Data Mining in CRM - Data Mining Techniques in CRM: Inside Customer Segmentation - page 2

with new ones from the competition. With respect to the second CRM goal of
customer development, the key message is that there is no average customer. The
customer base comprises different persons, with different needs, behaviors, and
potentials that should be handled accordingly.
Several CRM software packages are available and used to track and efficiently
organize inbound and outbound interactions with customers, including the man-
agement of marketing campaigns and call centers. These systems, referred to as
operational CRM systems, typically support front-line processes in sales, market-
ing, and customer service, automating communications and interactions with the
customers. They record contact history and store valuable customer information.
They also ensure that a consistent picture of the customer's relationship with the
organization is available at all customer ''touch'' (interaction) points.
However, these systems are just tools that should be used to support the
strategy of effectively managing customers. To succeed with CRM and address
the aforementioned objectives, organizations need to gain insight into customers,
their needs, and wants through data analysis. This is where analytical CRM comes
in. Analytical CRM is about analyzing customer information to better address the
CRM objectives and deliver the right message to the right customer. It involves the
use of data mining models in order to assess the value of the customers, understand,
and predict their behavior. It is about analyzing data patterns to extract knowledge
for optimizing the customer relationships.
For example, data mining can help in customer retention as it enables the
timely identification of valuable customers with increased likelihood to leave, allow-
ing time for targeted retention campaigns. It can support customer development
by matching products with customers and better targeting of product promotion
campaigns. It can also help to reveal distinct customer segments, facilitating the
development of customized new products and product offerings which better
address the specific preferences and priorities of the customers.
The results of the analytical CRMprocedures should be loaded and integrated
into the operational CRM front-line systems so that all customer interactions can
be more effectively handled on a more informed and ''personalized'' base. This
topic is about analytical CRM. Its scope is to present the application of data
mining techniques in the CRM framework and it especially focuses on the topic of
customer segmentation.
WHAT CAN DATA MINING DO?
Data mining aims to extract knowledge and insight through the analysis of large
amounts of data using sophisticated modeling techniques. It converts data into
knowledge and actionable information.