People seem to routinely overestimate the impact of new technologies in the short-run and under estimate the long-run impact. As a rule of thumb, a new technologies impact will be overestimated for 3-7 years looking forward, and underestimated 7-10 years out
THE BLOOMBERG EXPLANATIONS
As I am writing these lines I see the following headlines on my Bloomberg: —-+ Dow is up 1.03 on lower interest rates. —-+ Dollar down 0.12 yen on higher Japanese surplus and so on for an entire page. If I translate it well, the journalist claims to provide an explanation for something that amounts to perfect noise. A move of 1.03 with the Dow at 11,000 constitutes less than a 0.01 % move. Such a move does not warrant an explanation. There is nothing there that an honest person can try to explain; there are no reasons to adduce. But like apprentice professors of comparative literature, journalists being paid to provide explanations will gladly and readily provide them. The only solution is for Michael Bloomberg to stop paying his journalists for providing commentary. Significance: how did I decide that it was perfect noise? Take a simple analogy. If you engage in a mountain bicycle race with a friend across Siberia and, a month later, beat him by one single second, you clearly cannot quite boast that you are faster than him. You might have been helped by somerhing.ior it can be just plain randomness, nothing else. That second is not in itself significant enough for someone to draw conclusions. I would not write in my pre-bed-time diary: cyclist A is oetter than cyclist B because he is fed with spinach whereas cyclist B has :2 diet rich in tofu. The reason I am making this inference is because he beat him by 1.3 seconds in a 3,000 mile race. Should the difference be one week, then I could start analyzing whether tofu is the reason, or if there are other factors.
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.
Much ballywho is often made over the promise of micro-payment or “tipping” flavored monetization models online. The consensus view is that if you can just crack the code and lower the barrier enough for tipping to become dead-simple, then the floodgates of donations will start pouring in. A more bleak (but realistic) view is outlined in an article over at MonetizeThis. In the real world you get tipped when you make eye contact. A bigger crowd doesn’t always mean more tips. Tipping creates price uncertainty. Why pay for something that’s free?
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.