There are already 120 potential game-changing machine studying functions in 12 industries — and the evolution is accelerating. Be part of our newest VB Stay interactive event for a deep dive into how machine studying can immediately influence your online business outcomes.

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What machine studying does best: apply huge and complicated units of knowledge to advanced issues, and provide you with options no human might have.

At REX, an actual property service platform, the advanced drawback appears deceptively easy at first look.


“What we’re doing at REX is trying to take a very very complicated consumer transaction — the selling of a house and the buying of a house — and we’re trying to use data and algorithms to go way beyond what a traditional real estate agent can do,” says Andy Barkett, VP of AI and machine studying at REX.

One of many greatest points they sort out is likely one of the most difficult all realtors knock into: lead qualifying. Seriousness, dedication, and timeframe fluctuate considerably when sellers begin eager about placing their residence in the marketplace, and sifting via the detritus has been all the time been a stumbling block that may result in wasted time and misplaced gross sales.

Knowledge to the rescue, Barkett says.

“When someone creates a profile on our site or signs up indicating they may have interest in selling their house, we get about 1300 pieces of data about that person,” Barkett explains.

They get the data from conventional client advertising firms, which implies they’re capturing a rare number of data. It’s every little thing from variety of children and demographic information to chance of shopping for residence workplace furnishings.

That information will get poured into what Barkett calls traditional supervised machine studying, which implies the algorithm is definitely tuned to succeed in a particular consequence, like signing as much as listing their home or truly selecting up when somebody from REX calls.

However although they don’t truly know upfront which data may truly correlate to the outcomes they’re in search of, it’s not simply throwing a vat of jello salad in opposition to the wall and seeing what sticks.

“We let the machine figure out which of those variables are actually predictive, and we continually adjust that over time,” explains Barkett. That’s as a result of whereas, for instance, being in a sure earnings bracket is predictive one month, the market will change, liquidity will dip, and a month later, that variable may not be as predictive.

“So rather than try to speculate about what’s predictive, and rather than try to do a simple, one-time static analysis of variables to figure out which is interesting, we just continuously run a machine learning process and let the machine decide which variables are predictive of those outcomes,” Barkett says.

However the necessity to persistently iterate, take a look at, and iterate some extra requires setting expectations throughout the nation, he’s discovered. His gross sales executives had been initially beneath the impression that it was a one-and-done course of.

“The expectation was essentially you run the machine learning process, and then a model comes out of it,” he says. “And then once that model has been built, they get better information about which leads are good ones. The reality is that we actually ran the process, and the first time we ran it,I’m not sure it gave us anything useful.”

It’s a course of, he emphasizes.

“As we continually try things, every time we try a new experiment or add a new piece of data or tweak something, the model gets one percent better,” Barkett says. “It took something like 30 iterations before we got to a place I think we were genuinely adding value for them.”

To be taught extra about how machine studying is frequently disrupting industries throughout the spectrum by truly producing outcomes — and the way your organization can get in on the motion — don’t miss this VB Stay event.


Don’t miss out!

Register here for free.


On this webinar you’ll:

  • Learn the way cognitive applied sciences scale throughout cellular devices (together with automobiles)
  • Consider the worth of a machine studying product to your group
  • Tailor your information construction to optimize for future machine studying initiatives

Audio system:

  • Andy Barkett, VP of AI and Machine Studying, REX
  • Daniel Lizio-Katzen, Senior VP Product and Income, Fareportal
  • Stewart Rogers, Director of Advertising and marketing Expertise, VentureBeat
  • Wendy Schuchart, Moderator, VentureBeat
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