INTERFACE, by apidays - Technology, Digital Transformation and Platforms.pptx

Technology, Digital
Transformation &
Platforms
Marshall Van Alstyne
API Days| June 9, 2022
Boston University
Questrom School of Business

@InfoEcon
© Van Alstyne 2021
Page 2
2022 SERIES OF EVENTS
New York
JULY
(HYBRID)
Australia
SEPTEMBER
(HYBRID)
Singapore
APRIL
(VIRTUAL)
Helsinki & North
MARCH
(VIRTUAL)
Paris
DECEMBER
(HYBRID)
London
OCTOBER
(HYBRID)
Hong Kong
AUGUST
(VIRTUAL)
JUNE (VIRTUAL)
India
MAY
(VIRTUAL)
APRIL (VIRTUAL)
Dubai & Middle East
JUNE
(VIRTUAL)
Check out our API Conferences
www.apidays.global
Want to talk at one of our conferences?
apidays.typeform.com/to/ILJeAaV8

@InfoEcon
© Van Alstyne 2021
Page 3
Technology as Strategy
APIs Determine
• Permissions & Contracts
• Who‘s a partner
• Outside-in Innovation
• Inside-out Innovation
APIs Enable
• Modularity & Recomposition
• End-to-End: What‘s in & What‘s out
• Evolution
• Scale
Source: SOA Software

Agenda
• Why business models are manifestly different + evidence
• How firms adopting APIs grew an average of 38% over 16
years compared to non-adopters
• Using tech & data to drive network effects
Boston University

@InfoEcon
© Van Alstyne 2021
Page 5
What’s Different
This Time?

@InfoEcon
© Van Alstyne 2021
Page 6
The traditional largest
firms and drivers of
the economy have
been…
These are joined by…
Traditional giants were based on “rival” resources such as energy and money.
New giants are based on “nonrival” information.
RDS/A
PTR
BP
TOT
XOM
CVX
Saudi
Aramco
JPM
IDCBY
BAC
WFC
CICHY
HSB
C
ACGB
Y
AAPL
TECHY
GOOG
FB
AMZN
MSFT
BABA
Sector
Bank (1.4T)
Oil (2.6T)
Tech (8.2T)

@InfoEcon
© Van Alstyne 2021
Page 7
Proportion of value from intangibles / tangibles has reversed
Source: http://www.oceantomo.com/intangible-asset-market-value-study/

@InfoEcon
© Van Alstyne 2021
Page 8
World’s Most Valuable Public Companies
@InfoEcon
2010 2021
Company Market Cap (bn USD)
Exxon Mobil 315
PetroChina 271
259
Petrobras 221
Industrial & Commercial Bank of China 213
Microsoft 211
China Mobile 205
Berkshire Hathaway 205
China Construction Bank 203
BHP Billiton 197
2,405
2,140
Alphabet 1,809
Amazon 1,680
Facebook 992
Tencent 592
Berkshire Hathaway 636
Saudi Aramco 1,862
Alibaba 544
Tesla 702
Company Market Cap (bn USD)
Apple
The most valuable and powerful businesses are built around platform-based ecosystems
Apple
Microsoft

@InfoEcon
© Van Alstyne 2021
Page 9
Platforms employ an order of magnitude fewer resources
@InfoEcon
12x
45x
19x
10x
5x
Platforms are "inverted firms" having shifted production
from inside to outside
Ratio
389,300
4,750,000
220,300
10,100,000
819,000
15,766,667
357,000
3,514,300
1,621,600
7,575,800
Firm Start year Employees Mkt Cap (bn USD)
BMW 1916 131,000 51
Uber 2009 16,000 76
Marriott 1927 177,000 39
Airbnb 2008 10,000 101
Walt Disney 1923 199,000 163
Facebook 2004 30,000 473
IBM 1911 350,000 125
Salesforce 1999 35,000 123
New York Times 1851 3,700 6
Twitter 2006 3,300 25

InterBrand: 2021 Best Global Brands

How Value Creation
Is Different

@InfoEcon
© Van Alstyne 2021
Page 12
Giants of Supply Side Economies of Scale
Ford Model T 1908
BMW Motorcycles 1948
Acklam Ironworks 1924
Standard Oil 1909
BASF Indigo 1921
Electric Dynamo 1893
Vanderbilt Colossus
of (Rail) Roads
1.1 Why platforms & ecosystems

@InfoEcon
© Van Alstyne 2021
Page 13
Giants of Demand Side Economies of Scale
Desktop OS
Online Books
Merchant Markets Mobile
Social Networks Social Commerce

@InfoEcon
© Van Alstyne 2021
Page 14
Platforms as inverted firms
In any market with network effects,
the focus of attention must shift
from inside to outside the firm
Reason: You can't scale network effects inside
as easily as outside

@InfoEcon
© Van Alstyne 2021
Page 15
Network effects & inverting the firm lead to fundamental changes
Marketing
• From push to pull
• From outbound to inbound
IT
• From back office (ERP) to front office
(CRM) to out-of-office (Social & Big Data).
Communication among users
Operations & Logistics
• Shed marginal costs
• Uber: no taxis, Airbnb: no real estate,
Facebook: creates no content, Alibaba: no
inventory
Finance
• Records outside firm
• From Shareholder Value to Stakeholder
Value, Incorporate network effects
Human Resources
• From internal employees to external
communities
R&D
• From experts & specialized departments to
crowdsourcing & open innovation
Strategy
• From entry barriers & inimitable resources
to ecosystem husbandry & long tail

@InfoEcon
© Van Alstyne 2021
Page 16
Resources scale more externally
Scale increases up
and to the right
1.5 Launch & Scaling
HR implications as
move from
employees to
crowds and people
to algorithms
Algorithms APIs
Employees Crowds
External
Internal
People
Software

@InfoEcon
© Van Alstyne 2021
Page 17
Forthcoming
Digital Transformation
As Firm Inversion Technology Use Case
• No APIs: lost market share
• Internal Use Case: Small + gains
• External Use Case: 38% growth over 16 years

@InfoEcon
© Van Alstyne 2021
Page 18
API Value

© 2021 Van Alstyne
Twitter: @InfoEcon :: mva@bu.edu :: PlatformEconomics.com 19
EMPIRICAL
STRATEGY
Match Firms’ API use to $$$
Data from:
Private API usage data from 239 public companies,
All Public APIs from Programmable Web
➔ Rigorous econometric testing

© 2021 Van Alstyne
WHERE to
IMPLEMENT? Apply APIs facing the upstream suppliers?
Apply APIs for internal platforms?
Apply APIs for downstream customers?

© 2021 Van Alstyne
RESEARCH FINDINGS:
Market capitalization after API adoption
Firms without API adoption Firms with API adoption
Firm market capitalization
+4%
average
Statistically significant correlation between API
adoption and market capitalization
Given $2.3B average market cap of API
adopting firms, a 4% typical increase in market
cap indicates a valuation increase of $86M
After controlling for ‘fixed efforts’, impact varies
between 2.23% and 12.7% … Average is 38%
after 16 years!

© 2021 Van Alstyne
RESEARCH FINDINGS:
Financial impact differs by type of API
B2C APIs B2B APIs Internal APIs
Revenue
Cost
Net Income
4%
-5.4%
0.86%
66.2%
-7.4%
38%
16.5%
-22%
4%

RESEARCH FINDINGS:
© 2021 Van Alstyne
Twitter: @InfoEcon :: mva@bu.edu :: PlatformEconomics.com
R&D budget falls for firms with a developer portal
Firms without API adoption Firms with API adoption
R&D Budget
-12x
Statistically significant correlation between R&D
spend and whether a company had an API
developer portal
After controlling for ‘fixed efforts’, impact is
significant
Suggests APIs can allow companies to capture
‘external’ innovation

© 2021 Van Alstyne
RESEARCH FINDINGS:
Financial impact differs by type of API
B2C APIs B2B APIs Internal APIs
Revenue
Cost
Net Income
4%
-5.4%
0.86%
66.2%
-7.4%
38%
16.5%
-22%
4%
APIs
Hack Risk
13.5%

© 2021 Van Alstyne
HACK EVENTS:
SMART FIRMS
VS
CLIFF OF THE
CLUELESS Breach happens 1Q before reporting
Detecting firms immediately shut down data leak
Unaware firms see spike in data outflow as their data lake
drains

© 2021 Van Alstyne
API Adopters
VS
Non-Adopters
Average market values 20Q before & 20Q after adoption
Implied gap is $8.4B

@InfoEcon
© Van Alstyne 2021
Page 27
Which Investments Matter?
… Importance of Architecture

@InfoEcon
© Van Alstyne 2021
Page 28
Which Platform Investment is better?
1) Marketing to
acquire users (CAC)
2) Discounts to
attract users
3) Design network
effects for user value
4) Architecture to
keep users
Econometrics
for 1800
values
Baseline =
$54
Net after CAC
-$16
Net after
discount
-$0.75
Net after NFX
+$37
Net after no
disintermediation
+$87.5
“How Users Drive Platform Value” (2020) Zhou, Zhang, Van Alstyne

@InfoEcon
© Van Alstyne 2021
Page 29
Platform Design (same business)
29
Meituan
Meituan has 5x per user value, 56x market cap value
 Coupon redeemed with merchant, off platform
 Subsequent business likely direct with merchant
 New matches must be generated fresh
 User order avg 2x per year
 Groupon is a “leaky bucket”
 Order & scheduling through platform
 Delivery through platform
 Payment through platform
Data used to ID missing products, SKUs that sell
Data identifies people in 1km who will buy
 Recommend new store locations
Users order avg 26 x per year!

@InfoEcon
© Van Alstyne 2021
Page 31
A Two-Sided Network Has Four Network Effects
Restricted | © Siemens 2021 | ADV & SLE with Boston University
Side 1
Platform
• A same-side effect for each side, i.e.,
preference regarding number of other users
on own side
• A cross-side effect in each direction, i.e.,
preference regarding number of users on
other side
Side 2
1.3 Overview: Key Strategic Decisions
Page 31

@InfoEcon
© Van Alstyne 2021
Page 32
Polarity can be positive or negative
Same-side: Player-to-player contact in
Xbox MMOG, end-user PDF sharing
Cross-side: Merchants & consumers for
Visa, developers & end-users for Windows
Same-side: Competing suppliers in
Covisint auction, competing dates on
Match.com
Cross-side: Digital Rights Management
costs to consumers. Advertising clutter to
viewers
Side 1
Platform
Side 2
Page 32

@InfoEcon
© Van Alstyne 2021
Page 33
Types of Network Effects
Ratings & User
Generated Content
Recommender
Systems
Connectivity Magnetism
Side 1 Side 2
Explicit Implicit
How can users create value for other users?
Test: Does it create interaction potential?
Page 33

@InfoEcon
© Van Alstyne 2021
Page 34
• Dramatically improves match
• Reduces customer churn
• Improves inventory churn
Machine Learning Able to Make Long Tail Recommendations
Long tail matches create enormous inventory value

@InfoEcon
© Van Alstyne 2021
Page 35
• Dramatically improves match
• Reduces customer churn
• Improves inventory churn
• But, a recommender system can become
a design tool
Recommender Systems as Design Tools
Predict matches for NEW products!
© 2019 Parker& Van Alstyne

@InfoEcon
© Van Alstyne 2021
Page 36
• In 2000, Reed Hastings offered to sell 49%
of Netflix to Blockbuster
• Film & TV execs saw them as no threat
• Bereft of "Blockbusters", Netflix developed
a recommender system to match users to
content
• In 2013, Netflix used recommender engine
to predict success of House of Cards,
creating new content
• It didn’t stop there… 7 Primetime
Emmy Awards
The content industry’s House of Cards problem
Content providers underestimate ability of a weak platform to innovate content
© 2019 Parker& Van Alstyne

@InfoEcon
© Van Alstyne 2021
Page 37
Recommender Systems as Design Tools
Netflix – 10 Golden Globe Awards (2021) Amazon – 1 Golden Globe, 8 Primetime Emmy Awards
The
Queen’s Gambit
Netflix won more than anyone & over half the possible TV awards

@InfoEcon
© Van Alstyne 2021
Page 38
Tech & Transform..
 Technology is strategy (not just infrastructure)
 Giant platforms are different – “inverted firms” driven by network effects.
• Create as much value outside as inside
 Harnessing 3rd party innovation changes the game. Entrants overtake
incumbents
 APIs are key to innovation & scale
• Firms that to them 4% mkt cap gain! 38% gain over 16 years!
• BUT experience security risk 13% hacked
• Non-adopters lose!
• Smarter firms do better
 You can design network effects to grow your platform
• Architecture can drive more value than marketing
• Data, ML, APIs are critical
Boston University

Research
2016
Pipelines,
Platforms &
New Rules
of Strategy
Strategy
2018
Innovation,
Openness &
Platform
Control
Platform IP
&
Competition
Policy
2017
Platform
Ecosystems:
How Developers
Invert the Firm
Firm
Inversion
2011
Platform
Envelopment
Strategy
2016
Platform
Revolution
Strategy
2019
Platforms &
Ecosystems
2020
Opening
APIs &
Performance
Platform
Definition
Technology
<link> <link> <link> <link> <link> <link> <link>

Contact
© 2021 Van Alstyne | CTO Forum | December 9th, 2021
Page 40
Marshall
Van Alstyne
Boston
University
Phone
+01 617 358-3571
Mobile
+01 857 636-8676
E-mail
mva@bu.edu

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INTERFACE, by apidays - Technology, Digital Transformation and Platforms.pptx