After you get a new lead -- either through a referral source or a tech platform -- do you vet them briefly to determine their intent? By qualifying a lead, you are on the path to lead scoring: a system where you give value to different behaviors or properties exhibited by potential buyers or sellers.
Traditional lead scoring
As Hubspot explains, there are two ways to score leads: the traditional method and the predictive method. The traditional method is a manual approach. You (or your lead gen specialist) puts together criteria or properties that make for a good buyer or seller, and then you score the lead accordingly. For example, a buyer with a 750 credit score may earn 10 points, while a buyer with a 650 credit score would only earn five. Or, a homeowner who requested a CMA may earn 10 points if they have 80% equity, while homeowner with no equity would earn zero points.
The traditional lead scoring approach will help you prioritize your leads, but it can also be limited. It forces you to determine the weight given to various properties and in vetting your prospects, you have to be careful not to demand too much. Even the strongest buyer lead will be turned off by a 60-point questionnaire meant to determine their worth as a client.
Predictive lead scoring
Predictive lead scoring, on the other hand, uses algorithms and models to determine the leads in your database who are most qualified. Within the real estate industry, these models can assess big data on everything from property, mortgage and financial information to more personal insights about a homeowner.
Not only are predictive models able to assess more information, they also weigh the data for you -- removing the guesswork. In the case of SmartZip’s predictive algorithms, our goal is to find the homeowners most likely to sell in any neighborhood across the U.S. When analyzing a neighborhood for qualified seller prospects, our predictive algorithm has four main tasks:
Find local seller patterns and triggers
Analyze homes and homeowners in the area to find those exhibiting similar seller patterns and triggers
Test predictions against real-life turnover
Refine results until they have the highest degree of accuracy possible
It may sound easy but in reality, the algorithm has been programmed to find significance that exists only when the right combination of data points exist. Sellers in east San Jose, for example, may exhibit different selling triggers than sellers in west San Jose -- and these differences would likely not be visible to the naked eye (even the naked eye of the smartest local listing agent).
Making predictive leads matter
After SmartTargeting produces a ranked list of likely sellers for an area, it’s up to an agent to convert them into listings. And in many cases, having a narrowed focus is the best gift an agent can receive.
One agent in southern California, has used her ranked list of sellers to prioritize her follow-up. In some cases, she has gotten in touch with acquaintances she knows from her community who she wouldn’t have otherwise contacted. The result? Five listings from people she already knew peripherally, but who may not have hired otherwise.
“SmartTargeting’s data empowered me to reach out to the some people I knew only a little from within my community. I’ve landed five listings by following up with these “warm leads at the right time."
Land more listings using seller predictions
Whether you are a true community expert or just getting started in real estate in your area, having a data-backed understanding of likely sellers can change your business. Reach out today to see selling predictions in your area -- and to see how SmartTargeting can also help you get in touch with likely sellers to land more listings.