1. Customer Relationship Management Wagner & Zubey 11 Copyright (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

2. Customer Relationship Management Wagner & 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

3. Customer Relationship Management Wagner & 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’s  Corporate and other.

4. Customer Relationship Management Wagner & Zubey 4 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).

5. Customer Relationship Management Wagner & 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

6. Customer Relationship Management Wagner & Zubey 6 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

7. Customer Relationship Management Wagner & 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 development  Use of SAS Enterprise Miner for data modeling.  Hire highly skilled Analysts to create a flexible highly synergistic environment.

8. Customer Relationship Management Wagner & 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.

9. Customer Relationship Management Wagner & 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.

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11. Customer Relationship Management Wagner & Zubey 11 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

12. Customer Relationship Management Wagner & Zubey 12 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)

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14. Customer Relationship Management Wagner & Zubey 14 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

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16. Customer Relationship Management Wagner & Zubey 16 Data Warehouse Issues  Data Marts -support different segments of information users  Data types  Query Performance  OLAP – Online Analytical Processing

17. Customer Relationship Management Wagner & Zubey 17 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

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19. Customer Relationship Management Wagner & Zubey 19 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

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21. Customer Relationship Management Wagner & Zubey 21 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

22. Customer Relationship Management Wagner & 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

23. Customer Relationship Management Wagner & 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

24. Customer Relationship Management Wagner & 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

25. Customer Relationship Management Wagner & 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.

26. Customer Relationship Management Wagner & 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).

27. Customer Relationship Management Wagner & 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

28. Customer Relationship Management Wagner & Zubey 28 Keys for Digital Dashboards and Portals  User friendliness  Easy access to information  Easy customization

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34. Customer Relationship Management Wagner & 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.

35. Customer Relationship Management Wagner & Zubey 35 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

36. Customer Relationship Management Wagner & Zubey 36 Questions?