So, in two days, that blog post generated 535 views of the Amazon page and 40 purchases. The affiliate fees associated with those 40 purchases add up to $6.50.

But those 535 views are pretty valuable. Those 535 clicks translated into a total of 118 orders in the past two days, including a Kindle. The total affiliate fees associated with those 535 clicks were $25.20.

But even including all the commerce that was generated from that link, that $25.20 is a cost per click of roughly 5 cents. I think that’s low for a bunch of reasons.

The post inspired some interesting conversation around how in a perfect world, each player in the chain of purchasing-intent generating events e.g blogs, social media pundits all the way to affiliate marketers should be compensated in some way, as opposed to the way affiliate links function now, where only the last attribution is compensated.

Inspired by the post, I decided to construct my own experiment in recommendation commerce to see if I could influence anyone to buy a product through an affiliate link, answering questions on Aardvark. I decided to only present an affiliate link as an answer when it was relevant to the question being asked, and after a series of un-monetizable questions, I was presented with the following.

The book I recommended was Mental Models: Aligning Design Strategy with Human Behavior (notice affiliate code in link again).

Notice how my answer was hyper-relevant to the question being asked of me. I would guess that in any other scenario, my affiliate link given as an answer would not have been treated with such gratitude. Also of note is the added benefit of my referral, Aaron purchased another two books, sweetening my take by $1.98.

Does this form of monetization scale? Maybe. Does it make sense at such a small one-to-one level? Probably not.

Therefore, I think it remains to be seen, which model (if any) is waiting to be unearthed to monetize the value that is generated in the form of social reviews and recommendations, but it is an interesting space, and one that I will be keeping my eye on.

A scary thought experiment…

Wikipedia is entirely supported by donations. So why isn’t it a good model for everyone else? Last year they raised $6.2 million from 125,000 donors. While that’s a lot of money and more than covers their operating budget for the year, Wikipedia gets 190 million visitors a day. Think about that for a second, one of the most import information sources in human history with daily traffic bigger than most nations only got 125,000 donors? How much is one of Wikipedia’s 5 billion monthly visitors worth when it comes time to donate? $.00124. That’s a fraction of a penny. If you’re providing a human experience enriching service on par with Wikipedia, multiply your total audience size by $.00124 to figure out how much you can expect to make from donations. That comes to about 8 million people to stay above the poverty level ($10,000). Wikipedia’s efforts worked well enough, but it’s a frightening proposition for anyone who doesn’t one of the most trafficked websites on the planet.

- Ludwig von Mises, Human Action: A Treatise on Economics

Is this a reason to be optimistic or sign of a bubble forming?

In the third quarter, and this was with the benefit of a 3.6% lift in property taxes (amazing), tax receipts at the lower levels of government fell 7% in Q3 from a year ago. Sales taxes are down 9% (seems just a bit at odds with the retail sales data, no?) and income taxes off 12%. If that is what a recovery does to fiscal finances, imagine what the backdrop will look like if the economy actually does pull a ‘double dip’.”

Via Business Insider

Work expands so as to fill the time available for its completion

In Software:

Software Bloat: Data expands to fill the space available for storage.

Example: Too many to list.

In Academics:

Student syndrome refers to the phenomenon that many people will start to fully apply themselves to a task just at the last possible moment before a deadline

Example: Cramming before a midterm.

In Business:

Expenditures rise to meet income and NO amount of computer automation will reduce the size of a bureaucracy.

Example: Australian Tax Office.

Source: http://en.wikipedia.org/wiki/Parkinson%27s_Law

Case 1: Professional Athletes

The general consensus among sports fans is that professional athletes appear grossly overpaid. How can one player truly be worth the $20 million dollars a year that their team is shelling out for their salary?

In fact, I would argue that professional athletes are probably the most fairly compensated in our society. Why would I say such a thing?

The numbers don’t lie. Professional athletes are the most accountable because every aspect of their production is meticulously tracked and measured. At the end of the day when A-rod’s agent goes to negotiate his contract, he is selling a body of work based almost exclusively on measured results and future projections based of those past results. Of course there are some intangibles that are taken into account like personal character etc.. but for the most part, the fair market value of a player is determined exclusively by the numbers.

Case 2: Wall St. Traders and Hedge Fund Managers

In the business world, the equivalent of the professional athlete is the Wall St Trader or Hedge Fund Manager. Their value is determined strictly on the revenue’s generated from their trading activity. While the performance of each individual in an organization is not completely measurable (compared to athletes), there is still a high level of accountablity and thus measurability of one’s performance.

This situation generally leads to fair value compensation in this industry under normal circumstances (putting aside the obvious manipulations and distortions of late). At the end of the day, your trades either lost money or made money. It is not hard to justify giving out a $1million dollar bonus to an employee who directly generated $10million in trading revenues for his firm.

Case 3: Everyone Else

In just about every other profession, there is decidedly less transparency and measurability when it comes to job performance. This can lead to enormous asymmetries in compensation packages and advancement opportunities. Instead managers have to rely on qualitative proxies for output such as performance reviews from fellow managers and employees.

Of course there are other good quantitative signals to use to gauge performance such as Sales #’s (for Sales Associates) and Students Grades (for Teachers). However, for the most part performance is still a black box.

Will this change as technology progresses and we acquire the means to effectively measure performance on a more granular level? I’m betting that it will.