If you thought you were done hearing about machine learning, you were wrong. As I’ve discussed in these recent articles, Google has started implementing the use of machine learning in search. By using machine learning to better understand user intent and uncover new search insights, Google is able to roll out increasingly sophisticated algorithms that improve both the quality of search results and the experience of users. So far, artificial intelligence has been used to find patterns and become more sophisticated over time, adapting and evolving like users so that Google can adapt and evolve the search experience to keep up. To many marketers, this initially felt like machine learning would exclusively benefit users and narrow the margin for advertising and SEO. However, Google AdWords recently announced a couple of machine learning developments that will benefit marketers and their efforts.

What’s new?

In-market audiences: In the same way that machine learning has been used to better understand search intent, it is also being used to better understand purchase intent. The step being taken towards this is the implementation of in-market audiences, which allow businesses to target users who have already search within their product and/or services category and are therefore much more likely to make a purchase. Per a statement on in-market audiences from Google, “It analyzes trillions of search queries and activity across millions of website to help figure out when people are close to buying and surface ads that will be more relevant and interesting to them.” This is good news for marketers because it ultimately means less wasted effort target broad audiences and more high-converting effort targeting an audience with specific purchase intent.

Google Attribution: This one isn’t yet available to everybody yet, but when it’s eventually rolled out from its current nest in beta it will likely be a favorite tool for online advertisers. In short, Google Attribution uses machine learning to aggregate data from AdWords, Analytics, and DoubleClick Search to analyze and view the results all in one place:

google-attribution Is Google using machine learning in AdWords?

The goal behind this machine learning innovation is to provide a clear and complete picture to an advertiser that lets them know whether or not their marketing efforts are working. Whereas existing attribution tools were difficult to set up, couldn’t track across multiple user devices, and didn’t integrate well with ad tools, Google Attribution makes it easy. It also makes switching to data-driven attribution easy, which uses machine learning to determine how much credit to assign to each step in the consumer journey (you can learn more about this here). Data-driven attribution follows a consumer from when they first engage with your brand down to the final clicks before a purchase is made. It also analyzes your account’s unique conversion patterns to compare paths of customers that end up converting to those who bounce. You can report, update bids, or move between your advertising channels all from one easy place.

Conclusion

What all these snazzy new updates go to show is that machine learning will be a good thing for both users and advertisers. Everyone stands to get from more efficient search and advertising tools, so online marketers need not fear the doom and gloom chatter occasionally surround machine learning. In the future, we can expect more innovations and updates such as these.