Schema code and other types of structured data has been around for years and its use has become pretty much mandatory if you want to achieve peak SEO performance.
But what is its true purpose and how has it changed the way consumers interact with search? In short, schema has turned websites into repositories of data that algorithms can mine, organise and evaluate against the query consumers are asking to be answered.
We are now in the world of the “contactless website experience”. Devices such as knowledge panels or cards within Google Search and even non-physical search environments through voice search and Google Home surfaces are becoming common place.
In 2019 schema code will continue to change how consumers interact with your websites and advance Google’s ability to categorise signals and data to better its machine learning platform.
Here are some examples of how scheme could change 2019:
In September 2018, Google announced that it changed what is considered “close variants” of an exact match keyword to include variations that share the same meaning as the keyword – implying that Google is moving towards the intent of a keyword more than how it’s structured.
In 2019, marketers should be saying goodbye to vast keyword lists within SEM campaigns and should instead focus on how they can match keyword intent to personal signals such device, location, interests, interactions and demographics. Using data to better target consumers will allow marketers to create tailored search messaging which will invariably lead to an increase in CTR, conversions and brand favourability.
Humans are lazy, we really are… Our attention spans have become short and deadlines to achieve performance results even shorter. In search, we tend to not analyse all the variables and data to strategically market to a consumer insight or trend.
This is where machine learning becomes your best friend and to not use it in 2019 will be a colossal mistake. It’s capability to digest tonnes of data, seek out trends, match them against key marketing signals and correlate them back to your business objective is amazing, and something no human can do so within a quick turn around.
So please start to use machine learning to do the heavy lifting and focus your time on the strategic planning and understanding the consumer behaviour to meet your clients objectives.
As as said last time, personalisation of content is where we are all headed. But has anyone asked the question; What is the negative impact of curating content based on signals such as view rate, likes, shares and subscriptions?
Content personalisation is highly evident every time you open the YouTube App. Instantly you are greeted with variations of the same content you watch or have liked but from potentially new sources.
Based on a sample size of 1 – myself – I continuously scroll down that page and let the tailored content come to me. The algorithm is so attuned to my preferences now that discovering new pieces of video content doesn’t happen through the search bar, it is suggested.
But what of the impact? Straight off the bat, we are seeing a reduction of search queries within the platform as content is delivered on auto-play or suggestion.
But what about a revolt against personalised content streams? We often hear friends or family “quit” a platform or – for the more tech savvy – disabled search/viewing history tracking as a way to feel less “watched” by algorithms. This type of user revolt against personalisation in an effort to break free from their confined content viewing behaviour would see them move back to the search bar, having to look for new, relevant content specifically.
Google has a global market share of 93% and it will continue to grow if it wins the platform wars that are occurring within India and China. But remember that search isn’t just relegated to Google and its products, it is a universal platform featured within highly used apps and websites.
In 2019, I believe we will see more retailers (both global and local), content and gaming platforms opening up their search inventory to advertising to increase revenue into the business.
Amazon, Apple App Store, Waze and Dan Murphy’s are all great examples of how big brands are tapping into their widely used search bars to drive incremental growth back into their business.
For marketers, this allows you to diversify your biddable portfolio and to reach new in-market consumers and to potentially drive down your costs by taking advantage of the lack of competitors within these emerging environments.