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Havard Business School has an interesting study titled Do Friends Influence Purchases in a Social Network?. I would like to get my hands on the raw data (which is from the Korean social site Cyworld), but the outline conclusions seems plausible:
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The results from the KDD Cup 2009 are both interesting and fundamentally not interesting. For this public data mining challenge Orange, the mobile telecommunications company, provided anonymous data sets on mobile customers: 50,000 records each of training and testing data with 15,000 variables. (The data set are still available for download and there are also smaller data sets with only 230 variables.) The competition was to provide the best models for churn, cross-sell (“appetency”), and up-sell.
The problem with the competition is that we do not know what the data means: the variables are simply named Var1, Var2, ..., Var15000. This means that this is purely a statistical exercise and no understanding of the business problem is required or helpful. Which is really disappointing and made the challenge much (much) less interesting for me.
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The financial crisis is all my fault. Or so David Smith from our friends REvolution seems to suggest in his post Physicists, models, and the credit crisis:
I remember working in The City in the late 90's and Wall Street in the early 00's and remarking then that just about every quant had a physics or engineering background. I met very few statisticians. Quantitative models have taken a hefty share of the blame for the credit crisis, but I wonder whether the blame lies more in their application, rather than the models themselves. Statisticians are trained on the limitations of models, and how to detect when models are breaking down, but statisticians were woefully underrepresented amongst quants. Do physicists and engineers get similar training?
I was a physicist who left the CERN research facility to work as a quant in an investment bank in the days before “banker” became a four-letter word, so I do have an opinion on this. I firmly believe that no statistician should ever be allowed out in the wild unsupervised, and this gives me an opportunity to also comment on the current crisis: We have the luxury to be angry with the bankers. I do not want to take that luxury away from us: it is wonderful that we can afford to indulge ourselves. But I would like to remind us that is is a luxury that we can afford, and that we can afford it at least in part due to those bankers.
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Twenty years ago, on 13th March 1989, Tim Berners-Lee wrote the original proposal for what was to become the World Wide Web. Happy birthday!
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All too often marketing departments thinks that database analysis is the first, last, and only step in segmenting the base of existing customers. In fact, identifying clusters of common behaviors is only the first activity you should undertake in creating a customer base segmentation.
In this article we identify the five steps you need to follow for success. We also discuss when you can cut short the five step process.
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Over the last years we have been doing a tremendous amount of customer segmentation work with the marketing departments in companies across a number of industries. We have experienced that there are many misconceptions about what “segmentation” really is, why we do it, and what we can expect to achieve from it.
In this first article in a series, we look at the goals and objectives you should set yourself for the customer segmentation effort.
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Marc Andreessen, who as founder of Netscape and other companies knows a thing or two about the subject, has a nice little series on The Truth About Venture Capitalists, part 1, part 2, and part 3. We've said something similar before, but Marc puts it very well.
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We are keen on innovation here at CYBAEA, so I feel obliged to mention two articles on the subject that I noticed this week. One talks about urban growth and what we might call the innovation horizon while the other argues that there are no age limits on innovation.
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McKinsey writes about Better Strategy Through Organizational Design and come out strongly in favor of Enterprise Social Software and collaboration techniques:
[T]he new element that can help 21st-century corporations create more wealth is large-scale collaboration, across the entire enterprise, enabled by digital technology. ... Digital technology provides the means not just to promote efficient, effective, and large-scale collaboration but also to measure each person’s “assists” and thus motivate employees to collaborate in ways that were not possible in the past.
Their arguments are closely linked to the profits per employee measure we introduced back in October last year. This is the most important metric for the CEO to manage.
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If you are into security, classification, and document sharing, then you need to read Jeff Jonas' post "Need to Know" vs. "Need to Share" – A Very Fine Line Indeed. Otherwise you should probably skip it.
Jeff, whom I met at the ETech conference, is one of the smartest guys around, and if you are interested in information sharing, data privacy, and mining shared data, then you will be interested in his blog.
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I am in Las Vegas for the Unica user conference where we are considering the future of marketing (and how Unica can help you spend a lot of money, naturally). Darcy Bevelacqua from Harte Hanks had a neat line. As a consultant, she gets called in to help companies to define their direct marketing (and customer relationship) approach and to implement it. Her first question is Do you have a customer strategy?
, and the answer is almost inevitable yes
.
She then goes to Sales, Marketing, and Customer Service and seperately asks the question: Who is your best qustomer?
If she doesn't get the same answer, then she knows you do not have a customer strategy, not matter what your corporate slide-ware says.
She never gets the same answer.
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The BBC is reporting on a row over HD-DVD encryption which was broken almost half a year ago by some customer who actually wanted to watch the movie he (thought he) had purchased.
I think it is terrible that anybody would even think about bypassing digital restrictions management (DRM) technologies, much less actually distribute the 16 bytes that makes up the decryption key in hexadecimal digits or otherwise. Truly terrible. I would never do such a thing. Ever.
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The Post-GUI era
. I like the expression and I think what it tries to encapsulate is important.
I am back from O’Reilly’s 2007 Emerging Technologies conference. The recurring theme of the conference, at least to my mind, was this: as technology becomes ubiquitous we need to think much harder about how technology interfaces with humans.
The world already has an interface, and it isn’t a GUI. We should learn from that.
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Enterprise Social Software has gone mainstream. I say this based on the fact that the analysts are now releasing stacks of research on this area. Forrester is a good example, and McKinsey is also in on it (yes, Web 2.0 is a strategic management issue now).
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We don't normally post just links, but sometimes it is just too hard to keep up with the good stuff. Mike writes from South Africal about lessons learned from social software implementations, and Centopeia (almost as difficult a name as CYBAEA!) comments on Lee's presentation at LIFT.
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Interest in productivity and how to manage innovation and know-how appears to be growing. Our article on employee productivity gets about four times more hits than the next most popular post. I imagine that more and more people in the West are waking up to the fact that innovation and high productivity is the only thing that keeps jobs here.
A recent paper from Harvard considers the optimal implementation of knowledge management. The maths is ridiculous, but the conclusions broadly ring true. With a slight reformatting for clarity:
We derive three main results [about the optimal management of know-how].
- First, information about successes is typically more useful than information about failures, since successful methods can be replicated while failures can only be avoided. This supports firms' focus on 'best practice'.
- Second, recording mediocre know-how can actually be counter-productive, since such mediocre know-how may inefficiently reduce employees' incentives to experiment. This is a strong-form competency trap.
- Third, the firms that gain most from a formal knowledge system are also the ones that should be most selective when encoding information (i.e., the ones that are most at risk from the competency trap); namely, large firms that repeatedly face problems about which there is little general knowledge and that have high turnover among their employees.
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You are not nearly as influential as you think you are. One of my friends are doing research on influencers in mobile networks, and he is going to be crushed (or maybe not) by this entry in a recent HBR list which basically says that influentials are not very ... influential. That is to say that the spread of new ideas in a social network is not dependent on a few super-connected, highly influential members, contrary to popular assumptions. The article offers some specific strategies for marketeers:
Because the ultimate impact of any individual—highly influential or not—depends on decisions made by people one, two, or more steps away from her or him, word-of-mouth marketing strategies shouldn’t focus on finding supposed influentials. Rather, marketing dollars might better be directed toward helping large numbers of ordinary people—possibly with Web-based social networking tools—to reach and influence others just like them.
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Maths is the new India. Really! That's what the HBR says in a new list:
The rate at which [mathematical algorithms] are intelligently adopted could be a differentiator in the wider marketplace. Cheap algorithms are like cheap labor and cheap capital—a valuable resource when judiciously employed.
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Since it is Sunday I guess we can link to a nice little time-waster that you may already have seen. If you are a Trivial Pursuit fan, read on.
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When it comes to being an entrepreneur or venture capitalist, is it better to be lucky or good? According to a new working paper, Skill vs. Luck in Entrepreneurship and Venture Capital: Evidence from Serial Entrepreneurs, skill carries the day most of the time. "We show that entrepreneurs with a track record of success are more likely to succeed than first-time entrepreneurs and those who have previously failed," the authors observe. "Similarly, more experienced venture capitalists are able to identify and invest in first-time entrepreneurs who are more likely to become serial entrepreneurs."
The 42-page paper is a $5 download. Be warned that the shopping system is very slow.
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More posts from CYBAEA Journal: 0-20 | >Next 20
On 2009-07-02 20:33:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
I am a sucker for good quality data. I wrote about data.gov, the US Government data site before, and now I find OECD Statistics which has some 300 data sets, many of which seems to be readily accessible (though some may require subscription)
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On 2009-06-16 10:27:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
I like the "multicore" library for a particular task. I can easily write a combination of if(require("multicore",...)) that means that my function will automatically use the parallel mclapply() instead of lapply() where it is available. Which is grand 99% of the time, except when my function is called from mclapply() (or one of the lower level functions) in which case much CPU trashing and grinding of teeth will result.
So, I needed a function to determine if my function was called from any function in the "multicore" library. Here it is.
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On 2009-06-12 10:23:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Somebody on the R-help mailing list asked how to get Rmpi working on his Fedora Linux machine so he could do high-performance computing on a cluster of machines (or a single multicore machine) using the R statistical computing and analysis platform. Since it is unusually painful to get working, I might as well copy the instructions here.
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On 2009-06-09 11:23:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
O’Reilly has published Data Mashups in R as a $4.99 PDF download in their Short Cut series. In 27 pages it takes you through an example of how to combine foreclosure information with maps and geographical information to produce plots like the one here. This is all done with the R statistical computing and analysis platform.
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On 2009-06-01 07:07:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Hugh Miller, the team leader of the winner of the KDD Cup 2009 Slow Challenge (which we wrote about recently) kindly provides more information about how to win this public challenge using the R statistical computing and analysis platform on a laptop (!).
Read more (~456 words).