To help you meet rising consumer expectations, over the next three weeks we’ll share insights and best practices from brands that have made machine learning an enabler for new opportunities in this “age of assistance”–instead of another challenge to figure out.
Solving problems with machine learning
At its core, machine learning is a new way of problem solving. Rather than spending hundreds of hours manually coding computers to answer specific questions, we can save time by teaching them to learn on their own. To do that, we give the computer examples until it starts to learn from them–identifying patterns, like the difference between a cat and a dog.
To illustrate how machine learning can help solve some of the most complex problems in the world, take the latest advances in medicine. In the US, doctors know survival rates for skin cancer increase dramatically with early detection.1 That’s why researchers at Stanford University used Google’s machine learning platform, TensorFlow, to train a model that can identify cancerous skin conditions from healthy ones with 91% accuracy–on par with 21 board-certified physicians.
New opportunities to accelerate growth
As marketers, you don’t wake up everyday expecting to save lives. But we do ask ourselves a very different question: how can I grow my business faster? This is where Google’s machine learning technology can help.
We know that choosing where your ads show and manually adjusting bids is time consuming, leaving less time for strategic tasks, like capturing the latest trends or entering new markets. Google’s machine learning considers billions of consumer data points everyday, from color and tone preference on mobile screens, to purchase history, device and location. With products like Universal App Campaigns and Smart Bidding, it’s now possible to use this data to help deliver millions of ads customized for your customers, and set the right bid for each of those ads–in real time.
Even if you’re not using these AdWords innovations, you’re still seeing the benefits of machine learning. Google uses information about search queries, historical ad performance and other contextual signals combined with machine learning, to predict whether or not someone will click on your ad. This predicted click-through rate helps determine the selection, ranking and pricing of your ads–meaning machine learning is already working to show the right ads to the right customers.
Over the next three weeks, we’ll continue exploring how you can use machine learning to reach your marketing goals and grow your business faster. To get the latest updates on this series, follow along on the Inside AdWords blog or subscribe to our Best Practices newsletter.
1. Stanford News, 2017