It’s a story that’s taken on legendary status in the field of predictive analytics and predictive marketing: Target, after analyzing the buying habits of a teenage girl in Minnesota, sent her mother-to-be product offers in the mail. Her dad called to complain about the coupons targeting a young woman who he said was too young to have a child. Then a week later, he called back… to apologize and admit that his daughter was, in fact, expecting a baby.
At the time, the news made national headlines. Reactions ranged from, “How cool is big data?!” to “Big brother is watching!” — and everything in between. And most people agreed that while it was an interesting story, there was no way that algorithms could truly predict smaller, reactionary human behaviors.
In hindsight, that story can likely be boiled down to Target sending a mass coupon campaign to anyone who purchased a pregnancy test with a debit card in a certain time frame. So while it’s the narrative referenced most often when people discuss predictive analytics, it actually sells predictive analytics short.
That’s because predictive analytics isn’t about drawing a straight line from one behavior (like buying a pregnancy test) to the next (like needing expectant mother products and coupons). True predictive algorithms take thousands of data points and uncover hidden trends and potential behaviors that are invisible to the naked eye.
And these “real” predictions? They have the power to change how we live and how we make decisions — though most of us don’t pay attention to their daily influence.
Predictive analytics isn’t the future.
The Target prediction story happened more than five years ago, in February 2012. And in the time since, companies large and small have been honing their algorithms to better predict consumer behavior — and secure their place in the big leagues.
Amazon has been said to get 35% of their revenue as a result of their robust recommendation engine. The engine surfaces products that consumers may like, based on past or recent purchases, browsing history and other customer’s purchase patterns.
After years of struggling to perfect their recommendation algorithm, Netflix recently changed their review system from a 1-5 star rating to a “thumbs up or thumbs down” rating. While plenty of users grumbled about their lost reviews, Netflix saw a 200% increase in user rating entries under the new system. The executive team is confident that the gamble will lead to more reviews by more users over the long-term — and a better prediction engine for ALL users as a result.
Predictive analytics for real estate is changing the game TODAY.
Predictive analytics is changing what we buy, what we watch, what we eat, and what jobs we’ll be hired for.
It’s also changing real estate — quickly. If you still think predictive analytics is a buzzword that will go the way of QR codes, we urge you to reconsider.
Here are three ways predictive analytics is changing the game for TODAY’s real estate agents:
1. Go beyond FSBOs and Expireds
Plenty of companies offer “seller lead” solutions, but most are basic marketing or robo-calling products working from FSBO and Expired lists. This Inman article details how these products sell the same data to hundreds of agents, who then go on to “stalk” homeowners who fall under the FSBO or Expired category of sellers.
Predictive analytics doesn’t just review current or recently expired listings to serve up seller prospects. Algorithms dive into thousands of data points — including time in home, demographic and neighborhood patterns, equity and personal financial factors — to identify homeowners who are the most likely to sell in the next 6 to 12 months.
That means that when you work from seller predictions, you won’t be the hundredth agent to call a predicted seller; you’ll likely be the first.
2. Become a listing specialist without a decade in the industry
Most industry experts agree that the typical agent path is to start as a buyer’s agent, then transition to listings as you develop referral or repeat business opportunities. It’s a safe, traditional route reinforced by the convenience of dime-a-dozen buyer leads that newbie agents can use to build their business on the cheap.
With a list of predicted sellers from your sphere or market area, new agents can skip the traditional path and go after listings earlier in their career. Rather than waiting for their first round of buyers to turn around and sell 5-7 years later — or to refer them in the years to come — they can pitch listing presentations to predicted sellers who haven’t yet signed on with another agent.
And in today’s targeted and retargeted online world, it only takes a few listings to start branding yourself as a listing agent who is ready for even more. Once they land their first seller clint, newbie listing agents can use Just Listed and Just Sold campaigns to drive in even more seller interest.
With predictive analytics, you can bypass years of open houses and nighttime showings in favor of becoming your market or sphere’s listing expert.
3. Work from value instead of desperation
We hear all the time from agents who win listings after having frank conversations with predicted sellers who had NEVER previously considered listing their home.
That’s because predictive analytics can actually predict consumer behavior before the consumer has considered it. Just like Amazon knows you need a new Kindle right as yours is about to bite the dust, our predictive seller algorithms can understand that specific market conditions in a neighborhood will entice an empty nester who is unsure of her equity position or current home value.
Of course, selling a house is a bigger decision than buying a new-edition Kindle — so these sellers don’t list the minute they get a postcard in the mail. Instead, they tend to react to personal outreach where an agent reaches out to discuss market trends or buyer opportunities in the area.
By being the first (and likely only) agent to reach out to a small pool of predicted sellers in your area or sphere, you can provide the value and one-to-one approach that traditional “farm-based” agents simply don’t have time for.
Ready to get started with the technology of TODAY?
As you can see, predictive analytics isn’t as out-of-reach as self-driving cars or hovercraft. It’s changing the game for real, working agents just like you.