searchable Attributes determine which product fields the full-text search engine indexes and matches against when a buyer enters a keyword. Only attributes included in this set contribute to keyword matching. Adding a new product field to your product model does not automatically make it searchable.

Facetable Attributes determine which fields are surfaced as filterable facets in the search results sidebar (e.g., Brand, Color, Size). A field must be independently declared in the facetable set — being searchable does not make a field facetable. Each facetable attribute requires a displayType (SINGLE_SELECT or MULTI_SELECT) and an optional displayRank integer to control ordering.

Sort Rules determine the available sort options presented to the buyer and the default sort order on page load. Sort rules reference field API names and accept sortOrder (Ascending or Descending) and a priority integer. Without explicit sort rules, search results fall back to platform default relevance scoring, which buyers often find unpredictable.

All three sets are managed through POST /commerce/webstores/{webstoreId}/search/indexes — a single endpoint that accepts all three attribute sets in one payload.

Index Rebuild Is Required After Every Configuration Change

Changing searchable attributes, facetable attributes, or sort rules does not take effect immediately. An explicit index rebuild must be triggered via POST /commerce/webstores/{webstoreId}/search/indexes/rebuild. Until the rebuild completes, the storefront continues serving results based on the previous index configuration.

The platform enforces a hard cap of 60 index rebuilds per hour per store. This limit is easy to hit during iterative testing or CI/CD pipelines that apply and rebuild on every deployment. Rebuilds must be spaced deliberately; there is no way to queue a rebuild — if the cap is reached, the rebuild call returns an error and the index is not updated.

Index rebuild time scales with catalog size. Large catalogs (tens of thousands of SKUs) can take several minutes. Rebuilds run asynchronously; poll the index status endpoint to confirm completion before testing search behavior.

BuyerGroup Entitlement Enforcement at Query Time

Commerce search enforces BuyerGroup entitlement visibility at query time, not at indexing time. Products that exist in the catalog and are correctly indexed may still be silently absent from a buyer's search results if the buyer's BuyerGroup does not have an entitlement policy covering those products.

This is the most common cause of unexpected product disappearance in production. The platform does not return an error — the product simply does not appear. There is a hard limit of 2,000 BuyerGroups per product per entitlement policy. Products assigned to more than 2,000 BuyerGroups require a re-architecture of entitlement policy grouping.

Einstein Activity Tracking Is a Prerequisite for Recommendations

Einstein product recommendations (collaborative filtering, trending, frequently bought together) depend on buyer interaction data collected through the Activity Tracking API. The storefront must instrument page views, product views, add-to-cart events, and purchases via the Activity Tracking API before Einstein can generate recommendations. Recommendations will return empty or generic fallback content until sufficient interaction data exists. On B2B storefronts, Einstein recommenders auto-detect the anchor type from context; on D2C storefronts, the anchor type must be specified in the recommendation request.

Common Patterns

Pattern: Configure Faceted Navigation for a Custom Product Attribute

When to use: A new product attribute (e.g., Material, Voltage Rating, Lead Time) has been added to the product model and the merchandising team wants it available as a filter in the storefront search results sidebar.

How it works:

1. Identify the field API name of the custom attribute on the ProductAttribute or Product2 object.
2. GET /commerce/webstores/{webstoreId}/search/indexes to retrieve the current index configuration.
3. Add the field to both the searchableAttributes array (so it contributes to keyword matching) and the facetableAttributes array (so it appears as a filter).
4. Set displayType to SINGLE_SELECT or MULTI_SELECT depending on whether buyers should be able to select multiple values.
5. Assign a displayRank integer to control where the facet appears relative to other facets.
6. POST /commerce/webstores/{webstoreId}/search/indexes with the updated payload.
7. POST /commerce/webstores/{webstoreId}/search/indexes/rebuild to apply the change.
8. Poll the index status until the rebuild status is COMPLETED.

Why not the alternative: Setting field-level search indexes on Salesforce objects directly (e.g., marking a custom field as indexed in Setup) has no effect on Commerce search. Commerce search uses its own attribute-set-driven index separate from the platform search index.

Pattern: Adjust Search Result Ranking with Sort Rules

When to use: Buyers are getting results in an unexpected order and the team wants to promote in-stock items, highest margin products, or recently added items to the top of search results.

How it works:

1. Retrieve the current sort rule configuration with GET /commerce/webstores/{webstoreId}/search/indexes.
2. Identify the field API names to use for ranking (e.g., StockKeepingUnit, LastModifiedDate, a custom MarginScore__c field).
3. Build a sortRules array with objects containing fieldName, sortOrder (Ascending or Descending), and priority (lower integer = evaluated first).
4. Include the updated sortRules array in the POST /commerce/webstores/{webstoreId}/search/indexes payload alongside the existing searchableAttributes and facetableAttributes (omitting either will reset them to empty).
5. Trigger an index rebuild and wait for completion.

Why not the alternative: Attempting to override search result order client-side (in an LWC component that re-sorts the result array) produces inconsistent behavior when buyers paginate — page 2 is served from the server with the original ranking, so re-sorted page 1 and server-ranked page 2 present contradictory result sets.

Decision Guidance

Recommended Workflow

Step-by-step instructions for an AI agent or practitioner working on this task:

1. Gather context: Confirm the WebStore ID, storefront type (B2B or D2C), the specific search behavior goal (new facet, ranking change, recommendation setup, or product visibility fix), and the current index rebuild count for this hour.
2. Retrieve current index configuration: GET /commerce/webstores/{webstoreId}/search/indexes and read the existing searchableAttributes, facetableAttributes, and sortRules arrays before making any changes — a POST that omits an array resets it to empty.
3. Audit entitlement policy if products are missing: Before touching index configuration, verify the affected products have an entitlement policy covering the buyer's BuyerGroup. Check the 2,000 BuyerGroup-per-product limit if the catalog uses broad entitlement assignment.
4. Build and POST the updated index configuration: Include all three attribute sets (searchable, facetable, sort rules) in a single POST /commerce/webstores/{webstoreId}/search/indexes payload. Batch all changes into one request to minimize rebuild count.
5. Trigger a single index rebuild: POST /commerce/webstores/{webstoreId}/search/indexes/rebuild. Verify you have rebuild budget (fewer than 60 rebuilds in the current hour). Poll the index status endpoint until status is COMPLETED.
6. Instrument Einstein Activity Tracking if recommendations are in scope: Confirm the LWC components on the storefront fire page-view, product-view, add-to-cart, and purchase events through the Activity Tracking API before enabling recommendation components. On D2C, verify the anchor type is explicitly specified in each recommendation request.
7. Test and document: Verify search results, facet rendering, sort behavior, and recommendation output against the intended configuration. Record the final POST payload and rebuild timestamp in the work template for audit trail.

Review Checklist

Run through these before marking work in this area complete:

• All three attribute sets (searchable, facetable, sort rules) are present in the POST payload — none were accidentally omitted, which would reset them to empty
• Index rebuild was triggered after configuration changes and polled to COMPLETED status before testing
• Rebuild count for the current hour was verified to be below 60 before triggering the rebuild
• Products reported as missing were audited against BuyerGroup entitlement policy — not just index configuration
• Einstein Activity Tracking API events are instrumented on the storefront before recommendation components are enabled
• For D2C storefronts, Einstein recommendation anchor type is explicitly specified in all recommendation API calls
• The 2,000 BuyerGroup-per-product limit was checked for any products with broad entitlement assignment

Salesforce-Specific Gotchas

Non-obvious platform behaviors that cause real production problems:

1. POST to search/indexes resets omitted attribute sets — The POST /commerce/webstores/{webstoreId}/search/indexes endpoint treats the absence of an attribute set in the payload as an intent to clear it. If you POST only an updated facetableAttributes array without including the existing searchableAttributes and sortRules, those sets are erased from the index configuration. Always read the current configuration first and include all three sets in every POST.

2. 60-rebuild-per-hour cap has no queue — When the cap is reached, the rebuild API call returns an error immediately. There is no automatic retry or queue. CI/CD pipelines that call rebuild on every metadata deployment will exhaust the budget within minutes on an active development day. Design deployments to batch all search configuration changes and trigger exactly one rebuild per deployment.

3. Entitlement policy gaps produce silent product omissions — A product correctly indexed and visible in catalog management can be completely absent from a buyer's search results if the buyer's BuyerGroup lacks an entitlement policy for that product. No error is thrown, no warning is logged in the storefront. This is the most common cause of production escalations about "missing products" that are not actually missing from the catalog.

Output Artifacts

Related Skills

• lwc/experience-cloud-search-customization — Use this for Experience Cloud site search (federated search, search scope, search bar LWC customization) rather than Commerce storefront search
• apex/commerce-integration-patterns — Use when Commerce search must be extended via custom Apex search provider override or external search engine integration
• architect/b2b-vs-b2c-architecture — Use when deciding which Commerce platform to build on and understanding how entitlement and catalog architecture differ between the two models