customer relationship management wagner & zubey 11 copyright (c) 2006 prentice-hall. all rights...

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


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.


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).


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


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


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.


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.


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.


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


Steps in an ETL process

Extract data with a batch ProcessTransform data with a metadata libraryLoad data into an operational data store

(ODS)


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


Data Warehouse Issues

Data Marts -support different segments of information users

Data typesQuery PerformanceOLAP – Online Analytical Processing


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


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


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



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



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



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


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.



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).



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


Keys for Digital Dashboards and Portals

User friendlinessEasy access to informationEasy customization


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.


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


Questions?

customer relationship management wagner & zubey 11 copyright (c) 2006 prentice-hall. all rights...

Documents

use of data

business segments

useful data slide

data modeling

terabytes of data

casualty insurance business

business opportunities

data warehouse design