성공적인 마케팅 통합관리를 위한 분석 적용 · regain customer confidence-adapt...

Copyright © 2013, SAS Institute Inc. All rights reserved.


성공적인 마케팅 통합관리를 위한 분석 적용 Analytics for Integrated Marketing Management

Eunjoung Kim

Advance Analytics, SAS Korea


Analytics trends in the Big Data era

Three keywords in Big Analytics

Case study

SAS CI6

VA DEMO

Agenda


IMM (Integrated Marketing Management)

Risk Customer Service

Corporate Affairs

Merchandising Finance

Operations Radio TV

Mobile Online

In Person Call Center

Direct Mail

Social

Consistent Customer Experience

Text Data ERP Social CRM EDW Online

Orchestration

Marketing

Digital/Social Marketing

Data

Quality

Data

Integration

Event Stream

Processing

Log Data /

Unstructured data

Customer

Profit Analytics

Predictive

Customer

Analytics

Online Customer

Experience

Analytics

High Performance

Analytics

Social

Network/Media

Analytics

Visual Analytics

Marketing Optimization Behavior based EBM Real-time marketing Customer Experience

Personalization

Operations Management & Strategy

Performance Management

Channels Departments

Decisions

Insight

Data Management

Experience


Analytics Trends in Big Data Era

Marketing Trends Analytics Trends

Social Media Smarter

Simplicity

Real Time

Mobile

Marketing -to revenue generation

Big Data

Simplicity-visualization

Technology innovation

Mobile Device

Analytics -competitive edge


3 Keywords Big Analysis

Predict Timely

1 Understand Deeply

Optimize Precisely

2

3


Understand Deeply: 360 degree view

future-predicted

online&social

WhoTheyAre

WhatTheyBuy

HowTheyUse

It

HowTheyInteract

WhatTheySay

WhoThey

Influence

TheirLoyalty

TheirValue

TheirPoten al

360CustomerInsight

O

behavioural

socio-demographic

Social Media

Current Data

︙ Big Data

︙

새로운 영역의 정보 확장

데이터

볼륨

확장

Relational Data

Text

Web log Manufacture IPDR

Data is Bigger!

http://www.google.co.kr/url?sa=i&rct=j&q=%ED%99%94%EC%82%B4%ED%91%9C&source=images&cd=&cad=rja&docid=bqShDRbtnRlFXM&tbnid=dqsinmejt-2FeM:&ved=0CAUQjRw&url=http://m.cafe.daum.net/cbnu02/6Qpx/39202?listURI=/cbnu02/6Qpx?prev_page=6&firstbbsdepth=00ACH&lastbbsdepth=00ABs&page=5&ei=J0VlUYWHIYbDkAXskYDoBg&bvm=bv.44990110,d.dGI&psig=AFQjCNESe4gh-oJf4g_uepWlL2pLmUG3Uw&ust=1365677732893751


Understand Deeply

Social Network Analysis & Text Analysis

CDR

Commu

nities

Role

Assign.

Links &

Nodes

Social Network Analysis

Online Conversations

Brand & Market

Monitoring

PR & Reputation Monitoring

Customer Satisfaction Monitoring

Product

Developm

ent

Text Analysis

f(x) Node

B Node

A

f(x)

f(x) = rel. force

Network

Community

Structure

리더

서브리더

멤버

Outlier Customer Value Analysis

Customer Experience

Analysis 등

Combine


Understand Deeply

Transaction Data 내부 웹/ 콜센터 행동데이터

Internal Big Data 신용카드 결제이력

o 소비패턴을 분석

o 카드론을 받기 직전의 소비

특징을 알 수 있음

Non-monetary Transaction Data

고객의 행동 데이터

o 고객의 웹페이지 방문 기록

o 콜센터 기록

고객의 Needs를 가장 잘

나타내어 주는 데이터. 시간이

중요함

Transaction

패턴 분석

:

병원비패턴

이사패턴

Transaction Data Real Time Data

예시) 카드론 고객 적용 외부

및 파생변수

Personalized

Offer

Transaction Data Analysis

http://www.google.co.kr/url?sa=i&rct=j&q=%ED%99%94%EC%82%B4%ED%91%9C&source=images&cd=&cad=rja&docid=bqShDRbtnRlFXM&tbnid=dqsinmejt-2FeM:&ved=0CAUQjRw&url=http://m.cafe.daum.net/cbnu02/6Qpx/39202?listURI=/cbnu02/6Qpx?prev_page=6&firstbbsdepth=00ACH&lastbbsdepth=00ABs&page=5&ei=J0VlUYWHIYbDkAXskYDoBg&bvm=bv.44990110,d.dGI&psig=AFQjCNESe4gh-oJf4g_uepWlL2pLmUG3Uw&ust=1365677732893751


Complex analysis

Case Study: KDDI

이탈

(시초) 이탈 가능성

(?)

추가적 이탈 가능

이탈 방지 프로그램의 효율성 개선, ROI 개선

네트워크 위험도가 큰 Influencer를 파악함 (고객 충성도 관리 부서)

RT

KDDI에 대한 입소문이빠르게 확산됨

KDDI Influencer를 신규 휴대폰 런칭 행사에 초대 (광고 부서)

RT

RT

RT Tweet

1 2

*RT: Re-tweet


Predict Timely

Right Time Real Time Scoring

Visualization

고객의 최근 행동이나 이벤트를 반영하기 위한 Time Gap축소

월 배치 일 배치Near real time

Cross Channel 적용을 통한 마케팅 효과 극대화

적절한 시기에 여러 데이터들을 다양하게 탐색하여 간단한 business insight를 얻거나 in-depth 분석을 위한 사전 분석 수행이 필요

거대한 양으로 오랜 기간 보관하기 어려운 데이터들, 즉 흘려버리는 데이터들을 탐색적 분석을 통해 간단히 insight 획득

http://www.google.co.kr/url?sa=i&rct=j&q=right+time&source=images&cd=&cad=rja&docid=5cLu3OoRzX8tcM&tbnid=h2ZFfawUTyggJM:&ved=0CAUQjRw&url=http://www.123rf.com/photo_12117976_now-the-right-time-concept-clock-closeup-on-white-background-with-red-and-black-words.html&ei=ehViUfr7Fe2OiAfq74HYBQ&bvm=bv.44770516,d.aGc&psig=AFQjCNEGtyu2Obt_PW2CMPL60IlvIu87NA&ust=1365468864930218


Predict Timely

Our model data

Customer is identified

Determine Customer Value

•Route immediately to

agent

High Churn

Propensity

•Route down billing

enquiry IVR path

Billing

Enquiry

•Route down payments

enquiry IVR path

Payment

Extension

Real Time Scoring


Predict Timely

Customer Data

Develop and Train Analytical Model

Productionise in Model Manager

Deploy the Scoring Model in RTDM Score Customer in Real-Time

IVR

Real Time Scoring


Case Study: ABN AMRO

Regain customer confidence-adapt marketing from mass campaigns to many more

personalized targeted campaigns to fewer customers in real-time.

Leverage move of customer interactions moving from branches to online and mobile

channels. ABN AMRO needed to adapt to the changing market to stay in business.

Reduce churn and increase cross channel selling by matching the same tailored customer

experience other banks are delivering to customers.

Challenge

Offline Behavior Scoring Web offer

Online in Web Behavior Scoring Best Offer

X


Predict Timely

IDENTIFY /

FORMULATE

PROBLEM DATA

PREPARATION

DATA

EXPLORATION

TRANSFORM

& SELECT BUILD

MODEL

VALIDATE

MODEL

DEPLOY

MODEL

EVALUATE /

MONITOR

RESULTS Domain Expert

Makes Decisions

Evaluates Processes and ROI

BUSINESS

MANAGER

Model Validation

Model Deployment

Model Monitoring

Data Preparation

IT SYSTEMS /

MANAGEMENT

Data Exploration

Data Visualization

Report Creation

BUSINESS

ANALYST

Exploratory Analysis

Descriptive Segmentation

Predictive Modeling

DATA MINER /

STATISTICIAN

데이터 탐색

In Depth 분석

Visualization


Predict Timely

Visualization

실시간 거래 로그데이터 수집

Customer Behavior정보

Market Sensing

지역별, 시간대별, 고객군별 마이크로 센싱

통계적 분석기법 활용 (상관분석, 회귀분석, Decision Tree, What-if Analysis, Market Basket Analysis)를 통해 변수간 인과 관계 파악

비즈니스 인사이트를 웹이나 모바일(Tablet PC) 로 즉각적 공유

즉각적인 의사결정 가능


Case Study Telecom Italia

SAS의 VA를 활용하여 accessibility, drop rate, call setup time 등 네트워크 서비스 관련 데이터를 정교하게 시각화해서 다양한 KPI 및 변수들간의 상관관계를 파악하여 네트워크의 문제점을 정의하고 빠른 개선에 활용하고, 보다 심화된 분석에 활용함

Multimedia Service Management in Telecom Italia is a dedicated structure to ensure optimum delivery of value-added services in

mobile and fixed networks. Services that range for the mobile network from SMS to WAP, from broadband to MMS, from the email

integrated with voice mail to availability notification services, and for the landline from IPTV to Web delivery of multimedia contents,

up to wi-fi for high-speed trains.

Big Data에 대한 직관적이고 빠른 분석을 하고, 분석 결과를 빠르게 리포트로 제공 함으로서, C레벨 매니지먼트가 은행의 포트폴리오 퍼포먼스에 대한 정보를 거의 실시간으로 제공받고, 비즈니스 의사결정에 활용

RHB Bank

RHB Bank is specializes in commercial banking, corporate and investment banking

and international banking services. RHB Bank was the one of largest bank in Malaysia.

http://www.fondazionemps.it/ita/default.asp

http://www.google.co.kr/url?sa=i&rct=j&q=rhb+bank+logo&source=images&cd=&cad=rja&docid=eSVXS2zUfCY_nM&tbnid=fWNg0KR9xAB8zM:&ved=0CAUQjRw&url=http://www.malaysialoan.com.my/2011/02/rhb-bank-home-loan-packages/&ei=8CBiUZDPJY-uiQeuoYCoDg&bvm=bv.44770516,d.aGc&psig=AFQjCNF8FjBNGqxNnkPVh6jRFCtMV2WI4Q&ust=1365471840154717


Optimize precisely

Next Best Offer

Optimization

Recommendation

Customer propensity Model

http://www.google.co.kr/url?sa=i&rct=j&q=customer&source=images&cd=&cad=rja&docid=1BMcANixQChRiM&tbnid=jcVlv1WMyaJ1VM:&ved=0CAUQjRw&url=http://www.designerscsr.com/2012/04/bad-social-media-presence-can-turn-into.html&ei=NsphUc3tKsSYiAeY5oGAAQ&bvm=bv.44770516,d.aGc&psig=AFQjCNE4l0XNQiWR9UddGyttTLjC97ViRQ&ust=1365449417942690


Optimize precisely

Do Not Disturbs

(Sleeping Dogs)

Lost Causes

(마케팅 효과 측정 불가)

Sure Things

(마케팅 효과 측정 불가)

Persuadables

(True Responders)

마케팅 대상일 때 구매여부

No

Yes

마케팅 대상이 아닐 때 구매여부

No Yes

Incremental Response analysis

Incremental Response

Rate

Test group Response Rate 𝑃(𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒|𝑂𝑓𝑓𝑒𝑟)

Control group Response Rate 𝑃(𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒|𝑁𝑜 𝑜𝑓𝑓𝑒𝑟)

(총 반응율) (자신의지 반응확률)

Response = Yes

Response = No

Response = Yes

Response = No

Incremental Response

Treatment Group(Promotion)

Control Group(No Promotion)

Difference score model

Net lift modeling


Recommendation

Optimize precisely

Segmentation

Product hierarchy

Sequence

http://www.google.co.kr/url?sa=i&rct=j&q=market+basket+analysis&source=images&cd=&cad=rja&docid=cpWQNu7LlWyYBM&tbnid=FcO6542UC6coFM:&ved=0CAUQjRw&url=http://www.11antsanalytics.com/products/11antsinsightPortal/&ei=n9JhUeGVCq68iAfdrIHQAQ&bvm=bv.44770516,d.aGc&psig=AFQjCNEZm_sIcz6jQwq8wiAiUML_7o_LQQ&ust=1365451093751818


Optimization

Optimize precisely

Product A

Product B

Product C

Product D

Scoring Selection Optimized

offer

Customer preference / Propensity

Constraints

고려조건


Case Study: Deutsche Telekom

Analytics

Marketing Optimization

과거 6주가 소요되던

Campaign Planning Time

단 몇 시간내에 해결

더 적은 은 Outbound-Calls

수행으로 반응률 향상

Problem Solution Value

• 유럽의 No.1 통신사업자, 15000만 이동통신 가입자

• 50개국에 자회사

• Full Service: Mobile, Fixed-Net, Internet, IPTV, ICT Solutions

• 이동통신사업 경쟁의 심화

• 가격 cut, 이익 down

• 모든 서비스 가입으로 인한 수익의 극대화

• Business Unit간의 No Alignment

• 매달 150개 이상의 오퍼 처리하는데 발생하는 문제점 해결

• Manual Campaign process


Case Study: Coles

목적함수 예측 스코어 Optimization

구매가능성 극대화

Health Category

Cereal Category

목욕용품 Category

세제 Category

각 제품 카테고리별 구매가능성 스코어 산출

Optimization

최적 오퍼 제공

15% 10%

Coupon offer

15% 10%

Coupon offer

제약 조건

- 고객별 오퍼 갯수 예산 등 고객별 최적의 쿠폰 오퍼 제공

고객 가치의 향상 (Up about 33%), 고객의 Quality 향상 (Up over 50%) 전년대비 매출 4.2% 향상, 전년대비 이익 15.8% 향상 ,


SAS CI6 – Real Time Decision Manager

Event Filtering

• Event 대상 여부 • Event 우선 순위

Right Offer

• 고객별 최적 오퍼 • 행동 패턴 / 실시간

스코어링 기반

Right Channel

• Cross Channel • Next best offer 를

채널로 전송

New capabilities to allow for categorization and segmentation with fewer nodes or without creating custom processes

Allow for more interactive construction of campaigns with reduced dependency of IT or advanced users

Operationalized Analytics


SAS CI6 – Marketing Optimization

Define Optimization Scenarios

SAS® Marketing Optimization process

Run & Review

Optimization Results

“What-If Analysis”

Optimization

Objective

Suppression

Rules Constraints

Contact

/Blocking

Policy

Optimization

Sensitivity

Analysis

Scenario

Comparison

Objective

Range Analysis

Input data

set Optimization

Summary report

Create

Scenario

Create new measure

Customized

Report


SAS CI6 – Marketing Optimization

Setting the objective Constraint/contact policy Report


SAS Visual Analytics

Central Entry Point Integration Role-based Views

모바일 BI 데이터 준비 비주얼 탐색기 리포트 디자이너

• 리포트 디자이너에서 생성한 인터렉티브 리포트를 모바일 앱을 통해 공유

• In-Memory(LASR)서버 모니터링

• 데이터 로드 및 조인

• 파생 변수 생성

• 비정형 분석 및 인사이트 탐색

• 웹과 모바일을 위한 대시보드 스타일의 리포트 생성

SAS® LASR™ ANALYTIC SERVER

성공적인 마케팅 통합관리를 위한 분석 적용 · regain customer confidence-adapt...

Documents