Startup Genome Report 01 free pdf ebook was written by Björn on May 30, 2011 consist of 68 page(s). The pdf file is provided by gallery.mailchimp.com and available on pdfpedia since May 15, 2012.
may 28th, 2011
startup genome report 01
a new framework for understanding..stanford university
fadi bishara, blackbox
contact: startupgenome@blackbox.vc
web: startupgenome.cc, blackbox.vc
startup benchmark: http://startupgenome.cc/pages/startup-genome-benchmark
report: http://startupgenome.cc/pages/startup-genome-report-1
methodology:..ability to act on feedback
v. miscellaneous observations
c. appendix
i. stages
ii. types
d....
May 28th, 2011
Startup Genome Report 01
A new framework for understanding why startups succeed
In this report we reveal in-depth research about what makes Silicon Valley
startups successful. The report is a 50 page analysis based on data from 650+
web startups. The report was coauthored by Berkeley & Stanford faculty
members. Other contributors include Steve Blank, the Sandbox Network, and 10
accelerators from around the globe.
The goal of the report is to lay the foundation for a new framework for assessing
startups more effectively by measuring the thresholds and milestones of
development that Internet startups move through.
This report is the Startup Genome Project’s first step toward cracking the
innovation code of Silicon Valley and spreading it to the rest of the world.
Authors
Max Marmer,
blackbox
Bjoern Lasse Herrmann,
blackbox
Ron Berman,
UC Berkeley
With collaboration and support from
Chuck Eesley,
Stanford University
Steve Blank,
Stanford University
Fadi Bishara,
blackbox
contact:
StartupGenome@blackbox.vc
web:
startupgenome.cc, blackbox.vc
startup benchmark:
http://startupgenome.cc/pages/startup-genome-benchmark
report:
http://startupgenome.cc/pages/startup-genome-report-1
methodology:
http://www.systemmalfunction.com/2011/05/deciphering-genome-of-
startups.html
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Table of Contents
A. Executive Summary
I. Summary of Main Results
II. The Startup Lifecycle
III. Types of Internet Startups
IV. Entrepreneurial Learning
V. Looking Forward
B. The Startup Genome Report
I. Introduction
II. The Stages To Success
1.1 Milestone Based Assessment Vs. Snapshot Based Assessment
1.2 The Startup Lifecycle
1.3 Marmer Stages vs. Traditional Indicators of Success
1.4 Consistency As An Indicator Of Success
1.5 Improving our Stage Assessment
1.6 Conclusion
III. Startup “Personality” Types
1.1 Types of Internet Startups
1.2 Types of Startups vs. Traditional Indicators Of Success
1.3 Areas of Improvement
1.4 Network Effects vs. Virality vs. User Data
1.5 Differentiating Types
IV. Learning As The Key To Successful Startups
1.1 Willingness To Listen
1.2 Drive To Learn
1.3 Ability To Act On Feedback
V. Miscellaneous Observations
C. Appendix
I. Stages
II. Types
D. Acknowledgments & Sources
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A. Executive Summary
The goal of the Startup Genome project is to increase the success rate of
startups and accelerate pace of innovation around the world by turning
entrepreneurship into a science.
With the first Startup Genome report we aim to lay the foundation for a new
paradigm of assessing startups and understanding the drivers of entrepreneurial
performance.
Many entrepreneurs that we have talked with, especially younger ones,
considered describing the repeating patterns of startups an impossible task or
even a disgraceful reduction of the artistry of entrepreneurship to numbers and
graphs. With this report we do not mean to imply that there is no art to
entrepreneurship but rather that entrepreneurship is strongest at the intersection
of science and art. By gaining a deeper understanding of the repeating patterns
underlying success and failure entrepreneurs can dramatically increase their
ability to innovate.
The window of opportunity for this project has only recently been opened. In just
the last 2-3 years the number of people extracting and codifying the informal
learning of Silicon Valley has hit a point of critical mass. Concurrently the costs
of startup creation have fallen dramatically triggering a huge increase in
technology entrepreneurship all over the world.
The theories and models that have had the most widespread adoption are
effectively applying scientific management principles to startups, with the two
most well known theories being Customer Development and the Lean Startup.
Yet despite this huge knowledge base emerging about how startups work,
startups have been able to absorb little more than the basic patterns of how to
build a startup. Most founders don't know what they should be focusing on and
consequently dilute their focus or run in the wrong direction. They are regularly
bombarded with advice that seems
contradictory,
which is often paralyzing.
And while startups are now gathering way more qualitative and quantitative
feedback than they were just a few years ago, their ability to interpret this data
and use it to make better business decisions is sorely lacking. The primary
cause of these problems is that we lack the necessary structure to assimilate
and build upon our accumulated knowledge on the nature of startups.
We believe the solution is the development of a flexible framework that enables
the integration of the principles, methodologies and wisdom that have been
discovered about how to create a successful startup.
For the last 5 months we've been working closely with Steve Blank, the
progenitor of Customer Development and the startup science movement, to
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build this framework, which currently serves as the foundation of the Startup
Genome Project.
In February we publicly announced the project and released a survey to test
three key aspects aspects of our framework. We received an overwhelmingly
positive response and 650+ startups filled out our survey.
The three key ideas we set out to test were:
1.
Startups evolve through discrete stages of development. Each stage can be
measured with specific milestones and thresholds.
2.
There are different types of startups. Each type evolves through the
developmental stages differently.
3.
Learning is a fundamental unit of progress for startups. More learning should
increase chances of success.
I. Summary of Main Results
The goal of the report is to lay the foundation for a new framework for assessing
startups more effectively by measuring the thresholds and milestones of
development that Internet startups move through.
Through analyzing the results from our survey we found that Internet startups
move through similar thresholds and milestones of development, which we
segmented into stages. Startups that skipped these stages performed worse.
We also identified three major types of Internet startups with various sub types.
They are segmented based on how they perform customer development and
customer acquisition. Each type has varying behavior regarding factors like time,
skill and money.
These 2 findings lay the foundation for us to begin organizing and structuring all of a
startup’s customer related data, which entrepreneurs can use to make better
product and business decisions. A first product based on this framework is
currently in development. Contact us at
startupgenome@blackbox.vc
if you would
like to know more.
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Summary of additional findings:
1. Founders that learn are more successful:
Startups that have helpful
mentors, track metrics effectively, and learn from startup thought leaders raise
7x more money and have 3.5x better user growth.
2. Startups that pivot once or twice times raise 2.5x more money,
have 3.6x
better user growth, and are 52% less likely to scale prematurely than startups
that pivot more than 2 times or not at all.
3. Many investors invest 2-3x more capital than necessary
in startups that
haven’t reached problem solution fit yet. They also over-invest in solo founders
and founding teams without technical cofounders despite indicators that show
that these teams have a much lower probability of success.
4. Investors who provide hands-on help have little or no effect on the
company's operational performance.
But the right mentors significantly
influence a company’s performance and ability to raise money. (However, this
does not mean that investors don’t have a significant effect on valuations and
M&A)
5. Solo founders take 3.6x longer to reach scale stage
compared to a
founding team of 2 and they are 2.3x less likely to pivot.
6. Business-heavy founding teams are 6.2x more likely to successfully scale
with sales driven startups than with product centric startups.
7. Technical-heavy founding teams are 3.3x more likely to successfully
scale with product-centric startups with no network effects
than with
product-centric startups that have network effects.
8. Balanced teams with one technical founder and one business founder
raise 30% more money,
have 2.9x more user growth and are 19% less likely to
scale prematurely than technical or business-heavy founding teams.
9. Most successful founders are driven by impact
rather than experience or
money.
10. Founders overestimate the value of IP before product market fit by
255%.
11. Startups need 2-3 times longer to validate their market than most
founders expect.
This underestimation creates the pressure to scale
prematurely.
12. Startups that haven’t raised money over-estimate their market size by
100x
and often misinterpret their market as new.
13. Premature scaling is the most common reason for startups to perform
worse.
They tend to lose the battle early on by getting ahead of themselves.
14. B2C vs. B2B is not a meaningful segmentation of Internet startups
anymore because the Internet has changed the rules of business.
We found
4 different major groups of startups that all have very different behavior
regarding customer acquisition, time, product, market and team.
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II. The Startup Lifecycle
Our foundational structure of startup assessment is the startup lifecycle.
Understanding where a startup is in their lifecycle allows us to assess their
progress. The startup lifecycle is made of 6 stages of development, where each
stage is made up of levels of substages. This creates a directed tree structure
and allows for more granular assessment by being able to pinpoint the main
drivers of progress at each stage. We call each of these stages the Marmer
Stages. However, in this report only the top level stages are discussed. Our first
four top-level stages are based loosely on Steve Blank's 4 Steps to the
Epiphany, but one key difference is that the Marmer Stages are product centric
rather than company centric.
Our 6 stages are:
1) Discovery
2) Validation
3) Efficiency
4) Scale
5) Profit Maximization (not covered in this report)
6) Renewal (not covered in this report)
Our assessment of the stages does not include traditional ways of assessment
like funding, team size, user growth, etc. They are based on milestones and
thresholds that vary based on the type of startup. An example for a milestone is
building a mvp and an example for a threshold is certain rate of retention.
We attempt to provide evidence for the existence of the Marmer Stages in two
ways:
1) That the Marmer Stages correlate with traditional indicators of progress.
2) That startups that don't move through the stages in order show less progress.
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Overview of Results:
Avg.
Months
Working
1. Discovery
2. Validation
11
3. Efficiency
17
4. Scale
25
$3,000,000
17
43%
$900,000
4
29%
$800,000
4
21%
7
Avg.
Funding
Raised
$227,000
Avg. Number Avg. % User
Top
of
Growth in Competitive
Employees last month Advantages
IP
1
6%
Technology
Partners
Insider Info
Traction
IP
Insider Info
IP
Traction
Technology
Top Challenges
Customer Acquisition
Over capacity
Customer Acquisition
Product Market Fit
Problem Solution Fit
Customer Acquisition
Team building
Fundraising
Customer Acquisition
Team Building
Avg. Number of Pivots Pivot Variance
Inconsistent Startups
Consistent Startups
1.6
1.2
5.0
2.0
Avg. Funding Raised
(Scale Stage)
$1,100,000
$3,400,000
Avg. # of
Employees
3
20
III. Types of Internet Startups
We created our types by defining a spectrum of 100% marketing to 100% sales
and created 3 points by selecting the two end points and the mid point. In the
future, we plan to define a more fluid spectrum with more than 3 points, as we
understand the underlying variables better and see where startups cluster. Our
fourth type, Type 1N (The Social Transformer), is the same as Type 1 (The
Automizer) but the product has network effects.
Type 1 - The Automizer
Common characteristics: self-service customer acquisition, consumer focused,
product centric, fast execution, often automize a manual process.
- technology heavy founding teams perform better than other teams
- market size is 2x bigger for Type 1 (The Automizer) compared to Type 2 (The
Integrator)
- more likely to tackle existing markets
- need the least capital of all types
Examples:
Google, Dropbox, Eventbrite, Slideshare, Mint, Pandora, Kickstarter, Hunch, Zynga,
Playdom, Modcloth, Box.net, Basecamp, Hipmunk, etc.
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Type 1N - The Social Transformer
Common characteristics: self service customer acquisition, critical mass,
runaway user growth, winner take all markets, complex ux, network effects,
typically create new ways for people to interact.
- need 50% longer than Type 1 (The Automizer) and Type 2 (The Integrator) to
reach scale stage
- business heavy and balanced teams perform better than technology heavy
teams
- market size is 2x bigger for Type 1N (The Social Transformer) compared to
Type 2 (The Integrator)
- more likely to tackle new markets
- more likely to have large team growth at the scale stage
- need more capital than Type 1 (The Automizer) and Type 2 (The Integrator)
- more likely to have large user growth
Examples:
Ebay, OkCupid, Skype, Airbnb, Craigslist, Etsy, IMVU, Flickr, LinkedIn, Yelp,
Aardvark, Facebook, Twitter, Foursquare, Youtube, Dailybooth, Mechanical Turk,
MyYearbook, Prosper, Paypal, Quora, etc.
Type 2 - The Integrator
Common characteristics: lead generation with inside sales reps, high certainty,
product centric, early monetization, SME focused, smaller markets, often take
innovations from consumer Internet and rebuild it for smaller enterprises.
- business heavy and balanced founding teams perform better than technology
heavy teams
- more likely to tackle existing markets with a product that is cheaper
- more likely to maintain small teams even when they scale
- monetize a high percentage of their users
Examples:
PBworks, Uservoice, Kissmetrics, Mixpanel, Dimdim, HubSpot, Marketo, Xignite,
Zendesk, GetSatisfaction, Flowtown, etc.
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Type 3 - The Challenger
Common characteristics: enterprise sales, high customer dependency, complex
& rigid markets, repeatable sales process.
- To reach scale stage they need about 2x more time compared to 1N and 3x
more time compared to Type 1 (The Automizer) and Type 2 (The Integrator).
- business heavy founding teams perform better than technology and balanced
founding teams
- market size is 6-7 times bigger than all other types
- more likely to either tackle existing markets with a better product or tackle a
new market
- are more likely to either pivot a lot or not at all
- more likely to have large team growth at the scaling phase
- need significantly more capital than the other types
- monetize a high percentage of their users
Examples:
Oracle, Salesforce, MySQL, Redhat, Jive, Ariba, Rapleaf, Involver, BazaarVoice,
Atlassian, BuddyMedia, Palantir, Netsuite, Passkey, WorkDay, Apptio, Zuora,
Cloudera, Splunk, SuccessFactor, Yammer, Postini, etc.
Avg. Months to
Move Through
Marmer Stages
Type 1 (The
Automizer)
Type 1N (The
Social
Transformer)
Type 2 (The
Integrator)
Type 3 (The
Challenger)
21
32
16
64
Primary Service
Providers Hired
User Experience, Backend
Development
User Experience, Backend
Development
Sales, Business
Development, PR
Sales, Business
Development, PR
Market Type
Existing Market
(Better or
Cheaper)
New Market
Existing Market
(Cheaper)
Existing Market
(Better) or New
Market
Avg. Team
Size (Scale
Stage)
20
28
11
46
Type of Founding Team
that is Most Successful
Technical Heavy Team
Balanced Team
Balanced Team
Business Heavy Team
Market size Estimation
(Efficiency & Scale
Stages)
$11B
$13B
$7B
$65B
Primary
Motivation
Type 1 (The
Automizer)
Type 1N (The
Social
Transformer)
Type 2 (The
Integrator)
Type 3 (The
Challenger)
Change the
World
Change the
World
Build a Great
Product
Build a Great
Product
Avg. Funds Raised
Avg. User
Percentage of
(Scale Stage)
Growth in Last User Base is
Month
Paid
$600,000
$2,300,000
$700,000
$4,100,000
14%
33%
11%
36%
8%
10%
20%
27%
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IV. Entrepreneurial Learning
We examined whether founders learned in the following ways:
a) Learning from best practice
Companies that follow startup thought leaders like Steve Blank, Paul Graham,
etc. are 80% more likely to raise money. Almost all companies that raised money
had helpful mentors. Companies without helpful mentors almost always failed to
raise funding.
b) Ability to listen to customer feedback
Companies that are tracking metrics average a monthly growth rate that is 7x
companies that are not tracking metrics and are 60% more likely to raise
funding than companies that don't track metrics.
c) Ability to act on feedback
Companies that fail to listen and act on feedback tend to scale without
validating the size and interest of the market. These companies tend to either
pivot not at all or more than 2 times. They also have a harder time raising money
and growing the team.
d) Conclusions
The Marmer Stages:
The stage-based developmental model seems to
correlate well with traditional measures of startup behavior and success.
However, it shows clearly that a snapshot analysis of startups is lacking, since
conclusions about specific startups cannot be drawn without a longitudinal
gathering of data.
Stage Consistency:
The concept of consistency, introduced in this report,
seems to be a strong predictor of "problems". Having a simple measure for
consistency is yet stronger evidence for the validity of the stages model.
Types of Startups:
Although intuition directs us to think startups behave
differently by type, our data shows the differences quite clearly, and can provide
startups with useful benchmarks. Firms can now more properly align their
actions according to their type, and not act on general advice that does not
pertain to them.
Learning:
We have given initial evidence that the ability to learn affects startups
in the long run. Our future work will focus on modeling and measuring what
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