IT and CRM A basic CRM model Data source & gathering system Database system Data warehouse Information delivery system Information users



Similar documents
from Larson Text By Susan Miertschin

Data Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms

Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA

Data Warehousing and Data Mining in Business Applications

Chapter 4 Getting Started with Business Intelligence

DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM

Business Intelligence Solutions for Gaming and Hospitality

Customer Analytics. Turn Big Data into Big Value

3/17/2009. Knowledge Management BIKM eclassifier Integrated BIKM Tools

Distance Learning and Examining Systems

University of Gaziantep, Department of Business Administration

Database Marketing, Business Intelligence and Knowledge Discovery

Enhanced Boosted Trees Technique for Customer Churn Prediction Model

Introduction to Data Mining

Data Mining for Fun and Profit

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

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP

Alexander Nikov. 5. Database Systems and Managing Data Resources. Learning Objectives. RR Donnelley Tries to Master Its Data

Chapter 5. Warehousing, Data Acquisition, Data. Visualization

5.5 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall. Figure 5-2

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

Overview, Goals, & Introductions

Session 10 : E-business models, Big Data, Data Mining, Cloud Computing

Supply chain intelligence: benefits, techniques and future trends

Customer Classification And Prediction Based On Data Mining Technique

Fluency With Information Technology CSE100/IMT100

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

PRACTICAL DATA MINING IN A LARGE UTILITY COMPANY

DATA MINING TECHNOLOGY. Keywords: data mining, data warehouse, knowledge discovery, OLAP, OLAM.

Introduction to Data Mining and Business Intelligence Lecture 1/DMBI/IKI83403T/MTI/UI

Introduction to Data Mining

Data are everywhere. IBM projects that every day we generate 2.5 quintillion bytes of data. In relative terms, this means 90

Web Data Mining: A Case Study. Abstract. Introduction

A Review of Data Mining Techniques

OLAP Theory-English version

New Approach of Computing Data Cubes in Data Warehousing

Data Mining and Marketing Intelligence

Index Contents Page No. Introduction . Data Mining & Knowledge Discovery

Data Mining for Successful Healthcare Organizations

Building Data Cubes and Mining Them. Jelena Jovanovic

Principles of Data Mining by Hand&Mannila&Smyth

A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS

Ignite Your Creative Ideas with Fast and Engaging Data Discovery

Data Mining & Knowledge Discovery: Personalization and Profiling Technologies

Chapter 6. Foundations of Business Intelligence: Databases and Information Management

Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10

ORACLE BUSINESS INTELLIGENCE, ORACLE DATABASE, AND EXADATA INTEGRATION

Retail POS Data Analytics Using MS Bi Tools. Business Intelligence White Paper

Information Systems and Technologies in Organizations

Business Intelligence Osvaldo Maysonet VP Marketing & Customer Knowledge Banco Popular

CONCEPTUALIZING BUSINESS INTELLIGENCE ARCHITECTURE MOHAMMAD SHARIAT, Florida A&M University ROSCOE HIGHTOWER, JR., Florida A&M University

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

Federico Rajola. Customer Relationship. Management in the. Financial Industry. Organizational Processes and. Technology Innovation.

Management Accountants and IT Professionals providing Better Information = BI = Business Intelligence. Peter Simons peter.simons@cimaglobal.

Delivering Business Intelligence With Microsoft SQL Server 2005 or 2008 HDT922 Five Days

Data Warehousing: A Technology Review and Update Vernon Hoffner, Ph.D., CCP EntreSoft Resouces, Inc.

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

ISSN: (Online) Volume 3, Issue 4, April 2015 International Journal of Advance Research in Computer Science and Management Studies

White Paper. Data Mining for Business

Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management

Chapter 6 - Enhancing Business Intelligence Using Information Systems

Data Mining: Overview. What is Data Mining?

Data Mining Algorithms and Techniques Research in CRM Systems

Subject Description Form

MBA Data Mining & Knowledge Discovery

Marketing Advanced Analytics. Predicting customer churn. Whitepaper

Data mining in the e-learning domain

ORACLE TAX ANALYTICS. The Solution. Oracle Tax Data Model KEY FEATURES

Chapter 20: Data Analysis

Information Management course

Foundations of Business Intelligence: Databases and Information Management

STATISTICA. Financial Institutions. Case Study: Credit Scoring. and

Data Mining Techniques

Big Data Strategies Creating Customer Value In Utilities

DMDSS: Data Mining Based Decision Support System to Integrate Data Mining and Decision Support

ECLT 5810 E-Commerce Data Mining Techniques - Introduction. Prof. Wai Lam

Foundations of Business Intelligence: Databases and Information Management

Data Mining Solutions for the Business Environment

Easily Identify Your Best Customers

Easily Identify the Right Customers

How To Use Big Data To Help A Retailer

A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH

Maximize Sales and Margins with Comprehensive Customer Analytics

Review. Data Warehousing. Today. Star schema. Star join indexes. Dimension hierarchies

Chapter Managing Knowledge in the Digital Firm

Implementing Data Models and Reports with Microsoft SQL Server

Dashboard Reporting Business Intelligence

Machine Learning and Data Mining. Fundamentals, robotics, recognition

Chapter 6 8/12/2015. Foundations of Business Intelligence: Databases and Information Management. Problem:

Mobile Phone APP Software Browsing Behavior using Clustering Analysis

Transforming big data into supply chain analytics

KNOWLEDGE BASE DATA MINING FOR BUSINESS INTELLIGENCE

Foundations of Business Intelligence: Databases and Information Management

not possible or was possible at a high cost for collecting the data.

RFM Analysis: The Key to Understanding Customer Buying Behavior

Transcription:

1

IT and CRM A basic CRM model Data source & gathering Database Data warehouse Information delivery Information users 2

IT and CRM Markets have always recognized the importance of gathering detailed data for the purpose of producing information that enable them to develop marketing strategies. Both hardware and software technology enable the retention of massive files of indepth data for long periods of time in a data warehouse. 3

A Basic CRM Model Data sources and gathering Database Date warehouse Information deliver Information users Data sources provide data that describes relationships with customers. Data gathering converts this incoming data to a database. The data warehouse prepares the data for storage, stores the data, describes the data so that it might later be retrieved, and performs a management and control function. An information delivery makes the contents of the data warehouse available to the information users. 4

A Basic CRM Model Data sources and gathering Database Date warehouse Information deliver Information users 5

1. Data sources and gathering Data consists of facts and figures that are difficult to uses because of their volume. Information consists of meaningful compilations and summaries of data that tell the user something he or she needs to know. 6

1. Data sources and gathering 7

1. Data sources and gathering Company collect data from contact points which provides the opportunity to learn more about the customer s behavior, background, and needs. Contact points, or touch point is any transaction or customer interaction with the organization. A purchase transaction, telephone call, sending email, are all contact points. 8

1. Data sources and gathering For example, a warranty card can ask for demographic information. Point of sale input (POS) or electronic data interchange (EDI) can scan product data from the bar codes and customer data from credit cards. Internet input, a web-based allow customer to enter their data when make purchases. 9

1. Data sources and gathering Research confirms that 70% of consumer decisions are made at Point of Sale, so it s critical to keep your product and brand in full view of the consumer. 10

A Basic CRM Model Data sources and gathering Database Date warehouse Information deliver Information users 11

2. Database Database is an accumulation of computer-based data that is arranged in a format to facilitate retrieval. It is a corporate resource, and a firm can have more that one. 12

2. Database For example, the marketing department or financial department can have its own customer and salesperson databases. 13

2. Database Database structure : Relational structure The beauty if this structure is that it makes use of data elements already in the data tables to integrate the contents of multiple tables. It is the current standard for the storage of business data, but they were not specifically designed to handle data views that involve large number of dimensions. 14

2. Database Database structure : Relational structure 15

2. Database Database structure: Multidimensional structure To overcome the relational structure limitation, software vendors have developed database management s for multidimensional databases. 16

2. Database Database structure: Multidimensional structure The term hypercube has been coined to describe data arrayed by three or more dimensions. The 3D analysis of customer, product and time. The star identifies the location in the cube where a quantitative measure (such as sales amount) for customer 5, product F, ad year 2001 is stored. 17

A Basic CRM Model Data sources and gathering Database Date warehouse Information deliver Information users 18

3. Data warehouse A data warehouse is a large reservoir of detailed and summary data that describes the firm and its activities, organized by the various business units in way to facilitate easy retrieval of information that describes the firm s activities. The data warehouse is the central element in the CRM. Data sources and gathering Database Date warehouse Information deliver Information users 19

A Basic CRM Model Data sources and gathering Database Date warehouse Information deliver Information users 20

4. Information delivery Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information. A natural source resides in the ground, if the resource is to be used, it must be mined. 21

4. Information delivery Data mining 4 main functions - Classification - Clusters - Associations - Sequences or patterns 22

4. Information delivery Data mining functions 1. Classification : determining classifications for the data Business often classify their customers based on the customers behavior so that special products and services can be offered to the more valuable classes- the best customers. 23

4. Information delivery Data mining functions 1. Classification : determining classifications for the data We can use RFM = Recency, Frequency, Monetary RFM enables customers to be arrayed in terms of recent purchases, frequency purchases, and average purchase amount. The data warehouse is queried to obtain the data on each customer, and then a quantitative analysis is conducted to arrive at the classifications. 24

4. Information delivery Data mining functions 1. Classification : determining classifications for the data The goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks. 25

4. Information delivery Criteria Watching move time/month Heavy users = 10-12 days Medium users = 6-9 days Light users = 1-5 days 3 3 Customers Watching move time/month Gary = 10 days Peter = 7 days Kel = 2 days Mimi = 5 days Ano = 4 days 1 Heavy Medium Light 26

4. Information delivery 27

4. Information delivery Data mining functions 2. Clusters: Data items are grouped. Marketers like to be able to identify clusters of customer with similar characteristics. For example-demographics, geographic, activity, psychographic, and behavioral. 28

4. Information delivery People love fishing People always go watch movie People love playing football 29

4. Information delivery Data mining functions 3. Associations: Data is mined to anticipate behavior patterns and trends. Company wants to know whether such association exist, and their strength, so that they can make decision based on the related products. For example, when deciding on store layout, a food retailer can locate items in the same area if the items are frequently bought together. Beer and diaper case. 30

4. Information delivery Data mining functions 4. Sequences or patterns : Data is mined to anticipate behavior patterns and trends. Companies are also interested in any patterns or sequences in a customer s purchase behavior. Perhaps certain behaviors occur in a certain sequence. 31

4. Information delivery Look at the time customer made a purchase Buying order (sequence) What is the most frequent pattern? 32

4. Information delivery Data mining functions 4. Sequences or patterns : Data is mined to anticipate behavior patterns and trends. Not only discovers a sequential pattern but it also identifies other behaviors with similar pattern. A customer cluster may make purchases in a certain sequence and similar time sequence discovery could be used to identify other clusters that follow similar sequences. 33

4. Information delivery Data analysis In order for the CRM to discover patterns in the warehouse data largely independent of user direction, the must exhibit some type of intelligence. Several methodologies have been employed. Among the more popular are decision trees, genetic algorithms, memory based reasoning, and neural network. 34

4. Information delivery Information delivery software (to do an analysis we need a software) OLAP is geared to the needs of the data warehouse user, enabling the interactive user to perform multidimensional analyses and obtain quick responses. OLAP also supports drill down, roll up, drill across, and drill through. 35

4. Information delivery OLAP also supports drill down, roll up, drill across. Roll up Drill across Drill down Summary info. (Net sales for Phuket sales region) Hierarchy 1 (Customer) Hierarchy 2 (Salesperson) Detailed info. (Net sales for Customer No.21198) Detailed data. (Sales items for Customer No.21198) Hierarchy 3 (Product) 36

A Basic CRM Model Data sources and gathering Database Date warehouse Information deliver Information users 37

5. Information users The information will be used by problem solvers and decision makers throughout the firm. Within each businesses areas, user can be - novices - analysts - power users 38

5. Information users Novice is someone with no special computer training, who typically wishes to obtain information without the need for a great deal of analysis. (Executives) Analyst is someone who is skilled in the use of analysis and statistical tools and transform data into a form that is usable by someone else. (Manager s staff) 39

5. Information users Power user is someone who can sophisticatedly perform the work of analyst plus perform more advanced operations. Information professional. (Marketing researcher, industrial engineers) 40