sfmobile: founder labs mobile edition 01/09/11

Founder Labs – Mobile EditionWeek 1 – Ecosystem Overview & Business Models

January 8, 2011

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Lars KampLars Kamp

Work Network

Management Consulting

2

www.sfmobile.org

San Francisco, CA415.894.5423lars@sfmobile.org

Suite 1200560 Mission StreetSan Francisco, CA 94105415.894.5423lars.kamp@accenture.com

Some history (of mobile).

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Q: Whose mission statement is this?

“We have a dream of improving the lives of many millions

of people by means of small, intimate life support

systems that people carry with them everywhere.

These systems will help people to organize their lives, to

communicate with other people, and to access

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communicate with other people, and to access

information of all kinds.

They will be simple to use, and come in a wide range of

models to fit every budget, need, and taste. They will

change the way people live and communicate.”

A: General Magic, 1990. You could say it all started here.

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General Magic’s “Magic Cap”. Looks familiar?

“Magic Cap”

6

Maybe now?

7

Three people from the team that architected Magic Cap.

Andy Rubin Tony Faddel Kevin Lynch

8 Source: Wired, SF Mobile analysis.

Mobile economics.

9

Mobile is the single biggest global distribution platform.

PC Installed Base TV Households Mobile Subscribers

PC TV Mobile

20091.2 Billion

20091.3 Billion

20094.0 Billion

10

BroadbandSubscribers

Pay TVSubscribers

20131.6 Billion

2009420 Million

2013648 Million

20131.33 Billion

2009600 Million

2013739 Million

4.0 Billion

20135.5 Billion

Source: Gartner, PWC, ITU, IDC, Accenture analysis.

Evolution of “the stack”: Shift from hardware to software.

Phone

ApplicationMiddleware

Middleware

Shell & UIUser Interfaces, App Stores &

User Software

Exte

rnal In

terfa

ces,

e.g

. US

B, S

peaker, F

lash C

ard

CommsSoftware

Early days Today

Mobile Device Stack

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Chipsets,Processors, Basebands

Drivers Drivers

Core Operating System

PhoneMiddleware

Hardware

Platform / OS

Middleware

Exte

rnal In

terfa

ces,

e.g

. US

B, S

peaker, F

lash C

ard

Hardware

1-2 MB of closed software

>1 GB of open software

Hardware Software

Source: Accenture analysis.

Value in mobile is moving up the stack…

Services and Content

Screen, User Interfaces,User Software

e.g

. US

B, S

peaker, F

lash

Card

Cost to build ($M)

Per-unit Revenue ($)

Break-even # of units

$0.1M $1.00 0.1M

$20M $0.20 100M

Mobile Handset Stack & Elements

DIRECTIONAL

12

Chipsets, Processors, Radio Basebands

Core Operating System

DeviceMiddleware

ApplicationMiddleware

Exte

rnal In

terfa

ces,

e.g

. US

B, S

peaker, F

lash

Card

$10M $0.10 100M

$1,000M $5.00 200M

Valu

e F

low

Hardware Software

Source: Estimates based on industry interviews; see David Wheeler “Linux Kernel 2.6: It's Worth More!” for estimating the cost of the Linux Kernel.

… and is fueling the app store economy.

149,000

211,000

2008 2009 2010

Size of Catalog (K) – Apple App Store vs. Android Market2008-2010, as of Q2 2010, by Number of Available Apps at End of Quarter, Excluding Books

Android

~20,000 monthly submission

13

740 4,40013,200

25,300

52,610

74,500

97,000

600 2,900 5,200 11,500

20,100

35,200

56,200

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2

App StoreJuly 11

Android MarketOct 22

Day 1 500 Apps

Day 1 62 Apps

~7,000 monthly submission

Source: Apple press releases & earnings calls, Google, AndroLib, PCWorld, Distimo, Accenture analysis. Catalog size for Apples excludes books. All numbers rounded.

But: An app is not a business model.

60%

70%

80%

90%

100%

60%

70%

80%

90%

100%

60%

70%

80%

90%

100%

Re

ten

tio

n R

ate

60%

70%

80%

90%

100%

Loyalty and Retention Rates of Mobile Apps Over Time, 2010

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0%

10%

20%

30%

40%

50%

60%

0 30 60 90 120 150 180

0%

10%

20%

30%

40%

50%

60%

0 30 60 90 120 150 180

0%

10%

20%

30%

40%

50%

60%

0 30 60 90 120 150 180Days After First Measurement

Re

ten

tio

n R

ate

News (9.1%)

Games (2.4%)0%

10%

20%

30%

40%

50%

60%

0 30 60 90 120 150 180

News (9.8%)

Enter-tainment (2%)

Days After First Measurement

Source: Flurry, Accenture analysis. User retention defined by the number of users who downloaded an application and launched the application at any time in the past, and also launched the app within the last seven days, e.g. "30 days ago" represents any new user that launched a given app in January and also again within the last seven days. "60 days ago" represents new users identified in December and also used within last 7 days. Sample based on relevant 5-6 apps per category with at least 120 days of data availability in the Flurry system.

90% dead after 90 days.

52%

40%

20%

9%

58%

38%

18%

5%

iPhone App RetentionAs of January 2010, by Application Category

30 Days 90 Days

Android App RetentionAs of January 2010, by Application Category

News

Social Networking

30 Days 90 Days

15

34%

35%

33%

10%

9%

4%

34%

38%

42%

10%

7%

16%

Games

Lifestyle

Enter-tainment

39% 10% 42% 11%Average

Retention Rates

Source: Flurry, Accenture analysis.

The cloud.

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What is “The Cloud”?

Cloud Origins Cloud Today Cloud Benefits

• Cost ReductionLower infrastructure, energy, licensing and maintenance costs

• Speed to Market

VirtualizationOne computer

acting like

many

• Virtualization and

Grid abstracted

• Computing as a

A style of computing that provides on demand access to a shared set of

highly scalable services.

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• Speed to MarketReduces time requiredto pilot projects

• Elasticity / ScalabilityOn-demand capacity and high business agility

• High Performance ComputingProvides “infinite” computingcapacity as needed

many

Grid Computing

Many

computers

acting like one

+• Computing as a

utility

• Scale economies

of central supply

• Uses massively-

parallel processing

• Geo-distributed

with massive

redundancy

Who is building a cloud?

Facebook – Prineville Google – The DallesYahoo – Lockport

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Apple – Maiden Microsoft – DublinAmazon – Morrow

The cloud: massive off-deck computing for mobile.

19 Source: Amazon press release, December 2010.

Silicon.

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Moore’s Law – since ~1965 on the desktop.

21 Source: Intel.

The original paper from 1965.

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Coming your way in mobile as well.

Baseband Processors

“Fat Modems” Baseband & Application Processor

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Low power silicon for voice/SMS and long

battery life.

OS-enablement of light apps running on top of

baseband.

High performance, low power application

processors.

Massive on-deck computing power.

2GHz

2.5GHz

Cortex-A9

Cortex-A15

20nm

2 cores

4 cores

Mobile Silicon: Chip Size, Cores & Clock Speed Over Time

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533MHz667MHz

800MHz833MHz

1GHz

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

ARM9

ARM11

Cortex-A8

Cortex-A9

130nm90nm

65nm45nm

32nm

28nm

20nm

Clockspeed:

Cores:

Node:

1 core

1 core

1 core

Source: ARM.

Development before knowing the actual device.

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Business models.

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Four quadrants and generic business models.

Cloud • SDK and developer fees to marketplace

• Software sales to aid app development and design

• Transaction fee

• Subscriptions

• Advertising

• In-app

• Display / search

• Multimedia

Pre-Load Post-Load

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Device

• IP licensing

• Pre-installed app licensing

• Software sales

• Hardware sales/materials

• App sales

• Virtual goods

• Digital media sales

• Subscriptions

and design

Source: Adjusted based on Vision Mobile (visionmobile.com).

Business models applied.

Cloud

Pre-Load Post-Load

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Device

Expect the center of gravity to shift to post-load.

Post-Load Revenue Streams

Pre-Load Revenue Streams

100%

ILLUSTRATIVE

Ecosystem Revenue Mix Over Time.

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Primary Revenue Models

• Licensing• Software sales• Hardware sales• Service subscriptions

• Licensing• Ads• Software sales• Hardware sales• Service subscriptions

• Social• Ads• Service subscriptions• Transaction fees• Privacy (User data)

0%

“Yesterday”2000

“Today”2010

“Tomorrow”2015 Onwards

A few ideas…

Enterprise Apps

Security

Cloud Services

Untapped market w/ customers who are used to paying lots of money for bad software.

Apps need to connect; remote service access; solves fragmentation – runs in browser.

Prevent abuse of sensitive data that resides on or can be accessed over mobile device.

Consumer Apps Gaming, social networking, dating, digital marketing are still in the nascent stage for mobile.

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Enablers

Analytics

Shannon’s Law

Devices

Smart Grid

On-device engagement requires enablers, e.g. authentication, payments, middleware

Engagement data provides opportunity for data mining for e.g. tracking, reporting purposes

Between powerful devices and clouds, wireless links remain the bottleneck.

Power-efficient devices, enabled with mobile OS, for vertical purposes, e.g. healthcare

Drive efficiency in electrical grids through mobile web services and devices.

And with that… Good luck!

“I have come here to

Nada, 1988

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“I have come here to

chew bubblegum and

kick ass...and I'm all

out of bubblegum.”