Information Technology And Collecting Customer Data
This document was uploaded by user and they confirmed that they have the permission to share it. If you are author or own the copyright of this book, please report to us by using this DMCA report form. Report DMCA
Overview
Download & View Information Technology And Collecting Customer Data as PDF for free.
More details
- Words: 1,778
- Pages: 30
CHAPTER 3 INFORMATION TECHNOLOGY AND COLLECTING CUSTOMER DATA
1
INFORMATION TECHNOLOGY AND CRM
data warehouse— a large reservoir of detailed and summary data that describes the firm and its activities, organized by the various business units in a way to facilitate easy retrieval of information describing the firm’s activities data— facts and figures that are difficult to use because of their volume. information— meaningful compilations and summaries of data that tell the user something that he or she did not already know CRM architecture— facilitates the gathering of data, storing it, transforming it into information, and presenting the information to users. 2
EXHIBIT 3.1 A BASIC CRM MODEL
Data sources
Data gatherin g system
Data warehouse system
Informatio n delivery system
Information users
3
A Basic CRM Model
Data Sources
Data Acquisition
internal—business units, such as a manufacturing, finance , or sales external—organizations and individuals outside the firm. computer-readable formats acquired from internal sources, data entry operators, or compatibility with touch points for external sources
Data Storage
record file database data mart—a subset of the data warehouse that contains data relating to a portion of the firm’s transactions. 4
A Basic CRM Model
Data Management
Management and Control
data security—achieved by use of passwords, supplemented with directories that specify the operations Exhibit 3.3: A Data Dictionary Entry
Information Delivery
database management system (DBMS)— software that maintains the data and makes it available for use data dictionary—a detailed description of each data element Exhibit 3.2: A Database Management System Model
query responses—answers to user questions that are displayed on the users’ workstations
Information Users
CRM user interface—designed to facilitate navigation through the data and to enable the users to easily make queries 5
EXHIBIT 3.2 A DATABASE MANAGEMENT SYSTEM MODEL Data description language processor
Informatio n requests Displayed informatio n
Database description (schema)
Database manager
Database
Printed information 6
EXHIBIT 3.3 A DATA DICTIONARY ENTRY
C : Documents and Sett Table : tblCustomer Data Created:
5/15/02 7
COMPUTER ARTHITECTURES
client/server— the stored data and functions that are performed on the data are allocated to the central server and to the user, called the client
Exhibit 3.4: Tiered Client/Server Configurations Three commodities—(1) control over the user interface, (2) the location of the software that performs the user’s functions, and (3) the location of the data—reside at the client level
8
EXHIBIT 3.4 CLIENT/SERVER ARCHITECTURES
Client
Client
Client
Server
Client A. Two-tiered architecture
Client
Server
Server
Server
Client B. Multi-tiered architecture 9
DATA INPUT
Contact Points
Point of Sale Input
POS terminals—scan product data from bar codes and obtain customer data from credit cards, checks, or store identification cards
Keyed and Scanned Data Input
touch point—any transaction or customer interaction with the organization
keyed input—when POS terminals and EDI cannot be used, the data most likely will have to be keyed into workstations by data entry operators scanned input—when data can be optically scanned, i.e. credit card invoices and airline tickets
Internet Input
Web-based systems—allow tracking of customer information for search and purchasing behavior
10
DATA STORAGE
main memory secondary storage direct access storage storage area network (SAN)— allows business units throughout the organization to store data on different servers. storage resource management (SRM) software— allocates storage in the most efficient way by locating unused storage and allocating it where it can best be used
11
DATABASE STRUCTURES
database design— arrange the data so that it can easily be retrieved hierarchical and network— the first structures, required that special physical links be built into the records to integrate data from multiple files relational— structure that makes use of data elements already in the data tables to integrate the contents of multiple tables Exhibit 3.5: Data Attributes Enable Relations 12
EX 3.5 DATA ATTRIBUTES ENABLE RELATIONS
Salesperson Number
Sales Region Number
Salesperson Name
123
1
Carolyn Wright
150
1
Ronald Hudson
188
1
Wally Collins
198
1
Sandy Lee
205
2
Richard Glenn
220
2
Vincent Garza
235
2
Ray Cox
A. Salesperson table
13
EX 3.5 DATA ATTRIBUTES ENABLE RELATIONS (Cont.) Customer Number
Customer Name
Salesperson Number
Year-to-Date Sales
30788
Austin Auto
123
2,500
30381
Jitney Jungle
235
16,283
30885
Central Repair
123
432,850
31246
Ace Body Shop
198
325
31980
Armadillo Imports
123
37,098
32659
Southern Motors
123
2,375
32776
Bonham Bearings
150
16,201
32829
Wrecking Bar
188
88,567
35294
Continental Cars
150
14,219
36291
Cowboy Trailers
220
59,263
41283
Nomad Motors
205
12,504
B. Customer table 14
Multidimensional Databases
data dimension— an array of data in a particular order one-dimension analysis two-dimension analysis—for example, customer sales by month (customer and time) multidimensional databases (MDDBs)— software developed to overcome the decreased effectiveness of relational database structures as the number of dimensions increases hypercube— data arrayed by three or more dimensions Exhibit 3.6: Data Stored in Hypercubes Exhibit 3.7: More than Three Data Dimensions 15
EXHIBIT 3.6 DATA STORED IN HYPERCUBES
16
EXHIBIT 3.7 VISUALIZING MORE THAN THREE DATA DIMENSIONS Salesperson Salesperson
Customer Customer
Product Product
Time Time Hour
Sales branch
Customer territory
Day Product line
Sales region
All regions
Customer category
All customers
Month Quarter
All products
Year
17
DATA ANALYSIS AND INFORMATION DELIVERY
analysis tools—include reports, database queries, and mathematical modeling, or online analytical processing (OLAP) Reports and Database Queries
repetitive report (or periodic report)—prepared automatically according to a schedule, such as monthly, without requiring requests by users special report—prepared when a special information need arises, such as a response to a database or data warehouse query Exhibit 3.8: A Report or Query Response Showing Two Dimensions of Data drill down—successively increasing the degree of detail, or granularity, of the data Exhibit 3.9: Drilling Down to Finer Granularity 18
EXHIBIT 3.8 A REPORT OR QUERY RESPONSE SHOWING TWO DIMENSIONS OF DATA
Customer Sales by Salesperson Report Sales Region Number
Salesperson Number
1 1 1 1
123 150 188 198
2 2 2
205 220 235
Salesperson Name
Y-T-D Sales
Carolyn Wright Ronald Hudson Wally Collins Sandy Lee
474,823 30,420 88,567 325
Region 1 Total
594,135
Richard Glenn Vincent Garza Ray Cox
12,504 59,263 16,283
Region 2 Total
88,050
Company Total
682,185 19
EXHIBIT 3.9 DRILLING DOWN TO FINER GRANULARITY Product Sales in Dollars May 2003 Product Line
Quota
Actual
Variance%
CD/tape/radio TV
200,000 750,000
182,305 831,200
-8.8 +10.8
Computer
375,000
402,117
+7.2
1,325,000
1,415,622
+6.8
Total
A. Product Sales by product line CD/Tape/Radio Sales in Dollars May 2003 Product Patriot
Quota
Actual
Variance%
150,000
104,900
-30.1
Series30
30,000
31,200
+4.0
Series50
20,000
46,205
200,000
182,305
Total
+231.0 -8.8
B. CD/Tape/Radio sales Patriot Model CD/Tape/Radio Sales by Retail Store May2003 Retail Store
Quota
Actual
Variance%
Phoenix
45,000
20,010
-55.5
Santa Fe
50,000
25,877
-48.2
Rapid City
32,500
33,338
+2.6
Boise
22,500
25,675
+14.1
150,000
104,900
Total
-30.1
C.Patriot model CD/Tape/Radio sales by retail store
20
DATA ANALYSIS AND INFORMATION DELIVERY
Mathematical Modeling
constructed in a software form and uses data and users’ instructions to project what might happen in the future
On-line Analytical Processing (OLAP)
an approach to quickly conduct analysis of data in a data warehouse where the user is on-line with the system
21
DATA ANALYSIS AND INFORMATION DELIVERY
Data Mining
how the user extracts previously unknown information from the large reservoir of the data warehouse, similar to the way that miners extract gold, coal, diamonds, and so on from the earth. verification mode— to believe that the warehouse contains data in certain forms or patterns and conducts repetitive queries to support this hypothesis. knowledge discovery— the user lets the system determine the path to follow in conducting the analysis Exhibit 3.10: Hypothesis Verification and Knowledge Discovery by Successive Queries
22
EXHIBIT 3.10 HYPOTHESIS VERIFICATION AND KNOWLEDGE DISCOVERY BY SUCCESSIVE QUERIES Sale Date
Customer
Product
02/12/03 02/15/03
Ed Flynn Adele Rice
TV Computer
02/18/03 03/01/03
Ric Knowles Ed Flynn
TV Computer
03/19/03 03/30/03
Angela Forest Robin Lin
TV Computer
04/05/03 04/11/03
Robin Lin Ed Flynn
CD/Tape/Radio CD/Tape/Radio
04/21/03 05/16/03
Adele Rice Richard Rodriguez
TV TV
05/17/03 05/26/03
Robin Lin Joe Wardlaw
TV Computer
05/29/03 05/29/03
Angela Forest Richard Rodriguez
CD/Tape/Radio CD/Tape/Radio
05/30/03
Cynthia Garfield
Computer
A. Query 1 for transaction data City store February through May
for the Rapid 23
EXHIBIT 3.10 HYPOTHESIS VERIFICATION AND KNOWLEDGE DISCOVERY BY SUCCESSIVE QUERIES (Cont.)
Product Sales Sequence
Customers
TV,Computer,CD/Tape/Radio
Ed Flynn
Computer,CD/Tape/Radio,TV
Robin Lin
Computer,TV
Adele Rice
TV,CD/Tape/Radio
Angela Forest
TV,CD/Tape/Radio
Richard Rodriguez
Computer
Joe Wardlaw, Cynthia Garfield
TV Ric Knowles B. Query2 for product sales sequences 24
EXHIBIT 3.10 HYPOTHESIS VERIFICATION AND KNOWLEDGE DISCOVERY BY SUCCESSIVE QUERIES (Cont.)
Support
Product Sales Sequence
Customers
TV,Computer
Flynn
TV,CD/Tape/Radio
Flynn,Forest,Rodriguez 0.375
Computer,CD/Tape/Radio
Flynn,Lin
0.250
Computer,TV
Lin,Rice,
0.250
TV,Computer,CD/Tape/Radio
Flynn
0.125
Computer,CD/Tape/Radio,TV
Lin
0.125
Factor 0.125
C. Query 3 for support factors for product sales sequences
25
CLOSED-LOOP MARKETING
CRM system loop (1)
data
Data gathering Data storage
(2)
Information delivery
(3)
information (CRM system) strategy (managers)
Exhibit 3.11: CRM-Based Marketing Strategies Close the Loop 26
EXHIBIT 3.11 CRM-BASED MARKETING STRAGEGIES CLOSE THE LOOP
Customers
Data
CRM system
Information
Managers
Marketing strategy
27
COLLECTING CUSTOMER DATA
Internal Data Sources
transaction processing systems—the multiple systems used by organizations to process their various transactions with customers, suppliers, employees, etc. Exhibit 3.12: Gathering Data From OrderProcessing Systems
External Data Sources
external sources—government, suppliers within the supply chain as well as those that provide syndicated data, and marketing intelligence about competitive actions are examples
28
EX 3.12 GATHERING DATA FROM ORDER-PROCESSING SYSTEMS Sales orders
Customers Rejected sales order notices
Customer statements
4 Accounts receivable system Accounts receivable data
Accounts receivable master file
Customer invoices
Billed sales order file
1 Order entry system
Approved sales order file
2 Inventory system 3 Billing system Customer data
Customer master file
Filled sales orders file Product data
Inventory master file 29
What is the difference between a data warehouse and a database?
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 a way to facilitate easy retrieval of information describing the firm’s activities. A database is an accumulation of computer-based data that is arranged in a format to facilitate retrieval. A data mart is a subset of the data warehouse that contains data relating to a portion of the firm’s transactions. 30
1
INFORMATION TECHNOLOGY AND CRM
data warehouse— a large reservoir of detailed and summary data that describes the firm and its activities, organized by the various business units in a way to facilitate easy retrieval of information describing the firm’s activities data— facts and figures that are difficult to use because of their volume. information— meaningful compilations and summaries of data that tell the user something that he or she did not already know CRM architecture— facilitates the gathering of data, storing it, transforming it into information, and presenting the information to users. 2
EXHIBIT 3.1 A BASIC CRM MODEL
Data sources
Data gatherin g system
Data warehouse system
Informatio n delivery system
Information users
3
A Basic CRM Model
Data Sources
Data Acquisition
internal—business units, such as a manufacturing, finance , or sales external—organizations and individuals outside the firm. computer-readable formats acquired from internal sources, data entry operators, or compatibility with touch points for external sources
Data Storage
record file database data mart—a subset of the data warehouse that contains data relating to a portion of the firm’s transactions. 4
A Basic CRM Model
Data Management
Management and Control
data security—achieved by use of passwords, supplemented with directories that specify the operations Exhibit 3.3: A Data Dictionary Entry
Information Delivery
database management system (DBMS)— software that maintains the data and makes it available for use data dictionary—a detailed description of each data element Exhibit 3.2: A Database Management System Model
query responses—answers to user questions that are displayed on the users’ workstations
Information Users
CRM user interface—designed to facilitate navigation through the data and to enable the users to easily make queries 5
EXHIBIT 3.2 A DATABASE MANAGEMENT SYSTEM MODEL Data description language processor
Informatio n requests Displayed informatio n
Database description (schema)
Database manager
Database
Printed information 6
EXHIBIT 3.3 A DATA DICTIONARY ENTRY
C : Documents and Sett Table : tblCustomer Data Created:
5/15/02 7
COMPUTER ARTHITECTURES
client/server— the stored data and functions that are performed on the data are allocated to the central server and to the user, called the client
Exhibit 3.4: Tiered Client/Server Configurations Three commodities—(1) control over the user interface, (2) the location of the software that performs the user’s functions, and (3) the location of the data—reside at the client level
8
EXHIBIT 3.4 CLIENT/SERVER ARCHITECTURES
Client
Client
Client
Server
Client A. Two-tiered architecture
Client
Server
Server
Server
Client B. Multi-tiered architecture 9
DATA INPUT
Contact Points
Point of Sale Input
POS terminals—scan product data from bar codes and obtain customer data from credit cards, checks, or store identification cards
Keyed and Scanned Data Input
touch point—any transaction or customer interaction with the organization
keyed input—when POS terminals and EDI cannot be used, the data most likely will have to be keyed into workstations by data entry operators scanned input—when data can be optically scanned, i.e. credit card invoices and airline tickets
Internet Input
Web-based systems—allow tracking of customer information for search and purchasing behavior
10
DATA STORAGE
main memory secondary storage direct access storage storage area network (SAN)— allows business units throughout the organization to store data on different servers. storage resource management (SRM) software— allocates storage in the most efficient way by locating unused storage and allocating it where it can best be used
11
DATABASE STRUCTURES
database design— arrange the data so that it can easily be retrieved hierarchical and network— the first structures, required that special physical links be built into the records to integrate data from multiple files relational— structure that makes use of data elements already in the data tables to integrate the contents of multiple tables Exhibit 3.5: Data Attributes Enable Relations 12
EX 3.5 DATA ATTRIBUTES ENABLE RELATIONS
Salesperson Number
Sales Region Number
Salesperson Name
123
1
Carolyn Wright
150
1
Ronald Hudson
188
1
Wally Collins
198
1
Sandy Lee
205
2
Richard Glenn
220
2
Vincent Garza
235
2
Ray Cox
A. Salesperson table
13
EX 3.5 DATA ATTRIBUTES ENABLE RELATIONS (Cont.) Customer Number
Customer Name
Salesperson Number
Year-to-Date Sales
30788
Austin Auto
123
2,500
30381
Jitney Jungle
235
16,283
30885
Central Repair
123
432,850
31246
Ace Body Shop
198
325
31980
Armadillo Imports
123
37,098
32659
Southern Motors
123
2,375
32776
Bonham Bearings
150
16,201
32829
Wrecking Bar
188
88,567
35294
Continental Cars
150
14,219
36291
Cowboy Trailers
220
59,263
41283
Nomad Motors
205
12,504
B. Customer table 14
Multidimensional Databases
data dimension— an array of data in a particular order one-dimension analysis two-dimension analysis—for example, customer sales by month (customer and time) multidimensional databases (MDDBs)— software developed to overcome the decreased effectiveness of relational database structures as the number of dimensions increases hypercube— data arrayed by three or more dimensions Exhibit 3.6: Data Stored in Hypercubes Exhibit 3.7: More than Three Data Dimensions 15
EXHIBIT 3.6 DATA STORED IN HYPERCUBES
16
EXHIBIT 3.7 VISUALIZING MORE THAN THREE DATA DIMENSIONS Salesperson Salesperson
Customer Customer
Product Product
Time Time Hour
Sales branch
Customer territory
Day Product line
Sales region
All regions
Customer category
All customers
Month Quarter
All products
Year
17
DATA ANALYSIS AND INFORMATION DELIVERY
analysis tools—include reports, database queries, and mathematical modeling, or online analytical processing (OLAP) Reports and Database Queries
repetitive report (or periodic report)—prepared automatically according to a schedule, such as monthly, without requiring requests by users special report—prepared when a special information need arises, such as a response to a database or data warehouse query Exhibit 3.8: A Report or Query Response Showing Two Dimensions of Data drill down—successively increasing the degree of detail, or granularity, of the data Exhibit 3.9: Drilling Down to Finer Granularity 18
EXHIBIT 3.8 A REPORT OR QUERY RESPONSE SHOWING TWO DIMENSIONS OF DATA
Customer Sales by Salesperson Report Sales Region Number
Salesperson Number
1 1 1 1
123 150 188 198
2 2 2
205 220 235
Salesperson Name
Y-T-D Sales
Carolyn Wright Ronald Hudson Wally Collins Sandy Lee
474,823 30,420 88,567 325
Region 1 Total
594,135
Richard Glenn Vincent Garza Ray Cox
12,504 59,263 16,283
Region 2 Total
88,050
Company Total
682,185 19
EXHIBIT 3.9 DRILLING DOWN TO FINER GRANULARITY Product Sales in Dollars May 2003 Product Line
Quota
Actual
Variance%
CD/tape/radio TV
200,000 750,000
182,305 831,200
-8.8 +10.8
Computer
375,000
402,117
+7.2
1,325,000
1,415,622
+6.8
Total
A. Product Sales by product line CD/Tape/Radio Sales in Dollars May 2003 Product Patriot
Quota
Actual
Variance%
150,000
104,900
-30.1
Series30
30,000
31,200
+4.0
Series50
20,000
46,205
200,000
182,305
Total
+231.0 -8.8
B. CD/Tape/Radio sales Patriot Model CD/Tape/Radio Sales by Retail Store May2003 Retail Store
Quota
Actual
Variance%
Phoenix
45,000
20,010
-55.5
Santa Fe
50,000
25,877
-48.2
Rapid City
32,500
33,338
+2.6
Boise
22,500
25,675
+14.1
150,000
104,900
Total
-30.1
C.Patriot model CD/Tape/Radio sales by retail store
20
DATA ANALYSIS AND INFORMATION DELIVERY
Mathematical Modeling
constructed in a software form and uses data and users’ instructions to project what might happen in the future
On-line Analytical Processing (OLAP)
an approach to quickly conduct analysis of data in a data warehouse where the user is on-line with the system
21
DATA ANALYSIS AND INFORMATION DELIVERY
Data Mining
how the user extracts previously unknown information from the large reservoir of the data warehouse, similar to the way that miners extract gold, coal, diamonds, and so on from the earth. verification mode— to believe that the warehouse contains data in certain forms or patterns and conducts repetitive queries to support this hypothesis. knowledge discovery— the user lets the system determine the path to follow in conducting the analysis Exhibit 3.10: Hypothesis Verification and Knowledge Discovery by Successive Queries
22
EXHIBIT 3.10 HYPOTHESIS VERIFICATION AND KNOWLEDGE DISCOVERY BY SUCCESSIVE QUERIES Sale Date
Customer
Product
02/12/03 02/15/03
Ed Flynn Adele Rice
TV Computer
02/18/03 03/01/03
Ric Knowles Ed Flynn
TV Computer
03/19/03 03/30/03
Angela Forest Robin Lin
TV Computer
04/05/03 04/11/03
Robin Lin Ed Flynn
CD/Tape/Radio CD/Tape/Radio
04/21/03 05/16/03
Adele Rice Richard Rodriguez
TV TV
05/17/03 05/26/03
Robin Lin Joe Wardlaw
TV Computer
05/29/03 05/29/03
Angela Forest Richard Rodriguez
CD/Tape/Radio CD/Tape/Radio
05/30/03
Cynthia Garfield
Computer
A. Query 1 for transaction data City store February through May
for the Rapid 23
EXHIBIT 3.10 HYPOTHESIS VERIFICATION AND KNOWLEDGE DISCOVERY BY SUCCESSIVE QUERIES (Cont.)
Product Sales Sequence
Customers
TV,Computer,CD/Tape/Radio
Ed Flynn
Computer,CD/Tape/Radio,TV
Robin Lin
Computer,TV
Adele Rice
TV,CD/Tape/Radio
Angela Forest
TV,CD/Tape/Radio
Richard Rodriguez
Computer
Joe Wardlaw, Cynthia Garfield
TV Ric Knowles B. Query2 for product sales sequences 24
EXHIBIT 3.10 HYPOTHESIS VERIFICATION AND KNOWLEDGE DISCOVERY BY SUCCESSIVE QUERIES (Cont.)
Support
Product Sales Sequence
Customers
TV,Computer
Flynn
TV,CD/Tape/Radio
Flynn,Forest,Rodriguez 0.375
Computer,CD/Tape/Radio
Flynn,Lin
0.250
Computer,TV
Lin,Rice,
0.250
TV,Computer,CD/Tape/Radio
Flynn
0.125
Computer,CD/Tape/Radio,TV
Lin
0.125
Factor 0.125
C. Query 3 for support factors for product sales sequences
25
CLOSED-LOOP MARKETING
CRM system loop (1)
data
Data gathering Data storage
(2)
Information delivery
(3)
information (CRM system) strategy (managers)
Exhibit 3.11: CRM-Based Marketing Strategies Close the Loop 26
EXHIBIT 3.11 CRM-BASED MARKETING STRAGEGIES CLOSE THE LOOP
Customers
Data
CRM system
Information
Managers
Marketing strategy
27
COLLECTING CUSTOMER DATA
Internal Data Sources
transaction processing systems—the multiple systems used by organizations to process their various transactions with customers, suppliers, employees, etc. Exhibit 3.12: Gathering Data From OrderProcessing Systems
External Data Sources
external sources—government, suppliers within the supply chain as well as those that provide syndicated data, and marketing intelligence about competitive actions are examples
28
EX 3.12 GATHERING DATA FROM ORDER-PROCESSING SYSTEMS Sales orders
Customers Rejected sales order notices
Customer statements
4 Accounts receivable system Accounts receivable data
Accounts receivable master file
Customer invoices
Billed sales order file
1 Order entry system
Approved sales order file
2 Inventory system 3 Billing system Customer data
Customer master file
Filled sales orders file Product data
Inventory master file 29
What is the difference between a data warehouse and a database?
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 a way to facilitate easy retrieval of information describing the firm’s activities. A database is an accumulation of computer-based data that is arranged in a format to facilitate retrieval. A data mart is a subset of the data warehouse that contains data relating to a portion of the firm’s transactions. 30
Related Documents
Information And Communization Technology
July 2020 20
Information And Communications Technology
April 2020 21
Information And Technology
May 2020 15
Information And Communication Technology
May 2020 21
Business And Information Technology
June 2020 16More Documents from "lovesstudy"
