Improve Customer Service and Grow Revenue with a Business Intelligence Competency Center. May. 23, 2007

Transcription

1 Improve Customer Service and Grow Revenue with a Business Intelligence Competency Center May. 23, 2007 NTT DoCoMo, Inc. Shunichi Haijima Copyright 2007 NTT DoCoMo, Inc.

2 Contents 1. Background of Business Intelligence at NTT DoCoMo 2. Strategic Information Management through BICC at NTT DoCoMo 3. Future Outlook 1 Copyright 2007 NTT DoCoMo, Inc.

3 1. Background of BI at NTT DoCoMo 2 Copyright 2007 NTT DoCoMo, Inc.

4 Introduction of NTT DoCoMo, Inc. NTT DoCoMo, Inc. DoCoMo Subscribers (as of Apr. 30, 2007) Mobile Phone 52.7 Million (FOMA 36.4 Million) (i-mode 47.6 Million) Market share :54.2% Financial Information Operating revenues 4,788 Billion of yen [ 40 Billion of U.S. dollar] (March 31, 2007) Income before income taxes 773 Billion of yen [ 6.5 Billion of U.S. dollar] (March 31, 2007) [1dollar =118.8yen] 3 Copyright 2007 NTT DoCoMo, Inc.

5 The Direction of Mobile Development Three initiatives; From Voice to non-voice", New Lifestyle Infrastructure", From Domestic to International". From Voice to Non-voice Multimedia Existing Business Ubiquitous Service Globalization Lifestyle Infrastructure Future business area From Domestic to International 4 Copyright 2007 NTT DoCoMo, Inc.

6 Development History of Mobile Phones Move from handset rental service to purchase only Million s Communication Infrastructure Mobile phone handset i-mode service launch IT Infrastructure i-mode service e-wallet Handsets : service launch 52.7 Millions (as of Apr. 30, 2007) 47.6 Millions (as of Apr. 30, 2007) 20.8 Millions (as of Apr. 30, 2007) Lifestyle Infrastructure Osaifu-keitei e-wallet handset Stage1 Communication Infrastructure Stage2 IT Infrastructure Stage 3 Lifestyle Infrastructure 5 Copyright 2007 NTT DoCoMo, Inc.

7 Osaifu-keitai TM e-wallet Handsets Sony Finance ampm Japan Circle K Sunkus CoCa-Cora Japan ONLINE SHOPPING bitwallet Y s mart JR / ANA/ JAL TRANS- PORTATION SHOPPING * The list of service providers include those planning to start service in the future. User base of Osaifu-Keitai 20.8 million (as of Apr. 30, 2007) KEY/ID Kesaka Systems TOPPAN FORMS TICKET MEMBER CARD TOHO CINEMAS FINANCE JCB DC CARD UC CARD Sumitomo Mitsui Card Bank of Tokyo-Mitsubish UFJ UFJ NICOS GEO SEGA TOWER RECRDS MatsumotoKiyoshi Wonder J-WAVE DAIICHIKOSHO Yodobashi Camera Bic camera 6 Copyright 2007 NTT DoCoMo, Inc.

8 Create a New Lifestyle Infrastructure -credit payment business- Create a new business model that does not rely on traffic income - New credit card brand business id for mobile commerce (Dec. 1, 2005) - New credit card issuance business DCMX for mobile commerce (Apr. 28, 2006) Reader/Writer + Sub card (subordinate use) Main card (main use) Sumitomo Mitsui Card 三井住友カード三井住友カード Other credit cards Credit Card Brand business Main Main card card (main (main use) use) Sub Sub card card (subordinate (subordinate use) use) No. of DCMX members: Over 2 million No. of installed id payment terminals : Approx 150,000 (As of Mar. 31, 2007) Credit Card Issuance Business + Credit card issued by DoCoMo 7 Copyright 2007 NTT DoCoMo, Inc.

9 Quintuple Play? Vertical Principal Investments for Future Growth and Their Objective Application Rakuten Auction 4.2billon yen / 40%stake Platform Internet Auction Credit payment Sumitomo Mitsui Card 98billon yen / 34%stake Tower Records Japan 12.8billon yen / 42%stake Music UC Card One-segment/ Data broadcasting 1billon yen / 10%stake Fuji Television Network 20.7billon yen / 2.6%stake oversea s KTF (South Korea) 65.5billon yen / 10%stake PLDT (The Philippines) 52.1billon yen / 7% stake Mobile phone Internet Fixed phone... FMC Triple play Quadruple play Horizontal 8 Copyright 2007 NTT DoCoMo, Inc.

10 i-mode Services Around the World 9 Copyright 2007 NTT DoCoMo, Inc.

11 2. Strategic Information Management at NTT DoCoMo 1) Establishment of a Corporate Official BICC 2) Project team with BICC and Business Departments 3) IT infrastructure for BI 4) Automation of Mining 5) Mechanism of Real Time Actions 6) Spread of Advanced Analysis 7) Example of Predictive Model 10 Copyright 2007 NTT DoCoMo, Inc.

12 1) Establishment of Corporate Official BICC (1/2) NTT DoCoMo BICC was established in The objective was to provide company-wide support for knowledge creating activities and utilization of information The The roles roles of of the the organization inside inside the the Information System Department Present System Planning System Development Operation & Maintenance Usage of Data Mission critical system 1 Mission critical system 2 Mission critical system 3 Mission critical system 4 Other Division Development Systems A, Section B, Section C, Section D, Section Other Divisions System Operation & maintenance section BICC (Including System Planning and System Development of DWH etc.) 11 Copyright 2007 NTT DoCoMo, Inc.

13 1) Establishment of a Corporate Official BICC (2/2) < BICC Organization > BICC General Manager Needs of Business Departments Manager Chief Analysis Group Data Extraction Group Security Group Data Mining [20 - / year] (Number of Data mining) Complex Data Extraction [800 / year] (Number of Extraction ) Analysts Chief DWH Group Traffic DWH Group R-OLAP M-OLAP [800,000 / year] (Number of Access) Information Explorers Information Consumers 12 Copyright 2007 NTT DoCoMo, Inc.

14 2) Project team with BICC and Business Departments (1/3) The BICC enabled a comprehensive Support and Data Analysis environment for Business Departments to make decisions. Past The main business department requests and receives the data. They need the knowledge of information systems, individual data, mining tools, large-scale data processing and mathematical statistics. Present The main business department decides its theme for analysis and enhances knowledge creation activity with the support from the BICC. Business Department Business Knowledge Business Department Business Knowledge (Knowledge of mining tool) (Knowledge of large-scale data processing) (Knowledge of mathematical statistics) Information System Department Knowledge of information systems Knowledge of individual data Knowledge of mining tools Knowledge of large-scale data processing Knowledge of mathematical statistics Knowledge of information systems Knowledge of individual data BICC 13 Copyright 2007 NTT DoCoMo, Inc.

15 2) Project team with BICC and Business Departments (2/3) <Business Departments> BICC Project A (theme a) Project B (theme b) Marketing Division Corporate Marketing Division Information Systems Department Project C (theme c) Project D (theme d) Product & Service Division Network Division C E O Project E (theme e) Global Business Division Project F (theme f) Corporate Strategy Department Project G (theme g).. R&D Division.. 14 Copyright 2007 NTT DoCoMo, Inc.

16 2) Project team with BICC and Business Departments (3/3) Key points in promoting the projects 1. Top Down Analysis are made for a reason. In order to effectively make use of the analysis results, we would have meetings with relevant department heads before any analysis is made. 2. Start Small and Expand Do not try complex analysis from the beginning. Starting with simple data extraction and then to gradually identify the business user needs and utilize complex mathematical models can lead to far better effectiveness of the project. 3. Data & Decision Making Support There can be analysis results which deny the direction of the business department. In such cases, discussions should be based on facts, not with any prejudice nor pre-conception. The analysis department will help the business interpret the results and make decisions. 15 Copyright 2007 NTT DoCoMo, Inc.

17 3) IT infrastructure for BI (1/2) IT Infrastructure Needs of Business Department 60TB (1Q 2007: 125TB) Mining server Math Models (Predictive Analysis) Mining (Data/Text/Weblog) Data Mining <20- / year> Complex Data Extraction <800/year> Analysts DWH 200TB (1Q 2007: 400TB) R-OLAP M-OLAP <800,000 /year> Information Explorers Information Consumers Mission-critical system 16 Copyright 2007 NTT DoCoMo, Inc.

18 3) IT infrastructure for BI (2/2) Mission-critical system Account System Billing System other Customer Transfer DWH Information Transfer questionnaire (External Data) Questionnaire Information of SFA Shop Mining Server Mission-critical system data Information questionnaire External Data Questionnaire Information of SFA (24CPU 60TB) 1Q 2007 : 120CPU 125TB Mining data Text Weblog Math Models Platform for Information analyses Platform for Information analyses scenario parts オ scenario type ンン processing engine usage analyze tool Internet Home page Call center DM One-to-One Marketing Plan Campaign Cross-sell, Up-sell Retention Action Analysis (Marketing) Predictive Analysis Targeting Segmentation 17 Copyright 2007 NTT DoCoMo, Inc.

19 Main tools for Mining SAS (Ver.9.1.3) SAS Enterprise Guide GUI tool SAS Enterprise Miner Data Mining SAS Data Integration Studio Data Extraction, Transformation & Loading 18 Copyright 2007 NTT DoCoMo, Inc.

20 4) Automation of Mining We can use math models at any time since 2005 DWH Information Mining Server Transfer Information 1. Create Analytical Data Create Models Verification models Scoring Programming (Automatic process) Analytical Data (Variables) Confirm models 2. Create models 3. Verification 4. Scoring Models Accuracy OK NG No.1: XX point No.2: YY point. 19 Copyright 2007 NTT DoCoMo, Inc.

21 5) Mechanism of real time actions Recommendations made by Predictive Analytics have been executed for all channels in the country since 2005 <Action> <Data Collection> service order opinion service order Shop Contract Data Usage Data Account System Billing System NTT DoCoMo Shop DM etc. Result Recommendation Result opinion service order opinion Call Center Internet site i-mode site Call Center Dialogue Record Profile of Members DWH Mining Sever Call center Internet site i-mode site Recommendation Result Recommendation Result 20 Copyright 2007 NTT DoCoMo, Inc.

22 6) Spread of Advanced Analysis As the activities of BICC spreads across the organization, the business departments have increased their strategic use of analysis number of data extraction Number of departments which utilize the Data Mining System (SAS) In Current 15 Over Marketing Department, Corporate Development Department, Sales Department, Corporate Marketing Department, Headquarters of regional Corporation, Etc. 21 Copyright 2007 NTT DoCoMo, Inc.

23 Churn rate per Month at NTT DoCoMo (%) 1.3 Present state Down MNP (mobile number portability) was implemented on Oct. 24, FY2003 FY2004 FY2005 FY Copyright 2007 NTT DoCoMo, Inc.

24 7) Example of Predictive Model (1/5) Theme : Analysis of Customers who are likely to Churn' Analysis through Cancellation Questionnaire Classification of Cancellation Cancellation by by the the Customer Questionnaire information Cancellation Questionnaire Cancellation and and new new contract Non-usage cancellation Churn Churn Analysis 23 Copyright 2007 NTT DoCoMo, Inc.

25 7) Example of predictive model (2/5) Typical process to develop a Churn Propensity Analysis Model. Decide Theme Create Hypotheses in Brainstorming meeting Create Variables Create Math Model Execute Actions Decide theme of churn prevention Decide theme with the responsible person in business department. Create one hundred hypotheses Create as many hypotheses as possible. (There may conflict between certain hypothesis.) Incorporate needs and wants of customers. Create several hundred Variables Data from DWH is not originally created for analysis. Therefore, it is necessary to reformat the data for analytic requirements. Create a predictive model Verify hypotheses. Create a predictive model from behavior and Variables. Execute Actions 24 Copyright 2007 NTT DoCoMo, Inc.

26 7) Example of predictive model (3/5) [ Deriving the hypothesis ] A person who changes handset models frequently, tends to churn easily? A person who buys the latest handset, tends to churn easily?... About 100 hypothesis Service Order Billing Traffic Questionnaire. Variables 483 About 100 hypothesis needs 483 Variables set Variables Age Generation, Student Classification Gender Male, Female Addresses Prefecture, City, Area, No. of times the address has changed. Tariff Plan No. of times the plan has changed, Contract period for the plan Handset Model, Price, Period of usage. Service Order Status Billing Information Traffic Information Created Variables External Data [The number of Variables used in the analysis ] main items (example) Customer information, service contract status, order history Monthly Invoice Information ( Base Charge, Communication Charge, Packet Communication Charge) Traffic Information Newly created items (Model Usage Period, No. of Increase/Decrease in the Invoiced Amount. ) Questionnaire Information, etc... Total The number of Variables Copyright 2007 NTT DoCoMo, Inc.

27 7) Example of predictive model (4/5) The accuracy of the model created in this occasion is verified. 100% 90% The rate (percent) that churn persons are selected 80% 70% 60% 50% 40% 30% 20% 10% The most suitable model The most suitable model chosen statistically In the case of random selection 0% % sampling Sampling Rate (%) 26 Copyright 2007 NTT DoCoMo, Inc.

28 7) Example of predictive model (5/5) We categorized customers by Churn Forecast and Customer value, and executed measures. high Customer Value B A Retention Measure Plan Send Direct Mails to The Customers 1. Promote DoCoMo Points Service D C 2. Promote Discount Services low low high Cancellation Probability 27 Copyright 2007 NTT DoCoMo, Inc.

29 3. Future Outlook 28 Copyright 2007 NTT DoCoMo, Inc.

30 Next stage of Evolution Feb 9, 2007 NTT DoCoMo Achieves World s First 5Gbps Packet Transmission in 4G Field Experiment 1G 4G 100M 3G & Enhanced 3G Super 3G 10M 1M 100K WCDMA HSDPA HSUPA WCDMA :Wideband Code Division Multiple Access HSDPA :High Speed Downlink Packet Access HSUPA :High Speed Uplink Packet Access Copyright 2007 NTT DoCoMo, Inc.

31 Concept of Context Awareness <Context Awareness> : Context is the current situation of the user. Awareness is understanding the situation of the user. Context Awareness is a system which supports the activity of each individual at that specific time, the specific place and for that specific individual. Activity person Recommendation Corporation Analysis Product Service Time Space Contents Permission DWH Information Collection Privacy Protection Technology 30 Copyright 2007 NTT DoCoMo, Inc.

32 NTT DoCoMo, Inc. SHUNICHI HAIJIMA 31 Copyright 2007 NTT DoCoMo, Inc.