Elisabete Silva, Henrique Nicola SOGRUPO - SI CGD GROUP

Transcription

1 Elisabete Silva, Henrique icola SOGRUPO - SI CGD GROUP elisabete.silva@cgd.pt henrique.nicola@cgd.pt 1

2 Agenda The requirement... Data Mining as part of Business Intelligence Data Mining projects at CGD Bank ext / current projects Conclusions 2

3 The requirement... A pressing reality of the new global environment is the emergence of a new era of competition. Competition is arising not only from traditional adversaries in traditional markets, or from new entrants to a specific industry or economic sector, but also from disintegration of barriers to previously insulated and protected markets. Don Tapscott & Art Caston, em Paradigm Shift 3

4 The requirement... The highest priorities for CGD Group are: Increase Profitability (Customers, Transactions, Products,...) Decrease Risk (Customers, Credit products,...) Some of the main goals for CGD are: to be Customer Oriented predict Customer needs solve Customer problems... not just answer their questions To satisfy these goals it s necessary: to improve Knowledge about Customers (using Data Mining) to invest in Data Quality Projects (to have correct information) 4

5 Business eeds: Improving Information Quality of CGD Customers One of the objectives of DMK credit cards area is to increase the number of its customers. The objective was to identify the customer segment at which marketing and sales actions were to be particularly targeted. An analysis of the profile of credit card customers may help to refine the definition of customers at whom a campaign should be targeted and the most suitable approach to be made reducing campaign costs and increasing its efficiency. 5

6 Data Mining as a part of Business Intelligence Data Marts Ad-Hoc Queries, Reporting External Data Operational Systems DW Temporary Data WEB Server Data Mining Server DB2 Connect Server... OLAP Data Mining Information System Communications platform OS390 Servers T Server LA Windows 95 SA TCP/IP DB2 DB2 6

7 Data Mining - What is it? Data Mining is the process of exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. «Mastering Data Mining» «The art and science of customer relationship management» Michael J. A. Berry Gordon S. Linoff 7

8 Data Mining - What is it? Data Mining is the process used to select, exploit and model large volumes of data with a view to identifying previously unknown consistent patterns or systematic relationships between variables, with a view to generating major value to an organisation s business or activities. 8

9 Data Mining - What is it? ame Age Scoring Own Credit Card John Mary Julia Pamela Michael Tom George Cindy Age Scoring Age 80 Age Scoring Scoring 9

10 Data Mining - What is it? Age Age Age Scoring Age < 60 Age >= 60 Scoring Decision Tree Scoring Scoring<30 Scoring >=30 Scoring <20 Scoring >=20 10

11 Initial Work - Extraction and Analysis of Data Quality Definition of data set to be analysed; Extraction of data required for the defined analyses from legacy systems (operational systems); Loading of extracted data in SAS ; Definition of rules for the preparation of data and new files; Analysis and validation of data. 11

12 Work Performed Using SAS Enterprise Miner TM Training on use of tool; Installation of tool on T server and team PC workstations; Study of profile of CGD customers: differentiating characteristics between credit card and non credit card customers Creation of a credit card acquisition propensity model (standard model); Creation of a classic (i.e. standard) credit card acquisition propensity model (Classic model); Creation of a gold credit card acquisition propensity model (Gold model); Creation of a gold credit card acquisition propensity model for classic credit card customers (upselling model); Creation of customers segments. 12

13 Several Tool Outputs ator ard Indic Credit C June 1999 AGE on 30 13

14 Results by Segment Cluster 4 Cluster 2 Cluster 3 Top-top (very low%) Cluster 6 Cluster 1 Cluster 45% 1 Traditional of customers savers are middle (medium aged %)(50-65); Cluster 94% 539% High have of customers Spenders deposits amounts are (low retired %) more and than 18% PTE are middle Cluster 2,000,000; 4 age 62 0 Debit (50-65); Borrowers Active Inactive Cards Investors (medium (low Users %) (low (low%) %) most of these customers are aged between and 82% 83% few around low of of asset these 66% levels of customers customers - 57% of (3% have have customers are and more investments over than respectively) 50; have one less of current less belong these to are the customers working population with loan product (82%); propensity than - use than 76% account debit PTE 98% have PTE and 100,000 have and 200,000 taken credit 22% deposits in out cards; investments; have although residential amounts overdrafts; they mortgage have savings more than loans, PTE 31% accounts; these customers have high credit levels and do not have 2,000,000; these 99% only taken of 4% customers out these have personal customers residential are loans investors have mortgage and 26% more - 68% loans have make investments - 6% have recorded payment than have PTE and 200,000 only 2% overdrafts; securities they in have are these Deposits taken transactional customers portfolio, and out 37% personal do 61% customers have not loans; have more use one credit than or11% PTE but more are defaults; 2,000,000; have term CaixaDirecta deposit these savings accounts Debit Card Users they customers customers subscribers, Active do accounts, and not Investors use with are Credit weak some moderate 56% 79% ties products; financial have have savings to CGD, their debit products accounts; Borrowers having use cards of ranging little credit and 34% these have are credit customers cards. with a high level of credit product - the from 86% more payment the of than these most a default current customers traditional rate account. is not have to the particularly taken most out sophisticated. high. propensity a residential they prefer 91% term have deposits taken more out residential traditional customers mortgage Cluster 0 mortgage loan; Cluster 5 76% have loans, more 25% than have one taken term out deposit personal account; loans and 18% 24% are have classified overdrafts. as preferred customers; Cluster 3 4% are classified as preferred customers. these are sophisticated, investing customers who make use of a wide range of the bank s products. Top-top Inactive High Spenders Traditional Savers 14

15 Results by Segment Cluster 4 Cluster 2 Cluster 6 Cluster 1 Debit Card Users Active Investors Borrowers Cluster 0 Cluster 5 Cluster 3 Top-top Inactive High Spenders Traditional Savers 15

16 Results by Segment Marketing Campaign 16

17 Work done in a Corporate Customers Project Extraction from DW corporate customers data Add external data to improve information about these customers Apply Data Mining techniques to discover and understand customers profile: What s the profile of a customer that has purchase a corporate product? Creation of a Global Industry Offer acquisition propensity model Credit customers propensity model Deposits customers propensity model Potential customers propensity model 17

18 Work done in a Corporate Customers Project SAS Enterprise Miner screen shot: 18

19 Work done in a Corporate Customers Project ow, CGD Group: Have improved knowledge about corporate customers; Understand the profile of some corporate customers; the characteristics of customers that have bought the Industry Offer Global product; Understand the characteristics of customer segments according to the products that they have: current accounts, credits, insurance,... 19

20 SAS modules at CGD SAS BASE SAS ACCESS to DB2 SAS COECT SAS Enterprise Miner SAS COECT SAS EIS SAS Enterprise Miner SA TCP/IP DB2 T Server LAs Windows 95 e T 20

21 Conclusions The quality of information obtained on CGD customers was highly relevant to CGD s Marketing Department; The It was shown The that right the use of a data mining tool - SAS information right Enterprise Miner to the TM right - made it possible decision to obtain fresh information of person at vital the right business interest; at the time right time Much of the project s success was, in part, based on the multi-disciplinary (IT and business) composition of the project team. 21

22 ext / current Projects... Data Mining with the Electronic Channels Department; The 4th iteration of Data Warehouse is about Risk and Channel Analysis... The 5th iteration of Data Warehouse is about Profitability... Goal: a consistent approach to customer servicing, independent of channel utilised. 22

23 ext / current Projects... Clickstream Analysis Click Through Click From caixadirecta.cgd.pt 23

24 ext / current Projects... Customer Churn (Attrition) Customer Retention Customer Acquisition Customer Profitability Targeted Marketing Cross Selling Market-basket analysis Upgrading or Upselling Fraud Detection Channel Management Risk Analysis Attributing of Credit Limits Web Mining 24

25 Data Marts 25