On 2005-01-27 20:53:00, Allan Engelhardt wrote in CYBAEA Journal:
Tim Oren is one of the consistently insightful venture capital bloggers, and his latest post, New Model Software Startups: Two Stage Ventures articulates a very strong trend that I have seen for some time but has not been exactly able to express. Interesting that it should be the same story at the same time on both sides of the Atlantic.
Tim identifies a new pattern in startups which he calls the two stage venture
. He focuses on software, but I don't think that is important. In the first stage, the venture builds a useful product as cheaply as possible
focusing ruthlessly on the value differentiators.
Build as little as possible, as fast and cheaply as possible, while demonstrating some unique value.
The second stage is either a trade sale or a venture capital expansion phase. For the second option, Tim predicts that the entrepreneurs will receive valuations well above what they would have commanded before achieving a first stage takeoff
because some of the risk has been taken away. For the first, he suggests that the market has genuinely changed:
While the [trade] sale may result in only a few million dollars, that outcome may be quite profitable to the founders and the individual backers. This may even be true on a risk adjusted basis, and that may be a new thing.
This is exactly the pattern I have seen here in Europe, especially over the last year or so. Consistently, the companies I have seen that have been successful have followed this path and consistently the ones that have failed to achieve success have not followed this approach.
Are other people seeing the same thing? Why this change now?
On 2010-07-13 07:47:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
I am not sure apeescape’s ggplot2 area plot with intensity colouring is really the best way of presenting the information, but it had me intrigued enough to replicate it using base R graphics.
The key technique is to draw a gradient line which R does not support natively so we have to roll our own code for that. Unfortunately, lines(..., type="l") does not recycle the colour col= argument, so we end up with rather more loops than I thought would be necessary.
We also get a nice opportunity to use the under-appreciated read.fwf function.
Read more (~535 words).
On 2010-06-22 11:45:00, Allan Engelhardt wrote in CYBAEA Journal:
We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is scary.
We now re-do the analysis four years later and, just because we can, we are using the leading companies of the London stock exchange instead of the largest American companies.
The results still hold. We called it the 3/2 rule: treble the number of workers and you halve their individual productivity. Large companies with ten times the number of employees are ¼ as productive as their smaller competitors.
Employee productivity is a big issue. If all the FTSE-100 companies achieved their average profits per employee, then the index would generate almost £1 trn of additional net profits for the economy.
Read more (~245 words).
On 2010-06-22 11:20:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is mildly scary.
We revisit the analysis for the FTSE-100 constituent companies and find that the relation still holds four years later and across a continent.
Read more (~763 words, 5 comments).
On 2010-06-17 09:05:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Following on from my previous post about improving performance of R by linking with optimized linear algebra libraries, I thought it would be useful to try out the five benchmarks Revolutions Analytics have on their Revolutionary Performance pages.
Read more (~300 words, 2 comments).
On 2010-06-15 10:21:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Can we make our analysis using the R statistical computing and analysis platform run faster? Usually the answer is yes, and the best way is to improve your algorithm and variable selection.
But recently David Smith was suggesting that a big benefit of their (commercial) version of R was that it was linked to a to a better linear algebra library. So I decided to investigate.
The quick summary is that it only really makes a difference for fairly artificial benchmark tests. For “normal” work you are unlikely to see a difference most of the time.
Read more (~934 words, 1 comments).
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