Revamp your online lead generation - fast
Previously in DM News, I wrote about a familiar scenario of steadily
declining online lead quality and the five easy steps you could
immediately take to turn your campaign around: use targeting; revamp
creative; add a qualifying checkbox; change to real-time data
delivery; and send triggered e-mail.
Here are five advanced changes that you can make right now to improve
lead quality. If you don't have the expertise in-house, sophisticated
network partners should be able to help you implement these steps free.
Use auto-scrub. Automatically exclude leads who incorrectly answer
key qualifying questions in your offer form. For example, if you
require consumers to have allergies and they indicate in your offer
form that they do not, then you should not receive or be required to
pay for those leads. Scrubbing bad leads upfront saves you the time
and effort of contacting consumers who have expressed interest but do
not qualify for your services.
Add dynamic fields. This versatile feature allows you to customize a
field in your offer form based on the answer provided in the previous
field. For example, if a consumer indicates he or she lives in a ZIP
code where you offer only certain services, then customize the next
field with those services only. Don't give consumers options that
aren't available to them.
Customize validation. Beyond standard data validation like physical
address, e-mail and phone, consider customizing validation to your
offer form. For example, if you require a certain loan-to-property
value percentage for a mortgage offer, then create the logic to
calculate that percentage upon submission and reject the lead if it
Share conversion data. The best targeting methods incorporate your
conversion data into a custom model that is continuously adjusted
according to your offer's performance on the back end. Your provider
should be able to accept data from your positive customer leads and
create a custom look-alike target that identifies and targets the
offer to consumers most likely to convert based on similar data in
Determine credit worthiness via predictive modeling. If your criteria
for lead quality hinge on a consumer's credit worthiness or
likelihood to pay for your service on the back end, predictive
modeling can be particularly effective. The process combines
demographic information with transaction data to create custom models
that target consumers with similar characteristics to those who meet
your lead-quality standards.
These suggestions, and the others in my last column, are by no means
the only steps you can take to improve lead quality. Talk to your
provider before proceeding to determine which are right for your
campaign and if there are other actions you can take to reverse
declining lead quality for good.