Database Reference
In-Depth Information
2. Undirected or Unsupervised models: In these models, there are input fields or attributes, but
no output or target field. The goal of such models is to uncover data patterns in the set of
input fields. Undirected models are also of two types, namely, cluster models, and, associa-
tion and sequence models. Cluster models do not have predefined target field or classes or
groups, but the algorithms analyze the input data patterns and identify the natural group-
ings of cases. In contrast, association or sequence models do not involve or deal with the
prediction of a single field. Association models detect associations between discrete events,
products, or attributes; sequence models detect associations over time.
Segmentation is much more complex than it may seem; simplified segmentation
models, when tested in real life, seem to imply that people as customers change
behavior radically. If this was really true, there would be no trust, no loyalty, and,
consequently, no collaboration. The apparent paradox gets resolved only when it is
recognized that while people as customers do not possess multiple personalities, they have
differing customs and, hence, play differing roles based on different contexts or scenarios.
The problem arises on persisting with the stance of
one-segment-fits-for-all-contexts-for
all-people-on-all-occasions
.
Data mining can provide customer insight, which is vital for establishing an effective CRM
strategy. It can lead to personalized interactions with customers and hence increased satisfaction
and profitable customer relationships through data analysis. It can support an
individualized
and
optimized customer management throughout all the phases of the customer lifecycle, from the
acquisition and establishment of a strong relationship to the prevention of attrition and the win-
ning back of lost customers.
a.
Segmentation
: It is the process of dividing the customer base into distinct and internally
homogeneous groups in order to develop differentiated marketing strategies according to
their characteristics. There are many different segmentation types based on the specific cri-
teria or attributes used for segmentation. In behavioral segmentation, customers are grouped
by behavioral and usage characteristics. Data mining can uncover groups with distinct pro-
files and characteristics and lead to rich segmentation schemes with business meaning and
value. Clustering algorithms can analyze behavioral data, identify the natural groupings of
customers, and suggest a solution founded on observed data patterns.
Data mining can also be used for the development of segmentation schemes based on the
current or expected/estimated value of the customers. These segments are necessary in order
to prioritize customer handling and marketing interventions according to the importance of
each customer.
b.
Direct Marketing Campaigns
: Marketers use direct marketing campaigns to communicate a
message to their customers through mail, the Internet, e-mail, telemarketing (phone), and
other direct channels in order to prevent churn (attrition) and to drive customer acquisi-
tion and purchase of add-on products. More specifically, acquisition campaigns aim at
drawing new and potentially valuable customers away from the competition. Cross-/deep-/
up-selling campaigns are implemented to sell additional products, more of the same
product, or alternative but more profitable products to existing customers. Finally, retention
