Startup Genome Report 01

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....

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Startup Genome Report 01 pdf




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Startup Genome Report 01 - page 1
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 Report version 1.0 . Copyright 2011, contents under creative commons license . Page 1
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Startup Genome Report 01 - page 2
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 Report version 1.0 . Copyright 2011, contents under creative commons license . Page 2
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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 Report version 1.0 . Copyright 2011, contents under creative commons license . Page 3
Startup Genome Report 01 - page 4
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. Report version 1.0 . Copyright 2011, contents under creative commons license . Page 4
Startup Genome Report 01 - page 5
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. Report version 1.0 . Copyright 2011, contents under creative commons license . Page 5
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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. Report version 1.0 . Copyright 2011, contents under creative commons license . Page 6
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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. Report version 1.0 . Copyright 2011, contents under creative commons license . Page 7
Startup Genome Report 01 - page 8
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. Report version 1.0 . Copyright 2011, contents under creative commons license . Page 8
Startup Genome Report 01 - page 9
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% Report version 1.0 . Copyright 2011, contents under creative commons license . Page 9
Startup Genome Report 01 - page 10
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 Report version 1.0 . Copyright 2011, contents under creative commons license . Page 10
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