Database Reference
In-Depth Information
14.9.1.2.2 CRM Decision Patterns through Data Mining
CRM systems like SAP CRM are used to track and efficiently organize inbound and outbound
interactions with customers, including the management of marketing campaigns and call centers.
These systems, referred to as operational CRM systems, typically support front-line processes in
sales, marketing, 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. These systems are just tools that should be used to support the
strategy of effectively managing customers.
However, to succeed with CRM, 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, allowing time for targeted retention campaigns.
◾
Data mining can support customer development by matching products with customers and
better targeting of product promotion campaigns.
◾
Data mining can also help to reveal distinct customer segments, facilitating the development
of customized new products and product offerings, which better address the specific prefer-
ences and priorities of the customers.
The results of the analytical CRM procedures 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.
Marketers strive to get a greater market share and a greater share of their customers, that is,
they are responsible for getting, developing, and keeping the customers. 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. Data mining
models consist of a set of rules, equations, or complex functions that can be used to identify useful
data patterns, understand, and predict behaviors.
Data mining models are of two kinds:
1. Predictive or Supervised Models: In these models, there are input fields or attributes and an
output or target field. Input fields are also called predictors, because they are used by the
model to identify a prediction function for the output or target field. The model generates an
input-output
mapping function, which associates predictors with the output so that, given
the values of input fields, it predicts the output values. Predictive models themselves are of
two types, namely, classification or propensity models and estimation models. Classification
models are predictive models with predefined target field or classes or groups, so that the
objective is to predict a specific occurrence or event. The model also assigns a propensity
score with each of these events that indicates the likelihood of the occurrence of that event.
In contrast, estimation models are used to predict a continuum of target values based on the
corresponding input values.
