Machine learning can automate the handling of huge troves of data to help companies make and save money. However, they’re not without pitfalls, as the real estate tech company Redfin learned.
Machine learning can automate the handling of huge troves of data to help companies make and save money. However, they’re not without pitfalls, as the real estate tech company Redfin learned.
As Redfin began building its own machine-learning capabilities, it ran into a problem: Employees weren’t using them. Bridget Frey, the firm’s CTO, said in an interview that there was a key reason for that: At first, Redfin didn’t leave room in these systems for the real estate agents who were supposed to use them to make modifications.
For example, a Listings Matchmaker feature generated a list of personalized recommendations for homebuyers, based on their interests. In its initial iteration, agents weren’t able to add recommendations they thought would be useful.
“And the agents were saying ‘I’m an expert in this neighborhood, I know that this house is perfect, why are you not letting me add it in?’ And we just realized that our engineers … were relying too much on the machine learning,” Frey said. “We needed to take what was special about our agents and use that to enhance the algorithm and enhance the experience for our customer.
