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Why Startups Fail (infographic based on Startup Genome data)

76 points| danberger | 14 years ago |visual.ly | reply

14 comments

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[+] mattront|14 years ago|reply
"Inconsistent startups write 3.4 times more lines of code in the Discovery stage and 2.25 times more lines of code in the Efficiency stage."

This is interesting. Either inconsistent startups (startups that are more likely to fail) over-do development - or - they tackle problems that are more complex and thus require more code. The implication of the later point is that startups working on difficult problems are more likely to make mistakes (like premature scaling) and fail.

[+] sthlm|14 years ago|reply
I always find there to be a very poor correlation between complexity, quality or efficiency and lines of code written. Therefore, I find the conclusion that they over-do development or approach more complex problems highly questionable. It might just be that they are not the most thoughtful programmers. This would go along much better with inconsistency. Instead of developing software, you set your mind on scaling fast, write a bunch of code and fall into the inconsistent startup category.
[+] klistwan|14 years ago|reply
I'd imagine it's due in large part to a lack of focus on the problem they're actually aiming to solve. "Even though we're solving Problem X, why don't we add Feature X, Y and Z?"

Lesson to learn? Focus on your solution to the problem people are having, and listen to your customers. Don't worry about the features you can add 3 months from now--your startup may not even last that long. Worry predominantly about getting the initial traction, sustaining it and developing a positive relationship with your customers.

[+] toblender|14 years ago|reply
This stat really stood out to me.

I think it's simply because a lot of start-ups don't realize that what they are making isn't that unique.

They can leverage many components that already exist in the market.

[+] todsul|14 years ago|reply
I'm sceptical about the usefulness of the dataset. One of the first things we learn as startup founders (or interns for Dr House) is that if you ask a person to analyse or predict their own behaviour, chances are the answer is way off. Even upon careful reflection and introspection, too many biases are at play.

I first learned this doing customer development for our current startup. We surveyed potential customers until almost being arrested at a private conference. We thought "this time is different" because we planned to validate the concept until we went numb. But we relied too much on others' self-assessments.

I'm not suggesting self-assessment is pointless (clearly it underpins our personal development), but rather in fleeting engagements with people who lack vested interest (e.g. surveys), it can do more harm than good.

Additionally, I found the Startup Genome survey so long-winded that my answers ended up being rubbish. It would have taken all day to get passed my own biasses and really think through that many questions. I understand there's more to the project than the survey, but that's the part I'm particularly sceptical about.

[+] truthseeker|14 years ago|reply
* But we relied too much on others' self-assessments.* Can you explain? Do you mean customer development brings in everyone's biases into the picture and not the truth?

What do you think is a better way to do customer development?

[+] projectileboy|14 years ago|reply
Great content; fair-to-poor presentation. The designers should re-read Tufte.
[+] binarysolo|14 years ago|reply
Love the Startup Genome Project report... great idea well executed by some ambitious folks.

re: infographic... I guess I'm kinda old-school, but I'm in the camp of data lovers who sees the utility of infographics as one that enriches viewers by easily bringing to light some otherwise difficult-to-intuit metrics and comps.

This is a really pretty picture, but don't treat it as a TL;DR version of the actual thing, which I find much more informative: http://startupgenome.cc/

[+] chintan|14 years ago|reply
ouch.. that infographic hurts! pls don't call it an infographic. Thank You.
[+] bragen|14 years ago|reply
I'm left unsure how they define "premature scale." If they simply mean "started paying more for customers than they're worth, and doing so on a massive scale," then, well, duh.

It would be more interesting to know what successful startups do at Stage 3. I doubt it's just wait longer.