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4 min readHow AI and Machine Learning Help to Power Multifamily Websites

Kristy Esch Administrator
4 min read

The concept of marketing intelligence, AI and machine learning may sound complicated when it comes to running a multifamily website with diverse locations and residents, but it’s the advanced technology that makes closing a deal on a signed lease more effective than ever before.

 

AI guided software collects a consumer’s information as they engage with interactive web technology and tracks a rental prospect’s online behavior, culling that data to personalize their digital interactions and deliver content tailored just for them. It’s called intelligent because the software is programmed to react to the information entered by the consumer, offering the next best step in the apartment-searching process.

 

Machine learning takes AI one step further by processing all of a property management company’s centralized data to identify trends and make predictions about consumer behavior. That company-centric information, when consolidated with industry benchmark data, allows the open-source software to predict likely outcomes and map out ideal customer profiles.

Keep Leads Engaged to Convert Better

 

Think about how renters search for apartments online. Nearly half say they’re not ready to move for 90 days and the majority visit a multifamily property website numerous times before making a commitment. They want all of the information in one place, such as details on local shopping, gyms, restaurants and more in the community or a personal expense calculator to determine monthly budgets.

 

A renter’s online journey often takes place over several days and on different devices. PERQ research shows the more experiences a prospect interacts with on a website, the more likely they are to convert. Of the prospects who scheduled a tour on a multifamily website, 72% went through at least one other experience and 58% went through at least three total experiences.

 

One day, they may use a mobile phone to check out a property’s photo gallery and then take a floor plan assessment to determine best fit. The next day, they may use their desktop computer to see if they pre-qualify on your website (without having to talk to a leasing specialist) and later check for special offers. Several days later, they may sign a lease and return to the website on their tablet to learn more about their new neighborhood in the community section.

 

Keeping rental prospects engaged with multiple experiences on the website makes a difference in signing more leases versus losing an online visitor to a competitor, says Caleb Bartlett, digital sales and media manager at Nolan Real Estate. “We have increased the likelihood of them taking that next step in the sales cycle.”

 

Combine AI with Machine Learning

 

While the terms AI and machine learning are often used interchangeably, they are not the same thing. AI involves machines that carry out tasks based on smart algorithms, whereas machine learning involves the ability of machines to receive data and learn for themselves, then change algorithms based on the processed information.

 

Combining advanced technologies like AI and machine learning automates much of the sales process for property management companies and delivers predictive analysis to help create a comprehensive marketing strategy based on actionable insights. The dynamic technology responds to real-time consumer data collected, making it easier for multifamily marketing teams to adapt as consumer trends change.

 

Richard Bullen, Property Manager of Grand Villas Apartments in Katy, Texas, says the data collected by his property’s website gives his leasing agents information on a prospective renter’s preferred contact method, move-in time frame, budget needs and pre-qualification status.

 

“It helps tremendously,” Bullen says. “It allows us to get a better sense of what they are looking for before we even make contact. And when we do make contact, we are able to specialize and pitch to them more effectively. It eliminates a lot of back-and-forth.”