Vpon Taiwan Mobile Ad Statistics and Trends Q2 2014

Vpon 行動廣告 � 2014年第二季 台灣行動市場數據報告�

Agenda

Taiwan Mobile Market - Mobile device market share (smartphone, tablet and mobile browser)

User Behavior Analysis - Location, telecommunication companies, online behavior, App usage analysis

Special Occasions - Dragon Boat Festival, Taipei metro attack, World cup 2014

Video App User Analysis - Video App user analysis

Big Data Analysis and Application - Using mobile data to optimize performance

Vpon in Taiwan

17 Million Unique users per month

2 Billion Requests per month

1,000 Renowned advertisers

Source: UU, Q2 2014, Vpon Inc.

Vpon in Asia

Pan Asia Mainland China, Hong Kong, Macau, Taiwan, Singapore, Malaysia, Thailand, Philippines, Indonesia, Japan and Korea.

10 Billion Impression per month

Source: UU, Q2 2014, Vpon Inc.

Vpon SDK

Multi platform Support iPhone / iPad / Android / Mobile website.

SDK 4 Provide CPM & CPC Ad revenue. Comply with MRAID standard for international mobile Ad.

MRAID, Mobile Rich Media Ad Interface Definitions, Source: http://www.iab.net/mraid

Mobile device share

14%�

81%�

5%�

iPhone� Android� iPad�

Source: UU, Q2 2014, Vpon Inc.�

Android : iOS = 8 : 2

l  The number of Android devices grows comparing to previous quarter.

l  With relatively friendly prices and more new device model choices, Android devices grow rapidly all over Asia (South Korea, China, and Malaysia). Meanwhile, the global platform ratio is also close to 8:2.

Android device share

46 � 44 � 43 � 46 �

11 � 10 �

Q1� Q2�

Phone� Phablet� Tablet�

Phablet: http://en.wikipedia.org/wiki/Phablet, Source: UU, Q2 2014, Vpon Inc.

Phablet rock�

l  Phablet becomes the main choice of Android users. 46% users own 5”-6.9” phablet whereas 44% users with 4.9” phones.

l  Android tablet with screen size larger than 7” get 10% users.�

%�

Top 10 Android models�

Source: UU, Q2 2014, Vpon Inc.�

Samsung Note 3 & S4

l  Phones listed in 2013: HTC One, Samsung Note 3 & S4, Sony Xperia Z, Samsung Note 3 & S4 is included in the Top 10 for the first time.

l  Samsung Note 3 took the fifth place after listing only for 8 months, showing the strong demand of the big screen mobile phones. Half phones of Top 10 are all the big screen mobile phones.

l  4.7 "- 5" is most widely used screen. �

Samsung Note 2�

Samsung S3�

HTC One�

Samsung S2�

Samsung Note 3�

HTC Butterfly�

Samsung S4�

Samsung Tab 2�

Sony Xperia Z�

HTC One X�

5.5”�

4.8”�

4.7”�

4.3”�

5.7”�

5”�

5”�

7”�

5”�

4.7”�

2013/3/29�

2013/10/29�

2013/4/25�

2013/3/6�

Android OS versions share

Froyo 0.4%

Ginger Bread 9.7%

Honey Comb 0.3%

Ice Cream Sandwich

11.4%

Jelly Bean� 64.2%�

Kitkat 13.9%

Source: UU, Q2 2014, Vpon Inc.�

Most people use Android 4.3

l  4.3.x Jelly Bean has accounted for 64.2%, is still the mainstream Android version in Taiwan.

l  The proportion of 4.4x Kitkat increased from 4% of last quarter to 13.9% of this quarter, showed that the consumers generally pursued to update the system.

l  The proportion of 2.x Froyo and Ginger Bread dropped sharply, only for 10.1%.

iOS Device Share

74 � 64 �

18 � 26 �

8 � 10 �

Q1� Q2�

iPhone� iPad� iPad mini�

Source: UU, Q2 2014, Vpon Inc.�

Increase of iPad Share

l  More people prefer to use tablets to connect the Internet. iPad Air and iPad mini 2 launched in 2013, lead to a replacement trend for the iOS tablets. The usage is enhanced due to the smaller size and easy to carry.�

%�

Top 10 iOS models

iPhone 5

iPhone 5s

iPhone 4s

iPad 3

iPhone 4

iPad mini 1

iPad 2

iPad Air

iPad 4

iPad mini 2

Source: UU, Q2 2014, Vpon Inc.

Most people use iPhone 5

l  Currently, the iPhone 5 is the most popular one of iOS mobile phone. iPhone 5s listed in 2013, took the second place rapidly, and quickly replaced iPhone 4s.

l  More and more people prefer to use iPad. There are six models of iPad and iPad mini on the list, showing the strong demand of iPad.�

iOS versions share

iOS 5� 4%�

iOS 6� 16%�

iOS 7� 80%�

Source: UU, Q2 2014, Vpon Inc.�

iOS 7 is the mainstream

l  iOS 7 is built in iPhone 5s and 5c. The share increases due to the growing penetration of the new phones. Since becoming available for download in September 2013, iOS 7 has become the most popular version taking 80% share.

No.1� No.2� No.3� No.4� No.5�

No.6� No.7� No.8� No.9� No.10�

iPhone 5�

Samsung Galaxy S2�

iPhone 4S� Samsung Note II�

iPhone 4� HTC Butterfly�

iPhone 5S� Samsung Galaxy S3�

HTC One� Samsung

Note 3�

Top 10 mobile device�

Source: UU, Q2 2014, Vpon Inc.�

l  Although the iPhone 5 is the most popular one, there are 4 Samsung mobile phones listed in Top 10. Samsung remains the most popular brand.�

Mobile browser share

Safari 23%

Android Browser

72%

Chrome Mobile

5%

Source: UU, Q2 2014, Vpon Inc.�

Built-in browser has the highest usage

l  The users prefer to use Android or iOS built-in browsers to access to the Internet.

l  About 5% of users download Chrome, as a commonly used mobile phone browser.�

User Behavior Analysis

Regional analysis of mobile devices

Northern� 52.2%

Middle� 23.0%

Southern� 21.1%�

Eastern� 3.4%�

Outling Island� 0.3%�

Source: UU, Q2 2014, Vpon Inc.�

Mobile devices become more popular l  In this quarter, there is no obvious

regional distribution change. Mobile devices penetration continues to grow in Taiwan. According to the latest survey of Institute for Information Industry, the market share of mobile devices has reached 58.7%. (2014/6)�

� �

Operators analysis

CHT� 37.3%�

Far Eastone� 28.6%�

Taiwan Mobile� 27.5%�

Vibo� 4.1%�

Asia Pacific� 2.5%�

Source: UU, Q2 2014, Vpon Inc.�

The market share of operators might change � l  Top 3 operators cover 93.4% share,

Chunghwa is the top one, but the market share has declined slightly over the previous quarter, followed by Far Eastone Telecom.

l  Asia Pacific telecom provides EVOD Internet service, Vibo telecom provides WCDMA Internet service, the penetration rate of both is 6.6%, has increased slightly.

l  Due to the emergence of 4G, the market share of operators may change in the future.�

Mobile internet access�

50.5 � 44.7 �

49.5 � 55.3 �

Q1� Q2�

3G� Wi-Fi�

Source: UU, Q2 2014, Vpon Inc.�

Special occasions affect the approach to connect� l  Compared with the last quarter, the

penetration of Wi-Fi increased, similar to the end of last year. With the influence of the holidays and recent events, the penetration of 3G or Wi-Fi changed slightly. (ex. Chinese New Year & Sunflower Event result in more often outdoor activities. So 3G was more than Wifi in Q1)

l  Along with growth of the video demand, the Wi-Fi penetration increased. Will see what happen after the launch of 4G.�

%�

Weekday usage pattern

0� 1� 2� 3� 4� 5� 6� 7� 8� 9� 10� 11� 12� 13� 14� 15� 16� 17� 18� 19� 20� 21� 22� 23�

Wi-Fi� 3G�

Source: UU, Q2 2014, Vpon Inc.�

Use 3G during the day, Wi-Fi after 8PM l  3G was adopted more often than Wi-Fi during the office and school hours. It

generally begins to increase at 7AM in the morning, reaches the peak at noon.

l  Wi-Fi usage begins to increase after 8PM, 1 hour ahead compared to last quarter. Generally reaches the peak at 10PM, showing that the online behavior is closely related to the consumer's lifestyle. �

Weekend usage pattern

Source: UU, Q2 2014, Vpon Inc.�

Usage at night increases, obviously begins to increase after 9PM l  Mobile Internet usage begins to increase after 9pm. due to days off during the

weekends. No big difference between 3G and Wi-Fi usage during the day time.

l  Usage of Wi-Fi is more than 3G in the evening. And both of them increased at night, showing that people often sleep late during weekends.�

0� 1� 2� 3� 4� 5� 6� 7� 8� 9� 10� 11� 12� 13� 14� 15� 16� 17� 18� 19� 20� 21� 22� 23�

Wi-Fi� 3G�

0� 1� 2� 3� 4� 5� 6� 7� 8� 9� 10� 11� 12� 13� 14� 15� 16� 17� 18� 19� 20� 21� 22� 23� 0� 1� 2� 3� 4� 5� 6� 7� 8� 9� 10� 11� 12� 13� 14� 15� 16� 17� 18� 19� 20� 21� 22� 23� 0� 1� 2� 3� 4� 5� 6� 7� 8� 9� 10� 11� 12� 13� 14� 15� 16� 17� 18� 19� 20� 21� 22� 23�

Mobile devices usage patterns

Source: UU, Q2 2014, Vpon Inc.�

iPad usage at night is higher l  iPad usage begins to increase after 9PM and continue to 1AM of the next day,

showing that the tablet usage when users at home is higher.

l  Compared with the Android systems, iPhone usage reached the peak around 7PM. Due to the smaller screen of iPhone, tablet usage increases from 6PM. However, Android does not have this issue due to the larger screens.�

Android iPhone iPad�

Android most popular apps�

Education� 0.2%� Entertain

ment� 35.4%�

Finance� 0.9%�

Life� 15.3%�

News� 3.1%�

Social� 4.6%�

Tech� 4.1%� Travel�

7.2%�

Videos� 29.1%�

Source: UU, Q2 2014, Vpon Inc.�

Android users prefer to use the entertainment Apps

l  Android users mostly use the entertainment Apps covering 35.4%. Followed by the audio, 29.1%. This may be related to the bigger screen of Android phone.

l  15.3% of Android users often use food, lottery, bus, beauty makeup, clothing and other life Apps.�

iPhone most popular apps

Education� 0.1%�

Entertainment

28.7%

Finance� 0.2%�

Life 29.5%

News� 5.2%� Social

8.1%

Tech� 0.2%� Travel�

0.8%�

Videos 27.1%

Source: UU, Q2 2014, Vpon Inc.�

iPhone users prefer to use the life Apps

l  iPhone users mostly use the life and other Entertainment Apps, including life, food, beauty makeup and clothing Apps.

l  Less users go for video Apps due to the smaller screen size.�

iPad most popular apps

Education� 0.2%�

Entertainment�

20.6%�

Finance� 0.2%�

Life� 5.0%�

News� 3.2%�

Social� 9.0%�

Tech� 0.2%�

Travel� 0.4%�

Videos� 61.3%�

Source: UU, Q2 2014, Vpon Inc.�

iPad users prefer to see the videos

l  More than 60% of iPad users download video related Apps, including Taiwanese, Korean, Mainland and Japanese dramas. Followed by the entertainment, games and other Entertainment Apps.

l  Unlike the mobile phone use behavior, iPad users prefer to use video related Apps compared with the iPhone and Android.�

Special Occasions

Thu� Fri� Sat� Sun� Mon� Tue�

Mobile traffic in Dragon Boat Festival

Dragon Boat

Festival�

Consecutive holidays brings traffic up l  As usual of consecutive holidays, mobile traffic goes up. The dragon boat

festival of this year has only three holidays, while comparing to the same period of the week before, traffic grows 1.2 times.�

Source: UU, Q2 2014, Vpon Inc.�

5/22-5/27� 5/29-6/3�

Mobile traffic during metro attack

Tue Wed Thu Fri Sat Sun Mon

Metro Attack�

5/13-5/19� 5/20-5/26�

Source: UU, Q2 2014, Vpon Inc.�

News affect mobile traffic l  By studying the mobile traffic around Banqiao and Nangang line, it is found

that the traffics is relatively smaller(-11%) than the week before. It may implies that relatively fewer people take Metro and use mobile on Metro that week than the week before.�

(the mobile traffic around Banqiao and Nangang line)�

6/29� 6/30� 7/1� 7/2� 7/5� 7/6� 7/9� 7/10�

5/29-6/10　� 6/29-7/2(Ro16), 7/5-7/6 (Ro8), 7/9-7/10 (Ro4)�

FIFA inflates mobile traffic by 1.15 time l  Comparing the traffic of the week before, the mobile traffic during the World Cup has

significantly increased. Especially for the first day round of 16, users have higher attention to the game. Moreover, for the games involving Argentina(7/2 & 7/6), the same upward trend is observed. The games started earlier at 12AM shed some light to the reason why and it also reflects that Taiwanese’s preference to Argentina and the famous player Messi. The traffic grow in apps of video, social and news categories.

l  While comparing to the traffic to rounds of 4, the traffic increase was minimal. The reason can be the timing of the games which only started at 4AM and it logically follows that users using mobile when watching the games is correct. �

Source: UU, Q2 2014, Vpon Inc.�

Mobile traffic during FIFA �

0� 1� 2� 3� 4� 5� 6� 7� 8� 9� 10� 11� 12�

6/8(日)� 7/13(日) Brazil vs. Holland �

Mobile traffic of FIFA quarter final�

Source: UU, Q2 2014, Vpon Inc.�

Lower attention to quarter final l  By comparing the mobile traffic of the week before and that of quarter

quarter final, it shows users did not pay extraordinary attention to the quarter final. The traffic is comparable to normal days. �

Game Starts�

0� 1� 2� 3� 4� 5� 6� 7� 8� 9� 10� 11� 12�

6/9(一)� 7/14(一) Germany vs. Argentina�

Mobile traffic of FIFA final�

Source: UU, Q2 2014, Vpon Inc.�

FIFA inflates 1.2 times in 3 hours l  Mobile traffic surges significantly during the 3 hours around the games and

reach 1.2 times to the traffic of normal days. The closer to the end of the game, this higher the traffic, it shows users highly cared about the results. The average traffic remains high until 6AM and back to normal�

Video App User Analysis

Sponsored by�

Mobile video, man VS women

34%�

35%�

16%�

15%�

31%�

52%�

4%�

13%�

Taiwanese�

Korean�

Japanese�

Chinese�

Men� Women�

Source: iOS, UU, Q2 2014, Vpon & CHOCOLABS�

Women fall for Korean drama l  Most user watch the Korean drama

and women prefers the Korean drama to 52%

l  Taiwanese drama are popular l  Men prefers work related Japanese

drama series�

Mobile video top 10

Rank� Drama� Men’ Top 10�

1� KR� 來自星星的你�

2� KR� 奇皇后�

3� TW� 世間情�

4� TW� 女人30情定水舞間�

5� TW� 我的自由年代�

6� KR� 繼承者們�

7� CN� 步步驚情�

8� TW� 愛上兩個我�

9� TW� 雨後驕陽�

10� TW� 廉政英雄�

Rank� Drama� Women’ Top 10�

1� KR� 奇皇后�

2� KR� 來自星星的你�

3� TW� 世間情�

4� TW� 女人30情定水舞間�

5� CN� 步步驚情�

6� KR� 繼承者們�

7� TW� 我的自由年代�

8� KR� 百年新娘�

9� TW� 愛上兩個我�

10� KR� 急診男女�

Source: iOS, UU, Q2 2014, Vpon & CHOCOLABS�

Mobile video, age defines

Aged 13-17� Aged 18-24� Aged 25-34� Aged 35-44� Aged 45-54�

Taiwanese� Korean� Japanese� Chinese�

Source: iOS, UU, Q2 2014, Vpon & CHOCOLABS�

Aged 35+ like Korean drama l  Aged 13-24 prefers to using mobile for Taiwanese and Korean drama.

l  Aged 35+ watch Korean drama on mobile the most�

Big Data Analysis and Application

Vpon big data�

•  Click Preference •  Mobile Device •  APP Category •  Ad Personality •  Timing •  Location •  Social Pattern�

•  Categories •  Grouping •  Recommendation •  Retargeting

•  Audience Targeting

•  Personalize Marketing

•  Cross Media Platform�

Integrate to other data base. ie. Customer’s existing CRM data base、social fans page (facebook fans)…

Collect User Pattern information�

Link to Advertiser data base � Big Data Analysis� Optimize Targeting�

Vpon has more than 380 million unique users, by collecting user behavior on advertisment, we are handling more than 10TB data each month. With multi-dimensional data mining, we find the specific prospective customers for advertisers. Vpon has not settled for accurate targeting by optimization, sending the ad to the right audience. We lower the cost and promote higher CTR at the same time and hence improve the effectiveness of the mobile advertisement.

Second-Party Data� First-Party Data�

Successful case: optimized Ad � Click Through Rate�

+3.6 times�

� 81%�

� 85%�

TOTAL Impression�

- 5 times�

l  Vpon leverages big data to assist advertisers to preform dependence analysis, through click pattern, we can spot out the prospective customers, such optimized CTR outperforms the normal one 3.6 times.�

l  Using recommendation algorithm to locate the target segment, it effectively reduce excess impression and lower advertising cost. In optimized case, even the total impression shrinks 5 times than before, the actual reach to target segment has only a tiny change. �

Target Reach� TA reach rate �

Normal Optimized Normal Optimized

+3.6 times� CTR(Click Through Rate)�

CVR(Conversion rate)�

CVR�

l  Use of analytic algorithm allocates ads to precise target customers, thought only a minor lift on the CTR while Vpon achieves to 3.6 times conversion rate. �

l  Through accumulation of data as time goes by, Vpon will be able to perform continuous optimization. After multiple algorithm calculation, Vpon is able to achieve 4.9 time conversion upon the first optimization and another 1.8 times upon the second run. �

+4.9 times�

+1.8 times�

Optimized conversion�

Normal Optimized Normal Optimized Further Optimized

Looking at big data at economic perspective, in classical theory, rich and richness is a trade off to each other; while in nowadays digital times, with the advancement of technology, it is possible that we can break through the compromise to create bigger value for all. Regarding big data, apart from Reach and Richness, another critical information of mobile data is Range which is the variety of mobile usage scenario.

Big data critical 3R�

High� Reach

Richness

High�

Low�

Classical-> Digital Economics� Big Data Economics�

Reach�

Richness�

High�

High�

Low�

Reach of unique users

The power source of behavioral forecasting

Range�

High� The customer affiliate of whole context

Classical Economics�

Digital Economics�

Range affects the accuracy of big data�

Brand Awareness�

View�

Rating�

Reach�

TV campaign�

Conversions�

Click�

Impression�

Request�

Range�

Mobile Campaign�

Actions�

Traffic�

Buzz�

Reach�

Offline Campaign� Reach�

Richness�

Cross use of multiple mobile device has become the trend, for every ad campaigns, the effectiveness and achievement of itself is closely related to the interaction and synchronization of other media. �

3R for best performance�

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

0

5000

10000

15000

20000

25000

30000

35000

40000

Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun

APP下載率� 優化轉換率� App Download Rate Optimized Conversion Rate

Resonance of integrated media advertisement l  By studying the app download and conversion rate through big data optimization, we find

resonance of TV and mobile advertisement and effectively improve the ad performance as a whole. By synchronizing the campaign and ad material, executing TV and mobile campaign at the same time, the conversion rate of mobile advertisement is even better than normal app download rate and it vividly depicts that in the time customers cross-use of multiple mobile devices, the consideration of range becomes the critical factor. �

TV Ad�

Embrace big data� n  Big Date is not just quantitative analysis

l  Big data analyzes various external factors l  Consumers’s cross-use of multiple mobile devices affects the accuracy of

analysis

n  Reach, Richness, Range l  Reach：Base of analysis, the larger the sample size, the more accurate the

analysis l  Richness：The richer the source information, the better we understand user

behavior and the higher precision of targeting. l  Range：The variety of usage context affect the accuracy of big data analysis

ü  Total strategy of cross media: matching and integration ü  Synchronization of creatives

n  Vitalize advertisers’ data base(i.e: member info) to combine with big data analysis l  M-CRM l  Personalized Marketing l  Audience Targeting

About the report

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All analytic data comes from Vpon Inc.

For the most updated reports, please go to http://www.vpon.com/

Thank You�