VenChar

Eliyon Technologies

Eliyon Technologies is an interesting startup in a search vertical doing web mining and information extraction through natural language processing to create an extensive database of executives at various companies to aid in executive search, sales prospecting, business development, competitive intelligence, etc.

Eliyon has over 21MM individuals in a searchable database that's reportedly growing by 0.5MM monthly. For each individual, Eliyon creates a profile that includes title, bio, educational background, past employment history, other affiliations including board memberships, etc. Information is drawn from such sources as press releases, corporate websites, SEC filings, etc. They appear to have been cash-flow profitable for the last seven quarters and added 230 customers this last quarter. They just raised an institutional round from Venrock.

Click-through fraud

ZDNet has an article on click-through fraud problems with search engine advertising. Basically the problem is that you could run ads on your page and employ bots or low-wage offshor labor to click through those ads. Those click-throughs generate revenue for the web site through revenue-sharing agreements with the search engines running the contextual ads.

Now, since search engine advertising is a big business, you (predictably) have fraud-detection technologies emerging to address this problem, creating another arms race:

Fraud-detection technologies are emerging to help advertisers analyse their campaigns and traffic. Some advertisers and search-engine marketing companies say they are compiling lists of sites that generate a high number of clicks but not sales.

Coremetrics, Urchin and Whosclickingwho.com are just a few that sell technology to examine click rates and sales that result from paid searches. Alchemist Media, which charges flat fees for its consulting services, has detected fraud while acting as an intermediary between search networks and marketers.

Many policing technologies can counter click fraud by analysing Web traffic logs or surfing behavior. If a page is turned every 1.8 seconds over a period of time, for example, fraud-detecting systems will flag the traffic as suspiciously uniform.

Human operations can be more difficult to detect because a wide network of people can click on ads from different computers across many regions, without a steady pattern. According to a report in the India Times, residents are being hired to click paid links from home, with the hopes of making between $100 to $200 per month.

The ATM revolution

Fortune has an article on the history of ATMs in which it argues that ATMs pioneered the human-to-computer transactional interface:

The success of the ATM inspired similar innovations (some more frustrating than others) in a number of nonfinancial industries as well. Full-service gas stations have all but given way to credit card-primed gas pumps. Delta Air Lines has 846 do-it-yourself check-in terminals in 83 U.S. cities. Kroger has self-check-out lanes in more than 1,400 supermarkets. And you can find similar aisles in 850 Home Depot stores. Coming soon to Hiltons near you: 45 hotels that allow you to check in without seeing a clerk. Even McDonald's is testing to see if consumers like ordering Big Macs and fries on a touchscreen. One might even make the case that the ATM made Internet commerce possible: Certainly Amazon.com could not exist without consumers feeling comfortable shopping on a computer. Look at almost any company on the FORTUNE 500—in fact, pick up the phone and call one just to see who answers—and it is clear how important it is to business that customers are willing to automate in one form or another.

Fooled by randomness

I just finished reading Nassim Taleb's excellent book Fooled by Randomness which combines insights from finance, behavioral economics, probability and statistics. I will write more about this later, but here’s an interesting example from the book on how we lack intuition for basic probability calculations:

You are a doctor. A certain disease tends to afflict one in every thousand individuals in the population. There is a test for the disease, but in 5% of the cases it yields a false positive. A person comes in one day and tests positive. What are the odds he has the disease? Stop for a moment and try to answer the question.

The naïve answer (and the one that most physicians gave when posed this problem) is that he has the disease with 95% certainty. The correct answer is about 2% (notice the wide discrepancy here – the naïve estimate misses the mark by a HUGE amount). Why? Because the test has a markedly higher error rate than the base rate of occurrence of the disease in the population. Consider that in a population of 1000, 1 person on average will have the disease, but the test will generate on average 50 false positives. Therefore the probability that a given individual who tests positive has the disease is 1/51, which is about 2%.

Why does almost everyone venture an answer of 95% as the odds of the subject having the disease? My theory is that people confuse the probability of event A conditional on event B (p(A|B)) with the probability of event B conditional on event A (p(B|A)): the probability of testing positive given that one does not have the disease is 5% whereas the probability of not having the disease given that one tests positive is 98% and the confusion of these probabilities leads to the wrong answer.

This is certainly interesting – no one would expect a physician to intuit the subtlety of the problem, much less pull out a scratchpad and compute the right answer. Now change the framing of the problem. You are a juror. You know that one in every thousand people has committed a criminal act in their lifetime. A crime has been committed and a fingerprint obtained from the crime scene. The fingerprint was correlated against a random collection of fingerprints drawn from people in the city and a positive match was obtained with one individual. The fingerprint matching program has a false positive rate of 5%. What are the odds the individual committed the crime?

This kind of problem can crop up in a number of different real-world decision-making scenarios and our lack of intuition for probability can lead to drastically bad calls. Physicians presumably have some basic grounding in probability and statistics, but it is probably safe to assume most jurors are mathematically illiterate. Makes you wonder how many false positives are sitting out prison sentences.

The problem caught me out the first time it was presented to me, as it did a mathematician friend of mine. I observed a tendency on my part to jump to an answer (the wrong one, in this case). It was only when I discovered how drastically wrong I was, that I sat down and did the calculation. If the problem had been presented to me on a math exam, it would probably have engaged the analytic part of my brain and I would have patiently worked it out and arrived at the right answer. When it jumped out at me out of a context where I wasn’t expecting a tricky calculation, I jumped to an easy, ill-considered and completely wrong answer. This is probably what is referred to as engaging System 1 versus System 2.