Source – zdnet.com
Google on Tuesday detailed a new suite of marketing tools that taps into the company’s vast repertoire of machine learning technology. Overall, the tools are aimed at helping marketers create more effective and optimized ads — but there’s a noticeable element of anti-Amazon underlining some of the key products.
Namely, Google’s new Local Campaigns service. The toolset uses machine learning to help drive brick-and-mortar store visits by optimizing ad placement across various Google platforms.
Businesses provide a location and ad, and Google automatically optimizes ads across properties “to bring more customers into your store.” Google said Local Campaigns report on store visits using anonymous, aggregated data from Google users who have signed-in and turned on their location history.
According to Google, people still make the majority of their purchases in physical stores, with mobile searches for nearby locations growing three-fold in the past two years, while “almost 80 percent of shoppers will go in store when there’s an item they want immediately.”
Local shopping is seemingly a direct swipe at Amazon on behalf of local brick-and-mortar retailers. With its focus on optimized ads and marketing campaigns to drive store visits, it’s essentially about everything but Amazon.
In April, Amazon stopped buying product listing ads, or PLAs, from Google’s Shopping ads, signaling that it was eying a push into the digital advertising market. Cut to today, and Google is upping the ante for Amazon’s local competitors.
As for the rest of Google’s new machine learning ad tools, the company also launched Responsive search ads, which use a machine learning ad format that “mixes, matches, and optimizes creative assets in real time to show the best-performing ad for each search query.”
Similarly, Smart Shopping campaigns use machine learning to optimize marketing efforts based on certain criteria and goals. Finally, Maximize Lift for YouTube, already in beta, automatically adjusts bids to optimize ad performance.
