It's true that consumers are creatures of habit, but not everyone follows the same habits.
To create good database marketing alerts, we need to get down to the details of what habits people follow in order to reach out to them at the critical time when they are ready to make a change.
When we started drilling into the details of consumer mortgage lending behavior we found there are thousands of combinations of data, that indicate intent behavior of a consumer gearing up to buy a home, sell their current home, refinance, or take out an equity loan.
Before we go into how to best use MonitorBase Predictive Alerts, I want to give a little insight into how we determine which consumers are most likely to be in the market at any given time. We use multiple forms of predictive analytics. More specifically, regression analysis and machine learning. These methods, coupled with hundreds of data points on each of the prospects in our system, allow us to model which consumers are in the market for a mortgage and when!
Now, that being said, we aren't necessarily tracking exactly what every consumer is doing at every moment in their buying cycle. We are identifying patterns in consumer behavior that indicate a mortgage transaction is likely to happen, based on what other consumers have done in the past.
What does this mean to me?
This means that your marketing efforts towards your predictive alerts will be much more effective than marketing to your overall list of consumers in your past-client database. Out of each 100 clients that you monitor we narrow the list down to the 5% that you should focus on each month!
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