China: Data Driven User Engagement and Acquisition

China
A look at the Opportunities in:
Optimization, Segmentation, Targeting,
CRM, Users Proﬁling.
Innovation
Strategy
Data
Data Driven User Engagement
and Acquisition

Media Optimization:
The Opportunity
Page 2Data Strategy and Innovation

Advanced Technologies
1. CPM = Cost per Thousand
Richer data
CPM1 is gone

Data, technology, media
and campaign optimization
give us enormous
potential to make spend
more efﬁcient
and to be
more
consumer
relevant

Data Optimization:
The Opportunity

1. Websites, apps, databases, media, social networks, micro blogs, emails, forums, bbs, ofﬂine events, newspapers, magazines, outdoor, etc…
most of these activities/media/platforms provide a huge amount of powerful data to be leveraged by the organization.
•  Platforms: plenty of useful data1
•  Data value: underestimated and
not made actionable
•  Easy to improve and make it scalable
across the organization

Media and Data
Optimization:
The Opportunity

For Paid Media this means:
Assigning budget in a
scientiﬁc way and optimizing
by using data driven
solutions giving positive
impact on spending,
performances, and
achievable goals/metrics.

For Unpaid media this
means:
Keeping users highly
engaged providing them
with the right content, at
the right time, in the right
place.

So, what does this look like
today?

The Online Media landscape
in Europe
•  Complex and sophisticated
•  Opportunity to operate
similarly in China within a
simpler environment

Source: http://www.lumapartners.com/lumascapes/display-ad-tech-lumascape/
2014 Global Display
Advertising Ecosystem

How this looks in China
today...

Source: http://www.rtbchina.com/rtb-redeﬁnes-media-buying-china.html (April 2012)
The Online Media landscape
in China

DMP (Data Management Platforms) On site optimization
Ad Serving / DSP (Demand Side Platforms)
Some of the “Global/Local”
Players

Targeting
Data and Optimization
Re-targeting
Behavioral
targeting
Smart Ads
Frequency
capping
Trading desks
Data
suppliers
Demand Side
Platform
(DSP)
Audience
Expansion
(Look alike
modeling)
Key Terminologies

Smart Ads: every ad is personalized and optimized for its
viewer
Retargeting: identify users who did a previous action (whether
on 1st or 3rd party websites), and expose them to a specific ad
accordingly
Behavioral Targeting: profiling users according to their online
activities (also offline where possible).
Audience Expansion: analyzes converters and identifies similar
profiles
DSP (Demand Side Platform): centralized media buying focused
on users rather than sites.
Data Suppliers: provide data on online/offline consumers
Trading Desk: buy and optimize media and audience using DSP
Frequency Capping: limit the times a user is exposed to an ad
Some Key Terminologies

Data Strategy and Innovation
Users:
Engagement, Optimization
and Acquisition

4 Key Audience/Target
Opportunities
1.  Registered users
2. Client and/or
Partner Database
3. Referrals
4. Unregistered Users and
New Users Acquisition

Action
Decision
NoYes Follow
Diagram Definitions

Main goal:
persuade already registered users1 to activate their
account and complete their proﬁle.
Follow up:
regular targeted emails based on their declared
information and web behavior to keep them highly
engaged.
1. In this speciﬁc case once a user registers, will need a further step to activate his account (mainly will need to click on a link sent to his
email address)
Objectives

Registered
user
Coming
from
platform
X?
Send
activation msg
Check
point 2
Action 3
Check
point 3
Action 4
Proﬁle
complete
d?
Action 7
Regular emails
(opted in)
based on
behavior, or
on site
targeting
(opted out)
Engagem
ent
score
evaluati
on
Action 6
Check
point 4
Action 5
N.B. Most of the original content in the boxes (actions and decisions) has been replaced with generic content.
For more details feel free to contact me: http://www.marcodecesaris.com
Action Plan

Main goal:
target new prospects1 and increase number of
registrations. Leverage existing Client’s database(s)
More:
In order to expand users’ proﬁle (and get to know more
and better about our users – including social connections2)
would be worthy doing db match with external data
partners.
Follow up:
Build a lookalike model1 and identify similar users (potential
converters).
2. Client and/or Partner Database
Objectives
1.  Building a lookalike model based on client’s db, would allow to identify a converter’s proﬁle. Then similar users could be found within the client db, within
the partners’ db or even across the internet. This would allow to identify new prospects and increase number of registrations.
2.  Identifying social connections could be leveraged for acquiring new prospects through social targeting.

Client DB or
Partner DB
Has the
user got
a
proﬁle?
Check
point 1
Action 2
Create proﬁleAction 1
Check
point 2
Action 3
Action Plan

Defining the Opportunity

1.  For example it could be used for geo-targeting with household income level indicators.
2.  N.B. Part of the content has been removed. This database could be used for interest based targeting, on and off line geo targeting.
3.  Possibility of creating partnerships with speciﬁc enterprises and get access to their customers: e.g. “pampers” (families with babies).
Data Partner
Objectives: new users acquisition, refining existing
users profile
Main Databases:
1. National Address Database – X Millions records, covers
all the household addresses in China1
2. National Magazines Subscription Database – Y Millions
records. It includes addresses and […]2
3. National Small and Medium Size Enterprise Database –
Z Millions records. It includes the type of industry and
contact of the enterprise3
N.B. Some of the content in this slide has been removed (e.g. information in the NATIONAL MAGAZINES SUBSCRIPTION DATABASE).

Main goal:
Leveraging the data once we know
users are interested in our brand,
products, services1.
3. Referrals
1.  The assumption is that ﬁrst we collect users’ data on a hub website, not on the ofﬁcial branded website. E.g. We could collect users data on a
game platform sponsored by Brand X. Then Brand X will use the users data in order to promote its products/services to the different users
who explicitly agreed on the data usage policy.
Objectives 1/2

Follow up:
Targeting registered users with
speciﬁc messages about client’s
products or services (through
banner on the hub1 website for
example).
3. Referrals
Follow up:
Referring those users to Client’s
website (rather than to the initial
hub1 for example)
1.  The assumption is that we collect users’ data on a hub website, not on the ofﬁcial branded website. E.g. We could collect users data on a
game platform sponsored by Brand X. Then Brand X will use the users data in order to promote its products/services to the different users
who explicitly agreed on the data usage policy.
Objectives 2/2

Referrals
Has the
user got
a
proﬁle?
Action 1
Send brand
(no hub) email
accordingly
Action 2
Check
Point 1
3. Referrals
Action Plan

Getting closer to
Global Best Practices

LOW HIGHAccountability & Effectiveness
CPD/CPM CPM/CPC/CPA CPM/CPC/CPA CPM with
CPC or CPA goal
Site
Speciﬁc
Ad Network
(Vertical)
Ad Exchange
DSP Real-
Time Bidding
Evolved Online Media
Strategy

Main goal:
Unlock the potential of Smart Advertising/DSP/Data
Providers, by targeting unregistered users (on and ofﬂine)
according to their 3rd party proﬁle, 3rd party behavior
and/or engagement with hub/brand/client website.
Follow up:
Paid media1, Unpaid media2
4. Unregistered Users and
Tapping into
“Smarter Data” - Objectives
1.  Such as: Accuen, Xaxis, MediaMind, Ipinyou, Google DoubleClick, Dratio, AdSame, MyThings, CognitiveMatch, Yahoo Dapper, Criteo, etc…
2.  Such as: Omniture Test and Target, Hubspot

Off-site On-Site
So, even if we don’t know
these users…
We can learn about them…

Off-site: Media

Hub
Ad
Target Audience: Male,
20-40 years old, interested
in Finance and Sport.
Publisher 1 site
N.B. Part of the information in this slide (ﬁgures, vendor’s name, publisher’s name, and percentages) has been voluntarily replaced with generic
names and characters (e.g. X%, a%, x, y, Vendor 1, Publisher 3, etc…).
For more details feel free to contact me http://www.marcodecesaris.com
Hub site
Home Page
Page 1
Page 2
Registration
started
Example.
Off-site: Media
1.  User lands on Publisher 1
site (where we bought
display ads). User qualiﬁes
to be served our ad.
2.  Our Ad (Hub Ad) is served
to the user.
3.  User clicks on “Hub Ad” and
lands on our “Hub site”.
1
2
1
2
3
3

Hub
Ad
Publisher 1 site
Hub site
Home Page
Page 1
Page 2
Registration
started
Example.
Off-site: Media
1.  User visits several pages in
our “Hub site”, and starts
the registration process on
the registration page.
2.  User leaves the “Hub site”
without completing the
registration.
4
5
4
5

Publisher 2 site Hub site
Complete
Registrati
on
Registration
completed
Example.
Off-site: Media
Hub
Ad
Publisher 1 site Hub site
Home Page
Page 1
Page 2
Registration
started
1.  Later on the same user
visits Publisher 2 site, and
the user (cookie) is
recognized by the ad
serving system, and a
customized ad is served
accordingly.
2.  User clicks on the ad and
land directly on the
registration page of our
“Hub site”.
6
7
6
7

Hub
Ad
Publisher 1 site Publisher 2 site Hub site
Complete
Registrati
on
Hub site
Home Page
Page 1
Page 2
Registration
started
Registration
completed
Example.
Off-site: Media
1.  User completes the
registration process
starting from the point
where he left during the
previous visit.
8
8
Despite the user being not
registered with us, the system is
able to identify the user once
again across the network and then
facilitates the ﬁnal registration by
serving a customized message at
the right time in the right place.

Who are
the best
smart players
to partner with
in China?

Major Adnetworks
•  Ad Network 1
•  Ad Network 2
•  Ad Network 3
•  Ad Network 4
Major Publishers
•  Publisher 1
•  Publisher 2
•  Publisher 3
•  Publisher 4
New Users AcquisitionOpportunity.
Vendor 1: adserving in China

Major Publishers
•  Publisher 1
•  Publisher 2
•  Publisher 3
•  Publisher 4

Buying the audience is more efficient
than buying inventory
Vendor 3 IS:
A platform combining data, media, technology & strategy
A means to enhance optimization and conversion
A tool providing more efﬁciency, greater control & deeper insights
Vendor 3 is NOT:
A 3rd party company
An account servicing team
Case study
X times higher CTR than past campaigns
Y times more cost-effective trafﬁc driving
Audience reach:
XX% - YY%

On-site

1. Case study: Targeted content results in xxx% increase in registration completions; Customized content drives response rate up yy%; Click-
through rates on homepage content slot jump zzz%
N.B. Part of the information in this slide (ﬁgures, vendor’s name, and percentages) has been voluntarily replaced with generic names and
characters (e.g. X%, a%, x, y, Vendor 1, etc…).
Our website
Home Page
Our website
Sporty Red Car
Features: bla bla bla bla bla
Engine: bla bla bla bla bka bka
Wheels: bla bla bla bla bla
Price: bla bla bla bla bla bla bla
Example.
On-site retargeting
2
3
3. User clicks on
the onsite banner
and lands on the
Sporty Red Car
page.
31.  User searches
for “sporty red
car” on Baidu
and click on the
link.
2. User lands on our Home
Page and according to the
search keyword an onsite
customized banner is
displayed (sporty red car)
21
1

•  Opportunity exists for players to
adopt and leverage these
advanced marketing technologies
in the Chinese market
•  To meet Client’s objective, there is
the potential to see media spend
work harder through optimization
•  Improve performance both in terms
of cost per acquisitions and
engagement Page 45Data Strategy and Innovation
Summary

What Are The Performances
And The Cost Involved?

Minimum Recommended Budget
(monthly or by campaign)
Performances from previous case
studies
Vendor 1 CPA: 8% of display
Vendor 2
w/o Vendor 2 w Vendor 2
CTR 0.05%-0.1% 1.5%-2%
Vendor 3
CPM: - 85% VS planned
CPC: - 61% VS planned
Impressions: + 683% VS planned
Clicks: + 255% VS planned
Vendor 4 # registrations: +1000%
Vendor 5
w/o Vendor 5 w Vendor 5
CTR 0.68% 1.30%
CR 0.71% 1.09%
Vendor 6 N/A
Vendor 7 CR: 108% increase
元 = X RMB = no min budget required
N.B. Part of the information in this slide (ﬁgures, indicative budget, vendor’s name, and percentages) has been voluntarily replaced with generic
names and characters (e.g. X%, a%, x, y, Vendor 1, etc…).
Minimum Budget Required
元元
元元元元
元元元元元元元元
元元
元
元

Recommendations

Testing these new
data and technologies for marketing
and advertising
with a minimum budget.
Goal: improving KPIs of X%.

Minimum budget required:
YY RMB
Target:
X% KPIs improvement
CPC
(RMB)
CPV
(RMB)
CPL
(RMB)
Average hub historical campaign
performances
x y z
TARGET by using the suggested
data/ad technologies
(1-J%)x (1-J%)y (1-J%)z

Ad/Data Technology Budget allocated (RMB)
Vendor 1 X RMB (over y months)
Vendor 2 X RMB (over z months)
Vendor 3 X RMB (over z months)
Vendor 4 X RMB + production cost
Vendor 5 X RMB ( j campaigns)
Vendor 6 X RMB ( j campaigns)
TOTAL XX RMB
Budget Allocation

No
Data/Ad
Technology
Duration M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12
1.1 Vendor 1 x months
1.2 Vendor 2 y campaigns
2 Vendor 3 y months
3 Vendor 4 y months
4 Vendor 5 y months
5 Vendor 6 y months
DSP
DMP
AS
DSP
AS
AS
Media Spending Timeline
(by month)

Thank You

EXPERTISES:
Data Strategy & Planning,
Custom Data Solutions,
Marketing Technologies,
Advertising,
Advanced Targeting,
Optimization,
Smart Advertising,
DSPs,
Measurement,
CRM,
Social Media,
Marketing,
Insights and Analytics,,
Innovation.
COMPANIES/CLIENTS I worked for:
About me

http://www.linkedin.com/in/marcodecesaris
Contact details
Marco De Cesaris

Backup

Vendors:
Cost and Timeline

Vendor 1
Ad Serving and Retargeting

TIMELINE: from x to y working days to setup
COST: it varies between X% of media spend and CPM model if
creative size > 40Kb
BUDGET: no minimum budget required
<ZKb
YYY: X%
>=ZKb
CPM
Reach:
XX%
Vendor 1
元

Vendor 2
Ad Serving and Retargeting

TIMELINE: from x to y working
days to setup
COST: Client usually pay by CPM.
Vendor 2 will put down the media
list, the CPM price, the estimated
CPC, impressions, and estimated
clicks etc in the media plan.
CPM:
X to Y
RMB
CPC:
Z to J
RMB
BUDGET: suggestion is to spend X
RMB per campaign (assuming 1
campaign per month)
¥ ¥¥ ¥ ¥
M1 M2 M3 M4 M5
¥ ¥¥ ¥ ¥
¥ ¥¥ ¥ ¥
Vendor 2 Reach:
YY%

Vendor 3
New Ad Technologies

TIMELINE: x working days to setup
COST: min y%; max z% of media spend
AS: a% M&A: b% AC: y%
BUDGET: recommendation is to spend J RMB over k months
¥ ¥¥ ¥ ¥
M1 M2 M3 M4 M5
Vendor 3 Reach:
ZZ%

Vendor 4
New Ad Technologies

TIMELINE: x to y working days to setup
COST: vendor 4 will make proﬁt out of the media spent. Details
not released.
BUDGET: min. Z RMB per month
¥ ¥¥ ¥ ¥
M1 M2 M3 M4 M5
¥ ¥¥ ¥ ¥
?
Vendor 4 Reach:
JJ%

Vendor 5
New Ad Technologies

COST: min y%; max z% of
media spend, depending on ﬁle
size and other add on
BUDGET recommended: >X RMB
M1 M2 M3 M4 M5
¥ ¥¥ ¥ ¥
¥ ¥¥ ¥ ¥
¥ ¥¥ ¥ ¥
¥ ¥¥ ¥ ¥
¥ ¥¥ ¥ ¥
¥ ¥¥ ¥ ¥
¥ ¥¥ ¥ ¥
¥ ¥¥ ¥ ¥
size 1
Cost: y%
size 2
Cost: z%
Clicks
tracking:
a%
Imps +
clicks
tracking:
b%
Add on:
c%
Vendor 5 Reach:
XX%

Vendor 6
OFF-LINE
Data Partnership

COST: y-z RMB per record
BUDGET: min. X RMB per campaign (assuming 1 campaign per month)
y-z ¥
¥ ¥¥ ¥ ¥
M1 M2 M3 M4 M5
¥ ¥¥ ¥ ¥
Reach:
>Y Mio
Vendor 6

In House Optimization: Site and CRM
Vendor 7

COST: initial setup X RMB + Y Mio server calls Z RMB
BUDGET: minimum J RMB over the ﬁrst 12 months, then
K RMB per year
Y MM
adcalls:
Z ¥
setup:
X ¥
Vendor 7
¥ ¥¥ ¥ ¥
M1 M2 M3 M4 M5

Credits
Page 2: http://upload.wikimedia.org/wikipedia/commons/f/f0/DARPA_Big_Data.jpg
Page 3: http://www.flickr.com/photos/uscensusbureau/6878356946/sizes/o/
Page 4: http://www.flickr.com/photos/scobleizer/4695267529/
Page 4: http://www.flickr.com/photos/68751915@N05/6355840185/
Page 21: http://www.flickr.com/photos/gsi-r/5341765493/sizes/l/
Page 33: http://www.flickr.com/photos/rohypnol/854518562/sizes/o/
Page 34: http://www.flickr.com/photos/mkhmarketing/8468788107/sizes/o/
Page 43: http://upload.wikimedia.org/wikipedia/commons/7/75/Internet1.jpg
Page 26: http://upload.wikimedia.org/wikipedia/commons/4/4c/Clouds_double_exposure.jpg
Page 31: http://upload.wikimedia.org/wikipedia/commons/1/1c/Evolution-des-wissens.jpg
Page 44: https://www.flickr.com/photos/motosclasicas/8726158779/sizes/o/

Credits
Page 52: http://www.flickr.com/photos/32307961@N06/3525061913/sizes/o/
Page 53: http://commons.wikimedia.org/wiki/File:Ignorance_is_bliss_-_shortbread_cookie_with_a_smile.jpg
Page 54: http://farm7.staticflickr.com/6109/6290003115_7788c41563_b_d.jpg
Page 56: http://www.flickr.com/photos/pathfinderlinden/7155072088/sizes/o/
Page 58, 60: https://c2.staticflickr.com/4/3346/3573644189_4fde8bbb9e_z.jpg?zz=1
Page 49: http://www.flickr.com/photos/snre/6946913449/sizes/k/
Page 50: http://www.flickr.com/photos/teegardin/6093690339/sizes/l/
Page 51: http://www.flickr.com/photos/comedynose/5043010086/sizes/o/
Page 48: http://www.flickr.com/photos/dgoomany/4976873914/sizes/o/
Page 46: http://www.flickr.com/photos/teegardin/5912231439/sizes/o/
Page 45: http://upload.wikimedia.org/wikipedia/commons/5/5b/Checkmate.jpg

Credits
Page 76, 77, 78, 79, 80, 81: http://upload.wikimedia.org/wikipedia/commons/4/4f/Copyright-_all_rights_reserved.png
Page 72: https://www.ﬂickr.com/photos/monana7/324669781/
Page 70: https://www.ﬂickr.com/photos/beantin/7649183772/sizes/l/in/photostream/
Page 68: http://upload.wikimedia.org/wikipedia/commons/f/f7/Sant'Olcese-villa_Serra_di_Comago-interno.jpg

Note
The following list of images are images used in this
presentation. I would like to thank the owners of
those images as those images perfectly match
the content of this presentation. I tried to look
for similar images covered by cc license but it
was practically impossible to ﬁnd suitable ones
able to replace the below list.
Hence I decided to use those original images (or slightly adapted) where the
copyright logo is clearly missing.
Despite of it, it could happen that I have to remove those images at a later
stage if I am asked to do so by the owners of the images.
Other Credits

http://www.gooddata.com/images/uploads/big-data-image.jpg
Other Credits
Page 7: http://www.befragungsinstitut.org/wp-content/uploads/2013/05/qm_97931741.jpg
Page 1: http://www.fromquarkstoquasars.com/wp-content/uploads/2013/02/blue-binary-code-jigsaw-puzzle.jpg
Page 6: http://www.filmofilia.com/wp-content/uploads/2011/06/moneyball_16.jpg
Page 8: http://media.cleveland.com/pdq_impact/photo/einsteinjpgjpg-4a389e85f92a0547.jpg
Page 9: http://nousygihs.files.wordpress.com/2011/03/youth_excited.jpg
Page 11: http://www.struggletovictory.com/wp-content/uploads/2012/03/Simple-4.jpg
Page 8: http://lh3.ggpht.com/_089TXf8rQcw/Si6S2nlmEVI/AAAAAAAABW0/WEkML4LGWS0/ing_2%5B4%5D.jpg?imgmax=800
Page 8: http://www.iconsdb.com/icons/download/white/accept-database-512.gif
Page 8:
http://colouringbook.org/SVG/2011/COLOURINGBOOK.ORG/
chovynz_money_bag_icon_black_white_line_art_scalable_vector_graphics_svg_inkscape_adobe_illustrator_clip_art_clipart_coloring_book_colouring-555
px.png

http://mystrategicplan.com/wp-content/uploads/2013/08/Glossary17.jpg
Other Credits
Page 16: http://www.bluebumblebee.co.uk/wp-content/uploads/2013/04/file0001817248786.jpg
Page 18: http://strategicsalesmarketingosmg.files.wordpress.com/2012/06/shutterstock_59234440.jpg
Page 19: http://marketwave-site.crane-west.net/wp-content/uploads/2012/01/hires.jpg
Page 20: http://michele-norris.com/wp-content/uploads/2012/02/writing-pencil.jpg
Page 22, 24, 29: http://charmedyogi.files.wordpress.com/2013/06/what-if.jpg
Page 23: http://profilesasiapacific.com/blog/wp-content/uploads/2013/05/puzzle.jpg
Page 12: http://www.sgeier.net/fractals/fractals/11/Tetris.jpg
Page 13: http://1.bp.blogspot.com/-X2AX0IVb4fA/T0hEbdVRhYI/AAAAAAAADQw/8GFG8bUv4ds/s1600/933320_13694080.jpg
Page 15: http://www.cindysfriendlytavern.com/PageArt/j0403725.jpg

http://1.bp.blogspot.com/_JtlCE5dJtCI/SwlJbxOGq0I/AAAAAAAAAlQ/rN-_Fc7yGhI/s1600/digital+1.gif
Other Credits
Page 32: https://news.slac.stanford.edu/sites/default/files/images/announcement/data-brain.jpg
Page 39: http://cina.quotidiano.net/wp-content/uploads/2011/11/yao-ming.jpg
Page 40: http://www.ontariosystems.com/sites/default/files/Ontario_Handshake.jpg
Page 41: http://www.ips-analytics.com/uploads/media/ips_technology_partners.jpg
Page 42: http://www.msktc.org/lib/docs/Reach_Your_Audience.jpg
Page 25: http://www.sam-welch.com/wp-content/uploads/2013/03/Defining-Successful-Data-Management-Programs.jpg
Page 27: http://www.indire.it/immagini/immag/scienzets/ts731-20.jpg
Page 55: http://www.cherryprinthd.co.uk/wp-content/uploads/blog/business-cards.jpg
Page 57, 59, 61, 63, 65, 67, 69, 71: http://boeddhamagazine.nl/wp-content/uploads/2013/10/6812481635_ed463ae1fa_b-800x600.jpg

, 64, 66: http://sequoiag.com/upload/iblock/c57/c57fbdb6c2674749702f45941f8082de.jpg
Other Credits

China: Data Driven User Engagement and Acquisition

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