How to find your target customer in the overwhelming sea of data

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Unearth “in-market” leads to power your sales and marketing departments.

“In-market” leads are the Holy Grail of marketing. But much like the legendary cup, these coveted prospects are often hidden within a jungle of consumers and buried under ever-growing mountains of data. The key to finding them lies in the relatively new science of behavioral analytics.

Nearly every consumer today is online, leaving a digital trail that can reveal deep insights into their lives and provide predictive clues into their future purchasing decisions. From social media “likes” to online searches, the data generated today is a treasure trove of information that, when combined with more traditional market and demographic datasets, can help marketers target customers more precisely.

Considering that 93% of all 2018 homebuyers used the internet in their search, mortgage lenders that can follow those digital breadcrumbs can engage with “in-market” prospects earlier in their journeys.

Identifying Predictive Behaviors
With the right analytics, the internet and social media can help marketers more precisely target active house hunters. For example, you can identify “affinity audiences” based on online searches. Consider someone searching for mortgage rates in a specific neighborhood or region. Savvy marketers can deliver an ultra-customized offer that matches that person’s unique search parameters.

Someone checking real estate sites for home values in a specific zip code and price range could be looking to move into the area. If someone is checking historical, current and future market value of an existing property, they could be contemplating a move.

Social media groups are becoming more localized every day. It’s easy to find one for people house hunting in a neighborhood or asking about local schools. Social media is also where people are comfortable sharing major life events, such as an engagement or a pregnancy. That person may be looking to buy their first home or looking to move up into a bigger house for a growing family.

Layering on more traditional market drivers, such as interest rates, affordability indices and employment rates, can add additional information to your predictive models. For example, if interest rates are rising, a potential borrower could be more receptive to a fast lock-in offer. If employment rates are going up, more people could be moving into the area, driving up demand.

Finding “R-Squared”
Without getting too deep into the weeds, R-Squared generally refers to that elusive sweet spot in the customer journey that predicts just when a prospect is ready to buy, given related data interactions that may otherwise go unnoticed. The more meaningful data points you can link, the closer you can get to your target.

And the deeper the analysis, the more surprising the results. For example, according to Equifax analysis, there is a high correlation between buying a new car and buying a home. Of course, the correlation of all of those data points is constantly evolving as more data become available. That’s where artificial intelligence and machine learning come in.

Using advanced analytics, models are constantly absorbing new data, learning how specific inputs impact the models, and updating themselves. Users always have the most up-to-date and relevant information available.

The Challenge
Even with all of this data and technology now available, though, there’s no silver bullet. One growing concern for marketers is understanding and complying with applicable laws, such as privacy, permissible use and fair lending regulations.

Consumers are increasingly aware of, and at times uneasy about, the amount of personally identifiable information (PII) out there and how companies are using it. Regulators and service providers are responding with tighter controls over how the data can be accessed and used.

Then there is sampling, managing and extracting insights from the volume of new data generated every day. At last count, there are 2.5 quintillion bytes of data created every day. (One quintillion has 18 zeros, for folks who are keeping score.)

The 5-Step Solution
The good news is that the capacity to gather and analyze all of the data already exists and can be plugged into your existing systems. While it’s important to always be evolving and examining any new data made available, the right solution can help you:

1. Capture: Integrate datasets pulled from both internal and external sources.
2. Prepare: Unique keying and linking solutions bring disjointed information into focus, creating account- and customer-level insights.
3. Store: Create a holistic view of the customer in a safe and secure environment, making the information available in real-time.
4. Analyze: Create value from the raw data by developing custom scores and models driven by advanced analytics. As the environment changes, you can change data weights or even inputs on the fly.
5. Deliver: Deliver greater customer insight into sales and marketing teams.

Most marketers agree that personalization is key to deepening customer relationships. Still, only 27% of the business leaders surveyed for Aberdeen’s CX Executive’s Agenda for 2019 say they are satisfied with their ability to use data to deliver a truly customized experience. Those mortgage lenders that are ready to invest in best-of-class data collection, integration and analysis, as outlined in the 5-step solution, can find an open lane in today’s increasingly crowded field.

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