In order to increase their customer bases, mortgage originators may find themselves wishing for a crystal ball that can tell them which people are most likely to want to purchase a home. Actually, thanks to a concept called predictive analytics and its ability to use current data to predict future actions, mortgage originators can get their wish.
Predictive Analytics 101: What It Is and How It Works
As SAS notes, predictive analytics uses data, statistical algorithms and machine learning techniques to identify the chances of future outcomes based on what has happened historically. In addition to giving industry leaders and others a solid knowledge of past events, predictive analytics offers that proverbial crystal ball to help assess what may happen next. Many organizations are using predictive analytics in a number of ways; this includes optimizing marketing campaigns. By being able to determine typical customer purchases and actions, it can help businesses of all types attract new clients by targeting the most suitable customers, increase customer loyalty and retain the ones they already have.
Statistics definitely bolster the notion of the popularity of predictive analytics. For instance, as MarketWatch.com notes, the global predictive analytics market is expected to grow from $3,426.75 million in U.S. dollars in the base year of 2016 to an impressive $12,577.92 million by 2023.
Predictive Analytics Is Poised to Change the Mortgage Industry for the Better
When it comes to the mortgage industry and predictive analytics, the two definitely go together like peanut butter and jelly. This is great news for mortgage originators, who may deal with clients who are frustrated by the historically long and detailed application process. Even clients who have a solid financial backing have to jump through a number of hoops to borrow money for a home. However, thanks to big data analytics, this situation is changing for the better.
While mortgage banking has always been about focusing on a financial institution’s goals, the willingness to take risks and the economy to determine its home loan procedures, big data analytics is changing all of that. Thanks to a broad base of data — which can include prospective customers’ locations and home buying histories — mortgage companies can now more accurately and quickly assess risk and behavior, both in existing customers and comparable clients who act in a certain way.
Another reason that predictive analytics will help mortgage originators boost their customer acquisition is due to the simple fact that there is a huge amount of data that is now available. The sheer volume of info that a company can collect on one person is enormous compared to the past and, once it is collected and analyzed, it can be used very efficiently. Most big data institutions have computerized infrastructures that allow this vast quantity of data to be stored in an inexpensive and efficient way; in turn, this leads to faster decision making in the mortgage industry.
Interestingly, notes HousingWire.com, customer expectations are behind a lot of the increase in predictive analytics. As more millennials enter the workforce and move out of their parents’ homes into places of their own, they expect that getting a home mortgage is as easy as ordering a pair of shoes online — it should be quick, easy and painless. While some companies do offer online mortgage applications, many mortgages are done the traditional way with stacks of paperwork to sign and info to process. Thanks to the large amount of digital data that is available — including property info, borrower income and credit scores — mortgage originators can greatly reduce the amount of time it takes to process a mortgage loan. This will please current clients and inspire them to return in the future for additional services.
How Prediction Scores, Demographic Information and Homeowner Marketing Data Will Increase Customer Acquisition
There are a number of specific types of data predictive analytics that can be very useful for mortgage originators. For example:
Home Finance Prediction Scores - This includes the Refi Intel Score, which allows mortgage originators to market directly to prospects or current customers who are likely to refinance in the next 3 to 4 months. This type of score can be created for FHA, conventional and cash-out refinance. The Purchase Intel Score will help maintain customer loyalty by identifying homeowners who are most likely to sell their current home and shop for a new mortgage within the next several months.
Homeowner Marketing Data - This type of data includes a Reverse Mortgage Refinance option, which allows mortgage originators to hone in on the most fruitful prospects for a reverse mortgage refinancing in a specific area.
Data for Insurance, Home Improvement and Beyond - This type of data features New and Nearby Homeowner information. In addition to letting those in the mortgage industry know where their current or prospective clients live, it also includes helpful data on how long they have owned their homes, if they might want to move soon, the square footage of the house and what amenities it might have, such as a pool. In addition, the Recent Home Equity Line can help mortgage originators learn if homeowners in their target area have gotten a home equity loan or HELOC.
DataTree Offers Accurate and Useful Homeowner and Mortgage Info
Clearly, predictive analytics can help mortgage originators use their marketing budgets in the most effective and successful ways possible. And DataTree is able to help them get the data they need. We understand that successful marketing begins and ends with data, and are happy to provide clients with the best homeowner, real estate and mortgage data the industry has to offer — this will help determine the likelihood that new or existing customers will buy a home, apply for a HELOC or look into refinancing. To learn more about this exciting service and how it can boost customer acquisition as well as increase client loyalty, visit DataTree online.