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

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

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.

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).

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

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

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.

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.

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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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

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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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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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

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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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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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

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

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

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

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.

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).

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

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

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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.

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

Customer Relationship Management Wagner & Zubey 36 Questions?