A Short (But Exhaustive) Marketing Guide to Schema Markup
- Plan out the Schema Markup strategy
- Generate JASON-LD code
- Customize and test the code
- Add code to a web page
- Monitor and measure the business impact
What is Schema.org?
Schema.org is a standardized semantic vocabulary of tags (or microdata) used to mark up web content and make it machine-readable. Marking up a webpage will drastically improve search engine’s understanding of the content and increases the possibility of appearing in knowledge panels, voice search results, answer boxes, rich snippets and featured snippets such as these:
Why Schema Markup Matters?
You should care about schema for two reasons.
The first reason is that schema markup is an efficient and underutilized marketing tactic. Content discovery and improved SERP-placement leads to higher click-through-rates and higher customer engagement.
This study by Schema App showed a 160% increase in YoY click volume after the SAP website was marked up.
Today less then 1/3rd of the websites have schema and only 12.29% of search queries have featured snippets in their search results. With the growth of digital voice assistants such as Alexa, Siri and Google Home consumers are shifting to voice search and 40.7% of all voice search answers come from a featured snippets.
The second reason is philosophical. Schema markup semantically connects and describes data by adding meaning and context. It facilitates machine-understanding and supports a creation of knowledge graphs which are the foundation of the semantic web, a future global brain of the internet.
In his seminal 2001 article The Semantic Web, Tim Berners-Lee, imagined a Magna Carta for the future web 3.0, where semantics, structure, and shared standards will allow humans to speak with intelligent machines in order to access automated services. Where the “day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines”.
What are knowledge graphs?
A knowledge graph is a knowledge base that describes real world entities and their interrelations, organized in a graph. It acquires and integrates information into an ontology that allows both humans and machines to understand. In 2012, Google introduced its Knowledge Graph, as a semantic enhancement of Google’s search that does not match strings, but enables rich representations in SERP as well as to answer voice questions by Google Assistant and Google Home.
Google’s Knowledge Graph, was leveraging DBpedia and Freebase and later incorporated microdata formats from the pages they index, based on the vocabularies published by schema.org. Today major companies, such as Facebook, LinkedIn, Airbnb, Microsoft, Amazon, Uber and eBay have created their own “knowledge graphs” that power semantic searches and enable smarter processing and delivery of data.
Ehrlinger, Lisa; Wöß, Wolfram (2016). Towards a Definition of Knowledge Graphs
McCusker, James P.; McGuiness, Deborah L. “What is a Knowledge Graph?“. www.authorea.com. Retrieved 21 March 2017.