Harnessing the Power of Predictive Analytics May 18, 2007 5:15 PM
, By Jeff Liebl
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Direct marketers
constantly wrestle with the never-ending challenge of maximizing campaign
response rates to generate more revenue. According to recent research from the
Direct Marketing Association, direct mail response rates currently stand at
2.18%, while e-mail response rates are slightly higher at 2.45%. In an era of
continuously-rising postal rates and clogged e-mail in-boxes, marketing
professionals spend inordinate amounts of time and effort endlessly dreaming up
new creative, tweaking campaigns, and procuring different mailing lists from
myriad providers in efforts to boost response percentages.
In reality, the
solution to this problem may not lie in finding a better mailing list, or
developing a better collateral piece. The answer may be found in more effective
customer segmentation and precision targeting — a critical component of direct
marketing strategy that's made easier by the latest advancements in predictive
analytics.
Predictive analytics
has now been widely adopted by businesses across several industries, including
by direct marketers, for whom it is a vital asset. Why? Because predictive
analytics allow direct marketers to easily identify individuals most likely to respond to certain offers, and hopefully,
become long-term customers. As a result, direct marketers are able to increase
response rates and maximize campaign return on investment (ROI) through the use
of statistically-derived targeting strategies. Let’s take a look at this
technology in greater detail.
Predictive analytics is a process, based on
statistical and data mining techniques, that models current and historical
customer performance data and traits to make “predictions” about future
outcomes and customer behaviors. These predictions can be expressed as
numerical values, or scores, that correspond to the likelihood of a particular
occurrence or behavior taking place in the future. In corporate America,
predictive scores are typically used to determine the risk or opportunity
associated with a specific customer or transaction. These evaluations assess
the relationships between many variables to estimate risk or response.
As a result, many firms rely on predictive scoring to make important
customer decisions. For example, credit scoring, used throughout the financial
services sector, is one of the most familiar and well-known predictive
analytics applications available today. Credit-scoring models, such as the
well-known FICO Score, analyze an individual’s credit history and other
financial information, and assign a relevant score that indicates the
customer’s likelihood to make future credit payments on time. Predictive
analytics are also used in many other industries, including retail, travel,
healthcare, pharmaceuticals, telecommunications, and more recently, online
marketing.
In consumer direct
marketing, predictive analytics is used to segment prospect lists by
identifying consumers more likely to respond to certain offers as well as to
reactivate past customers and prospects. Acquiring new leads and converting
them to paying customers can be rather costly, though the Internet has in
recent years proven a great new source of high volumes of low-cost leads. The
quality of those Internet-based leads can vary widely, however. Reactivating
old leads that failed to convert can be one of easiest and most cost-effective
means of generating incremental sales.
How can predictive analytics reactivate old leads…and create added
revenue?It is rather
straightforward, really. A predictive analytics solution provider uses an
existing file of unconverted consumer leads, updates, and augments the leads
with external data sources to ensure the lead is authentic, and that the
prospect can be contacted. Next, a set of custom affinity scores is applied to
each lead in the file to determine the scope of offers each prospect is most
likely to respond to during a given campaign. Then, those scored leads, and
each lead’s opt-in criteria, are mapped to the company’s available offerings to
ensure the marketing program is targeting the right people with the most
relevant goods or services. Time and time again, this approach has proven to
generate enhanced response rates, leading to added revenue, because of this improved,
data-driven targeting.
Direct marketers can
harness the power of advanced predictive analytics solutions to increase
campaign response rates, create added revenue streams, and elevate overall
marketing ROI. While the technology is quite sophisticated, the rationale
behind it is not:more effective
targeting of customers leads to more sales. Moreover, companies can fully
leverage existing assets, such as unconverted online leads, that are otherwise
of no value. Predictive analytics can truly solve a number of persistent direct
marketing problems, while delivering on many different objectives.
Jeff Liebl is vice president of
marketing and business development atSt. Cloud, MN-basedeBureau, a company that provides precision marketing, risk management,
and fraud prevention scoring and information solutions to multichannel
marketers. He can be reached at jeffliebl@ebureau.com.
