vpon 2015q1 hong kong mobile market statistics and trends

✦ Hong Kong Mobile Market

✦ Users Behavioral Pattern

✦ Mobile Campaign Optimization

✦ Case Study Men’s Shaving Product

✦ Case Study Mobile Game App Download

Hong Kong Mobile Market

iPhone34.5%

iPad3.5%

Android62.0%

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.

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.

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.

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 Mini6%

iPad8%

iPhone 45.1%iPhone 6

46.7%

iPhone 534.5%

iOS Devices

Source: UU, Q1, 2015, Vpon Inc.

Others1%

Chrome Mobile41.7%

Safari20.4%

Android Browser37.2%

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.

Wan Chai6%

Eastern 5%

Southern2%

Central and West6%

Outlying2%

Sai Kung5%

Kwai Tsing8%

Tuen Wan4%

Sha Tin9%

Tuen Mun7%

Tai Po5%

Northern3%

Yuen Long5%

Wong Tai Sin3%

Kwun Tong6%

Sham Shiu Po7%

Yau Tsim Mong7%

Kowloon City10%

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.

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.

Others2.7%

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.

Users Behavioral Pattern

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

%10

0%

Vide

o

Ente

rtain

men

t

New

s

Life

Soci

al

Educ

atio

n

Fina

nce

Tech

nolo

gy

Trav

el

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.

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.

Others2.7%

Life14.8%

News16.8%

Entertainment29.6%

Video36.1%

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.

Others1%

Life11.6%

Entertainment4.3%

News43.2%

Video40.1% 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.

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%

Entertainment12.5%

Video65.3%

Source: UU, Q1, 2015, Vpon Inc.

iPad Apps Categories

d

Mobile Campaign Optimization

Three Influential Factors to Advertising Effectiveness

Mobile Advertising Effectiveness Hinges on Consumer’s Scenario

Case StudyMen’s Shaving Product

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

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

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

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

Case StudyMobile Game App Download

Campaign Optimization Trilogy: Data Collection-Multi-Audience Sample-Strategy Adjustment

Incentive ads

App Retention Rate Should Come First than App Download Rate

Incentive ads

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

Maximize Campaign Effectiveness According to Different Scenarios

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

Campaign Optimization by Big Data