Make More Money with Scored Leads
Brokers may profit from working with lead-providers that score their results
By Joey Liner, co-founder and vice president of sales, DoublePositive Marketing Group
Many mortgage brokers’
businesses rely on the leads they
purchase. They turn to various
lead-providers hoping to receive strong
prospects that will become funded loans.
Increasingly, lead-providers are scoring their leads to predict their likelihood
of converting into funded loans or progressing to the next step in the loan-orig-ination process. The concept is similar to
how credit scores predict the likelihood of
borrowers repaying a loan.
Brokers who purchase their leads from
Internet lead-providers should understand
how the lead-scoring process works, as
well as how they can profit from using
Without an accurate feedback loop of
final lead outcomes, lead-scoring isn’t possible. The score’s quality depends on the
accuracy of the feedback loop. Feedback
that reports back only 50 percent of all
the amount of time mortgage professionals
spend on low-score leads.
A solution that is becoming popular
among lead-buyers that have begun scoring their leads is to redirect low-scoring
“For a lead to be ready for the loan process,
live contact is essential, and the consumer must be
genuinely interested and qualified. Anything less
is a waste of a mortgage broker’s time.”
To determine lead scores, lead-providers
analyze how data characteristics have influenced historical lead results. This is not
complex when there is only one data field
For example, analysis may indicate that
50 percent of e-mail addresses from a certain domain will convert into funded loans
compared to only 25 percent of e-mails
from another domain. The lead score of the
first domain’s e-mail leads would be 5 0, and
the other’s would be 25.
The process becomes more complicated
as we add more data fields, such as state
We may see that 60 percent of leads by
males convert into a funded loan, compared to 40 percent for females.
And looking at the state field, we may
find that Alaska has 70 percent of leads
converting into a funded loan compared
to 20 percent of Florida’s leads.
To determine a single, composite lead score, each field needs its own
In other words, it’s possible that state
is a more accurate field to determine the
likelihood of a funded loan than the e-mail
domain, or vice versa.
Weighting each field’s degree of influence is the best method for normalizing
these variations of influence.
the leads that convert into funded loans
will result in a lead score that is less than
Most lead-buyers simply seek to close
loans. But they also can optimize scores
for other events, such as a submitted application or a live, hot transfer. These are
milestones that occur before a loan actually funds and can be useful with loan cycles of 90 days or more.
Data accuracy on the front end often
is limited to the accuracy of the con-sumer-provided data, which consumers usually fill out via an online form.
More-accurate data and more fields of
accurate data on the front end can substantially add to a lead score’s accuracy
leads to a leads-provider to contact, qualify and transfer the consumers. Typically,
the lead-buyer is only charged for the
leads that result in a live, hot transfer.
Mortgage brokers then can use their
time for loan-related activity with genuinely interested and qualified consumers rather than wasting their time
chasing down consumers, leaving messages, scheduling callbacks, sending
e-mails or asking preliminary qualifying
A live-hot-transfer leads-provider
typically performs the time-intensive
tasks of mining the nuggets of gold from
the leads with the lowest scores.
Leads with the highest scores can be
delivered directly to the mortgage brokers
as usual. But because brokers’ lead flow is
limited to those with the highest probability of converting into funded loans,
they can fund more loans.
The typical outcome is a higher conversion rate of all received leads into funded
loans — sometimes 50-percent to 100-per-
cent greater. This can drastically lower the
average cost of acquiring new customers,
even with the cost of the leads-provider incorporated into the total average acquisition cost.
by Joey Liner
“Lead Your Way to New Business,”
“Pick the Right Lead for You,”
View these articles and more at
A means to an end
Predictive analysis through lead-scoring
essentially provides a way to predict the
future. But it does not create any value
on its own. Lead-buyers must find ways
to use lead scores to extract value from
The answer lies in first identifying the
economic consequences of leads with low
lead scores. Even leads with low scores can
be converted into funded loans. The problem is the amount of time required to mine
these leads for the gold nuggets.
Time is a mortgage broker’s most
valuable asset, and every effort should be
made to allocate as much time into loan-related activity as possible. But for a lead
to be ready for the loan process, live contact is essential, and the consumer must
be genuinely interested and qualified.
Anything less is a waste of a mortgage
Leads with high scores typically require
minimal time for the broker to establish
live contact and to assess the consumer’s
interest and qualifications. Leads with
low scores, however, can require excessive
amounts of time to contact and qualify.
The key to making money with lead-scoring is to find a process that minimizes
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From a data perspective, no two Internet
leads are alike. Thus, the more accurate the
score for predicting outcomes, the more
valuable the process becomes.
The key to the most accurate and useful
lead scores are:
1. Accurate and timely feedback loop
data (back end); and
2. As much accurate lead data to analyze
as possible (front end).
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Joey Liner is co-founder and vice president of sales at DoublePositive Marketing
Group ( www.doublepositive.com), the industry leader in the “hot transfers” space.
His day-to-day responsibilities include sales and vendor/supplier relations. Previously, Liner was the leading sales associate for TheLoanPage.com, an Internet
mortgage-lead-generation company. Liner graduated from Towson University in
Baltimore. Reach him at (443) 278-8566 or e-mail firstname.lastname@example.org.
Lending in Washington, Oregon, and Idaho