166 Enhance Your Business Applications: Simple Integration of Advanced Data Mining Functions
The models, then tightly integrated with the data itself in the database
management system (DBMS), facilitate automating the process of dynamically
evaluating and responding to individual customer preferences and behaviors.
Certain product items and price offerings, preferred color combinations, and
other micro campaigns can be offered to Web site visitors in a more dynamic
way, based on the underlying mining model.
8.2 Business Intelligence integration
This section discusses the integration of IM Scoring with other Business
Intelligence tools. Particularly, it includes the tools for online analytical processing
(OLAP) using the DB2 OLAP Server and tools for query reporting using QMF.
8.2.1 Integration with DB2 OLAP
Making data mining results available to the business analyst using
multidimensional OLAP front-end tools gives new insights to solve real business
problems such as to:
Find the most profitable customer segments
Understand customer buying behavior (find product associations using
market basket analysis)
Optimize the product portfolio
Keep profitable customers by understanding attrition factors
Knowledge that was previously hidden in the data warehouse and data mining
models and that was only available to data mining experts is now available for
cross-dimensional browsing using both DB2 UDB and DB2 OLAP Server.
Integrating IM Modeling and IM Scoring further into OLAP solutions, by
automating steps done manually by the OLAP designer manually, reduces the
steep learning curve for OLAP users when applying mining technology. Plus, it
brings faster time to market of marketing- and sales-related actions on the basis
of found knowledge as the automation eliminates the manual efforts.
Basic understanding of OLAP cube
An OLAP cube is a multidimensional view into a company's data. Typical
dimensions include time, products, market or geography, sales organization,
scenario (plan versus actual), and a measure dimension (includes such
measures as revenue, cost of goods sold (COGS), profit or ratios-like margin).
The structure of the dimension that defines a multidimensional view is called
Chapter 8. Other possibilities of integration 167
Each dimension can be hierarchical in structure. For example, the time
dimension can be broken down into years, the years can be broken down into
quarters, and the quarters can be broken down into months and so on.
Typically the outline contains a hierarchy that business analysts used for a long
time in their reporting. For example, the customer dimension in the banking
industry could be aggregated according to customer segments such as private
individuals, corporate customers, public authorities, and so on.
The cube typically does not contain attribute dimensions for all attributes that are
known in the warehouse about the base dimension “customer”. In the banking
industry, for example, the warehouse may have dozens of attributes, such as
age, marital status, number of children, commute distance, number of years as a
customer, and so on, for each customer.
Integrating a new dimension in the cube
The attributes described in Chapter 4, “Customer profiling example” on page 51,
can be represented as an additional dimension or as an attribute dimension. An
attribute dimension simply assigns the attribute as a label to the base dimension.
Defining a hierarchy for customers, such as using geographical information, is
easy to do in an OLAP system. Using market regions in an OLAP cube is
common practice. However, a hierarchy that is easy to define, such as a
geographical hierarchy, does not give valuable information about the business.
Data mining using IM Modeling and IM Scoring, instead, can produce a
segmentation of customers. It takes more information about customers into
account, such as family status, size of city, estimated income, and other
demographic data. Such segments, also called
clusters, can then be used to
define a customer dimension in OLAP as shown in Figure 8-3. The cluster
identifiers can then be added to the OLAP cube as additional attribute
dimensions. A meaningful short description of the separate cluster can be added
as a dimension.
Figure 8-3 OLAP outline with initial rough hierarchy to present customer groups