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KDDI CASE EXAMPLE HARNESSING BIG DATA ANALYTICS FOR CUSTOMER CARETakuya Kudo, Global Lead of Data Science, Accenture
APPLIED INTELLIGENCETAKUYA KUDO2018.6.13
THE ART OFDATA SCIENCE“KDDI FS NEW SERVICE DRIVENBY MACHINE LEARNING”
KDDI Corporation
Transforming KDDI from a telecommunications company into a "Life Design Digital Company” enables KDDI to provide diverse products, services and more relevant customer experiences that encompass the unique needs and requirements of valuable business partners and customers driven by Data Science Center of Excellence.
INNOVATION IN CUSTOMER LIFESTYLE CHOICES
• #2 telco in Japan.
o $38B revenue (+4.6%YOY), $7B OI(+25.2% YOY)
o 50M subscribers (38% of Japanese population)
• Via digitalization, KDDI aims to transform from “Traditional Telco” to “Life Design” company providing a variety of digital consumer services.
• To accelerate this journey KDDI defines the following as priority areas:
o Insight to Action (Data Analytics) o Digital Customer Experienceo Digital Service Expansion
• Near term KDDI imperatives:
o Enhance analytics capability at scale by leveraging customer data available across various KDDI affiliates
o Grow digital business through better customer understanding
KDDI CONTEXT KDDI Group Company
⋯ CATV (#1 in Japan)
Wireless
Wireline
Internet(ISP)
Electricity
eCommerce
Insurance
Mobile Video/Book
Mobile Wallet
⋯ Net Banking
⋯ MVNO
Establishing robust KDDI ecosystem to expand “Mobile Wallet” share by accelerating commerce, finance and other new services through KDDI points reward system
KDDI VISION Transform to “Life Design Company”
Grocery/Daily goods
(eCommerce)
Electricity
Mobile/Video, Book
etc BankWM
Insurance
Loan
Mobile Wallet
Shops(#2,500)
MobileOMNI CHANNEL
OPTIMIZATIONI.E.#HALLOWEEN
OPTIMIZATION
Target ∑ipixi pi:Max Snack Weight
Constraint ∑iwixi≤200 wi: Max Capacity
Feature ∀xi∈{0,1} xi: In or Out with size
4
8
16
32
64
128
256
512
1024
2048
4096
OPTIMIZATION
NOT SO FAST! REALITY BITES
WHAT IS THE LAST BUT MOST IMPORTANT ELEMENT TO FACE CUSTOMER?
Oh Yeah! Human!
AI + HUMAN ENGAGEMENT= KEY ELEMENT OF DATA SCIENCE
SERVICES AND ASSETS IN DATA ANALYTICS COE
Analytics
Channel
AI Single Brain
Virtual/Web
Dashboard
KDDI DMP Sub’sDMP IoT Data
Analytics Platform
Sophisticated Customer Targeting
ABAISingle Brain
IoT Data Analytics
Data Management
APAnalyticsPlatform
• Accenture Insight PF(AIP) utilization w/ KDDI group Data
• Design Nurturing/ • Recommend model
by KDDI group Data
AI Chat Service
VA VirtualAssistant
• Customer Support Chat Service w/ KDDI Data + AI
AA AdvancedIoT Analytics
• IoT Analytics• Service w/
KDDI Data + IoT Data
IoT Dashboard
Management Dashboard
MA ManagementAnalytics
• Support for KDDI’sManagement w/ KDDI Data
Custom made solution
Accenture Service Provided
CoE Management
AI Chatbot
Analytics Service Management
ANALYTICS SOLUTIONS
SCOPE DEFINITION:SERVICES PROVIDED
Service Definition
Design/Modelling
Model Coding/Model Tuning/Operation
SolutionManagement
KDDI+
ACN
AAAdvancedIoT Analytics N/A
VAVirtualAssistant
ACN D4C KEVChat operation
(Tier2)AI Engine
tuningChat log analytics
APAnalyticsPlatform
ACNAIP SW set up and tuning
ABAISingle Brain
D4COthers
(for Website, etc.)
D4CMAManagementAnalytics
An
aly
tic
s S
erv
ice
ACNRecommendation
for outbound
KDDI JV staff works together with ACN staff(Role Identified as “Two in-a-box” with Accenture & KDDI)
Data Analytics CoE (JV) Subcontracting(on-site work required)
* KDDI Group Company
PROPOSED JV STRUCTURE
KDDI ACN Other
A Shareholder Agreement
Long-term Consulting Service Agreement- MSA- SOW
C
B Long-term Staff Augmentation
D Secondment Agreement
Investment Business
Analytics CoE
KDDI Accenture
20%80%
JV
AKDDI &
Affiliates
Accenture
Subcontractor
C
Subcontractor
*KDDI 100% captive
Service Agreement
D4C
*KDDI 10-15% captiveApprox. 50 employeesEstablished in 2005
Subcontractor
Staff
B
Loan Seconded
D
KDDI Evolva
• Minority ownership (15 to 20%) for JV
• Expected capital infusion US$ 0.7M
• Equity accounting approach
16
JV CEO
CoE Management
Lead : TBD
Co-lead : Mizuno
Member : Hasegawa
Corporate Function(GA/HR/F&A)
Lead : TBD
AB MA VA AA AP
Seconded(ACN) : 3
Staff (ACN) : 32
Seconded(KDDI) : 7
Chief Science Officer COO/CFO
Analytics Service Mgmt.
Lead : TBD
PlatformAnalysis
KDDIPresident
Vice President
KDDI DMP DptChief of Biz Sector
PROPOSED JV ORGANIZATION (DRAFT)
CEO：IenakaCSO：Kudo/COO：Koketsu
Advanced Tech.
Horikoshi
IndustrialScience.
Horikoshi
ConsumerScience.
Asano
ExternalPlatform.Yamada
ProductPlanning.
Yoshie
Planning Koketsu Implementation Hasegawa
・Corporate Strategy
Finance/Accounting・HR/Recruting・Marketing & Com・Legal
・MPP architecture・Design ARISE
Enterprise Architecture
・Call Center
Analytics・Advanced
Analytics・Management
Dashboard・Sales Support
・au churn analysis・CPA optimization・Recommend
engine・Design customer
profile・SSS PJ
・SD-WAN solution・AI Taxi ・Deep Learning
SNS Chat bot・Call center
speech analytics
・Design data
monetization Platform
-AI PFetc
・Data business
planning
High Security Clearance Analytics
・Portal Credit Risk Analysis・au market place analytics・Debt analysis for delinquent customer
Total FTEs: 192Cost Center + Future Profit Division
Line of business profit Center
Management & Strategy Div.
Mizuno
Technology Div.Hirayama
Science Div.Sasaki
Business Development Div.Hasegawa
KDDI X ACCENTURETO DRIVE NEW DATA BUSINESS FOR TOYOTAWE PROVIDE NEW DATA SERVICE BEYOND TELECO.
DISRUPTION
AI Taxi
FORTUNECHANGE THE WORLD 2017
Using data analytics to help health care providers make life-saving differences.
Some projects of Accenture’s Analytics and Health and Public Services teams are literally matters of life and death. In Saga Prefecture, Japan, the consulting firm analyzed transport data and identified inefficiencies, helping to shave a critical 1.3 minutes off emergency transport times.
JAPAN GENERAL INSURANCE MAJOR
Deep Learning Algorithm to improve driver safety and reduce accidents
Accenture Analytics developed a deep-learning algorithm which harnesses in-car and outside video, geo-location and biometric data, to identify signs of drivers’ drowsiness and near-miss accidents from their heart-rate changes and driving behavior.. .
APPLYING SOCIAL ANALYTICS FOR INBOUND BUSINESS IN CG&S - LAWSONWe build SNS rating engine, which converts SNS big data into customer brand rating.
• Use rating (e.g., ratings in TripAdvisor) as the labels of the review data to train the model
• Apply the trained model to predict customer ratings by using SNS texts without rating
• Possible to further improve the model with customer profile ＆ location data
Reviews with Ratings
Da
ta R
etr
iev
ing
Inte
rfa
ce
Rating Model
Building
For Marketers: Personalized Recommendation
For Business Owners: Satisfaction Monitoring
For Public Sector: Resource & Infra Assessment
Recommendation
Service Monitoring
Resource Assessment
Rating Prediction
✓ User Profiles
✓ Purchase Data
✓Geo Logs
SNS Big Data(No Ratings)
+
Accenture SNS Rating Engine
✓ User Profiles
✓ Purchase Data
✓Geo Logs
+
SNS Big Datawith Ratings
~ BRAND EVALUATION MATRIX OF TOURISM ATTRACTIONSSNS ANALYSIS CASE
Brand power can be separated to 2 essential components: brand awareness (measured by number of posting) and brand rating (measured by our model prediction). By arranging the measurements within four quadrant matrices and investigating the results, we can help client making optimal market strategies.
Killer AttractionsPotential Attractions
Well Aware But Low-rated Attractions
MedianHigh
High
Low
These contents attract many tourists and provide them with high satisfaction.
We need to investigate why the evaluation points are low despite high degrees of awareness, and then consider taking appropriate actions.
If we start a promotion, there’s a high possibility that we can attract many tourists.
(*)
(*)Predicting evaluation points by using rating model
