News
 The
Power of Predictive Modeling
When you want to increase campaign response rates
and generate more revenues per customer, solutions
may be as close as your own customer data file, says
David Hadaway, president/CEO of AltairData
Resources.
The challenge is figuring out what data predict
which buyers will meet your business objectives.
That's why more companies are turning to predictive
analytics. By developing predictive models,
marketers can realize that lift in response by at
least 20 percent over more traditional list-building
methods, such as income-based selection or
geographic clustering.
Predictive modeling basically involves analyzing
data from past marketing campaigns or other
transactions to determine how various combinations
of attributes affect response or purchasing
behavior.
Each prospect or customer is scored based on
demographic and lifestyle characteristics, as well
as buying history. The marketer then targets the top
scorers and eliminates unlikely responders and
low-value customers.
To determine if modeling is practical for your
company or for a specific campaign, you need to
consider these questions:
What do you want your model to accomplish?
Predictive models can help:
Improve response rates to specific promotions;
Determine which consumers are likely to be repeat
buyers;
Identify customers for up-selling or
cross-selling;
Reduce cost by eliminating unqualified prospects
or non-responders; and
Rank leads for follow-up via telemarketing or
other methods.
Do you have enough good quality data to develop a
useful model? To create a response model, you need a
list of about 2,000 responders and 2,000
non-responders from a similar campaign. You will
also need demographic and psychographic attributes
for both responders and non-responders. If you don't
collect that data yourself, you can purchase it from
other data providers.
Will you have multiple opportunities to use your
model? Predictive modeling is an investment. To
maximize your return, you should plan to refine,
refresh, and reuse a model multiple times.
Do you have the expertise to produce relevant
models? Although software vendors sell sophisticated
analytical tools, it often takes significant
expertise to use these programs effectively. If your
company lacks the resources to build in-house
analytic capabilities, consider outsourcing to a
consultant or data company with experience in your
industry.
When predictive modeling is too expensive or
impractical, consider using shelf models. We've
seen financial companies use well-designed shelf
models to reduce cost per lead and cost per sale
dramatically, resulting in an average jump in
profits of 65 percent. That's a result any marketer
would be happy to take to the bank.
---Source:
Reprinted from DM News December 19, 2007 issue (www.dmnews.com).
David Hadaway is president/CEO of AltairData
Resources. Reach him at dhadaway@altairdata.com.
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