In today’s rapidly evolving digital landscape, website owners and content creators face a persistent challenge: identifying gaps in their existing content and continuously optimizing it for success and visibility in search. With user behaviors and search engine algorithms constantly changing, it is crucial to ensure that your new and old content remains comprehensive, relevant, and helpful to humans and machines. Enter Schema.org, a collaborative, community-driven initiative launched in 2011 by tech giants Google, Bing, Yahoo, and Yandex. The Schema.org vocabulary provides a standardized frame ..read more

This past June, we hosted a webinar with Digital Patient Experience Strategist, Chris Boyer, on How to Navigate AI Search: Key Insights for Healthcare Digital Marketers. Though the webinar was geared toward the healthcare industry, the content discussed offers universal value to any digital marketer adapting to AI-driven search. During the webinar, we discussed topics such as: The shifts in user experience and behavior after the introduction of AI search Strategies to enhance SEO and maintain relevance in the evolving search landscape How the measure of success in search is changing If you d ..read more

In today’s global marketplace, many organizations operate websites that serve customers across multiple countries and languages. These sites fall into two main categories: multilingual and multi-regional. Multilingual websites offer content in multiple languages—for example, a U.S. healthcare organization can provide users with both English and Spanish versions of its site. On the other hand, multi-regional websites target users in different countries or regions, often adapting content to local preferences and regulations. Some websites combine both approaches, serving as multilingual and mult ..read more

Schema Markup is a form of structured data that allows website owners to provide additional context and meaning to the content on their pages. It effectively communicates the purpose and relationships of different elements on your site to search engines. It’s crucial to express Schema Markup in a format accepted by major search engines to take advantage of the Schema.org vocabulary, become eligible for rich results, and accurately describe your website content. Popular search platforms like Google and Bing recognize three primary formats for Schema Markup: Microdata RDFa JSON-LD Implementing ..read more

Nesting in Schema Markup refers to the practice of structuring your markup hierarchically by grouping additional relevant entities on a web page under a defined main entity of the web page within your markup. This approach communicates clear relationships, giving machines context about the different entities described on your web pages. By improving search engine understanding of your content, it can lead to better visibility and more accurate presentation in search results. Here’s a breakdown of nesting in Schema Markup: Main Entity: This is typically the primary entity or item that your webp ..read more

In an era when search engines increasingly rely on semantic understanding and AI to generate results, Schema Markup has emerged as a vital SEO tactic for website owners striving to maintain visibility and relevance in search. However, Schema Markup is a very technical aspect of SEO, and navigating the complexities requires technical expertise, ongoing effort, and costs. As a result, we frequently encounter questions from our customers, digital marketers, and IT teams alike, seeking clarity on Schema Markup. This article addresses some of these common questions head-on, providing insights to he ..read more

Knowledge graphs have been central to semantic technology for decades. From healthcare and eCommerce to fraud detection and SEO, knowledge graphs empower organizations to harness the full potential of their information architecture. But even with a long history, knowledge graphs are more relevant than they’ve ever been. According to Gartner’s Emerging Tech Impact Report, a robust knowledge graph is imperative for organizations looking to implement generative AI technologies. Knowledge graphs can help organizations ground their AI initiatives—like LLMs—in factual data about the organization. Th ..read more

In the past, we’ve measured the value of Schema Markup purely through the lens of rich results. However, we’ve seen a lot of changes in rich results and the overall search experience this past year. The uprising of generative AI-powered search engines, accompanied by the volatility in rich results, has prompted our team to dive deeper into the semantic value of Schema Markup and entity linking as it pertains to search today. In this article, we will share the value of entity linking, the tools enabling you to do it at scale and the results we’ve seen from implementing entity linking with our E ..read more

On February 20th, 2024, Google added structured data support for product variants. This update comes as a relief to many eCommerce brands, as it allows merchants to display a wider range of product variations directly in their search results. The new structured data for product variants uses the Schema.org ProductGroup type in addition to Product structured data. Benefits of Implementing Product Variant Structured Data There are many products in the market with variants. For example, clothes can come in different colours and sizes. Phones can come in different capacities, colours, and models ..read more

What is a Knowledge Graph? A knowledge graph is a structured representation of knowledge that describes entities and the relationships between them. Knowledge graphs are a part of “knowledge representation“, a field of Artificial Intelligence (AI) that deals with presenting data in a way that enables machines to engage in reasoning, problem-solving, decision-making, and inferencing. The versatility of knowledge graphs extends across various domains, with use cases that include: Recommendation engines Targeted advertising Data analysis Semantic search The training of Large Language Models (LLM ..read more