Modern Metrix

Color Forecast

This is awesome! French clothing company Pimkie has installed cameras on the streets of Milan, Paris and Antwerp (supposedly, Europe's most fashion-conscious cities) to capture images of people's clothing. The captured data is then converted into real-time "color forecast" on their webisite, so you can see what colors are trending right now in these cities. You can also look at the colors that were trending yesterday, last week, and last month. For example, these were the most popular colors in Paris in past 30 days:

Imagine taking this concept further, by capturing gender and age of passerby's to further enhance the forecast? Or capturing the images of shopping bags on the streets  and then projecting which stores are currently trending, on the giant screens, or on websites and in mobile apps?

I am Appalled

Am I the only one who finds this recent article in Mediapost on how advertisers and cable companies continue to seek more elaborate ways to run ads on new TV and video watching platforms like Netflix and video-on-demand, very disturbing? I particularly found industry's findings that "consumers are not only embracing programming, but advertising on VOD platforms" simply jaw dropping. Are you f$%##ing serious?!!! You guys just don't get it! We hate ads! The very reason we cut the cable cord and switch to Netflix and similar platforms is because we are sick and tired of being bombarded by annoying commercials!

The future of advertising are search and INTENTIONAL or UNINTERRUPTED discovery, which imply full control by consumer of where, when, how and why of exposure to information about products and services, as opposed to "in your face", "spray and pray" approaches so favored by old media. 

The Rise of Data Scientists? Apparently, Not Fast Enough

As data become more abundant (and messy), there is increasing demand for more specialists who could not only separate noise from proverbial "golden nuggets" of information, but also make those "nuggets" useful and actionable:

"Hilary Mason, chief scientist for the URL shortening service bit.ly, says a data scientist must have three key skills. "They can take a data set and model it mathematically and understand the math required to build those models; they can actually do that, which means they have the engineering skills…and finally they are someone who can find insights and tell stories from their data. That means asking the right questions, and that is usually the hardest piece."

It is this ability to turn data into information into action that presents the most challenges. It requires a deep understanding of the business to know the questions to ask. The problem that a lot of companies face is that they don't know what they don't know, as former U.S. Defense Secretary Donald Rumsfeld would say. The job of the data scientist isn't simply to uncover lost nuggets, but discover new ones and more importantly, turn them into actions. Providing ever-larger screeds of information doesn't help anyone."

Gmail Meter Helps You Analyze Your Gmail Usage

Just as I started looking for better ways to measure my Gmail usage, Gmail labs rolled out the Gmail Meter script! Among the statistics, provided on a monthly basis and summarized via nice charts, are volume, traffic patterns and time analysis, length of messages and conversations, and even how long does it take for you to respond to e-mails. Check it out!

Where Did My Taxes Go: Data Visualization

Check out this awesome tool that provides detailed visualization of how your federal tax dollars are being spent by the US government.

Quantifying "Walkability"

Slate looks at how small start up Walk Score is attempting to quantify the "walkability" of geographical locations. 

How Americans Spend their Money: Break-Down of Expenditures for Average US Household

NPR has interesting visualization that summarizes findings of Bureau of Labor Statistics from their most recent Consumer Expenditures Survey:

The article also looks at how American's spending habits changed over last several decades. In particular, compared to 1949 data, Americans nowadays spend considerably less money on food (15% in 2011 vs. 40% in 1949) and clothes (3.6% in 2011 vs. 11.7% in 1949), but more on housing (41% in 2011 vs. 26% in 1949) and transportation (16.9% in 2011 vs 7.3% in 1949). 

Update: here is an alternative visualization, with more expenditure categories. 

Where Facebook Ads Work Better: States with the Highest and Lowest Click-Through Rates

This article in Mashable shares interesting statistics on geographical difference in click-through rates for Facebook ads,  provided by digital marketing agency:

"Facebook users in South Dakota, Tennessee and Colorado click through ads on Facebook at record rates. In South Dakota, Facebook users click on ads at a rate of 174% the national average.

Meanwhile, residents of Vermont, Alabama and Indiana are the least likely to take the bait. In Vermont, the click-through rate (CTR) is 57% the national average."

The differences are not explained. If you ask me, the first what comes to mind is demographics, most likely, education (states with less educated population have higher click-through rates). I also suspect that states with the higher click-through rates are are ealier stages of Facebook adoption (the novelty factor comes in play, hence more clicks). But that is just IMHO.

Free Twitter Data!

I am always on the look out for free data to play with, and today stumbled upon awesome depository of Twitter data on Infochimps website. Check it out!

 

Why "Pace of Life" is Faster in the Cities

This short but fascinating article in The Atlantic explains why pedestrians in larger cities walk faster:

"Levine measured three "pace of life" variables — walking speed, work speed, and clock accuracy — though for the purposes of this post, in keeping with the previous literature, we'll focus on the first. The top ten cities for walking speed, according to Levine, were as follows: Dublin, Amsterdam, Bern/Zurich, London, Frankfurt, New York, Tokyo, Paris, Nairobi, and Rome.

With the exception of Nairobi — insert joke here about Kenyans crushing everyone at the New York City Marathon — the fastest walking cities were from wealthy nations. The statistical analysis confirmed this general perception: two of the three strongest social predictors of walking speed were a country's G.D.P. and its purchasing power parity (the other was its individualism). Indeed, when Levine considered all his "pace of life" metrics, he found that pace of life was swifter in "economically productive countries" like Western Europe and Japan than in undeveloped countries."