Real-Estate

How big data is driving big retention in mortgage


Retaining customers is hard work during normal times, but retaining customers at scale while interest rates are at incredible lows is downright daunting. Retention models that worked in 2019, failed in 2020 and continue to fail in 2021 — but not for the reason many think. It’s not due to the rate environment, it’s the lack of knowing which individual customers are back in-market before your competition knows that is hindering your success at recapturing their business.

There are only a handful of servicers who have unlocked the secret to achieving high retention rates, and they are performing over 3x better than the industry average of 18%. How are they doing it? It’s all about the data. New data sets exist today to enable better identification and personalization of engagements, which didn’t exist previously and that is giving the competitive edge to the first movers.

Innovation in the World of Data
In 2018, PwC reported there was a total of 4.4 zettabytes of accumulated data in the world. For context, a single zettabyte is equal to a billion terabytes, and two terabytes of storage can be purchased for about $10/month from Google, Dropbox and others. Bottomline, 4.4 zettabytes is a lot of data.

In 2020, that number exploded to be 44 zettabytes of accumulated data in the world. A 10x increase occurred in the blink of an eye! The world of accessible data to make better decisions in all facets of business is moving at the speed of light. Yet many lenders are still relying on in-the-money models and credit triggers to try and retain customers. It simply is not good enough anymore.

What is working is understanding and reacting to consumer behavior. As consumers shop more online than ever before, their digital behavior can be monitored to inform growth strategies. Lenders can deliver personalized experiences with more robust customer data — intelligent data that provides a broad view of shopping behavior.

Prioritizing New Data Sets
We are in the middle of an important transformation at a time when so many lenders are satisfied with losing 4 out of 5 current customers since their origination numbers were sky high. 2020 was a wild and abnormal year and the tectonic shift that has occurred in the data world has largely gone unnoticed by many lenders. Basically, record-breaking loan volumes masked the retention issues for many lenders. But as rates begin to increase, there will be quite a separation of those who prioritized using new data sets to make better marketing decisions for customer retention, and those who didn’t.

This superior data that improves consumer experiences is behavioral data. Behavior, unlike demographics, changes constantly and provides a clearer view of the consumer in real-time.

How specific can you get with this data? Today’s technology can provide lenders insights on what consumers are currently shopping for, when they are in-market, how frequently they are shopping, what time of day they shop, and more.

Creating Exceptional Experiences
Data companies have created these proprietary data sets easily accessible in privacy-friendly ways which have made marketers’ lives incredibly simple. It transforms “Big Data” into easily actionable data creating exceptional customer experiences.

Using this data from outside of their organization, lenders can enhance their own first-party data to allow them to create tailored messaging and customized outreach strategies based upon behavior that lead to exceptional customer experiences.

The goalposts for identifying and engaging with customers early in their mortgage shopping journey have changed. Consumers are being identified as in-market for a new mortgage over 100 days before they complete an application and become a credit trigger. By the time traditional data models flag a consumer as a portfolio runoff risk, other lenders leveraging behavioral data for new customer acquisition strategies will already have forged a relationship with the consumer and be in prime position to close the consumer’s next loan transaction.


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