Mobile Monetization Scenario Design & Big Data Arther Wu - - PowerPoint PPT Presentation

Mobile Monetization Scenario Design & Big Data

—— Arther Wu Senior Director of Monetization and Business Operation

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

Publisher

• f Mobile Tool

Apps in GP

#1

Publisher

• f Mobile Apps

in GP Worldwide

#2

Users

1.6B

MAU

~500M

4.7

rank Junk Files Phone Boost Anti-virus App Manager

No.1

Android tool app

600+M

downloads

60+

countries

Rapid Growth of Global Mobile User Base

Mobile MAU

MM GLOBAL FOOTPRINT

∼71% Mobile MAU Overseas 1

494 2015-Jun

10.7X

San Francisco New York Mexico City London Paris Beijing Jakarta Taipei Tokyo Hong Kong New Delhi Berlin San Paolo

Global Presence for Advertisers

Mobile Monetization

In-app Purchase Paid Download

Ads

Advertising

US Total Media Ad Spending Share, by Media 2014 - 2019

0.0%$ 5.0%$ 10.0%$ 15.0%$ 20.0%$ 25.0%$ 30.0%$ 35.0%$ 40.0%$ 45.0%$ 2014$ 2015$ 2016$ 2017$ 2018$ 2019$ TV$ Digital$ Digital$6$Mobile$ Print$ Radio$ Outdoor$ Dictories$

Resource: eMarketer 2015 Sep

Mobile

Mobile Advertising Ecosystem

Advertiser! Ad Exchange! Publisher!

Cheetah Apps

Publisher

Advertisers Ad Network

Advertiser

CM Cloud Data

Supply Demand

Cheetah Mobile’s Strength

• n Mobile Advertising

How Does Cheetah Mobile Ensure Ads Performance

Appropriate Ad Format Scenario Design Big Data Usage

Multiple Ad Formats in Cheetah Apps

Advertisers’ objectives Cheetah Ads Cheetah Ads Satisfy Marketing Objectives

CPM / Branding

Awareness Consideration Conversion Loyalty

Advertisers’ objectives Cheetah Ads Cheetah Ads Satisfy Marketing Objectives Awareness Consideration

CPC / Brand Cognition

Conversion Loyalty

Advertisers’ objectives Cheetah Ads Cheetah Ads Satisfy Marketing Objectives Awareness Consideration Conversion

CPS / Customer Willingness

Loyalty

Advertisers’ objectives Cheetah Ads Cheetah Ads Satisfy Marketing Objectives Awareness Consideration Conversion Loyalty

CPA / Customer Loyalty, Retention

Users’ daily life cycle

CM Locker: Charging screen

Users’ daily life cycle

CM Locker: Weather flow

Users’ daily life cycle

Clean Master: Phone boost result page

Users’ daily life cycle

CM Security: AppLock

Users’ daily life cycle

PhotoGrid: Result page

Scenario Design

User Behavior Flow User’s Current Scenario

Relevant Information User’s scenario

Flight Search Hotel Info

User Engagement Non-intrusive Placement

CM Locker’s info page Clean Master: Phone boost result

Longer Time on Page Non-interruptive Placement User Engagement

CM Launcher’s News ballon

Yet, there’s NO such “law” in Scenario Design

What would people do after editing photos?

People share immediately either on social media or instant messenger.

User behavior flow cannot be interrupted But you can extend it.

Take a photo Edit Share

+ =

PhotoGrid Social Media PhotoGrid Community

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

Volume

MB GB TB PB

Velocity Variety Veracity

Batch Periodic Near Real-time Real-time Noise

Incomplete

Biased

Anomaly

Database Table Video Photo Audio Mobile Sensor Unstructured Social

4V of Big Data

Big Data plays notable role in Mobile Advertising

Precise Audience Targeting Big Data Business Intelligence User Profile

Cheetah Apps

Publisher

Advertisers Ad Network Facebook Twitter

Advertiser

CM Cloud Data

Supply Demand

Big Data

Raw Data Profile Click Preference

Machine Learning Many kinds of algorithms

Interest Tag Google Play App Category

How to Use Big Data? Audience Insights Data Mining

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 ...

Big data mining - Algorithms

One of algorithms: Gradient Boosting Regression Trees, GBRT

Android iOS Open shopping apps

• ver 3 times today

No or less than 3 Female who like to shop Make an order Didn’t make an order

User Type A User Type B

What is Machine Learning?

Representation Evaluation Optimization

Learning =

Profile Ad Performance A/B Testing

App Usage Analytics User Profiling Analytics

Cheetah App Cloud

4MM+ Apps Audience Insights

Better Performance of Apps

App Graph Ad Serving

Better User Targeting

Cheetah App Cloud

4MM+ Apps Audience Insights

App Graph Ad Serving - Showroom

$$$

Social Data Model Mobile Usage Derived Model

Audience Insights from Mobile Behavior and Consumption

Profile Targeting:
 Ad Display Based on App Usage Graph In-App Tracking:
 In-app events like purchases and sign- ups are attributed to the media source DataSync Optimization: 
 Campaign data gets smarter by the second to increase ROI Retargeting:
 Re-engage users to return to your app with targeted campaigns

Conver'ng)Data)into) Audience)Insight

Leverage Cheetah Orion for Precise Audience Targeting

Select the Right Audience

42+ proprietary tags Psychographics/ Demographics/Consumer Behavior Device/OS/Carrier Localization Purchase & Engagement Patterns Predictive algorithm

$

Yield Optimization - eCPM Comparison

Ad Network A Advertisers

eCPM

Ad Network B Ad Network C

Thanks.

arther@cmcm.com