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Copyright © 2000 , SAS Institute Inc. All rights reserved.
SAS Enterprise Miner for Analytical CRM
SAS Institute Ltd
15 February 2001
The world’s largest privately held software company
Meeting the needs of decision makers in business, government and beyond
Delivering The Power to Know for 24 years
Who we Are
0
200
400
600
800
1000
1200
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Worldwide Customer Base
796 Banking 292 Telecommunications 898 Insurance1142 Services361 Retail187 Transportation160 Publishing/Media
1575 Manufacturing1013 Pharma/Chemical163 Oil and Gas
2100 Public/Government1392 Universities
98%Fortune 10090%Global 50098%Customer Retention67%Customers Extend Investment Yearly
SAS in Hong Kong
Banking/Insurance
American Express
AXA Insurance
Bank of China
BPI
Citibank
Dao Heng Bank Group
Hang Seng Bank
HSBC
HK Mortgage Corp.
ING Baring
Standard Chartered Bank
Chase Manhattan Bank
Government
Census & Statistics
Department of Health
Education Department
HK Housing Authority
HK TDC
Hospital Authority
ITSD
Planning Department
Rating & Valuation Dept.
Security Bureau
Social Welfare Dept.
Transport Dept.
Communications
Cable & Wireless HKT
Cathay Pacific Airways
Federal Express
HK Air Cargo Terminal
HK Telecom CSL
MTRC
OOCL
SmarTone Mobile
Wharf Cable
Others
ACNielsen Ltd.
Asia Television
Caltex Oil
China Light & Power
Coca Cola China Ltd.
HK Electric Co. Ltd.
IBM China/HK
Park N’ Shop
Reader’s Digest
Television Broadcast Ltd.
Why SAS?
There are four components to the system we put in place – data management, statistical analysis, reporting and information delivery. Other vendors can provide one or two of these but SAS can provide all four, with powerful integration between each step.
Kelvin Poon, Statistician, Hospital Authority
Whether it’s credit scoring in Singapore, customer retention in Hong Kong or acquisition modeling in India, SAS helps us get the answers
Jim Thomason, Senior Information Systems Manager, Standard Chartered
With SAS Institute’s reporting and data mining software, we can improve our understanding of customers and deliver tailored solutions to them.
Y.B. Yeung, Head of IT, HSBC
The power to know your customers.Increase your revenues: Compile your customers’ entire buying history –
Web, catalog, storefront – everything. Identify your most profitable customers. Predict future buying behaviors. Target your marketing dollars where the payoff is
greatest.
ââ
SASSAS Enterprise Miner
SAS Enterprise Miner references
Banking & Financial ServicesAXA Financial, Bank of America, Wells Fargo (USA), First Union (USA), Generale de Banque (Belgium), Deutsche Bank (Germany), Old Mutual (South Africa), Caja de Ahorros de Asturias (Spain), Hang Seng Bank (Hong Kong), Hongkong and Shanghai Banking Corporation, NG Bank (Netherlands), Banque Sofinco (France), Banco Comercial Portugues, Nordbanken (Sweden), Standard Chartered (Singapore), MBNA Direct Limited (UK), Credito Italiano (Italy), Cariplo (Italy), Mapfre (Spain), Reale Mutua (Italy)
TelecommunicationsBelgacom (Belgium), Proximus (Belgium), British Telecom (UK), Telenor Media (Norway), Telia Mobitel (Sweden), Tele Danmark, France Telecom, T-Mobil (Germany), US West (USA), AT&T (USA), MT&T (USA), Hutchison Telecom (Hong Kong), PCCW HKT (Hong Kong), SKTelecom (Korea), Malaysia Telecom, Maxis (Malaysia), Orange (Australia), SmarTone (Hong Kong), Telstra (Australia)
OtherAmazon.com (USA), Outpost.com (USA), British Airways (UK), Cathay Pacific Airways (Hong Kong), Marks & Spencer (UK), Cecile (Japan), Eddie Bauer (USA), UCB Pharma (USA), Shanghai Baosteel (PRC)
1 SAS2 NCR3 Oracle Corporation4 Computer Associates5 Cognos Corporation6 MicroStrategy Incorporated7 Microsoft Corporation8 IBM9 Informix Business Solutions10 Hyperion11 SAP America12 SPSS Inc.
Top Vendors as Selected by theReaders of DM Review
Data Warehousing/Business Intelligence Products
Business Objective:
Direct marketing department of a retail products company wants to increase catalog sales revenue.
They want to target only current customers likely to make a catalog purchase.
An Example
Input Variables
Category Variables Description
Demographic AGE
INCOME
MARRIED
SEX
C0A6
OWNHOME
Age in years
Yearly income in thousands
1 if married, 0 otherwise
F or M
1 if change of address in last 6 months,
0 otherwise
1 if own home, 0 otherwise
Geographic LOC Location of residence, A-H
Monetary VALUE24 Total value of purchases in past 24 months
Input Variables
Category Variables Description
Recency/
Frequency
BUY6
BUY12
BUY18
Number of purchases in last 6 months
Number of purchases in last 12 months
Number of purchases in last 18 months
Purchase History
DISCBUY
RETURN24
1 if discount buyer, 0 otherwise
1 if product return in past 24 months,
0 otherwise
Response RESPOND 1 if responder to test mailing,
0 otherwise
Propensity To Buy
Customer Id Score
Cust_id_01 0.72
Cust_id_02 0.23
Cust_id_03 0.91
Cust_id_04 0.80
Cust_id_05 0.45
Cust_id_06 0.28
: :
: :
Customer Id Score
Cust_id_03 0.91
Cust_id_04 0.80
Cust_id_01 0.72
Cust_id_05 0.45
Cust_id_06 0.28
Cust_id_02 0.23
: :
: :
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