The document discusses mobile market trends in Hong Kong and case studies on optimizing mobile advertising campaigns. It finds that iPhone market share grew while Android declined, and the top devices are large screen phones. Video and social media see most mobile traffic. Case studies on a men's shaving product and mobile game app showed that optimizing ad placement based on user behavior analysis and testing multiple audience targeting strategies improved click-through rates by 2-3 times compared to non-targeted campaigns. Data-driven optimization of ad content and timing to match user scenarios and interests was found to be effective.

3. ✦ Hong Kong Mobile Market
✦ Users Behavioral Pattern
✦ Mobile Campaign Optimization
✦ Case Study Men’s Shaving Product
✦ Case Study Mobile Game App Download

4. Hong Kong Mobile Market

5. iPhone
34.5%
iPad
3.5%
Android
62.0%
62% Android
Compare to the last quarter, the share of
android users shows a drop from 80% to
62%, and it possibly reflects that some of the
android users switch to iPhone
The growth of iPhone is contributed to the
new iPhone 6 series and the iPhones starts to
regains its large screen phone market.
Android Vs iOS
Source: UU, Q1, 2015, Vpon Inc.

6. iPhone
being the most influential rival to Android
phones, four of the iPhone models hits top
six. In contrast, the Android phones are
highly fragmented, the number of unique
users is diluted by different phone
manufacturers
iPhone 6
iPhone 5S
Samsung Galaxy Note3
iPhone 6Plus
Samsung Galaxy Note2
iPhone5
Samsung Galaxy S3
Xiaomi Redmi Note
Samsung Galaxy Note 4
LG G3
Unique Users
4.0”
5.5”
5.0”
5.5”
Top 10 Devices
4.7”
5.7”
5.7”
5.5”
5.5”
4.0”
4.8”
Source: UU, Q1, 2015, Vpon Inc.

7. SAMSUNG
remains the market leader and 5 of its
phones hits top 10, its board portfolio
caters to the fragmented screen size market
LG
earns a major share of the market by its
phablet phones after Samsung. G3 is the
most popular among the portfolio
PHABLET
9 top 10 phones are Phablets whose screen
size are within 5’’ - 6.9’’. The screen size of
the top phablet models lie between 5’’ - 5.9’’
Samsung Galaxy Note3
Samsung Galaxy Note2
Samsung Galaxy S3
Xiaomi Redmi Note
Samsung Galaxy Note 4
LG G3
Samsung Galaxy S5
Sony Xperia Z1
LG G Pro2
LG G2
Unique Users
4.8”
5.5”
5.5”
5.7”
5.1”
5.2”
5.0”
5.0”
Top 10 Android Devices
5.7”
5.9”
Source: UU, Q1, 2015, Vpon Inc.

8. iPHONE 6 46.7%
iPhone 6 and iPhone 6Plus were released
on 19 Sep, 2014, soon it becomes the most
popular iOS devices and it is still growing
It tells that the large screen size strategies
fits perfectly to Hong Kong users and it
makes a success. Within the iPhone 6/6Plus
users, 43% users are using the 5.5” iPhone
6 Plus which is slightly fewer than that of
the standard 4.7” iPhone 6
iPad Mini
6%
iPad
8%
iPhone 4
5.1%iPhone 6
46.7%
iPhone 5
34.5%
iOS Devices
Source: UU, Q1, 2015, Vpon Inc.

9. Others
1%
Chrome Mobile
41.7%
Safari
20.4%
Android Browser
37.2%
41.7% CHROME
Chrome takes the biggest share and from
the trend observed in 2014, we expect it’s
growth should continue
Mobile Browsers
Source: UU, Q1, 2015, Vpon Inc.

10. Wan Chai
6%
Eastern
5%
Southern
2%
Central and West
6%
Outlying
2%
Sai Kung
5%
Kwai Tsing
8%
Tuen Wan
4%
Sha Tin
9%
Tuen Mun
7%
Tai Po
5%
Northern
3%
Yuen Long
5%
Wong Tai Sin
3%
Kwun Tong
6%
Sham Shiu Po
7%
Yau Tsim Mong
7%
Kowloon City
10%
48.2% N.T
New Territories shares near half of the
traffic, Kowloon shares 33% while Hong
Kong Island shares 18.8%
Hong Kong 18 Districts Traffic
Source: UU, Q1, 2015, Vpon Inc.

11. 54% in 7 DISTRICTS
Central and Western, Yuen Long and Yau Tsim Mong, Shatin, Kowloon City, Kwai Tsing and
Tsuen Wan are districts sharing top traffic which is expected to be generated by the mainland
tourists
Simplified Chinese Apps Location-Based Traffic
Source: UU, Q1, 2015, Vpon Inc.

12. Others
2.7%
SmarTone
20.0%
Hutchison
18.1%
China Mobile
21.2%
csl.
38.0%
38% csl.
Since the merge of CSL and PCCW, it
becomes the biggest player among all
Each of four operators provides 4G LTE
services
Network Operators
Source: UU, Q1, 2015, Vpon Inc.

13. Users Behavioral Pattern

14. 61% Video
on Wifi
Users prefers to use wifi for
apps in video category while
to use cellular for apps News
and Life categories
0%25%50%75%100%
Video
Entertainment
News
Life
Social
Education
Finance
Technology
Travel
53%54%52%54%53%
41%40%
50%
61%
47%46%48%46%47%
59%60%
50%
39%
Cellular Wifi
Mobile Internet Access
Source: UU, Q1, 2015, Vpon Inc.

15. PEAK HOURS at 1300, 1800 and 2200
Android and iPhone traffic spikes at lunch time at 12 am - 1 pm and night time 8 - 10 pm
iPad traffic increases drastically from 8 - 12 pm and in night time
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Android iPad iPhone
Hourly Traffic by Carriers
Source: UU, Q1, 2015, Vpon Inc.

16. Others
2.7%
Life
14.8%
News
16.8%
Entertainment
29.6%
Video
36.1%
36.1% Video
Video, Entertainment and News
categories takes the major share,
55% of Android Traffic
Android Apps Categories
Source: UU, Q1, 2015, Vpon Inc.

17. Others
1%
Life
11.6%
Entertainment
4.3%
News
43.2%
Video
40.1% 40.1% Video
With the larger phone screen size iPhone 6/6Plus,
video traffic shows a significant increase. It also
reveals the potential for more video ad on iPhone
iPhone Apps Categories
Source: UU, Q1, 2015, Vpon Inc.

18. 65.3% Video
Most of iPad users download video Apps for
Taiwan, Korea, China and Japan TV series or
movies
The large screen allows better video and
gaming experience and therefore the two
category shares almost more than 77% trafficOthers
12.0%
Life
10.3%
Entertainment
12.5%
Video
65.3%
Source: UU, Q1, 2015, Vpon Inc.
iPad Apps Categories

19. d
Mobile Campaign
Optimization

20. Three Influential Factors to Advertising Effectiveness
? ? ?

21. Mobile Advertising Effectiveness Hinges on
Consumer’s Scenario

22. Case Study
Men’s Shaving Product

23. Source: CTR, 2015, Vpon Inc.
First step to optimization is to observe, the campaign was subjected to no specific targeting and
to all app categories. 6 days was allowed for observation and formulating lookalike audience
strategies. Category B was selected for banner optimization and Category B and C were
selected for interstitial optimization based on the outstanding CTR performance.
Pre-optimization: Observation on different categories performance
Daily CTR(%)
on Various Categories - Banner
0
0.25
0.5
0.75
1
Campaign Run Time/Day
1 2 3 4 5 6
Category A Category B Category C Category D
Daily CTR(%)
on Various Categories - Insterstitial
0
3.25
6.5
9.75
13
Campaign Run Time/Day
0 1 2 3 4 5
Category A Category B Category C Category D

24. Source: CTR, 2015, Vpon Inc.
The CTR performance hinges on the unique behavioral patterns of audience, and hence
performance was track and compared. Lookalike audience A whose CTR average at 0.78% was
1.6 times better than normal ad allocation (non-specific audience) whose CTR average at 0.3%.
Due to the instability of the CTR of lookalike audience B, CTR fluctuation over a wide range
across days, optimization on lookalike audience B was forgone. Such lookalike optimization
was not adopted in interstitial since category optimization outperformed its lookalike
optimization.
Pilot Run of Two Lookalike Audience Optimization
Daily CTR(%) of Two Lookalike
Audience - Banner
0
0.35
0.7
1.05
1.4
Campaign Run Time/Day
1 2 3 4 5 6
Lookalike Audience A Lookalike Audience B Lookalike
Audience A
Lookalike
Audience B

25. Source: CTR, Q1 2015, Vpon Inc.
The step-wise optimization takes times, 10 days in this case, to formulate its best fit strategies -
prioritizing ad allocation to the combination of the best performing app categories and best
lookalike audience A. At the period of optimized allocation, the average CTR is 1.07% which 2.6
times better than non-specific targeting.
Daily CTR of Banner
0
0.35
0.7
1.05
1.4
Campaign Run Time/Day
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Optimized Allocation
Pre-optimization:
observation
Pilot-Run on two
lookalike audiences
Optimized
Allocation

26. Source: CTR, Q1 2015, Vpon Inc.
Same process like banner optimization was gone through in interstitial, yet lookalike
audiences were forgone, the optimization was formulated by prioritizing ad allocation to the
best performing app category and the campaign was continually optimized. At the period of
optimized allocation, the average CTR is 10.22% which 2.4 times better than non-specific
targeting.
Daily CTR of Interstitial
0
3
6
9
12
Campaign Run Time/Day
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Optimized Allocation
Pre-optimization;
observation
Pilot-Run on two
lookalike audience
Optimized
Allocation

27. Case Study
Mobile Game App Download

28. Campaign Optimization Trilogy:
Data Collection-Multi-Audience Sample-Strategy Adjustment
Incentive ads

29. App Retention Rate Should Come First than App Download Rate
Incentive ads

30. Simultaneous Delivery of Multiple Ad Material;
Profound Insight to Consumer’s Preference

31. Maximize Campaign Effectiveness According to Different Scenarios

32. Formulate Campaign Strategy Based on Observation of
Consumers’ Interests and Habits

33. Campaign Optimization by Big Data