June 5, 2017
For digital marketers, audience targeting is one of the single most important steps for a winning online strategy. It’s what helps brands strategically narrow their efforts in on their targets and get the insights they need to have more success.
Targeting is especially important for paid search, where having a clearly defined audience matters most because money is on the line. It’s no surprise that Google recently announced developments from the intersection of paid search and machine learning, as more powerful audience insights and targeting developments are underway. Here’s what you need to know from this recent announcement and what it means for digital marketers going forward.
It’s no secret that a lot of users search the internet with the specific intent of making a purchase. In response to this, Google has started using machine learning findings to better understand that intent and create opportunities for marketers. The machine learning analyzes trillions of search queries and activities across the web and then sorts that data to figure out when people are close to buying and surface ads that will be more relevant and interesting to users.
In-market audiences will use this advanced machine learning to allow marketers to reach people based on their specific interests as they browse pages, apps, channels, videos, and content. Marketers select audiences by categories, such as fashion, travel, or sports, and Google shows their ads to people who are likely to be interested in those categories:
This is a win-win for everyone, because by targeting audiences who are in the market users see more of information that’s relevant to them and marketers channel their efforts into a more direct line of communication. Overall, this will enhance the search experience for users and the advertising experience for marketers to be more efficient, pointed, and hopefully productive.
In keeping with Google’s continued use of machine learning, this development goes to show how insights and data from artificial intelligence in search will be used to drive new updates, features, and products. As I’ve mentioned before, the use of machine learning to enhance search is not a black and white issue or formula that marketers will be able to find shortcuts around or avoid. Use of machine learning stems from Google’s vested interest in improving the search experience for users and continually narrowing the margin for marketers who game the system. Because machine learning looks for, collects, and analyzes data based on real user behavior, the updates and developments we see from search engines moving forward will be increasingly based on quality and authenticity.
In addition to providing a better user experience, this showcases that marketers also stand to gain from machine learning findings. Among speculation and chatter that machine learning will eventually cause the death of organic search has developed a fear that advancements made by way of artificial intelligence will put marketers at a disadvantage, ultimately crowding them out space to target their audiences. What this development actually shows is that machine learning has the capacity to create as much opportunity for marketers as it does for users, especially in terms of creating opportunity and relationships more specific between users and brands.
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