In this age of digital transformation, organizations compete on their data. Every interaction, whether internal to the company or external, is facilitated by data and content. Being able to present the appropriate information to customers and prospects depends on how well that data is designed and managed. One critical element in data management is the development of a product schema.
The product schema is the overall structure for describing the product data. It contains the list of structured data attributes for a single product, a product type, or product family. The attributes contain the specific values that describe the product. For example, the product attributes schema for a laptop will includes attributes such as product ID, price, weight, dimensions, screen size, storage capacity, RAM, and processor speed. The structured data schema for a phone includes some of these elements but also includes additional elements such as carrier, wireless capability, number of cameras, etc. This information is typically stored in a product information management (PIM) system.
Product Schemas Improve Search
Product attributes and product schema (plus the taxonomy that organizes products on a B2B or B2C website) determine whether your products are visible and findable. The schema markup in a PIM identifies structured data for a search engine. If the wrong schema is used, customers will not be able to locate your products with an onsite search engine, on a distributor site search, or in marketplace such as Google shopping. If your customer can’t find it, they can’t buy it, and you will not be able to compete in the market.
Product schema and schema markup also allow for structured data about your product to appear in a “rich snippet” or a “rich result” in Google search result. For example, using structured data markup tells the search engine the details of your product – size, price, color, model. A rich result snippet using the correct product schema and product schema markup contains more information that will help your customer choose your product. A rich result snippet has a higher click through rate than a standard snippet – one study 58% versus 41%[1] A click-through can bring the customer directly to your product page, so it is important to have the correct product markup to encourage that conversion.
What is attribute schema design?
Multiple types of schema can be designed. A product attribute schema is one type of schema. It describes the details and characteristics of products and allows for customers to select the details that are most important to them. A product schema type delineates the data structure of different options for a product, such as size or color, brand, configuration, variations, types, and specifications, each of which is a metadata category. The product schema attributes contained on a product page allow your customers to tell your products apart (a blue sweater vs. a white sweater) and how they are different from those of your competitors. Product rich results might also include product reviews that would show up with product listings.
Another schema type could be for an event. This schema type would contain markup with required properties such as a date, time, location, and other recommended properties such as a short description. In the case of a concert, biographies of the performers or a history of the group could be included in the schema.
Having consistent standards for product attributes (including naming, definition and application across the entire portfolio of products) will improve the customer experience, leading to increased conversions, click-throughs, and sales. Good data governance also supports improved product onboarding and data quality.
How do you evaluate your product attribute schema design?
Design of product information attributes and schema is based on an understanding of users and the things they are trying to accomplish. It is important to define the personas – characteristics of your target prospect – so that designers can determine how the customers are making decisions about purchasing products. For example, a B2B customer in the procurement department has different factors in mind than someone in the engineering department. Procurement cares about pricing, supply chain, delivery, support and logistics, while engineering is more concerned about technical specifications. In each case, information about the appropriate factors needs to be available in the attribute model.
Attribute schema designs can be evaluated through a variety of approaches. One is compliance with best practices or “heuristics” – the rules of thumb for a well-designed information structure. Another approach is to analyze user behaviors on the website. For example, if visitors are coming to a product page and then leaving without drilling down to products, it may be because they do not see the specifications or details that would help them choose the product they want. The persona exercise includes development of use cases and user scenarios. Those use cases will reveal which attributes need to be in the schema design. User testing is then used to validate the design choices.
Who should be concerned about product attribute schema design?
Many executives do not understand the importance of product schema design. Often responsibility for attribute schema design is delegated to the IT organization or sometimes even outsourced to a low cost-offshore provider. But we advise against this because of the very real impact that poorly constructed product data models can have on your bottom line. Correct attribute design is a sophisticated process and requires expertise in a range of areas, including merchandising, user experience, product information management, ecommerce, taxonomy development, and information architecture. Understanding the correct data type design is an important element for Google analytics. Having the incorrect data type or missing attributes on a product page will skew analysis results.
A poor product attribute design has a far-ranging impact, including reduced findability, poor search performance, inability to offer solution bundles, problems with cross-sell and upsell, all of which ultimately reduce conversions and lower sales. If customers cannot locate what they need quickly, they will move on to your competitor’s site. Business leaders, heads of ecommerce, product managers and digital marketing executives should all be concerned about having the solid foundation of a correctly designed product attribute schema. Implementation requires technical SEO capabilities and tools such as the Schema App[2], Schema Markup Validator[3] as well as those from Yoast SEO[4] can ensure that data type, structure and markup are correct.
Best practices in attribute schema design
One of the critical elements for product attribute schema design is a deep understanding of users based on their role and their objectives. This is done by creating personas and user journey maps. One output of this process is a series of use cases that describe exactly what users need to do when they come to your site and are solving a problem. The goal is to understand how prospects think through their decision. How will they select a particular product? What are the most important factors? What language do they use when describing challenges and requirements? The goal is to understand the user’s “mental model” and reflect that model in the design of attributes so that the correct choices can be presented at each touchpoint. This is sometimes accomplished through refiners or facets on the left hand navigation bar, through identifying related products or through solution bundles, for example.
While the principles are the same across industries, the process will require different participants when the products are technically complex. For example, B2B industrial manufacturers will require more input from people who have deep expertise in the domain when developing product schema. Those subject matter experts will know more about how the product is used and how customers make decisions and solve problems – all of which need to be reflected in attribute design. The customer journey for consumers is quite different. B2C businesses require an understanding of the customer’s personal preferences and buying behaviors, rather than requiring deep technical expert input.
Product display taxonomies are closely related to attribute schemas and inform how those attributes will be managed and optimized. The display taxonomy leverages the attribute schema for faceted search and guided navigation. Consider the elements that one would click on to narrow a selection of products – size, brand, price, color – or in the case of technical product – the engineering specifications, tolerances and configuration options. These elements populate refiners that customers use to locate exactly what they want. In this way, taxonomy design is closely related to product attribute design.
Fixing product attributes and product taxonomies that were initially not well designed leads to directly measurable uplift in click-throughs, conversions, and sales. Every organization selling products and using a web presence (even if it is not conducting online commerce) can benefit from a well-tuned and tested product attribute schema. This aids in findability, cross-sell and upsell.
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[1] https://www.searchenginejournal.com/google-serp-study-which-rich-results-get-the-most-clicks/382445/