Transcript XHTML Essentials: Level 1 Chapter 8

Customer Relationship Management:
A People, Process, and Technology
Approach
William Wagner and Michael Zubey
Chapter 4: Business
Intelligence
Customer Relationship Management
2007 Thomson
Publishing: All Rights
Wagner & ZubeyCopyrightCopyright
(c) 2006 Prentice-Hall. All rights reserved.
Reserved
1
Objectives
 Apply CRM analytics to real-world scenarios within the
financial services market
 Describe the importance of the business intelligence
framework
 Describe the extract transform load (ETL) process and
its importance for CRM and business intelligence
processes
 Explain the role the people, processes, and technology
involved in the overall business intelligence (BI)
framework
 Discuss the future of BI and its value in the CRM
environment
Customer Relationship Management
Wagner & Zubey
2
CRM in Action
The Allstate Corporation
 the holding company for Allstate Insurance Company.
 engaged in the personal property and casualty insurance
business and the life insurance, retirement and investment
products business
 has four business segments:
Allstate Protection, which includes its personal property and
casualty business
Allstate Financial, which encompasses life insurance,
retirement and investment products business
Discontinued Lines and Coverage’s
Corporate and other.
Customer Relationship Management
Wagner & Zubey
3
CRM in Action
The Allstate customer data warehouse
took just over a year to implement
can hold up to three terabytes of data in an Oracle
database
Ab Initio is used for extract, transform, and load (ETL)
from nine different administration systems that support
Allstate’s life insurance, long-term care, annuities, and
mutual fund businesses.
SAS Enterprise Miner and Brio are used for analytics
 Proclarity is used for online analytical processing
(OLAP).
Customer Relationship Management
Wagner & Zubey
4
CRM in Action
 Application of the data warehouse
 Elimination of duplicate mailings
 Study economic value of producer relationships
 Flexibility in use of data in the future
 Identify business opportunities within targeted segments
 Analyze performance of intermediaries
 Gauge the effectiveness of specific customer-centric
marketing operations
Customer Relationship Management
Wagner & Zubey
5
CRM in Action
Installation Process
 Continued involvement of both business and IT in the
data warehouse design.
 Built an internal householding process using Trillium and
built a carrier presort mail file.
 To minimize current data extract issues and allow the
most future flexibility
 Used an ETL product to take all of the data in the
mainframe and drop it into a collection area
 Evaluated segments that were used on a regular
basis
 Then use the ETL tool to select the most useful data
Customer Relationship Management
Wagner & Zubey
6
CRM in Action
Installation process ( contd.)
 use analytics to track and gauge the effectiveness of
specific customer-centric marketing operations
 Trap bad variable data and replace with data to indicate
incorrect source system variable. This ensures
continuing scrubs in the data warehouse.
Further development
 Use of SAS Enterprise Miner for data modeling.
 Hire highly skilled Analysts to create a flexible highly
synergistic environment.
Customer Relationship Management
Wagner & Zubey
7
Business Intelligence
 A broad category of applications and technologies
for gathering, storing, analyzing, and providing
access to data to help enterprise users make better
business decisions.
Customer Relationship Management
Wagner & Zubey
8
Data Warehouse

“A data warehouse is a central repository for all or significant
parts of the data that an enterprise's various business systems
collect.”- as defined by defined by the self-proclaimed father of
data warehousing- Bill Inmon.
Customer Relationship Management
Wagner & Zubey
9
Customer Relationship Management
Wagner & Zubey
10
ETL Process
 The extraction, transform, and load process
of an enterprise data warehouse is referred to
as the ETL process
 Critical due to
Timeliness of data
Faster decision making process
Customer Relationship Management
Wagner & Zubey
11
Steps in an ETL process
 Extract data with a batch Process
 Transform data with a metadata library
 Load data into an operational data store
(ODS)
Customer Relationship Management
Wagner & Zubey
12
Customer Relationship Management
Wagner & Zubey
13
Phase 2 – Data Warehousing
 Data is assembled and prepared for reporting
and analytics
Break out into data marts, different data types,
etc.
Data mining may occur in phase two
Query performance analyzed and optimized
OLAP tools used
Good for end users
Customer Relationship Management
Wagner & Zubey
14
Customer Relationship Management
Wagner & Zubey
15
Data Warehouse Issues
 Data Marts -support different segments of
information users
 Data types
 Query Performance
 OLAP – Online Analytical Processing
Customer Relationship Management
Wagner & Zubey
16
Reporting and Analysis – Phase 3
 Externally-facing process
Data security and user interface design more
important here
 Analytics
Used to derive KPIs and special reports
Many off-the-shelf applications
 Reporting
Can include rudimentary calculations based on
historical data
Customer Relationship Management
Wagner & Zubey
17
Customer Relationship Management
Wagner & Zubey
18
CRM Analytics
 A form of OLAP
 Employs data mining
 Can provide
customer segmentation groupings
RFM analysis example
 profitability analysis
personalization
event monitoring
what-if scenarios
predictive modeling
Customer Relationship Management
Wagner & Zubey
19
Customer Relationship Management
Wagner & Zubey
20
Knowledge workers-consumers
 Explorers
 do not know what they want
 do "out-of-the-box" thinking
operate on intuition
 create huge queries, looking at much detail and
history.
Response time may range into multiple days.
look at data one way and then another
Customer Relationship Management
Wagner & Zubey
21
Knowledge workers-consumers
 Farmers
 do the same activity repeatedly, except on
different data.
know what they want before they set out to
execute a query.
operate in a very predictable manner.
 execute the same query repeatedly, against very
small amounts of data.
expect good performance for their queries
Customer Relationship Management
Wagner & Zubey
22
Knowledge workers-consumers
 Miners
methodically scan data (large amounts at a
detailed level)
look for suspected patterns. Once having found
the pattern, the data miner tries to explain the
pattern, in both the technical sense and the
business sense
Customer Relationship Management
Wagner & Zubey
23
Knowledge workers-consumers
 Tourists casual users ("just visiting" the data)
 know how to cover a breadth of material quickly but have
little depth
 know how to find things.
 Operators "run" the enterprise on a day-by-day basis
 functional area involves lots of data
 make key tactical decisions to improve business
conditions
Customer Relationship Management
Wagner & Zubey
24
Knowledge workers-Producers
 ETL specialists
work with the different business knowledge
workers to determine which data types are critical
to the business processes so that they are
extracted and then loaded into the data
warehouse.
will create, test and manage all of the application
that is engaged to deliver the ETL process within
the overall business intelligence environment.
Customer Relationship Management
Wagner & Zubey
25
Knowledge workers-Producers
 Meta data modelers
responsible for the technical architecture upon
which the physical Meta data repository, and the
access to it, is based
 responsible for the design and construction of
the Meta model (physical data model) that will
hold the Meta data (both business and technical
Meta data).
Customer Relationship Management
Wagner & Zubey
26
Knowledge workers-Producers
 Data warehouse architects
 develop the different information schemas that a data warehouse
uses
 design, development, and test and implement the data
warehouse
 OLAP developers
 design and develop information transformation and reporting tools to
support key intelligence areas within the business.
 Application developers
 will build information portals or dashboard applications for customers to
easily access the data
Customer Relationship Management
Wagner & Zubey
27
Keys for Digital Dashboards and
Portals
 User friendliness
 Easy access to information
 Easy customization
Customer Relationship Management
Wagner & Zubey
28
Customer Relationship Management
Wagner & Zubey
29
Customer Relationship Management
Wagner & Zubey
30
Customer Relationship Management
Wagner & Zubey
31
Customer Relationship Management
Wagner & Zubey
32
Customer Relationship Management
Wagner & Zubey
33
The Future and Value of Business
Intelligence in CRM
 GPS- for “real-time” tracking of shipments
 Artificial Intelligence- for unmanned customer
support systems, product support documents,
speech recognition software.
Customer Relationship Management
Wagner & Zubey
34
Chapter Summary
 In this chapter you learned:
 What is business intelligence (BI)
 The functional areas of BI and their importance for
CRM
 The three critical phases of a BI system
ETL
Data Warehousing
Reporting Services
 Data mining in a CRM context
Customer Relationship Management
Wagner & Zubey
35
Questions?
Customer Relationship Management
Wagner & Zubey
36