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SPL Workspace Case Study 2015


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The Startup Policy Lab Workspace Case Study 2015 examined the unique characteristics of the startup communities at two leading workspaces in Silicon Valley: Runway Incubator in San Francisco and Hacker Dojo in Mountain View.

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SPL Workspace Case Study 2015

  1. 1. Startup policy lab SPL develops tools to address public policy driven by emergent technology Workspace Case Study 2015 Charles Belle and Kathie Chuang Startup Policy Lab is a fiscally sponsored project of Community Initiatives – a 501c3 nonprofit organization
  2. 2. Startups live, work, and play in workspaces all over the world.
  3. 3. HackerSpaces, a community generated wiki, lists over 350 workspaces in the united states
  5. 5. 80 startups >200,000 Data points WE CONDUCTED ONE ON ONE INTERVIEWS: SPL spoke with 82 startups using a 10 question survey. LEVERARGED SECONDARY DATA SOURCES: One workspace partner shared >200,000 data points related to membership activity. WE ENGAGED IN INDEPENDENT RESEARCH: SPL collected third party data about startups from Angel List and Crunchbase. We started by comparing two workspaces
  6. 6. Learn the best ways to collect data about the dynamic startup community Compare two different types of workspaces to surface initial patterns and differences Better understand the characteristics of startups within workspaces Determine if there is sufficient support to launch an annual and more robust study our goal was to develop the foundation for more in-depth research
  7. 7. W E F O U N D P A T T E R N S , T A K E A W A Y S , A N D I D E A S F O R H O W T O E X P A N D T H I S P R O J E C T G O I N G F O R W A R D
  8. 8. Location was the primary factor for selection Nearly 50% of startups surveyed the workspace chose it because of its location. Funding needs were at the seed stage Median funding was just $1MM. Companies had traction (customers) and business models Median age of startups was 30 months. High expectation of churn At one workspace, the median time in residence was 9 months; and the total length of time startups expected to stay was 17 months. Patterns we found in both workspaces
  9. 9. Build to location: support existing communities Startups gravitate to each other and spaces that have other startups. Building a supportive ecosystem means going to that community, not building a workspace to locate a startup community in a particular location to drive outside capital investment. Bring in the Angels: Target seed funding for early stage startups Startups in workspaces are likely to be smaller and at the seed stage. As a result, these startups, for the most part, are not ready for venture capital. A robust Angel Investor community will provide great impact for seed stage startups. Think local & global: satellite offices are valuable additions to workspaces While it is important to support local startups, companies seeking to locate their satellite office in a workspace offer great benefits to local startups. Satellite offices of established startups from outside a region provide stability, bring in outside capital, and ideas that local startups can leverage. Three insights emerged 1 2 3
  10. 10. Workspace resource guide Create an online resource with information about types of workspaces. Analytic tools Enable workspaces (startups, investors, policymakers & workspaces) to compare performance metrics of workspaces. Partner with third party data experts Collaborate with Angel List and Mattermark to build automated collection of granular data about startups. Next steps to increase the value of our research Increase the number of data points Another year with our partners and add (1) more partner. increase frequency of data collection Collect data on a quarterly basis. Structure data Structure data to make it more consistent across workspaces. Research funding Raise funding to continue this initiative.
  11. 11. O U R A P P R O A C H : S P L C O L L A B O R A T E D W I T H T W O W O R K S P A C E S ( S A N F R A N C I S C O & S I L I C O N V A L L E Y )
  12. 12. Partnerships Two workspaces (Runway & Hacker Dojo) provided access to their community, promoted the survey, and shared their data. Time period Research and data collection occurred between July 2015 - Sept 2015. Research Methodology Conducted in person surveys, analyzed member check-in data, and pulled secondary research. Data collected • Total number of startups surveyed: 80 (Runway: 39, Hacker Dojo: 41) • Total number of survey respondents: 114 What we did: surveys and research (Runway: 49, Hacker Dojo: 65) • Total number of members at Hacker Dojo: 479 • Total number of member check-in data points collected: 215,253 • Type of check-in data collected: name, date of check-in, & time of check-in Third party data Collected information about participating startups from Angel List & Crunchbase, including funding, location, and company type. expertise Support provided by data science partner Datable.
  13. 13. Our Awesome Workspace partners For - Profit Non - Profit Member, month to month, pay for access to desks 479 members Company, month to month, pay for individual desks 80 companies Business model Payment model 2012 2009 Mountain Veiw, CASan Francisco, CA Year founded Location “World's Largest Non-profit Community Hackerspace” “Runway is a community and co- working space for entrepreneurs, influencers, and hustlers”Description number of companies/members Website
  14. 14. The survey was static, which reduced the depth of insight. There is a high rate of churn of startups at workspaces. The survey only provides a snapshot of a moment in time. Limited financial resources prevent more robust data collection and analysis. Robust research requires additional funding to create and maintain infrastructure for data collection and analysis. Data accuracy Is heavily dependent on self-reported data. Data collected was not standardized; proprietary tools might be able to provide more structure and accuracy, e.g. Mattermark. A few (massive) caveats about our approach
  15. 15. S U R V E Y F I N D I N G S P R O V I D E A C O M P A R I S O N O F F O R P R O F I T W O R K S P A C E V E R S U S A N O N - P R O F I T W O R K S P A C E
  16. 16. Member/company time cycle in innovation space How long are the startups in the space and how old are they? Runway Average: 10.87 Months Median: 9.00 months Standard deviation: 11.41 Average: 10.03 Months Median: 8.00 months Standard deviation: 7.85 Hacker Dojo Workspaces are perceived as temporary offices. Most startups have been in the workspace for less than a year.
  17. 17. Funding AmountFunding Amount NumberofCompanies NumberofCompanies $0 to $500,000 $500,000 to $1M $1M to $1.5M $1.5M to $3M $3M + what level of funding do these startups have? Runway Hacker Dojo The majority of the startups were, unsurprisingly, at the seed stage. The vast majority of startups had raised less than $500,000. 23 4 5 4 3 43 5 1 1 1
  18. 18. events/programs 22% mentors/networking 9% funding/capital 8% price point 4% location 53% collaborative space 4% events/programs 22% equipment135 location 53% investor link 14% mentors/networking 25% Why do startups select a particular workspace Runway Hacker Dojo LOCATION Location was overwhelmingly important in the work space at nearly 63%. LOCATION Important in the makerspaces as well as the workspace. Location accounts for 28% of the appeal of the space. MENTORS AND NETWORKING Refers to the availibility of mentors and networking oppertunities in the given space.
  19. 19. A D E E P E R D I V E I N T O T H E I N D I V I D U A L W O R K S P A C E S
  20. 20. R U N W A Y I N C U B A T O R I S A F O R - P R O F I T W O R K S P A C E L O C A T E D I N S A N F R A N C I S C O ( T W I T T E R B U I L D I N G )
  21. 21. B2C B2B HQ Satelite Software Hardware 46% 54% 31% 69% 10% 90% 90% of the startups in a general purpose workspace are in software. B2C and B2B are equally represented, but satellite offices are 30% of residents. companies @runway have clear characteristics b2c vs b2b companies hq vs satelite Hardware vs Software
  22. 22. events/programs mentors/networking funding/capital location price point collaborative space Why did the company choose runway 5% 5% 47% 7% 16% 20% Startups chose Runway primarily based on location Twitter Square Uber
  23. 23. $2.84M $1.OM $4.75M How much capital have companies at runway raised? MEDIAN FUNDING OBTAINED AVERAGE FUNDING OBTAINED STANDARD DEVIATION A few companies skew the data higher. The vast majority of companies have raised less than $1M — most, in fact, have raised less than $500k.
  24. 24. What are The average and median length of time companies are located at runway? Expect high levels of churn at workspaces. Startups perceive workspaces as a temporary location; with plans to stay for less than two years. 48.23 30.00 52.25 10.87 9.00 11.41 18.44 17.00 14.07 Total Time Since Company Founding (months) Average Median Standard deviation Total Time in Runway Currently (months) Total Time Estimated to Be in Runway (months)
  25. 25. H A C K E R D O J O I S a N O N - P R O F I T W O R K S P A C E L O C A T E D I N M O U N T A I N V I E W
  26. 26. Why did the company choose hacker dojo? 22 17 17 43 99 17 22 17 24 80 22 29 25 57 133 13 24 14 32 83 18 26 24 46 114 Regardless of the length of time companies expected to spend at Hacker Dojo, Location was the number one reason for being there. Length of time Member Planned to stay at Hacker Dojo Events & programs 0 - 3 months 4 - 6 months 6 - 12 months 12+ months Total Equipment Links to investors mentors & networking Location
  27. 27. 1-2 members 2-3 members 3-5 members 5-7 members >7 members Where hacker dojo members live based on zip code
  28. 28. What is the funding stage and size of companies at the hacker dojo? $0 - $50,000 1 - 2 2 - 4 2 - 5 3 - 5 $50,000 - $550,000 $230,000 - $810,000 $260,000 - $610,000 Length of time at Hacker Dojo does not necessarily indicate more funding. Length of time member planned to stay at Hacker Dojo average funding 0 - 3 months 3 - 6 months 6 - 12 months 12+ months “low estimate to high estimate” “low estimate to high estimate” average current company size
  29. 29. Thank you GENERAL INQUIRIES SUPPORT OUR WORK @StartupPolicy StartupPolicyLab SPL HEADQUArTERS 1355 Market Street, 4th floor, San Francisco, CA, 94103
  30. 30. The people behind this project CHARLES BELLE KATHIE CHUANG DATA PARTNER CEO, Startup Policy Lab Intern, Startup Policy Lab DatableApp team on the 1’s and 0’s. We love startups and they are awesome.