Great to see data being used to dissect entrepreneurship, but have to disagree on a few fronts.
-- "Steve Jobs isn’t doing anything radically different than other entrepreneurs. He just knows the rules of the game and plays it extremely well." I think that's nice in theory and may make folks feel good especially as its impossible to prove/disprove but arguing his success is the result of playing a game better seems wrong.
-- There isn't any factor which suggests the importance of the market the startup is going after. While management and momentum are alluded to in the 14 factors, market seems to be missing, no? And this seems to be a major gap.
-- It's not clear how success is measured which would be critical to dissecting patterns of success? It seems its measured in part by funding received. But given an overwhelming majority of funded startups don't succeed, not sure funding is an appropriate proxy for success. It may be necessary but is certainly not sufficient to guarantee success.
-- While large companies can use data to achieve better results, that's driven by the fact that they are generally more in the "exploit" portion of their maturity curve, i.e. eeking out another percent or two of market share, margin, revenue growth, etc. Startups on the other hand are about "exploration" (vs the exploit of the big cos) and while the practices of Ries/Blank make that exploration more rational, well-conceived, it remains an inherently uncertain thing.
-- there is quite a lot of data about existing markets that are differentiated into cheaper, better and niche and new markets. How do you think is market significant. I am happy run a few tests for you :)
-- We measure success based on the consistency of how someone moves through a certain set of predefined milestones and thresholds. Funding, user growth, employee growth, etc. are traditional indicators of success that we correlate with our milestone based assessment in this report. We hope to become more granular in how we can measure consistency.
-- not sure how what you say contradicts anything we say. Thoughts?
- Jobs appears to have that certain je ne sais quoi. There are exceptional folks and outliers and he is one of them IMO. I wish I was smart enough to what it is about him so I could bottle that and become rich selling it, but alas...
- Market is significant because the size and growth of it matters. Picking a niche market that is not growing is a fatal flaw even if you do everything else right. Or when a market turns on you - just ask anyone involved in real estate in 2008.
- Success is profit or wealth-creating exit (not acq-hire or asset sale). By the metrics of funding, user growth and employee growth, most dot-com startups in 1999 were successes. I worked at one of them (Kozmo.com) - by those measures, we were one of the most successful companies ever :) Again, funding, users, employees may all be conditions which are necessary for success (not sure that's the case to be honest) but definitely not sufficient.
Am a data junkie so see lots of value in what you're doing but worry about efforts to distill entrepreneurship into a 'playbook'. There may be correlations with what is being observed but causality is a whole different world.
I never was successful in the startup area. But as far as I learned about chess, go, poker and other strategy games, the idea of the great player always are is, that they can think ahead of the pattern. And if they have a pattern it can't be described or understood (if you think of zen or kungfu it definitely takes more time for the student to really understand the real concepts in a way that he can use them in practice). So I wonder, is it really worth to look for a pattern, when there probably is none?
I didn't understand why, after filling out the form, I was given access to an html version but told over e-mail that "We will send you the pdf manually within 48 hours."
This has been one of the very interesting questions that probably a lot of universities tried to tackle.
Taking the approach of just analyzing over 600 startups and finding relevant pattern is remarkable and smart.
I truly believe that this work can have a huge impact on how we're going to build high scalable companies in the future.
Taking a new approach is at it's time where business plan competitions are not "the way" to go any more.
While this is very much a nascent field, this is the best attempt I've seen so far to do meaningful collection and analysis of actual data around startup sucess.
pretty awesome analysis, i've definitely had some misconceptions dispelled and some suspicions confirmed. extremely helpful for an active angel investor like myself. i particularly like knowing the optimal range for how many times a startup should pivot.
-- "Steve Jobs isn’t doing anything radically different than other entrepreneurs. He just knows the rules of the game and plays it extremely well." I think that's nice in theory and may make folks feel good especially as its impossible to prove/disprove but arguing his success is the result of playing a game better seems wrong.
-- There isn't any factor which suggests the importance of the market the startup is going after. While management and momentum are alluded to in the 14 factors, market seems to be missing, no? And this seems to be a major gap.
-- It's not clear how success is measured which would be critical to dissecting patterns of success? It seems its measured in part by funding received. But given an overwhelming majority of funded startups don't succeed, not sure funding is an appropriate proxy for success. It may be necessary but is certainly not sufficient to guarantee success.
-- While large companies can use data to achieve better results, that's driven by the fact that they are generally more in the "exploit" portion of their maturity curve, i.e. eeking out another percent or two of market share, margin, revenue growth, etc. Startups on the other hand are about "exploration" (vs the exploit of the big cos) and while the practices of Ries/Blank make that exploration more rational, well-conceived, it remains an inherently uncertain thing.