Mobile Monetization Scenario Design & Big Data. Arther Wu Senior Director of Monetization and Business Operation

Transcription

1 Mobile Monetization Scenario Design & Big Data Arther Wu Senior Director of Monetization and Business Operation

2 Agenda Quick update of Cheetah Mobile Ad Scenario Design Big Data / Relation with Advertising

3 #1 #2 1.6B ~500M Publisher of Mobile Tool Publisher of Mobile Apps Users MAU Apps in GP in GP Worldwide

4 600+M downloads 60+ countries No.1 Android 4.7 rank tool app Junk Files Phone Boost Anti-virus App Manager

5 Rapid Growth of Global Mobile User Base Mobile MAU MM 10.7X 494 GLOBAL FOOTPRINT 71% Mobile MAU Overseas Jun

6 Global Presence for Advertisers London Berlin San Francisco Beijing New Delhi Tokyo New York Paris Mexico City Hong Kong Taipei San Paolo Jakarta

7 Mobile Monetization In-app Purchase Advertising Paid Download

8 US Total Media Ad Spending Share, by Media %$ 40.0%$ 35.0%$ 30.0%$ 25.0%$ 20.0%$ 15.0%$ 10.0%$ Mobile TV$ Digital$ Digital$6$Mobile$ Print$ Radio$ Outdoor$ Dictories$ 5.0%$ 0.0%$ 2014$ 2015$ 2016$ 2017$ 2018$ 2019$ Resource: emarketer 2015 Sep

9 Mobile Advertising Ecosystem Advertiser! Ad Exchange! Publisher!

10 Demand CM Cloud Data Supply Advertisers Ad Network Cheetah Apps Publisher Advertiser

11 Cheetah Mobile s Strength on Mobile Advertising

12 How Does Cheetah Mobile Ensure Ads Performance Appropriate Ad Format Scenario Design Big Data Usage

13 Multiple Ad Formats in Cheetah Apps

14 Cheetah Ads Satisfy Marketing Objectives Cheetah Ads Advertisers objectives Awareness Consideration Conversion CPM / Branding Loyalty

15 Cheetah Ads Satisfy Marketing Objectives Cheetah Ads Advertisers objectives Awareness Consideration Conversion Loyalty CPC / Brand Cognition

16 Cheetah Ads Satisfy Marketing Objectives Cheetah Ads Advertisers objectives Awareness Consideration Conversion Loyalty CPS / Customer Willingness

17 Cheetah Ads Satisfy Marketing Objectives Cheetah Ads Advertisers objectives Awareness Consideration Conversion CPA / Customer Loyalty, Retention Loyalty

18 Users daily life cycle CM Locker: Charging screen

19 Users daily life cycle CM Locker: Weather flow

20 Users daily life cycle Clean Master: Phone boost result page

21 Users daily life cycle CM Security: AppLock

22 Users daily life cycle PhotoGrid: Result page

23 Scenario Design User Behavior Flow User s Current Scenario

24 Relevant Information User s scenario Flight Search Hotel Info

25 User Engagement Non-intrusive Placement Clean Master: Phone boost result CM Locker s info page

26 CM Launcher s News ballon Longer Time on Page Non-interruptive Placement User Engagement

27 Yet, there s NO such law in Scenario Design

28 What would people do after editing photos?

29 People share immediately either on social media or instant messenger.

30 User behavior flow cannot be interrupted But you can extend it. Take a photo Edit Share

31 + PhotoGrid = Social Media PhotoGrid Community

32 Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it - Dan Ariely, a professor of Duke Univ

33 Variety 4V of Big Data Unstructured Social Mobile Sensor Photo Video Audio Database Table MB GB TB PB Veracity Anomaly Biased Noise Incomplete Volume Batch Periodic Near Real-time Real-time Velocity

34 Big Data plays notable role in Mobile Advertising Big Data Business Intelligence User Profile Precise Audience Targeting

35 Big Data Demand CM Cloud Data Supply Advertisers Ad Network Facebook Twitter Cheetah Apps Publisher Advertiser

36 How to Use Big Data? Audience Insights Google Play App Category Data Mining Interest Tag Raw Data Machine Learning Profile Many kinds of algorithms Click Preference

37 Big data mining - Algorithms Classifier (Supervised Learning) Logistic Regression Gradident Boosted Decision Tree SVM Neutral Network Naive Bayes Boosting Clustering (Un-supervised Learning) Gaussion Mixture Model K-Nearest Neighbour LDA...

38 One of algorithms: Gradient Boosting Regression Trees, GBRT Android ios Open shopping apps over 3 times today No or less than 3 User Type A User Type B Make an order Didn t make an order Female who like to shop

39 What is Machine Learning? Learning = Representation Evaluation Optimization Profile Ad Performance A/B Testing

40 Cheetah App Cloud Cheetah App Cloud App Graph Ad Serving App Usage Analytics User Profiling Analytics 4MM+ Apps 4MM+ Apps Audience Insights Audience Insights Better Performance of Apps Better User Targeting

41 App Graph Ad Serving - Showroom

42 Audience Insights from Mobile Behavior and Consumption $$$ Social Data Model Mobile Usage Derived Model

43 Profile Targeting: Ad Display Based on App Usage Graph In-App Tracking: In-app events like purchases and signups are attributed to the media source Conver'ng)Data)into) Audience)Insight DataSync Optimization: Campaign data gets smarter by the second to increase ROI Retargeting: Re-engage users to return to your app with targeted campaigns

44 Select the Right Audience 42+ proprietary tags Psychographics/ Demographics/Consumer Behavior Device/OS/Carrier Localization Leverage Cheetah Orion for Precise Audience Targeting $ Purchase & Engagement Patterns Predictive algorithm

45 Yield Optimization - ecpm Comparison ecpm Ad Network A Ad Network B Ad Network C Advertisers

46 Thanks.