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The following article appeared on http://www.dmnews.com.
Use Analytics to Satisfy
Consumers
Sept. 23, 2003
By: Robert Slaker
SPSS Inc.
If recent action by consumers is any indication, many seem fed up
with what they perceive as intrusive unsolicited marketing. The
huge response to the federal do-not-call list, combined with consumer
and Internet service provider use of e-mail blocking filters (despite
that these activities also have unintended consequences), has to
be a wakeup call for all direct marketers.
"How did it come to this?" is not easily
answered. As in most matters of mind and emotion, a complex set
of sociological and economic factors contributes to the seeming
consumer rebellion. But, being the practical direct marketers we
are, we can focus on specific actionable items.
One major factor is that we may have lost sight
of the basic marketing principle: Know thy customer and give him
what he wants. We are at a stage where we must avail ourselves of
all the tools available to maximize our value to our customers and
minimize the effect on consumers who don't find value in what we
offer. New technologies to work with consumer data are making that
objective much easier to attain.
Along with making the proper handling of consumer
do-not-call and do-not-e-mail requests part of our standard operating
procedures, we need to do better at getting to the root causes of
why people are dissatisfied and improve our relationships with customers
and prospects. That means starting from the consumer data and working
our way back to the products and services we offer.
Customers and prospects give us information about
themselves all the time. Databases are full of these useful tidbits,
and call centers and other customer management systems are overflowing
with details about our customers and contacts. The problem is that
the information is in the form of data — tons of it. Data
is good, and more data is better, but data by itself has no value
if it is not turned into information.
And while we take precautions to protect the privacy
of the data, we also have the chance to take advantage of the information
it provides to better manage the relationship with our customers
and prospects. To do so means using every tool at our disposal,
and one of those tools is the technology that lets us properly manage
the massive amount of data that spins off from our interactions
with customers and prospects.
Turning this data into useful information is where
analytical technology comes into play. A philosopher once wrote
that finding the patterns in the randomness of life is the way we
create beauty and make art. A similar statement could be made about
analytics, which find patterns in the randomness of data so that
you can discover valuable information and gain insight.
An array of analytical products is available for
desktop and enterprise systems and for pros and novices alike. Generally,
analytics fall into four categories:
Statistical analysis. This refers to a
collection of methods used to process large amounts of data to uncover
key facts, patterns and trends. Though there are numerous statistical
analysis procedures, the two used most commonly by direct marketers
are classification and segmentation.
Classification uses predictor fields to predict
a categorical target field, such as which groups will respond to
a mailing. Segmentation divides subjects, objects or variables into
relatively homogeneous groups (e.g., segmenting consumers into usage
pattern groups).
Popular statistical software can handle the entire
analytical process: planning, data collection, data access, data
management and preparation, data analysis, reporting and deployment.
Use of statistical analysis to classify and segment can increase
the likelihood that we communicate only with people who are more
likely to be interested in our offer.
Online Analytical Processing. With OLAP,
users easily and selectively extract data, then view it from different
perspectives. For example, a user can request that data be analyzed
to display a spreadsheet showing all of a company's widgets sold
in Wyoming in August, compare revenue figures with those for the
same products in October, and then see a comparison of other product
sales in Wyoming in the same time period.
To facilitate this analysis, OLAP data is stored
in a multidimensional database, which considers each data attribute
as a separate "dimension." This management tool lets marketers
quickly review history and trends to take advantage of emerging
opportunities and take corrective action on developing problems.
Data mining. This finds the meaningful
patterns and relationships in data and provides decision-making
information about the future. Data mining procedures include: association,
looking for patterns where one event is connected to another; sequence
or path analysis, looking for patterns where one event leads to
a later event; classification, looking for new patterns; clustering,
finding and visually documenting groups of facts not previously
known; and forecasting, discovering patterns in data that can lead
to reasonable predictions about the future.
Data mining gives a clear picture of what is going
to happen in time to change it. This includes: whom the best customers
might be, which customers are likely to defect or, if the right
data is gathered, which carry the risk of adverse reaction to marketing
offers.
Text mining. Text mining analyzes unstructured
textual data by finding patterns and relationships within thousands
of documents, such as e-mails, call reports and Web sites. Text
mining extracts terms and phrases, then classifies the terms into
related groups such as products, organizations or people using the
meaning and context of the text. This distilled information can
be combined with other data sources and used with traditional data
mining techniques such as clustering, classification and predictive
modeling.
Questions to explore include which concepts occur
together? What else are they linked to? What do they predict? The
answers make the marketer better able to identify and head off potential
consumer dissatisfaction and maximize consumer satisfaction.
With the massive amount of consumer data generated
every moment of every day, and the necessity of carefully managing
the relationship with the consumer, analytics no longer are a nice
thing to have, they are essential.
It comes down to building a reciprocal relationship
with the consumer. If we take the time to understand consumers based
on the data we already have, we'll be able to give them what they
want or tell them about something of interest to them, thereby developing
a relationship valued by the consumer.
Robert Slaker is senior manager of direct response
marketing at SPSS Inc., Chicago.
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