Combining Strength to Embrace the Future — Win the Era of Programmatic Buying

Shen Ying

General Manager

CTR Media & Consumption Behavior

Wang Zuo

Online Media Group Product Director

Tencent

September 16, 2015 Shanghai

Marketing Effectiveness is dependent

on the Quality of the Consumer Data…

怎么能够简单且精准的找到他？

通过什么 方式能找到他？

他有怎样 的行为？

Traditional Advertising Planning is focusing on

Consumer Segments

Consumer group 1:

Have individual character,

love fashion, chasing dreams

Consumer group 2:

Practicality is the key consumption concept,

brand is important to them,

rational consumption

Programmatic Buying Emphasis is on the

Individual’s Data Use internet at

university Visit cosmetics

websites very often

Been to many

different cities recently

Visit automotive

websites very often

University

students

Cosmetics

consumers

Business

people Car lovers

Fast food Ads Cosmetics Ads Business mobile phone Ads Automotive Ads

Tracking internet users

who surf internet at

university IP address

Track people whose IP

address always changes

from one city to another

Track internet users

who often visit

automotive websites

Track internet users

who often visit

cosmetics websites

2012

2013

2014

2015

2016

187.6

2012

8.1

From Groups to Individuals,

Is Individual based data more effective?

Data source：MAGNA GLOBAL & Enfodesk

Global Programmatic

Buying will reach 273

Hundred Million

Dollars in 2016,

China accounts for

10%

Market size of Programmatic Buying in China

(Hundred Million RMB)

Over 20 Times Increase

Current TA screening

method is not

effective enough

There are still Barriers to cross

User Recognition Consumer Behavior

Classification Advertising Plan

Current consumer

segmentations cannot

meet marketing demands

effectively

Online data cannot

serve offline

advertising plan

Barrier (1) User Recognition:

Difficult to ensure accuracy of online data

User information is difficult to

verify

Self-complete information can reduce the

data accuracy, and is difficult to verify

Low veracity of user data can lead to

inaccurate behavior definition

Cookies based data is inaccurate

Short period of validity

Delete or reset can lead to data loss

Cookie mapping technology is still in the

development phase

Online Behavior

Short Period of

Validity

Unstructured

Difficult to Verify

Barrier (2) Consumer Behavior Classification:

Analysis dimensions of online data cannot meet the

marketing needs properly

Dara source: CNRS 2014（1-12,60 cities）

20% 27.1%

Internet users who contact at least 3

traditional media per day

42%

Internet users: Used the internet last week

Traditional media: TV, newspaper, magazine, radio and outdoor media

Barrier (3) Advertising Plan:

Online data cannot serve offline advertising planning

‘Data’ + ‘Technology’

Provides More Effective Insights

CNRS-TGI is

One of Global

TGI Network

CNRS-TGI classifies Consumers on the basis of

their behavioral data.

From 1969 Global TGI…

Recruits

respondents

through home

interview

Data collection:

In-home

face to face interview

+

online self-completion

Data ETL

and

Weighted

TGI - 40 years

experience of

single-source

study

More accurate data

age, gender, income, career

……

ISO20252

Systematic & Scientific Research and Fieldwork

Working Systems ensure high Data Quality

Demographics

7 types of media

220+ Categories

6000+ Brands

200+

Lifestyle

statements

Single Source Research Product with Rich Data of

Media Contact and Consumption Behavior

TGRs

Multi-data products support both Online and

Offline Advertising Plan

Demographics

Media contacts

Product consumption

Lifestyle

CNRS

+

CSM

TAM data

CNRS

+

PC meter click data

+

Mobile device meter

click data

CNRS-TGI Clickstream

Make Big Data More Powerful

Bigger than Bigger

• By integrating and grouping the behaviors and interests of customers in different dimensions, as well as de-identifying and desensiting, a unified customer interest classification system is established, covering over 400 subdivided interests.

Tencent DMP，Bases On 800 Million QQ Users

Population

property

Social

contact

property

Others

Content preference

Game preference

Interest in E-business

User image

Primary

category Second-level category

Te

nce

nt b

as

ic la

bel s

ys

tem

Region Region 1 category in total

Demographic Age

10 categories in

total Gender

……

Behavior

Video 7 categories in

total Advertisement

……

Interest

Infant & mom 9 categories in

total Automobile

……

Relation

Spouses 9 categories in

total Students

……

User-defined

Own data upload 3 categories in

total Website traffic

Activity

• Independent analysis

• User profile

• Real-time, multi-dimensional analysis

• Mobile intelligent location tool

Data Analysis

• Precise advertising system

• User-specific recommendation engine

• Life-cycle customer management

Data Mining

• Task dispatching system

• Data monitoring

• Metadata management platform

• TD Bank

• TDW

Data Management

• Independent reporting tool

• Tencent Compass

• Tencent Analysis

• Tencent Cloud-based Analysis

Data Visualization

Tencent Big Data Foundation Establishment

Agency company

DSP

DMP 1

DMP 2

DMP 3

Tencent DMP

Medium A Medium B Medium C Medium D

TA Advertising Ten

cent

Co

okie

Map

pin

g

Co

okie

Map

pin

g

Build the Cookie Mapping Platform to Open Up Access to Data Transmission

Cookie Mapping

• Continuation of the Standard for Mobile

Internet Advertising

Expected to be issued in July 2015

− Standard for mobile video advertising

− Effect appraisal of mobile internet

− Standard for privacy protection of mobile

users

• Industrial Standard for Solving Problems in

Intelligent Routing

Expected to be completed by June 30th, 2015

Under planning Issued

Data Standards

• Partnered the Interaction Network Branch of

the China Advertising Association to issue the

Framework Standard of the Chinese Internet

for Protecting the Information of Targeted

Advertising Users (issued on March 15th, 2014)

• Partnered the Interaction Network Branch of

the China Advertising Association to issue the

China National Standard for Internet

Advertisements (issued on March 11th, 2015)

Primary Standard for Internet Digital Advertising

Standard for Advertising Monitoring on Mobile Internet

Standard for Mobile Internet Integration

• Engage in the establishment of an industrial standard for Cookie mapping and a form standard for user

identification and data exchange

• To cooperate with internationally renowned third-party data providers and research companies and use their

perfect data security management standards for reference in forming Tencent’s own management standard for

data security and data exchange, in order to establish an industrial standard

Engage in the Establishment of Industry Standards to Protect User Privacy and Create a Healthy Ecosystem

Leverage User Recognizable Level

01

02 03

Achieve Cross-Screen Advertising Placements Enrich Group Tags

Cooperative Value of CNRS-Tencent DMP

Complement Advantages of Resources

Movies

Pre-Order Brands

Average Cost

Income

Scene

Concepts of Cooperation Match in Tencent platforms to identify loyal customers.

Get more accurate insights from loyal customers

Redirecting

Audience Expansion

Remarketing to loyal users or marketing to similar potential customers (Lookalike) to transform them into new customers

Encryption

Attribute 1

Attribute 2

Attribute 3

Two Cases

DMP (TENCENT-CTR）

DMP（XXX）

DMP（YYY）

Medium A

DMP recognition rate: 17.1%

TA density: 12%

Medium B

DMP recognition

rate: 19.9%

TA density: 12%

DMP recognition

rate: 41.2%

TA density: 22.9%

Medium D

DMP recognition

rate: 21.5%

TA density: 11.4%

Medium C

Implications： • Using the total DMP data to integrate Reachmax and conduct programmed input, thereby realizing cross-media input

accuracy to targeted audiences.

Household necessities brand

Case 1： One household necessities brand——DMP data navigation and cross-media real-time accuracy becomes possible

DSP

Data Recognition

50.16% recognition rate 15 Thousand Car

owners information

Customers who visit the

store

Customers who bought

cars

80 million Potential TA

4. Monitoring and Analysis

1. Customer-owned data

2. Docking data with the Tencent DMP

Customer Diffusion

3. Lookalike Audience delivery

Monitoring and analysis the cross-platform ad impressions, clicks, and the properties and preferences of registered users

Project milestones Over half of the

Offline-online data matched ；

Increase company's own data 5330 times+

Audience Optimization

Case 2： One auto brand——data research projects

Ongoing projects

Ongoing projects

5. Establishing company DMP

Collect data assets of the brand itself, so that the audience profile will be clearer to achieve audience Remarketing

Introduce third-party partners： Achieve data identification efficiently through professional research methods to

Case 2 Optimization： One auto brand——the Brand builds its DMP to achieve precise delivery at large scale

4. Monitoring and analysis

50.16% recognition rate 80 million Potential TA

1. Customer-owned data

2. Docking data with the Tencent DMP

Customer Diffusion

3. Lookalike Audience delivery

Monitoring and analysis the cross-platform ad impressions, clicks, and the properties and preferences of registered users

Audience Optimization

Customers who visit the

store

Customers who bought

cars

In the Future, any business is data business.

Thank you |

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