Dimensional modeling for CRM Applications

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Dimensional modeling for CRM Applications"

Transcription

1 Dimensional modeling for CRM Applications

2 Learning objective CRM has emerged as a mission-critical business strategy that is essential to a company s survival The perceived business value of understanding the full spectrum of customers interactions and transactions has propelled CRM to the top of the charts. We examine the implications of CRM on the world of data warehousing 2

3 CRM Overview Need migrate from a product-centric orientation to one that is driven by customer needs Motivation the better you know your customers, the better you can maintain long-lasting, valuable relationships Goal maximize relationships with your customers over their lifetime Requirement develop a single, integrated view of each customer 3

4 CRM Overview CRM involves all aspects of the business: marketing, sales, operations, and service What does CRM achieve? attracts new customers, doesn t let the profitable ones leave, and converts unprofitable customers into profitable ones CRM leads to: increased sales effectiveness and closure rates, revenue growth, enhanced sales productivity at reduced cost, improved customer profitability margins, higher customer satisfaction, and increased customer retention 4

5 Operational CRM Synchronization of customer-facing processes across sales, marketing, operations, and service initial prospect contact, quote generation, purchase transaction, fulfillment, payment transaction, and ongoing customer service Each touch point in the customer contact cycle represents an opportunity to: collect more customer metrics and characteristics, and leverage existing customer data to extract more value from the relationship 5

6 Analytical CRM Data is created by operational CRM Need to store and analyze the historical metrics resulting from customer interaction and transaction systems Sounds familiar? 6

7 CRM and DW Data warehouses are at the core of CRM Serve as repository to collect and integrate customer information in operational systems (or external sources) Foundation that supports panoramic (360- degree) view of our customers, including customer data from various sources: transactional data, interaction data (solicitations, call center), demographic and behavioral data (typically augmented by third parties), and self-provided profiles 7

8 CRM and DW Analytic CRM is enabled via accurate, integrated, and accessible customer data in the warehouse Measure the effectiveness of decisions made in the past to optimize future interactions Identify up-sell and cross-sell opportunities, pinpoint inefficiencies, generate demand, and improve retention. Integrated data generate scores that close the loop back to the operational CRM 8

9 CRM and DW Model output translates into specific tactics recommended for the next point of customer contact, appropriate next product offer or antiattrition response 9

10 CRM and DW As the organization becomes more centered on the customer, so must the data warehouse Data warehouses grow as we collect more information about customers Data staging processes grow more complicated as we match and integrate data from multiple sources Need for a conformed customer dimension becomes paramount 10

11 Customer Dimension A well-maintained, well-deployed conforming customer dimension is the cornerstone of a CRM The customer dimension can be extremely deep (with millions of rows), extremely wide (with dozens or even hundreds/thousands of attributes), and sometimes subject to rather rapid change 11

12 Customer Dimension Name and Address Parsing Regardless of whether individual human beings or commercial entities, we typically capture customers name and address attributes Common (but wrong) approach: generalpurpose columns for names and addresses Name-1 through Name-3 and Address-1 through Address-6 12

13 Customer Dimension Name and Address Parsing Common data quality problems No consistent mechanism for handling salutations, titles, or suffixes What is the first name or how to be addressed in a personalized greeting? Multiple customers listed in a single name field No guaranteed conformance with postal authority regulations or support address matching or latitude/ longitude identification 13

14 Customer Dimension Name and Address Parsing Solutions: break name and location attributes into many elements Standardize ( Rd for Road ) Validate (ZIP code and state) name and address data cleansing and scrubbing tools 14

15 Customer Dimension International Names and Addresses Universal representation Design consistent from country to country Similar data appear in predictable places Cultural correctness Appropriate salutation and personalization for a letter, , or telephone greeting Differences in addresses Different addresses may be required foreign mailings from the country of origin to the destination country (presenting the destination city and country in capital letters) 15

16 Customer Dimension International Names and Addresses Include an address block attribute with a complete valid postal address including line breaks rendered in the proper order according to regulations of the destination country Telephone numbers presented differently depending on where the phone call originated attributes representing the complete foreign dialing sequence, complete domestic dialing sequence, and local dialing sequence 16

17 Customer Dimension Dates It s common to have dates in the customer dimension date of first purchase, date of last purchase, and date of birth Summarize these dates by the special calendar attributes of our enterprise such as seasons, quarters, and fiscal periods Dates changed to foreign key references to the date dimension Date dimension copies (role-playing dimensions) declared as semantically distinct views Example: First Purchase Date 17

18 A Case for Snowflaking Assume external data with 150 demographic and socioeconomic attributes of counties Rather than repeating this large block of data for every customer, we snowflake Save significant space since the customer dimension is large The data is administered and loaded at different times 18

19 Customer Dimension Segmentation Attributes and Scores Customer segmentation classifications or scores: Demographic Gender, Ethnicity, Age or other life-stage classifications Income or other lifestyle classifications Status new, active, inactive, closed Recency date of last purchase Frequency total purchase transaction count Intensity total net purchase amount Scores characterizing the customer purchase behavior, payment behavior, product preferences, propensity to churn, and probability of default 19

20 Large Changing Customer Dimension Snowflaking with minidimension 20

21 Large Changing Customer Dimension Snowflaking with several minidimensions 5 demographic attributes, each with 10 possible values, then 100,000 rows Cases where we need to support more Multiple minidimensions cluster similar attributes together demographic income and lifestyle purchase and credit behavioral scores Don t overdo it! No separate minidimension for each demographic attribute, e.g., age, gender, or income 21

22 Variable-Width Attribute Set The longer the relationship, the more we know about customers 10 million initial prospects described by few attributes 1 million customers with more attributes Not possible to store prospects and customers together in a single dimension 22

23 Variable-Width Attribute Set Break the dimension into: base dimension consisting of attributes common to both prospects and customers Minidimension with attributes known only for customers Many fact table rows join to an empty customer row in the minidimension 23

24 Customer Hierarchies Commercial customers often have a nested hierarchy of entities ranging from individual locations up through regional offices, business unit headquarters, or parent companies These hierarchical relationships may change frequently as customers reorganize themselves internally or are involved in acquisitions 24

25 Customer Hierarchies Fixed-Depth Hierarchies A customer dimension that is highly predictable with a fixed number of levels Example with 3 levels: corporate parent, business unit headquarters, and regional offices (from top to bottom) We add distinct attributes in the customer dimension corresponding to these levels 25

26 Customer Hierarchies Variable-Depth Hierarchies Set of commercial customers with relationships given by an organizational tree Need to summarize sales at any level in the tree Add a bridge table between customer dimension and fact table 26

27 Customer Hierarchies Variable-Depth Hierarchies The bridge table contains one row for each path from a node to each node below it: the number of levels between them bottom flag for leaves top flag for root An additional row for the zero-length path from node to itself 27

28 Customer Hierarchies Variable-Depth Hierarchies 28

29 Customer Hierarchies Variable-Depth Hierarchies We can constrain the customer table to a particular parent customer and request any aggregate measure of all the subsidiaries at or below that customer We can use the # of Levels from Parent to control the depth of the analysis We can use the Bottom Flag to jump directly to all the bottom-most customer entities but omit all higher-level customer entities 29

Easily Identify the Right Customers

Easily Identify the Right Customers PASW Direct Marketing 18 Specifications Easily Identify the Right Customers You want your marketing programs to be as profitable as possible, and gaining insight into the information contained in your

More information

Long before the customer relationship management (CRM) buzzword existed,

Long before the customer relationship management (CRM) buzzword existed, 8 Customer Relationship Management Long before the customer relationship management (CRM) buzzword existed, organizations were designing and developing customer-centric dimensional models to better understand

More information

IBM SPSS Direct Marketing

IBM SPSS Direct Marketing IBM Software IBM SPSS Statistics 19 IBM SPSS Direct Marketing Understand your customers and improve marketing campaigns Highlights With IBM SPSS Direct Marketing, you can: Understand your customers in

More information

Customer-Centric Data Warehouse, design issues. Announcements

Customer-Centric Data Warehouse, design issues. Announcements CRM Data Warehouse Announcements Assignment 2 is on the subject web site Students must form assignment groups ASAP: refer to the assignment for details 2 -Centric Data Warehouse, design issues Data modelling

More information

9. 3 CUSTOMER RELATIONSHIP MANAGEMENT SYSTEMS

9. 3 CUSTOMER RELATIONSHIP MANAGEMENT SYSTEMS Chapter 9 Achieving Operational Excellence and Customer Intimacy: Enterprise Applications 349 FIGURE 9-5 THE FUTURE INTERNET-DRIVEN SUPPLY CHAIN The future Internet-driven supply chain operates like a

More information

Data Mining with SAS. Mathias Lanner mathias.lanner@swe.sas.com. Copyright 2010 SAS Institute Inc. All rights reserved.

Data Mining with SAS. Mathias Lanner mathias.lanner@swe.sas.com. Copyright 2010 SAS Institute Inc. All rights reserved. Data Mining with SAS Mathias Lanner mathias.lanner@swe.sas.com Copyright 2010 SAS Institute Inc. All rights reserved. Agenda Data mining Introduction Data mining applications Data mining techniques SEMMA

More information

UNIVERSITY OF GHANA (All rights reserved) UGBS SECOND SEMESTER EXAMINATIONS: 2013/2014. BSc, MAY 2014

UNIVERSITY OF GHANA (All rights reserved) UGBS SECOND SEMESTER EXAMINATIONS: 2013/2014. BSc, MAY 2014 UNIVERSITY OF GHANA (All rights reserved) UGBS SECOND SEMESTER EXAMINATIONS: 2013/2014 BSc, MAY 2014 ECCM 302: CUSTOMER RELATIONSHIP MANAGEMENT (3 CREDITS) TIME ALLOWED: 3HRS IMPORTANT: 1. Please read

More information

Dimensional Data Modeling for the Data Warehouse

Dimensional Data Modeling for the Data Warehouse Lincoln Land Community College Capital City Training Center 130 West Mason Springfield, IL 62702 217-782-7436 www.llcc.edu/cctc Dimensional Data Modeling for the Data Warehouse Prerequisites Students should

More information

Using SAS Enterprise Miner for Analytical CRM in Finance

Using SAS Enterprise Miner for Analytical CRM in Finance Using SAS Enterprise Miner for Analytical CRM in Finance Sascha Schubert SAS EMEA Agenda Trends in Finance Industry Analytical CRM Case Study: Customer Attrition in Banking Future Outlook Trends in Finance

More information

IBM SPSS Direct Marketing 23

IBM SPSS Direct Marketing 23 IBM SPSS Direct Marketing 23 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 23, release

More information

IBM SPSS Direct Marketing 22

IBM SPSS Direct Marketing 22 IBM SPSS Direct Marketing 22 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 22, release

More information

Information Systems Roles in the Value Chain Customer Relationship Management (CRM) Systems 09/11/2015. ACS 3907 E-Commerce

Information Systems Roles in the Value Chain Customer Relationship Management (CRM) Systems 09/11/2015. ACS 3907 E-Commerce ACS 3907 E-Commerce Instructor: Kerry Augustine November 10 th 2015 CUSTOMER RELATIONSHIP MANAGEMENT (CRM) SYSTEMS Managing materials, services and information from suppliers through to the organization

More information

ACS 3907 E-Commerce. Instructor: Kerry Augustine November 10 th 2015. Bowen Hui, Beyond the Cube Consulting Services Ltd.

ACS 3907 E-Commerce. Instructor: Kerry Augustine November 10 th 2015. Bowen Hui, Beyond the Cube Consulting Services Ltd. ACS 3907 E-Commerce Instructor: Kerry Augustine November 10 th 2015 CUSTOMER RELATIONSHIP MANAGEMENT (CRM) SYSTEMS Managing materials, services and information from suppliers through to the organization

More information

PAST PRESENT FUTURE YoU can T TEll where ThEY RE going if YoU don T know where ThEY ve been.

PAST PRESENT FUTURE YoU can T TEll where ThEY RE going if YoU don T know where ThEY ve been. PAST PRESENT FUTURE You can t tell where they re going if you don t know where they ve been. L everage the power of millions of customer transactions to maximize your share of customer travel spend. Vistrio

More information

WHITEPAPER. Creating and Deploying Predictive Strategies that Drive Customer Value in Marketing, Sales and Risk

WHITEPAPER. Creating and Deploying Predictive Strategies that Drive Customer Value in Marketing, Sales and Risk WHITEPAPER Creating and Deploying Predictive Strategies that Drive Customer Value in Marketing, Sales and Risk Overview Angoss is helping its clients achieve significant revenue growth and measurable return

More information

ANALYTICS. Acxiom Marketing Maturity Model CheckPoint. Are you where you want to be? Or do you need to advance your analytics capabilities?

ANALYTICS. Acxiom Marketing Maturity Model CheckPoint. Are you where you want to be? Or do you need to advance your analytics capabilities? ANALYTICS Analytics defined Analytics is the process of studying data to identify potential trends, evaluate decisions, or assess the performance of a tool, event, or scenario. The process should include

More information

Marketzone. campaigns that may or may not be working. Marketers today live in the world of the always-connected customer

Marketzone. campaigns that may or may not be working. Marketers today live in the world of the always-connected customer marketzone Marketers today live in the world of the always-connected customer... and cannot afford to waste dollars on campaigns that may or may not be working. Marketers today live in a fast-paced world

More information

Engagements The Key to Understanding the Customer Journey: What to Measure and Why

Engagements The Key to Understanding the Customer Journey: What to Measure and Why The Key to Understanding the Customer Journey: What to Measure and Why Prem Uppaluru, President & Chief Executive Officer To date, contact-center technology has mainly focused on cost and operational efficiency:

More information

IBM Software A Journey to Adaptive MDM

IBM Software A Journey to Adaptive MDM IBM Software A Journey to Adaptive MDM What is Master Data? Why is it Important? A Journey to Adaptive MDM Contents 2 MDM Business Drivers and Business Value 4 MDM is a Journey 7 IBM MDM Portfolio An Adaptive

More information

Introduction: Modeling:

Introduction: Modeling: Introduction: In this lecture, we discuss the principles of dimensional modeling, in what way dimensional modeling is different from traditional entity relationship modeling, various types of schema models,

More information

What is Customer Relationship Management? Customer Relationship Management Analytics. Customer Life Cycle. Objectives of CRM. Three Types of CRM

What is Customer Relationship Management? Customer Relationship Management Analytics. Customer Life Cycle. Objectives of CRM. Three Types of CRM Relationship Management Analytics What is Relationship Management? CRM is a strategy which utilises a combination of Week 13: Summary information technology policies processes, employees to develop profitable

More information

D&B Optimizer Powered by Acxiom

D&B Optimizer Powered by Acxiom D&B Optimizer Powered by Acxiom Increase campaign efficiency, improve response rates, and reveal new opportunities by identifying and enriching more business and commercial records in your databases Enable

More information

Supply Chain development - a cornerstone for business success

Supply Chain development - a cornerstone for business success Supply Chain development - a cornerstone for business success Agenda 1. Supply chain considerations 2. Benefits of a developed SCM strategy 3. Competitive advantage by using a LSP 4. CRM/SCM key to business

More information

Chapter 3: Strategic CRM

Chapter 3: Strategic CRM Chapter 3: Strategic CRM Overview Topics discussed: CRM perspectives The components of strategic CRM Steps in developing a CRM strategy Case Study: CRM implementation at International Business Machines,

More information

Achieving Operational Excellence and Customer Intimacy: Enterprise Applications

Achieving Operational Excellence and Customer Intimacy: Enterprise Applications Chapter 8 Achieving Operational Excellence and Customer Intimacy: Enterprise Applications 8.1 2007 by Prentice Hall STUDENT OBJECTIVES How enterprise systems achieve operational excellence by integrating

More information

Customer Relationship Management (CRM)

Customer Relationship Management (CRM) Customer Relationship Management (CRM) Dr A. Albadvi Asst. Prof. Of IT Tarbiat Modarres University Information Technology Engineering Dept. Affiliate of Sharif University of Technology School of Management

More information

Three Fundamental Techniques To Maximize the Value of Your Enterprise Data

Three Fundamental Techniques To Maximize the Value of Your Enterprise Data Three Fundamental Techniques To Maximize the Value of Your Enterprise Data Prepared for Talend by: David Loshin Knowledge Integrity, Inc. October, 2010 2010 Knowledge Integrity, Inc. 1 Introduction Organizations

More information

ACS 3907 E-Commerce. Instructor: Kerry Augustine March 3 rd 2015. Bowen Hui, Beyond the Cube Consulting Services Ltd.

ACS 3907 E-Commerce. Instructor: Kerry Augustine March 3 rd 2015. Bowen Hui, Beyond the Cube Consulting Services Ltd. ACS 3907 E-Commerce Instructor: Kerry Augustine March 3 rd 2015 CUSTOMER RELATIONSHIP MANAGEMENT (CRM) SYSTEMS Managing materials, services and information from suppliers through to the organization s

More information

Analytics: A Powerful Tool for the Life Insurance Industry

Analytics: A Powerful Tool for the Life Insurance Industry Life Insurance the way we see it Analytics: A Powerful Tool for the Life Insurance Industry Using analytics to acquire and retain customers Contents 1 Introduction 3 2 Analytics Support for Customer Acquisition

More information

Decisioning for Telecom Customer Intimacy. Experian Telecom Analytics

Decisioning for Telecom Customer Intimacy. Experian Telecom Analytics Decisioning for Telecom Customer Intimacy Experian Telecom Analytics Turning disruption into opportunity The traditional telecom business model is being disrupted by a variety of pressures from heightened

More information

CONNECTING DATA WITH BUSINESS

CONNECTING DATA WITH BUSINESS CONNECTING DATA WITH BUSINESS Big Data and Data Science consulting Business Value through Data Knowledge Synergic Partners is a specialized Big Data, Data Science and Data Engineering consultancy firm

More information

Master Data Management and Data Warehousing. Zahra Mansoori

Master Data Management and Data Warehousing. Zahra Mansoori Master Data Management and Data Warehousing Zahra Mansoori 1 1. Preference 2 IT landscape growth IT landscapes have grown into complex arrays of different systems, applications, and technologies over the

More information

Business Intelligence (BI) Data Store Project Discussion / Draft Outline for Requirements Document

Business Intelligence (BI) Data Store Project Discussion / Draft Outline for Requirements Document Business Intelligence (BI) Data Store Project Discussion / Draft Outline for Requirements Document Approval Contacts Sign-off Copy Distribution (List of Names) Revision History Definitions (Organization

More information

Get Better Business Results

Get Better Business Results Get Better Business Results From the Four Stages of Your Customer Lifecycle Stage 1 Acquisition A white paper from Identify Unique Needs and Opportunities at Each Lifecycle Stage It s a given that having

More information

CRM Predictive Analytics: From Buzzwords to Business Value

CRM Predictive Analytics: From Buzzwords to Business Value CRM Predictive Analytics: From Buzzwords to Business Value Liz Roche Vice President & Director CRM Infusion Program liz.roche@metagroup.com Customer Marketing Backlash Reaching Epic Proportions Technology,

More information

Tutorial Customer Lifetime Value

Tutorial Customer Lifetime Value MARKETING ENGINEERING FOR EXCEL TUTORIAL VERSION 150211 Tutorial Customer Lifetime Value Marketing Engineering for Excel is a Microsoft Excel add-in. The software runs from within Microsoft Excel and only

More information

Oracle Transactional Business Intelligence Enterprise for Human Capital Management Cloud Service 11.1.1.10

Oracle Transactional Business Intelligence Enterprise for Human Capital Management Cloud Service 11.1.1.10 Oracle Transactional Business Intelligence Enterprise for Human Capital Management Cloud Service 11.1.1.10 Human Resources Workforce Learning Enrollment and Completion July 2015 Contents Human Resources

More information

26/10/2015. Enterprise Information Systems. Learning Objectives. System Category Enterprise Systems. ACS-1803 Introduction to Information Systems

26/10/2015. Enterprise Information Systems. Learning Objectives. System Category Enterprise Systems. ACS-1803 Introduction to Information Systems ACS-1803 Introduction to Information Systems Instructor: Kerry Augustine Enterprise Information Systems Lecture Outline 6 ACS-1803 Introduction to Information Systems Learning Objectives 1. Explain how

More information

Knowing the customer: this time it s personal. How analytics can help banks achieve superior CRM, secure growth and drive high performance

Knowing the customer: this time it s personal. How analytics can help banks achieve superior CRM, secure growth and drive high performance Knowing the customer: this time it s personal How analytics can help banks achieve superior CRM, secure growth and drive high performance Table of Contents Introduction How advanced analytics changes customer

More information

NCOE whitepaper Master Data Deployment and Management in a Global ERP Implementation

NCOE whitepaper Master Data Deployment and Management in a Global ERP Implementation NCOE whitepaper Master Data Deployment and Management in a Global ERP Implementation Market Offering: Package(s): Oracle Authors: Rick Olson, Luke Tay Date: January 13, 2012 Contents Executive summary

More information

INTRODUCTION TO BUSINESS INTELLIGENCE What to consider implementing a Data Warehouse and Business Intelligence

INTRODUCTION TO BUSINESS INTELLIGENCE What to consider implementing a Data Warehouse and Business Intelligence INTRODUCTION TO BUSINESS INTELLIGENCE What to consider implementing a Data Warehouse and Business Intelligence Summary: This note gives some overall high-level introduction to Business Intelligence and

More information

Customer Analytics. Turn Big Data into Big Value

Customer Analytics. Turn Big Data into Big Value Turn Big Data into Big Value All Your Data Integrated in Just One Place BIRT Analytics lets you capture the value of Big Data that speeds right by most enterprises. It analyzes massive volumes of data

More information

Decisioning for Telecom Customer Intimacy. Experian Telecom Analytics

Decisioning for Telecom Customer Intimacy. Experian Telecom Analytics Decisioning for Telecom Customer Intimacy Experian Telecom Analytics Turning disruption into opportunity The traditional telecom business model is being disrupted by a variety of pressures. From heightened

More information

Forging a Culture of Customer Centricity Using an Alternative Approach to Master Data Management

Forging a Culture of Customer Centricity Using an Alternative Approach to Master Data Management Solutions for Enabling Lifetime Customer Relationships Forging a Culture of Customer Centricity Using an Alternative Approach to Master Data Management W HITE PAPER: MASTER DATA MANAGEMENT WHITE PAPER:

More information

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Oman College of Management and Technology Course 803401 DSS Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization CS/MIS Department Information Sharing

More information

Hello, Goodbye. The New Spin on Customer Loyalty. From Customer Acquisition to Customer Loyalty. Definition of CRM.

Hello, Goodbye. The New Spin on Customer Loyalty. From Customer Acquisition to Customer Loyalty. Definition of CRM. Hello, Goodbye. The New Spin on Customer Loyalty The so-called typical customer no longer exists. Companies were focused on selling as many products as possible, without regard to who was buying them.

More information

Customer Analysis CUSTOMER VALUE CUSTOMER CUSTOMER ASSESSMENT SEGMENTATION MANAGEMENT

Customer Analysis CUSTOMER VALUE CUSTOMER CUSTOMER ASSESSMENT SEGMENTATION MANAGEMENT Customer CUSTOMER VALUE CUSTOMER CUSTOMER ASSESSMENT SEGMENTATION MANAGEMENT Companies are continuing to adopt a more customer-centric approach to doing business, realizing that in this competitive marketplace,

More information

Chapter 1: Strategic Customer Relationship Management Today

Chapter 1: Strategic Customer Relationship Management Today Chapter 1: Strategic Customer Relationship Management Today Overview Topics discussed: From the marketing to the customer concept CRM and customer value The concept of CRM CRM from a business strategy

More information

Understanding the Financial Value of Data Quality Improvement

Understanding the Financial Value of Data Quality Improvement Understanding the Financial Value of Data Quality Improvement Prepared by: David Loshin Knowledge Integrity, Inc. January, 2011 Sponsored by: 2011 Knowledge Integrity, Inc. 1 Introduction Despite the many

More information

IBM SPSS Direct Marketing 19

IBM SPSS Direct Marketing 19 IBM SPSS Direct Marketing 19 Note: Before using this information and the product it supports, read the general information under Notices on p. 105. This document contains proprietary information of SPSS

More information

CHAPTER SIX DATA. Business Intelligence. 2011 The McGraw-Hill Companies, All Rights Reserved

CHAPTER SIX DATA. Business Intelligence. 2011 The McGraw-Hill Companies, All Rights Reserved CHAPTER SIX DATA Business Intelligence 2011 The McGraw-Hill Companies, All Rights Reserved 2 CHAPTER OVERVIEW SECTION 6.1 Data, Information, Databases The Business Benefits of High-Quality Information

More information

DIMENSIONAL MODELLING

DIMENSIONAL MODELLING ASSIGNMENT 1 TO BE COMPLETED INDIVIDUALLY DIMENSIONAL MODELLING Describe and analyse the dimensional modelling (DM) design feature allocated to you. (The allocation of a design feature to a student will

More information

CRM - Customer Relationship Management

CRM - Customer Relationship Management CRM - Customer Relationship Management 1 Customer power Consumer choices gains importance in the decision making process of companies and they feel the need to think like a customer than a producer. 2

More information

Outline. BI and Enterprise-wide decisions BI in different Business Areas BI Strategy, Architecture, and Perspectives

Outline. BI and Enterprise-wide decisions BI in different Business Areas BI Strategy, Architecture, and Perspectives 1. Introduction Outline BI and Enterprise-wide decisions BI in different Business Areas BI Strategy, Architecture, and Perspectives 2 Case study: Netflix and House of Cards Source: Andrew Stephen 3 Case

More information

CRM Initiatives: Taking it Personal. Key Steps for Personalization Success

CRM Initiatives: Taking it Personal. Key Steps for Personalization Success CRM Initiatives: Taking it Personal 7 Key Steps for Personalization Success CRM Initiatives: Taking it Personal 7 Key Steps for Personalization What comes to mind when you hear the word relationship? For

More information

Harness the value of information throughout the enterprise. IBM InfoSphere Master Data Management Server. Overview

Harness the value of information throughout the enterprise. IBM InfoSphere Master Data Management Server. Overview IBM InfoSphere Master Data Management Server Overview Master data management (MDM) allows organizations to generate business value from their most important information. Managing master data, or key business

More information

Product to Customer. through MDM. Presented by Luminita Vollmer, MBA, CDMP, CBIP

Product to Customer. through MDM. Presented by Luminita Vollmer, MBA, CDMP, CBIP Product to Customer A Fundamental Change through MDM Presented by Luminita Vollmer, MBA, CDMP, CBIP May 1, 2012 Atlanda, GA EDW 2012 Contents Introduction The Focus of the Presentation Disclaimer The story

More information

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

More information

MDM-Powered Cloud Computing to Boost Sales. Leverage Master Data Management(MDM) for operational effectiveness of sale organization

MDM-Powered Cloud Computing to Boost Sales. Leverage Master Data Management(MDM) for operational effectiveness of sale organization MDM-Powered Cloud Computing to Boost Sales Leverage Master Data Management(MDM) for operational effectiveness of sale organization Panigrahi, Subhrajyoti (Subbu) 3/1/2011 Table of Contents Building an

More information

Grabbing Value from Big Data: Mining for Diamonds in Financial Services

Grabbing Value from Big Data: Mining for Diamonds in Financial Services Financial Services Grabbing Value from Big Data: Mining for Diamonds in Financial Services How financial services companies can harness the innovative power of big data 2 Grabbing Value from Big Data:

More information

Five Steps to Integrate SalesForce.com with 3 rd -Party Systems and Avoid Most Common Mistakes

Five Steps to Integrate SalesForce.com with 3 rd -Party Systems and Avoid Most Common Mistakes Five Steps to Integrate SalesForce.com with 3 rd -Party Systems and Avoid Most Common Mistakes This white paper will help you learn how to integrate your SalesForce.com data with 3 rd -party on-demand,

More information

Marketing: it s the marketing portion of a CRM like Salesforce.com. This database comes with the following tables

Marketing: it s the marketing portion of a CRM like Salesforce.com. This database comes with the following tables Database Structure This demo dataset is based in a CRM standard structure on a B2B company in the computer components and software development industry. It s a snapshot of that CRM on July 5th, 2013, and

More information

ACHIEVING OPERATIONAL EXCELLENCE AND CUSTOMER INTIMACY: ENTERPRISE APPLICATIONS

ACHIEVING OPERATIONAL EXCELLENCE AND CUSTOMER INTIMACY: ENTERPRISE APPLICATIONS ACHIEVING OPERATIONAL EXCELLENCE AND CUSTOMER INTIMACY: ENTERPRISE APPLICATIONS Content How do enterprise systems help businesses achieve operational excellence? How do supply chain management systems

More information

Cablecom Delivers Unique Customer Experience Through Its Innovative Use of Business Analytics

Cablecom Delivers Unique Customer Experience Through Its Innovative Use of Business Analytics BUYER CASE STUDY Cablecom Delivers Unique Customer Experience Through Its Innovative Use of Business Analytics Dan Vesset Brian McDonough IDC OPINION Global Headquarters: 5 Speen Street Framingham, MA

More information

5 Steps to Optimizing Customer Value in Insurance

5 Steps to Optimizing Customer Value in Insurance 5 Steps to Optimizing Customer Value in Insurance LEVERAGING PREDICTIVE ANALYTICS TO ENGAGE YOUR CUSTOMER Tom King Senior Director, Industry Principal Insurance PEGASYSTEMS Despite the lure of customer

More information

Voice of the Customer: How to Move Beyond Listening to Action Merging Text Analytics with Data Mining and Predictive Analytics

Voice of the Customer: How to Move Beyond Listening to Action Merging Text Analytics with Data Mining and Predictive Analytics WHITEPAPER Voice of the Customer: How to Move Beyond Listening to Action Merging Text Analytics with Data Mining and Predictive Analytics Successful companies today both listen and understand what customers

More information

DISCOVER MERCHANT PREDICTOR MODEL

DISCOVER MERCHANT PREDICTOR MODEL DISCOVER MERCHANT PREDICTOR MODEL A Proactive Approach to Merchant Retention Welcome to Different. A High-Level View of Merchant Attrition It s a well-known axiom of business that it costs a lot more to

More information

Oracle Transactional Business Intelligence Enterprise for Human Capital Management Cloud Service 11.1.1.10

Oracle Transactional Business Intelligence Enterprise for Human Capital Management Cloud Service 11.1.1.10 Oracle Transactional Business Intelligence Enterprise for Human Capital Management Cloud Service 11.1.1.10 Human Resources Talent Profile Subject Area July 2015 Contents Human Resources Talent Profile

More information

OPTIMIZING YOUR MARKETING STRATEGY THROUGH MODELED TARGETING

OPTIMIZING YOUR MARKETING STRATEGY THROUGH MODELED TARGETING OPTIMIZING YOUR MARKETING STRATEGY THROUGH MODELED TARGETING 1 Introductions An insights-driven customer engagement firm Analytics-driven Marketing ROI focus Direct mail optimization 1.5 Billion 1:1 pieces

More information

Easily Identify Your Best Customers

Easily Identify Your Best Customers IBM SPSS Statistics Easily Identify Your Best Customers Use IBM SPSS predictive analytics software to gain insight from your customer database Contents: 1 Introduction 2 Exploring customer data Where do

More information

SmartBanker REDPORT INTERNATIONAL

SmartBanker REDPORT INTERNATIONAL Smart Analytics You Can Bank On The new age of data is transforming business, and financial institutions are grappling with growth and profitability challenges in large part due to the increasingly competitive

More information

Customer Lifecycle Management How Infogix Helps Enterprises Manage Opportunity and Risk throughout the Customer Lifecycle

Customer Lifecycle Management How Infogix Helps Enterprises Manage Opportunity and Risk throughout the Customer Lifecycle Customer Lifecycle Management How Infogix Helps Enterprises Manage Opportunity and Risk throughout the Customer Lifecycle Analytics can be a sustained competitive differentiator for any industry. Embedding

More information

Speeding ETL Processing in Data Warehouses White Paper

Speeding ETL Processing in Data Warehouses White Paper Speeding ETL Processing in Data Warehouses White Paper 020607dmxwpADM High-Performance Aggregations and Joins for Faster Data Warehouse Processing Data Processing Challenges... 1 Joins and Aggregates are

More information

Making Small Business Finance Profitable

Making Small Business Finance Profitable Making Small Business Finance Profitable Key Lessons Learned about Applying New Technologies to SME Finance Peer Stein, Banking Advisory Group December 5, 2002 Shifting the Productivity Frontier Productivity

More information

Data Warehouse Snowflake Design and Performance Considerations in Business Analytics

Data Warehouse Snowflake Design and Performance Considerations in Business Analytics Journal of Advances in Information Technology Vol. 6, No. 4, November 2015 Data Warehouse Snowflake Design and Performance Considerations in Business Analytics Jiangping Wang and Janet L. Kourik Walker

More information

e-crm: Latest Paradigm in the world of CRM

e-crm: Latest Paradigm in the world of CRM e-crm: Latest Paradigm in the world of CRM Leny Michael (Research Scholar, Bharathiyar University, Coimbatore) Assistnat Professor Caarmel Engineering College Koonamkara Post, Perunad ranni-689711 Mobile

More information

Predictive Customer Intelligence

Predictive Customer Intelligence Sogeti 2015 Damiaan Zwietering zwietering@nl.ibm.com Predictive Customer Intelligence Customer expectations are driving companies towards being customer centric Find me Using visualization and analytics

More information

Effective Segmentation. Six steps to effective segmentation

Effective Segmentation. Six steps to effective segmentation Effective Segmentation Six steps to effective segmentation Segmentation is a powerful tool to help achieve your business strategy and drive higher value to your brand. Sure, I hear you say, we all know

More information

Course 103402 MIS. Achieving Operational Excellence and Customer Intimacy

Course 103402 MIS. Achieving Operational Excellence and Customer Intimacy Oman College of Management and Technology Course 103402 MIS Topic 7 Achieving Operational Excellence and Customer Intimacy CS/MIS Department Enterprise Systems Management Information Systems Enterprise

More information

Chapter 7: Data Mining

Chapter 7: Data Mining Chapter 7: Data Mining Overview Topics discussed: The Need for Data Mining and Business Value The Data Mining Process: Define Business Objectives Get Raw Data Identify Relevant Predictive Variables Gain

More information

Data Warehouse and Business Intelligence Testing: Challenges, Best Practices & the Solution

Data Warehouse and Business Intelligence Testing: Challenges, Best Practices & the Solution Warehouse and Business Intelligence : Challenges, Best Practices & the Solution Prepared by datagaps http://www.datagaps.com http://www.youtube.com/datagaps http://www.twitter.com/datagaps Contact contact@datagaps.com

More information

CRM and One to One Marketing. Michael Collins Marketing and Data Strategist Travelosophy

CRM and One to One Marketing. Michael Collins Marketing and Data Strategist Travelosophy CRM and One to One Marketing Michael Collins Marketing and Data Strategist Travelosophy Travel Companies are Lucky! Travel Companies are Lucky! Traditionally they have collected: Customer and prospect

More information

View Point. Customer Centric banking: A 360 degree view. Abstract. - Ashok Gopinath, Navdeep Gill

View Point. Customer Centric banking: A 360 degree view. Abstract. - Ashok Gopinath, Navdeep Gill View Point Customer Centric banking: A 360 degree view - Ashok Gopinath, Navdeep Gill Abstract Banks today are moving back to basics, shifting attention from complex product offerings to developing greater

More information

Enterprise Data Quality

Enterprise Data Quality Enterprise Data Quality An Approach to Improve the Trust Factor of Operational Data Sivaprakasam S.R. Given the poor quality of data, Communication Service Providers (CSPs) face challenges of order fallout,

More information

Next Best Action Using SAS

Next Best Action Using SAS WHITE PAPER Next Best Action Using SAS Customer Intelligence Clear the Clutter to Offer the Right Action at the Right Time Table of Contents Executive Summary...1 Why Traditional Direct Marketing Is Not

More information

QAD Customer Relationship Management Demonstration Guide. May 2015 EE2015 / CRM 6.7

QAD Customer Relationship Management Demonstration Guide. May 2015 EE2015 / CRM 6.7 QAD Customer Relationship Management Demonstration Guide May 2015 EE2015 / CRM 6.7 Overview This demonstration shows how QAD Customer Relationship Management supports the vision of the Effective Enterprise;

More information

Maximizing Guest Experiences

Maximizing Guest Experiences Maximizing Guest Experiences One Platform: Cross Functional and Scalable Central Data Warehouse with Hospitality Architecture Profile De-duplication Engine 360 Degree Profile of Guests and Prospects ESP

More information

InfoGlobalData specialise in B2B Email Lists and Email Appending Services.

InfoGlobalData specialise in B2B Email Lists and Email Appending Services. InfoGlobalData specialise in B2B Email Lists and Email Appending Services. We provide high quality mailing lists for your email marketing needs. Our data intelligence service can provide valuable insight

More information

Lection 3-4 WAREHOUSING

Lection 3-4 WAREHOUSING Lection 3-4 DATA WAREHOUSING Learning Objectives Understand d the basic definitions iti and concepts of data warehouses Understand data warehousing architectures Describe the processes used in developing

More information

A SAS White Paper: Implementing the Customer Relationship Management Foundation Analytical CRM

A SAS White Paper: Implementing the Customer Relationship Management Foundation Analytical CRM A SAS White Paper: Implementing the Customer Relationship Management Foundation Analytical CRM Table of Contents Introduction.......................................................................... 1

More information

Maximize Sales and Margins with Comprehensive Customer Analytics

Maximize Sales and Margins with Comprehensive Customer Analytics Q Customer Maximize Sales and Margins with Comprehensive Customer Analytics Struggling to connect the dots between Marketing, Merchandising and Store Ops? With the explosion of customer interaction systems,

More information

CoolaData Predictive Analytics

CoolaData Predictive Analytics CoolaData Predictive Analytics 9 3 6 About CoolaData CoolaData empowers online companies to become proactive and predictive without having to develop, store, manage or monitor data themselves. It is an

More information

The Power of Personalizing the Customer Experience

The Power of Personalizing the Customer Experience The Power of Personalizing the Customer Experience Creating a Relevant Customer Experience from Real-Time, Cross-Channel Interaction WHITE PAPER SAS White Paper Table of Contents The Marketplace Today....1

More information

Banking the way we see it. A Tale of Two Banks. Focused Customer Experience Management Provides Crucial Competitive Advantage

Banking the way we see it. A Tale of Two Banks. Focused Customer Experience Management Provides Crucial Competitive Advantage A Tale of Two Banks Focused Customer Experience Management Provides Crucial Competitive Advantage Table of Contents 1 Overview...1 2 Loss of Faith...2 3 Customer Retention...3 4 Efficiency Ratio...4 5

More information

Creating Customer Value, Satisfaction, and Loyalty 9/5/2008. Building Customer Value and Satisfaction

Creating Customer Value, Satisfaction, and Loyalty 9/5/2008. Building Customer Value and Satisfaction Chapter 4 Creating Customer Value, Satisfaction, and Loyalty 4-1 Chapter Questions How can companies deliver customer value, satisfaction, and loyalty? What is the lifetime value of a customer, and why

More information

Increasing Retail Banking Profitability through CRM: the UniCredito Italiano Case History

Increasing Retail Banking Profitability through CRM: the UniCredito Italiano Case History Increasing Retail Banking Profitability through CRM: the UniCredito Italiano Case History Giorgio Redemagni Marketing Information Systems Manager Paris, 2002 June 11-13 UNICREDITO ITALIANO GROUP OVERVIEW

More information

Chapter. Enterprise Business Systems

Chapter. Enterprise Business Systems Chapter 4 Enterprise Business Systems Learning Objectives Identify and give examples to illustrate the following aspects of customer relationship. Business processes supported Customer and business value

More information

Microsoft Business Analytics Accelerator for Telecommunications Release 1.0

Microsoft Business Analytics Accelerator for Telecommunications Release 1.0 Frameworx 10 Business Process Framework R8.0 Product Conformance Certification Report Microsoft Business Analytics Accelerator for Telecommunications Release 1.0 November 2011 TM Forum 2011 Table of Contents

More information

6/10/2015. Chapter Nine Overview. Learning Outcomes. Opening Case: Twitter: A Social CRM Tool

6/10/2015. Chapter Nine Overview. Learning Outcomes. Opening Case: Twitter: A Social CRM Tool Opening Case: Twitter: A Social CRM Tool McGraw-Hill-Ryerson 2015 The McGraw-Hill Companies, All Rights Reserved Chapter Nine Overview SECTION 9.1 CRM FUNDAMENTALS Introduction Using Information to Drive

More information

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III www.cognitro.com/training Predicitve DATA EMPOWERING DECISIONS Data Mining & Predicitve Training (DMPA) is a set of multi-level intensive courses and workshops developed by Cognitro team. it is designed

More information