Category Archives: Thoughts

Algorithms work better than doctors

I came across this fantastic paper after reading about Meehl‘s 1954 book, “Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence”.  It’s a meta-analysis (review of studies) of the evidence that mechanical prediction techniques like fixed questionnaires or algorithm generate superior predictions compared with those made by clinicians, regardless of their level of expertise.

In plain words, it suggests that simple questionnaires and algorithms arrive in more accurate predictions compared with clinicians, doctors or psychologists.  I can’t believe this is not mandatory reading in every university.

The abstract:

The process of making judgments and decisions requires a method for combining data. To compare the accuracy of clinical and mechanical (formal, statistical) data-combination techniques, we performed a meta-analysis on studies of human health and behavior. On average, mechanical-prediction techniques were about 10% more accurate than clinical predictions. Depending on the specific analysis, mechanical prediction substantially outperformed clinical prediction in 33%-47% of studies examined. Although clinical predictions were often as accurate as mechanical predictions, in only a few studies (6%-16%) were they substantially more accurate. Superiority for mechanical-prediction techniques was consistent, regardless of the judgment task, type of judges, judges’ amounts of experience, or the types of data being combined. Clinical predictions performed relatively less well when predictors included clinical interview data. These data indicate that mechanical predictions of human behaviors are equal or superior to clinical prediction methods for a wide range of circumstances.

The full article is available here:

http://www.psych.umn.edu/faculty/grove/096clinicalversusmechanicalprediction.pdf

Advertisements

Is Mobile the Graveyard of VC money?

Are Mobile Start-ups there?

Can you name one successful mobile software start-up, e.g. one that sold for $100m+ and generated nice returns to its VCs?  If you cannot come up with a name, you might agree with a VC partner I recently met, who claimed that Mobile has so far been the graveyard of VC money.   With that in mind, I went crawling the web for the successful mobile exits.

The mobile telecoms industry is one of a mere few trillion-dollar industries, like automobiles, tourism and military.  It generates more revenues than either the advertising, TV, Internet or software industries.  Is it conceivable that despite its size it did not produce a single YouTube, Flickr, Hotmail, Skype or a Facebook?

I tried hard, but really could not find a single big exit for a mobile start-up. There are only a few that could be considered runner-ups to the titles “successful” but each has its issues:

  • AdMob is still private but going after the big prize of the #1 mobile advertising network.  They might make it to a $100m+ sale but with CPMs plummeting they will have to hang in there for a few more years at least.
  • Zyb generated a nice return to its VC but was still only a $49m deal.
  • M:Metrics at $44m, was not a huge deal either.
  • Loopt is still private, well hyped and with zero revenues, despite ABI’s dreamed-up target of $3bn for this segment.  With Google Latitude zero rating location-based-services, I doubt if their revenue model will stand.
  • JumpTap has with already raised $73m in funding since ’05, so I sure want to see how they drive a 3-5x return with Google, Yahoo and Microsoft buying operator deals for nine-figure sums.
  • Don’t get me started about MVNOs like this one, or this one or any of these ones.
  • Others like Shozu, Amobee are even further away from becoming the next big thing.

Unless someone can come up with a start-up which I have missed, it does indeed look like Mobile has been a graveyard for VC money so far.

Challenging the Long Tail Theory

Anita Elberse challenges the Long Tail theory in a well-written article in HBR this month.

The search-engine marketing (SEM) industry could serve as an excellent test case for the Long Tail theory and for Elberse’s criticism of that theory.  Advertisers bid on keywords in search engines (e.g. Google, Live Search and Yahoo) to advertise their services. An insurance company can bid on a keyword like “insurance” or on a long tail of keywords like “insurance mazda mx5 automatic” or “insurance 29 male boston ford focus”.
According to the “Long Tail” theory we would expect to find a long tale of obscure keywords. The tail is expected to generate more revenues than the head.
According to the critical theory we would expect to find that the long tail is flat/flattening and generates fewer revenues than the head. E.g. that Google generates >90% of revenues from less than 10% of keywords.

Myriad of bloggers and search-engine-optimisation (SEO) agencies reported that the “Long tail” theory is indeed applicable in their industry. Are they wrong? If they are not wrong, how can the critical theory explain this discrepancy? Is the critical theory universally applicable or are there special cases where the “Long Tail” theory does in fact prevail?

Trackback for discussion: http://conversationstarter.hbsp.com/2008/07/the_long_tail_debate_a_respons.html