Data Mining: The Brains Behind eCRM
While customer relationship management (CRM) gets a lot of attention,
one important component is often overlooked - how a company effectively
uses its existing customer data to improve customer loyalty while also
identifying new potential buyers of products and services. Data mining
has emerged from obscure beginnings in artificial intelligence to become
a viable and increasingly popular tool for putting data to work.
Customer Relationship Management is an information industry term for methodologies
and software that help companies manage customer relationships in a structured
way. For example, an enterprise might build a database about its customers
that describe relationships in sufficient detail. Ideally, the information
in the database can then be accessed by management, salespeople, helpdesk
personnel, and others to match customer needs with product plans and offerings,
remind customers of service requirements, and know what other products
a customer has purchased. CRM of course existed before there was an information
industry; even the great salesmen of literature such as Willie Loman and
"Professor" Harold Hill owed their successes to knowing their customers
and what those customers wanted.
has been a vital information industry segment for many years, but the
special needs of Internet businesses for real-time processing of huge
volumes of data led to the creation of a flock of startups and the coining
of the term "eCRM" to describe their niche. Some of the special characteristics
of eCRM were that it primarily served (and was offered by) dot-com businesses,
that it tended to be weak on the traditional kinds of CRM processing,
and that it made effective uses of Internet technology, such as connecting
an on-line customer with a phone representative and automatically displaying
the customer's data on the representative's screen. However, as bricks-and-mortar
firms expand to the Internet and old-line CRM and analytics firms do likewise,
the line between CRM and eCRM will become too hazy to matter.
objectives of eCRM are to:
marketing departments to identify and target their best customers, manage
marketing campaigns with clear goals and objectives, and generate quality
leads for the sales team
organizations in improving telesales, account, and sales management
by optimizing information shared by multiple employees, and streamlining
relationships with customers to improve customer satisfaction and maximize
the most profitable customers and provide them the highest level of
employees with the information and processes necessary to understand
their customers' needs and build strong relationships between the company,
its customer base, and distribution partners
is Data Mining?
Data mining is an automated approach for discovering or inferring hidden
patterns or knowledge buried in data. By "hidden" we mean patterns that
are not made apparent through casual observation. As a component of eCRM,
data mining can aid marketing personnel by helping to answer the following
types of questions:
- Are there
additional people whom I should market to, or whom I am currently missing?
product traits do my customers perceive most favorably?
- Can I
create additional products which contain the best of these traits?
techniques for data mining exist, most of these fall into one of the following
two categories, supervised and unsupervised learning. Users apply data
mining software employing one of these two learning algorithms to construct
a data mining model.
and Unsupervised Learning
In supervised learning, the user constructs the model by
supplying example sets of input data and corresponding sets of output
data. Training pets provides a good analogy for supervised learning. When
training a dog to roll over, the owner will say "roll over" (the input),
then grasp Fido on either side and physically rotate him laterally over
the ground to simulate the desired response: a roll (the output). Eventually
the dog will respond automatically to the "roll over" command with the
appropriate action. The analogy fails, however, when extending it to data
mining. In data mining, the trained model needs to respond to more than
just known inputs, but must give meaningful results when receiving novel
inputs. (Our dog would be confused by "roll out" or "roll up".)
data mining example might involve a marketing manager who wants to spur
the sales of a new CD. The manager would comb the database for records
of customers who had already purchased the CD, and present some of these
to the learning algorithm, instructing it to recognize from the other
purchases that these are likely purchasers of the target CD. The algorithm
would be tested with the remaining customer records. Records from customers
who didn't purchase that CD would also be used in the training and testing.
If the algorithm shows a high rate of predicting purchasers from non-purchasers
it would be used as a recommendation engine; when customers visit the
site those that score highly would be offered the target CD as a recommendation.
mining models created by supervised learning are often applied in cases
where users wish to understand the outcome or consequences of a given
set of customer profile data never before encountered. By supplying both
the inputs and expected outputs, supervised learning allows users to create
models for prediction and identification.
contrast to supervised learning, unsupervised learning constructs
data mining models independent of any supplied performance criteria. The
algorithm that creates these models searches through a large collection
of input data and finds relationships on its own without requiring the
user to supply an expected output. In our dog training analogy, this would
be equivalent to shouting a series of commands at Fido and letting him
decide how to respond. Similar commands (measured within a specified tolerance)
would presumably cause him to perform the same trick, but the trick might
be completely new, i.e., one that we might not have anticipated (like
fetching leftovers from the refrigerator).
learning models are ideal for identifying novel patterns in data that
might otherwise go undiscovered. For example, if a book is reviewed favorably
on nationwide radio show its sales might suddenly shoot up. A website
would want to recognize this and recommend the book to likely buyers.
Since the review was not known about in advance and the phenomenon only
lasts for about a day, the website needs to be able to recognize the pattern
as it emerges and respond to it without marketing intervention. Indeed,
on any given day there might be hundreds of such short-lived patterns
that the website could profit from.
A set of target outputs (actual) are used to compare between actual
and produced output during learning (i.e., model construction)
Used for constructing predictive models
Models are constructed independently of any supplied performance
Used for searching for novel patterns
mining capabilities can play an important role in successful customer
relationship management. In evaluating a data mining package, users should
ask in which category, supervised or unsupervised, it falls and whether
it is suitable for their needs. For instance, insurance companies seeking
to predict insurance fraud should consider packages that use unsupervised
learning, as there are typically no baselines to serve as predictors.
Consumer products retailers can use unsupervised techniques to discover
customer buying patterns in cash register receipt data in order to group
items optimally in their stores. Supervised learning should be used where
one wishes to measure the deviation from a known result. For example,
a marketing department may know what product combinations (e.g., cell
phones and accessories) are purchased by certain age groups. Supervised
learning can create a model that records these known "truths" about customer
buying habits which can then be used to qualify buying data that does
not conform exactly to these truths. This can help the marketers adjust
their strategy accordingly.
many companies the benefits of data mining are first realized in the area
of marketing. The process of collecting and segmenting customer data for
effective marketing campaigns has traditionally been more of an art than
a science. However, automated approaches such as data mining provide businesses
with better ways to plan and manage marketing campaigns. Companies with
large customer databases should consider a data mining solution for campaign
management and customer analysis that can leverage their data volumes
to improve customer loyalty, qualify and understand their customers, and
generate repeat business.
This article has been modified from its original form since the original