SAN FRANCISCO — “I’m not from the future, I’m just from Google,” said Dan Siegler to a laughing Inman Connect crowd. “I can just tell you where we’re placing our bets.” So, what exactly is Siegler, the head of industry, automotive and real estate at Google, putting his bets on? Machine learning. Siegler said from the beginning of time until 2003, humankind produced approximately 5 exabytes of data. Now, we produce almost 5 exabytes of data per day.
SAN FRANCISCO — “I’m not from the future, I’m just from Google,” said Dan Siegler to a laughing Inman Connect crowd. “I can just tell you where we’re placing our bets.”
So, what exactly is the head of industry, automotive and real estate at Google putting his bets on? Machine learning.
Siegler said from the beginning of time until 2003, humankind produced approximately 5 exabytes of data. Now, we produce almost 5 exabytes of data per day.
“Less than 1 percent of it is being analyzed,” he said. “And machine learning is helping us get into that other 99 percent.”
Machine learning is under the umbrella of artificial intelligence (AI), and it specifically refers to “using algorithms and tools to facilitate and enable machines to learn,” said Siegler.
The algorithm Siegler is referring to is called a neural network. A neural network is comprised of code that tries to think of everything that can happen to the program. Data scientists feed loads of data into the machine so the code can begin making decisions on its own.
Siegler said Google uses neural networks to make its proprietary products, such as Google Photos and Google Translate, work better. For example, Google Translate used to translate word-by-word, which rendered inaccurate results. Now, thanks to a robust neural network, the tool translates sentence-by-sentence to render contextually accurate translations.
So, how exactly will this technology impact real estate?
1. Smarter marketing
Siegler said real estate professionals will be able to use machine learning to make their marketing smarter, accurately targeted and better overall.
He said marketers are currently using best-guess targeting tactics to reach audiences across various platforms. Siegler said machine learning eliminates best-guess strategies and allows marketers to conduct deep analyses that produce highly specific audience segments that include “people who are much more likely to do what you want them to do when you get them into the funnel.”
2. Smarter ads
Next, he said machine learning enables marketers to send responsive ads that change based on the path a customer uses to get to your website or product.
Specifically, these responsive ads will be able to seamlessly change size based on whether viewers are on their mobile device or a desktop, or what social media site the ads are appearing on. Furthermore, the wording of the ad will change based on what is most likely to draw a consumer in.
3. Smart bidding
Lastly, Siegler said machine learning will enable businesses to “really understand what a click is worth.” He said current tactics include doing a CPC (cost per click) analysis — something that is inaccurate and “leaves a lot of money on the table.”
Instead, he said real estate professionals should learn about smart bidding, which is a strategy that allows marketers to know exactly where a potential consumer is coming from and optimize bids at the right time so you’ll effectively spend your ad budget.
Siegler says AI and neural networks are relatively new, and that we’re in the frenzy stage — a stage that describes the excitement and skepticism over a new technology.
For those who are skeptics, Siegler has one piece of advice: “Start now.”