In the past several months, it’s become increasingly clear that there’s a new precious commodity that some builders have, and others do not. Its importance will soon—perhaps this year—outweigh individual land positions and cost of goods sold. I believe the home building companies that learn to use this resource well will dominate their competition and find new avenues to profitability. I’m talking about data—along with artificial intelligence (A.I.) and machine learning—as both the current opportunity and a future risk if you’re not on board.
No need to check your calendar. You’re still in the year 2019 (not 2039), but this is housing’s new reality. The builders that have the right data, keep it clean and organized, and apply A.I. to it are already winning—and this is a race where you don’t want to play catch-up.
Get Your Data Ready for Machine Learning
I’m not prone to using hyperbole, but I believe builders that don’t begin to address this part of their business by the end of 2019 may be too late. And if you want the benefits of A.I.-enhanced data, you have to get your data scrubbed and ready so you can apply that machine learning quickly and effectively.
Consider how the combination of data and A.I. is already affecting the way new-home marketers do their jobs.
Today, if you have a large enough set of current customer data—names, phone numbers, and email and mailing addresses; your basic CRM database—you can upload that information to Facebook, Instagram, Twitter, Pinterest, and countless other platforms, allowing you to create a look-alike audience using the A.I. systems on those platforms.
The resulting audience will be comprised of the top 1 percent of the population with predisposed interest in your communities and homes. You no longer need to define your target market; in this scenario, your current customers do that for you, and do so far more accurately than you ever could on your own.
How Home Builders Can Create the Perfect Ad
It gets better. If you can clearly define specific actions you’d like your target audience to take on your website—such as spend more than 2 minutes on a page, visit more than three pages, or fill out a lead form—then you can program or teach the A.I. to monitor the target audience’s actions after clicking on your ads and become better at predicting who will accomplish a desired action in the future. This process makes every ad you deliver dramatically more efficient. In our tests, we’ve seen it reduce costs by as much as 63 percent per click without negatively affecting traffic quality.
Beyond targeting audiences and their online activity more effectively, A.I. is also helping marketers create the perfect ad.
Late last summer, Google rolled out what it calls “responsive search ads.” Google’s A.I. uses data inputs from marketing teams about the unique features of their homes and communities and then uses machine learning to determine the best possible combination of that data, build the “perfect” ad, and show it to each individual user. In our experience, you can achieve both cost savings (18 percent lower cost per click) and improved results (46 percent increase in click-through rate) using this method.
The first time you entrust one of these A.I. platforms with your data can be a bit unnerving. It’s a similar sensation to removing your hands from the steering wheel of a Tesla and letting it handle the driving for you. Marketers like direct control over their campaigns, but that’s a diminishing role in the digital world, perhaps evolving to a full-time A.I. teacher and babysitter within the next decade or so.
Artificial Intelligence as a Sales Department Disruptor
A.I. is beginning to disrupt the sales department, as well. If a salesperson only has time to call a handful of people each day, which ones will be selected? Or, if you have too many leads in your CRM, with no hope of consistently following up on all of them individually, then why not let an A.I.-powered follow-up app such as Conversica, among others, do the work for you?
This type of app uses machine learning and natural language processing to interact with each prospect as an individual. It listens and reacts as a human would, but it never needs time off or asks for a raise, and it can work forever. Once prospects are ready to take the next step, the app connects them with a human salesperson to complete the sale.
Each A.I. profile built by Conversica and its contemporaries is given a human name, such as “Stacy” or “Paul,” so prospects can more easily relate to it. Even though the email signature clearly states that the sender isn’t human, customers calling the office have been known to ask for “Stacy,” “Paul,” or the “person” who so diligently followed up with them.
The data revolution is here now, and the biggest challenge isn’t how to best use the data, but how to collect and organize the data in a way that A.I. systems can use it.
All of those filing cabinets full of paper in your office? They can’t help you. Your CRM system should be a treasure trove—unless you let each salesperson approach it in his or her own way, which will ultimately render the data incomplete, inconsistent, and therefore unreliable.
Beyond how the data is organized, you also need as much of the right data as you can get your hands on, in turn enabling more precise insights and results from A.I. All of the examples mentioned here aren’t possible unless you have the data to drive them. So spend time this year working on improving your approach to data collection, digitization, and organization. This is very much like a gold rush, where those who stay on the sidelines may never be able to overtake the competitors who get too far ahead.