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All Names Are Not the Same
By
Mary Conway
June 2008
All names are not the same.
That's the basic truth underlying successful customer acquisition campaigns.
Yet many marketers overlook this fact when they embark on a telemarketing
program. They accept a shockingly low response rate from a customer or a
prospect list as a "necessary evil," instead focusing their attention on
developing the most creative promotional concept and/or presenting an offer to
the consumer via the vendor with the lowest hourly cost.
The result in far too many
instances is hours of nonproductive calls to consumers who have little
likelihood of responding to the offer. This wasteful practice can be greatly
improved by tapping the growing power and sophistication of database modeling
protocols, now increasingly being deployed in advanced teleservices programs.
Effective database modeling is
built on the premise that all names are not the same – that better performing
ones can be reliably predicted through careful list analysis, modeling, and
prioritization.
It's said that past performance
is not a guarantee of future results, but it's one of the most effective
predictors of a successful telemarketing effort. Surprisingly, most
teleservices programs overlook the prior performance of names on a list, failing
to glean invaluable insights about how those leads are likely to respond in the
future.
Analysis of a list's prior
response rates reveals important insights by answering questions like these:
Does one geographic area, such as the northeast, respond better than another
region, such as the southwest? Do urban markets outperform suburban ones? Do
consumers residing in single-family dwellings respond better than consumers
living in multifamily dwellings? Do credit scores correlate with response
rates?
The analysis doesn't stop there.
Here's another factor to consider: How recently has a name been called?
Over-contact is a red flag in telemarketing. If a properly coded list indicates
a household has been called recently – say within the last month – that name
should probably be suppressed, as the likelihood of a positive response is low.
Many companies devise their own
coding schemes for names on their customer lists. These too can be analyzed for
varying response rates – even without knowing exactly what they mean!
Database modeling programs
consider all these inputs and then apply a scoring mechanism that ranks expected
response rates from highest to lowest. This evaluation is invaluable in guiding
the hourly efforts of telemarketing teams – focusing them on fully penetrating
prospect names with the best potential for sales success. Importantly, the
assessment process must be continuous. For optimal results, as waves of
a campaign are completed, the modeling program must evaluate performance on an
ongoing basis, re-ranking remaining names before they are called.
This
database modeling was recently employed for a credit card issuer. The client
supplied a list of customer names, which it had ranked for expected propensity
to enroll in a value-added program. This ranking was intended to drive the
campaign's list penetration strategy: over-penetrate the best leads and
under-penetrate the worst ones.
Unfortunately, as the campaign unfolded, there were frequent instances where the
performance scores did not correlate with actual results. Consequently, a
significant degree of sales opportunity was not being realized. The company
clearly needed a better database modeling strategy.
By
overlaying additional modeling criteria on the list, a revised
segmentation/scoring scheme could be generated. As the table below illustrates,
the new campaign achieved significantly higher conversion rates, resulting in a
cost-per-enrollment that was $10 lower than the previous strategy – a savings of
over 22 percent.
|
Metric |
Original Strategy |
New
Strategy |
Percent Improvement |
|
Sales per Hour: |
0.60 |
0.77 |
28% |
|
Contact Penetration: |
48.84% |
50.63% |
4% |
Contact Conversion:
|
5.5% |
7.0% |
27% |
|
Cost per Enrollment: |
$46.67 |
$36.38 |
↓22% |
As businesses relentlessly seek
the best return-on-investment for their marketing investments, database modeling
– skillfully applied – offers an impressive solution.
Mary Conway is chief marketing
officer for DialAmerica (www.dialamerica.com).
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