Upload
laurence-willis-stewart
View
215
Download
0
Tags:
Embed Size (px)
Citation preview
Customer Relationship ManagementWagner & Zubey 11Copyright (c) 2006 Prentice-Hall. All rights reserved.Copyright 2007 Thomson Publishing: All Rights Reserved
Chapter 4: Business Intelligence
Customer Relationship Management:
A People, Process, and Technology Approach
William Wagner and Michael Zubey
Customer Relationship ManagementWagner & Zubey 2
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 ManagementWagner & Zubey 3
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’sCorporate and other.
Customer Relationship ManagementWagner & Zubey 4
CRM in Action
The Allstate customer data warehousetook just over a year to implementcan 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 ManagementWagner & Zubey 5
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 ManagementWagner & Zubey 6
CRM in Action
Installation ProcessContinued 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
basisThen use the ETL tool to select the most useful data
Customer Relationship ManagementWagner & Zubey 7
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 developmentUse of SAS Enterprise Miner for data modeling.Hire highly skilled Analysts to create a flexible highly
synergistic environment.
Customer Relationship ManagementWagner & Zubey 8
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 ManagementWagner & Zubey 9
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 ManagementWagner & Zubey 10
Customer Relationship ManagementWagner & Zubey 11
ETL Process
The extraction, transform, and load process of an enterprise data warehouse is referred to as the ETL process
Critical due toTimeliness of dataFaster decision making process
Customer Relationship ManagementWagner & Zubey 12
Steps in an ETL process
Extract data with a batch ProcessTransform data with a metadata libraryLoad data into an operational data store
(ODS)
Customer Relationship ManagementWagner & Zubey 13
Customer Relationship ManagementWagner & Zubey 14
Phase 2 – Data Warehousing
Data is assembled and prepared for reporting and analyticsBreak out into data marts, different data types,
etc.Data mining may occur in phase twoQuery performance analyzed and optimized
OLAP tools usedGood for end users
Customer Relationship ManagementWagner & Zubey 15
Customer Relationship ManagementWagner & Zubey 16
Data Warehouse Issues
Data Marts -support different segments of information users
Data typesQuery PerformanceOLAP – Online Analytical Processing
Customer Relationship ManagementWagner & Zubey 17
Reporting and Analysis – Phase 3
Externally-facing processData security and user interface design more
important here
AnalyticsUsed to derive KPIs and special reportsMany off-the-shelf applications
ReportingCan include rudimentary calculations based on
historical data
Customer Relationship ManagementWagner & Zubey 18
Customer Relationship ManagementWagner & Zubey 19
CRM Analytics
A form of OLAP Employs data miningCan provide
customer segmentation groupingsRFM analysis example
profitability analysis personalization event monitoring what-if scenarios predictive modeling
Customer Relationship ManagementWagner & Zubey 20
Customer Relationship ManagementWagner & Zubey 21
Knowledge workers-consumers
Explorers do not know what they want do "out-of-the-box" thinkingoperate 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 ManagementWagner & Zubey 22
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 ManagementWagner & Zubey 23
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 ManagementWagner & Zubey 24
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 ManagementWagner & Zubey 25
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 ManagementWagner & Zubey 26
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 ManagementWagner & Zubey 27
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 ManagementWagner & Zubey 28
Keys for Digital Dashboards and Portals
User friendlinessEasy access to informationEasy customization
Customer Relationship ManagementWagner & Zubey 29
Customer Relationship ManagementWagner & Zubey 30
Customer Relationship ManagementWagner & Zubey 31
Customer Relationship ManagementWagner & Zubey 32
Customer Relationship ManagementWagner & Zubey 33
Customer Relationship ManagementWagner & Zubey 34
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 ManagementWagner & Zubey 35
Chapter Summary
In this chapter you learned:What is business intelligence (BI)The functional areas of BI and their importance for
CRMThe three critical phases of a BI system
ETLData WarehousingReporting Services
Data mining in a CRM context
Customer Relationship ManagementWagner & Zubey 3636
Questions?