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Product Information Structure

Product and catalog managementAdvanced Level

Product information structure is how product data is organized into categories, attribute groups, typed attributes and variants, so every product is described the same way.

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What is Product Information Structure?

Product information structure is the way product data is organized: which attributes a product has, how they are grouped, which values they accept and how products relate to categories, families and variants. It is the product data structure behind every product page, feed and filter. Product information itself is everything you know about a product: identifiers such as SKU and GTIN, titles and descriptions, specs, prices, images and documents. The structure decides where each piece lives. A typical structure has a category tree, attribute groups per category (for example "Dimensions" or "Electrical"), typed attributes with units or fixed value lists, and a parent product with variants for size or color. It differs from a taxonomy, which only orders categories, and from a product page layout, which decides how the data is shown. Our guide to product hierarchy explains how to structure product data from category down to variant.

Why Product Information Structure matters for e-commerce

Filters, search, product comparisons, feeds and translations all read from the same structure. When attributes are free text, or the same detail is stored under three names, filters break, feeds get rejected and every new channel needs manual cleanup. A clear product information structure fixes that at the source. Structure also decides how fast you can grow. Adding a category, a language or a marketplace is a mapping job when the structure is clean, and a rebuild when it is not. In WISEPIM you set up attributes, attribute values and product families centrally and map your categories to each channel with the Category map.

Examples of Product Information Structure

  • 1Level: Category, Example: Kitchen > Cookware > Frying pans
  • 2Level: Attribute group, Example: Dimensions: Diameter (cm, number), Height (cm, number), Weight (kg, number)
  • 3Level: Attribute with value list, Example: Hob type: Gas, Electric, Induction, Ceramic (multi-select)
  • 4Level: Product family, Example: Parent "Pro frying pan" with variants by diameter: 20 cm, 24 cm, 28 cm
  • 5Level: Channel mapping, Example: Your category Frying pans mapped to the Google product category for skillets
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How WISEPIM Helps

  • Attribute management and the Attribute values page let you define typed attributes and fixed value lists in one place.
  • Product families and variants keep shared data on the parent and only the differences, such as size or color, on each variant.
  • The Category map links your own category tree to channel taxonomies, including the Google product taxonomy.
  • AI category classification suggests a Google product category for each product, with 95%+ accuracy.

Common mistakes with Product Information Structure

  • Using different names for the same product details across categories creates messy data. This inconsistency makes it hard for customers to find what they need.
  • Overcomplicating the Product Information Structure with too many details makes data entry difficult. Employees struggle to manage information when the system is too complex.
  • Building a structure without input from marketing, sales, or IT teams leads to gaps. The system must meet the needs of every department to be effective.
  • Designing a structure only for current products limits future growth. Failing to plan for new markets leads to expensive and time-consuming changes later.
  • Storing images and videos separately from product data causes errors. Linking these files to the PIM system ensures product pages always show the correct content.

Tips for Product Information Structure

  • Check your current data first. Look for missing or repeated info before you build a new Product Information Structure.
  • Start with a simple plan. Only add complex details as your business and product list grow.
  • Ask for feedback from every department. Ensure the structure helps marketing, sales, and IT teams work faster.
  • Create strict rules for naming. Use the same terms everywhere to keep your product data accurate.
  • Design your structure for all sales channels. Ensure your data meets the needs of webshops, marketplaces, and printed catalogs.

Trends around Product Information Structure

  • AI-driven data modeling: AI and machine learning assist in analyzing existing product data to suggest optimal attribute sets, categories, and relationships, streamlining initial setup and ongoing refinement.
  • Automated schema generation: Tools increasingly automate the creation of product information structures based on industry standards, competitor analysis, and market demand, reducing manual effort.
  • Headless commerce compatibility: Structures are designed with API-first principles, ensuring maximum flexibility and adaptability for seamless integration with various front-end experiences and channels.
  • Sustainability attribute integration: Product information structures are evolving to include specific attributes for sustainability data (e.g., recycled content, ethical sourcing, carbon footprint) to meet consumer and regulatory demands.
  • Semantic data modeling: Moving towards more semantically rich structures that allow for better machine readability and interoperability, enhancing data exchange with partners and AI applications.

Tools for Product Information Structure

  • WISEPIM: A comprehensive PIM solution offering flexible data modeling capabilities to define and manage complex product information structures efficiently across all channels.
  • Akeneo: An open-source PIM platform that provides powerful tools for structuring, enriching, and distributing product content.
  • Salsify: A Product Experience Management (PXM) platform that helps businesses create, manage, and syndicate product content with robust data modeling features.
  • Contentful: A headless CMS that allows for highly flexible content modeling, enabling businesses to define custom structures for product-related content beyond traditional PIM systems.
  • Shopify / Magento: E-commerce platforms that offer foundational product data structures, often integrated with PIM systems for advanced structuring and enrichment.

Related Terms

Attribute GroupAttributizationCategory ManagementCircular Product IdentityCountry of OriginDigital Catalog

Also Known As

product data structureproduct structureproduct information architectureproduct data model

Frequently Asked Questions

Product information is all data that describes a product: identifiers (SKU, GTIN), names and descriptions, technical specs, prices, images, documents and channel-specific content. In ecommerce it is what shoppers read on the product page and what marketplaces and feeds need to list the product.

Start with a category tree, then define which attributes each category needs and group them (for example Dimensions, Materials, Electrical). Give each attribute a type, a unit and, where possible, a fixed list of values. Finally decide which products are variants of one parent, such as sizes and colors.

A taxonomy is the hierarchy of categories, for example Home > Kitchen > Pans. The product information structure is wider: it also covers the attributes per category, their data types and value lists, and how variants and related products connect. The taxonomy is one layer of the structure.

Keep one internal structure and map it to each channel instead. Marketplaces and feeds each have their own categories and required fields, so you translate your categories and attributes to theirs at export. That way you maintain the data once.

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