Maybe I’m way behind on this — but here’s an idea. Write very detailed blog posts about a topic or question you have. Something worth speculating on — but isn’t really out there in the blogosphere or media. Then just use a halfway decent traffic analysis tool and read the tea leaves.

I posted a clearly over the top post, “Apple Acquires Last.fm“, on 19 August of this year. It’s a provactive title that gets a little bit of search traffic my way. I didn’t write the post to get traffic. The web 2.0 acquisition market seemed so heady. I thought it’d be funny to package my iTunes wishlist as a critique of the silly enthusiatic part of the web2.0 lexicon (my GenreFolksonomies). I also have written another “wishlist” post to Apple — about wanting to switch to Mac. And in some weird sort of synchronicity, they addressed most of my needs by introducing the Mini.

But looping back to where I started this post. While my intention in writing the Apple/Last.fm post wasn’t to “bug the internet,” I’m learning that, in fact, I have. I don’t have that strong of a signal on this blog — I haven’t consistently written or made people aware that it exists. So any traffic bumps are caused by waves and ripples of the web. I just had a spike of searches like: “how many last.fm users, last.fm acquire, last.fm itunes.” Sure, it’s a lot of noise — but think of the potential for parsing the signal?

Yes, you say, of course that’s what Digg and Technorati and the hundreds, if not thousands, of sites have been trying to do with blogs. But I think those are more about parsing the signals through the existing “microphones” on the web. I’m talking about strategically placing the microphones — not for traffic — but to learn.

Business 2.0 has an interesting feature this month: Road Warrior’s Guide to Travel (at the time of publishing, the site didn’t have the April issue content up). The best of the Road Warrior (?) tips involve air travel. Check out Flight Stats.

The best way to see the value immediately is through the Flight Report. I can imagine scheduling all my business travel through an engine like this. And imagine if you actually paid for performance? I’ll use LGA to ORD as the test flight. It would work something like this:

First, if you delve into on-time performance metrics, you’ll see that on-time is relative. I can travel from New York to Chicago and have a scheduled flight time of 2 hours and 26 minutes if I leave at 6 am and 2 hours and 35 minutes if I leave at 7 am. The later flight has 9 extra minutes built in. Why should I pay the same for a flight that is scheduled for 6% longer for me, all things equal.

The key there is “all things equal.” If price adequately reflects demand (which we shouldn’t assume) we have to understand the components of demand. Comfort, speed, convenience, meets schedule needs, etc…

So let’s go back to the LGA to ORD example. There are five flights that leave on a weekday between 6 and 7 am. Say I need to get to a 10 am meeting in the loop. My primary concern is making the meeting on time–but I also don’t want to get up any earlier than I need to. These five flights are all in play.

So I’ll start with the two 7 am flights. How much time will I have from the time I land until the meeting? I want to give myself 60 minutes from the arrival. That means I have to arrive by 9:00. Looks like I won’t have a problem with either flight. But what are the chances of landing on time? The United flight (669) is on time (within 15 minutes of sked arrival) 78% of the time. The American (305) does a little better at 81%.

The next check is, if it is late, how late will it be? United averages 14 minutes delay with a standard deviation around 11. American comes in with an average of 15 minutes and a high standard deviation of around 31 minutes. So while they have a good chance of being on time, the American flight has much more variance in arrival time–if it is late it will have a much greater chance of being significantly late.

So based on performance, how much more or less will I pay for the American flight over the United flight?
Rather than using Flight Stats on-time percentages, I’m using their average and standard deviation on delays to come up with the 90% Minutes metric: I have a 90% chance of being no more than x minutes late.
For American flight 305, my 90%m is 55 minutes — 90% chance of arrive by 9:29 am.
United flight 669 90%m is 29 minutes — 90% chance of arriving by 9:03 am.

or

I have an 85% chance of arriving by my target time on United 669–subtract a 6% chance of the flight being cancelled = 79% chance of success.
I have a 62% chance of arriving by my target time on American 305–subtract a 6% chance of the flight being cancelled = 56%

This is just the start. If the meeting start time is very important–I’m depending on other people also arriving at certain times, 79% might not even be good enough. This also is using an average that hasn’t accounted for seasonality or additional weather concerns, that I can tell. Without taking this all the way through, so far I decided I have a 24% probability advantage on United. I’ll work some more on this in a later post.

I’ve been thinking a lot about movie trailers over the past few months–but with the Oscars a month away, my attention has shifted to the movies themselves. Specifically, how do you quantify the success of movies beyond awards and box office sales?

I’ve just begun–and limited this first look to the movies nominated for best picture. Below are the daily box office grosses. The same scale is used on each graph.

Daily Box Office History of Oscar Nominated Best Pictures

Brokeback Mountain: continuing to rise.
Capote: flat with small spike with award buzz.
Crash: built early and flattened. Only film not currently in theatres.
Good Night: slow build with only minor Oscar-induced spike.
Munich: declining in growth. Doesn’t seem to be on the solid upward trajectory of Brokeback.

Daily Screens of Oscar Nominated Best Pictures

Brokeback Mountain: had a very slow rollout. Would it gross even more following a Crash-size release? Was it deemed to risky at first?
Capote: got no screen-time until award-buzz started to kick in. Not a proportional increase in revenue though–average revenue per screen is still declining.
Crash: kicks the trend established by the other slow-growers of the group–starting strong and slowly losing screens.
Good Night: rose and fell and gets and award boost.
Munich: shows all the signs of falling well short of the estimated cost of $75 million. The number of screens were already on the decline weeks ago, rescued only by a small award bump.

I created a one-pager of other metrics (pdf). Below is a low-res excerpt. If you’re interested in seeing it all, send me a note at fulminator at gmail dot com. Each of the 5 movies are broken out like Brokeback Mountain. I’ll include a cheat sheet on the metrics as well.