Adobe Merchandising
Learn how to create personalized experiences for your shoppers through product discovery and recommendations.
These Adobe Merchandising services are built on the composable catalog data model (CCDM), which separates product data from product context. Product data includes SKUs, attributes, metadata, assets, and prices. Product context defines how that data is delivered, including catalog views, data access policies, and catalog sources (locales). Because catalog views and policies control storefront data, you can compose custom catalogs for B2B, B2C, and D2C models and tailor shopping experiences without duplicating base data.
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Product discovery—Equips merchandisers with powerful tools to curate and optimize the shopping experience, ensuring that customers find the right products quickly, at the right time. By adjusting search behavior, you can directly influence product visibility, relevance, and conversion.
With product discovery, you can guide shoppers toward the most relevant products with minimal friction by configuring:
- Merchandising rules—Boost, bury, pin, or filter products in search results, default product listings, and category pages.
- Facets—Configure the filters shoppers use to narrow search and category results.
- Synonyms—Map equivalent terms so different search words return the same products.
Semantic search, which is enabled by default in Settings, improves relevance for natural-language and intent-based queries. In Settings, you can also configure price facets, catalog language, and advanced semantic and fuzzy search options.
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Recommendations—Use artificial intelligence and machine-learning algorithms to analyze aggregated visitor behavior and product interactions across your storefront. Combined with your catalog, this data delivers highly engaging, relevant, and personalized experiences. Recommendations are displayed on the storefront as units with labels, such as “Customers who viewed this product also viewed”. You can create, manage, and deploy recommendations directly from the Recommendations workspace and place them throughout the shopping journey to support promotional goals, cross-sell strategies, and personalized experiences.