Tag Archives: data

Data Laziness

Ridership on the New York City subways declined last year because, well, they’re not sure, really:

The [Metropolitan Transit] authority’s acting chairman, Fernando Ferrer, said on Thursday that several factors could be contributing to the decline: rising subway delays, the popularity of Uber and other apps, and weekend maintenance work that disrupts service.

“It may be all of the above,” Mr. Ferrer told reporters after an authority board meeting. “I’m very glad that our ridership is at historic highs. If it declines a little bit — and I’ve seen those numbers, and it’s a little bit — there is no reason for alarm.”

You want “reason for alarm?”  I’ll give you reason for alarm: the MTA’s chairman can’t be bothered to run a simple Excel spreadsheet.  Let’s call this “data laziness” and show you how easy it would be to get a more definitive answer.

Actually, Excel is a much more useful tool here

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Let’s Mess with Starbucks: A Datajacking Primer

Pity poor Starbucks.  Coffee snobs, a demographic that Starbucks all but created, love to hate them.  Whenever the American right wants to take a swipe at liberal values, they try to pull some stunt at Starbucks, such as mixing Berettas and cappuccinos.  Across the pond, British activists use the House of Mermaid as a stand-in for globalization and/or Yankee imperialism.  Since SBUX CEO Howard Schultz proudly supports Israel in his private life, some anti-Zionist organizations have suggested boycotts.

(On a personal note, I suggested a counter-boycott at the time and recommended that my Zionist friends buy multiple espressos in support.  Those were some very hyperactive Jews.)

Now I invite you to jump on the bandwagon and help me turn Starbucks cafes into rattling dens of death metal by messing with Starbucks’s data.

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Schroedinger’s Cart

As a blogger, I try to cover a lot of ground in marketing and marketing data.  My posts range from how-tos to POVs to the occasional bit of humor.  And then everyone once in a while, I like to go completely “out there” and tackle a marketing issue with a decidedly off-kilter approach.  This will be one of those times.

Lately, I’ve been thinking about how people shop, both in-store and online, and it’s given me some potential insight into how marketers might be able to develop more appealing experiences for customers.

Behold, Schroedinger’s Cart:


No cats were harmed in the creation of this extremely arduous pun

Physics Nobel Laureate Erwin Schroedinger (or Schrödinger, if you must have the umlaut) famously posited a thought experiment about a cat in a box.  Schroedinger asked the reader to imagine that a a random event inside the box would release a poison gas and kill the cat but that the outside observer would have no idea whether that random event occurred.  He famously asserted that the cat was both alive and dead until the observer opens the box.

This is ridiculous, of course.  Except this thought experiment perfectly describes how we often shop.

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Data’s Inigo Montoya Problem (Part II)

If you’ve had campaigns fail because of bad data, then maybe you’ve fallen victim to the “Inigo Montoya problem of data.”  That is, maybe you’ve used data that don’t mean what you think they mean.

Previously, I discussed the origins of bad marketing data.  Now I’d like to discuss how to fix the problem.

First, I recommend something so exciting that you probably will not finish reading this post before trying it: read the dictionary.


Yeah, yeah.  I know.  All characters, no plot.  Wait, no.  That’s the phone book.

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Data’s Inigo Montoya Problem (Part I)

Of all the quotes in the infinitely quotable movie “The Princess Bride,” (“Have fun storming the castle!”  “As you wish,” “Never get involved in a land war in Asia!”), one always stood out for me:


Inigo Montoya: You keep using that word. I do not think it means what you think it means.

If I’ve encountered one problem more than any other in marketing data, I’d call it the Inigo Montoya problem: the dangers of using misleading data.


Photo courtesy of, let’s see here, The Daily News.  Huh.

The problem seems so widespread–and so dangerous–that I’ll address it over two columns:

  1. How misleading data occur
  2. How to prevent or work around bad data

In theory, bad data shouldn’t exist.  Of course, as Homer Simpson noted, “In theory, Communism works. In theory.”  How do bad data arise?

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Taboo Data

A friend and former colleague coined the term “taboo data” recently, and I intend to steal it.

By way of background, my friend Trey Peden started seeing wedding ads online after visiting wedding-related sites ahead of his upcoming nuptials.  This targeting should come as no surprise to anyone familiar with the current state-of-play of online targeting.  Only one problem: none of the couples in the ads resembles Trey and his intended because his intended is also a man.

Without diving into the churning debate about gay marriage (I’m very much in favor of it, if it makes a difference to you), I find Trey’s take on the situation enlightening.  He knows enough about online targeting to know that if they could divine his imminent (within two months) wedding by his web tracking, they could also glean his sexuality.  In turn, there’s no reason he couldn’t have seen ads with two groom or two bride figurines on top of the cake.  He assumed that these marketers made the decision not to target this way to avoid controversy.  Hence, he coined the term “taboo data.”

Have we created a class of data that we can derive easily but that we can use only at our peril?  Let’s talk about the implications of taboo data.


Not in front of the monkey!

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Concussion evidence? There’s an app for that.

Fast on the heels of Major League Baseball’s Statcast, the National Football League has announced that it will start sharing data from its games as well.  Next Gen Stats will use sensors embedded in players’ shoulder pads to track them on the field.  Following key plays, Next Gen will break down the data into slick illustrations that fans can watch on certain Microsoft devices (a gimme to MSFT owing to their long-time NFL sponsorship, presumably).


Like TRON, only with PEDs

While undeniably cool and very appropriate for the Madden fans out there, this venture may end up biting the NFL in the ass.

While the NFL has been dealing with recent scandals involving one of their top players and a drumbeat of domestic violence cases, it has also had an issue with player concussions.

I have to imagine that some data-focused fan will start collecting velocity and acceleration/deceleration data from this app and start calculating the forces involved throughout the game.  In turn, these calculations will only underscore the risks of concussion on pretty much every play in the game.

And those risks do not make for a pretty highlight reel.

You see unnecessary landfill; I see data

The problem with analyzing data for a living is that you begin to look for data-driven decisions in EVERYTHING.

¡Por ejemplo! Home Depot’s plastic bags.

I bought some flying insect killer at HD today.  More specifically, I patronized their West 23rd Street store in Manhattan.  That’s when I noticed unusually thick plastic bags.


Rightfully reviled as they are, sturdy plastic bags mean a lot to city dwellers, at least the ones who don’t own cars.  Schlepping groceries, school supplies and whatnot down the street, on the bus or into the subway really brings out the bags’ value.

For drivers, cheap bags mean, at worst, spilled oranges in your trunk.  For pedestrian shoppers, it means piñata time.


What it feels like when your Trader Joe’s bag opens up on the #11 bus

Here’s the thing: I used to visit Home Depot’s 59th Street store all the time, the one where Alexander’s used to be.  Those bags, and the ones I’ve gotten at suburban HDs, seemed cheaper.

The data nerd in me wants to believe that someone at HQ in Atlanta, someone used credit card data to measure distances from home or business addresses of purchasers to the address of the store where they purchased and then used a proprietary algorithm to determine which stores need heavier bags.

OK, HD can’t possibly work this way, but a fella can dream, can’t he?

Behind the Numbers: 39% of us Totally <3 Big Brother!

I, for one, welcome our new wearable overlords!


In my case, I think I could put several brands to sleep with my “lifestyle”

Accent Marketing Services recently shared a survey with eMarketer about consumers’ interest in wearables.  As the market evolves, I’m sure these will change, but one figure really stood out: nearly four out of ten respondents interested in wearables (smart watches, fitness bands, glasses, codpieces, etc.) said they wanted to give “brands more insight into [their] lifestyle.”

Come again?


Wear the Apple Watch for your Protection, Please

The one thing that most of my friends of all political stripes can agree on is that they don’t want private companies or the government collecting more information than they need to collect.  Interestingly, the number doesn’t decline much with age (see chart above).

However, the deeper story really underlines what we generally know: people will exchange data if they get value in return.  A little additional information from the survey provides some context: nearly three quarters of respondents “think wearable tech will change how they engage with fitness providers and 22% say in-store and online shopping.”  [emphasis mine]

So, as I let my blood pressure drop to a reasonably healthy level, I think the numbers make more sense. People don’t mind (or even like) tracking when they see a direct benefit.  So:

  • Exchange data for better fitness: YES
  • Exchange data so you can buzz my wrist when I walk past a Starbucks: NO

On the other hand, The Ministry of Truth would like to have a word with the one-in-five of you who want retailers to have wearable data.

Metaball: Can Baseball Data Help Market Baseball?

Michael Lewis’s 2004 book Moneyball documented a revolution in how baseball teams evaluate players.  More than a decade after the book, all Major League teams use statistics like WAR, Fielding Independent Pitching and Range Factor per Game.  Now, Major League Baseball wants fans to get in on the act with Statcast,  Using both radar and special cameras, Statcast gives incredibly detailed information on nearly every movement on the field.


Not pictured: Explanation of how a Major League infielder flubs a cutoff throw

I’ve written extensively on how sports and data combine to make sports themselves more marketable.  So I thought I’d discuss what impact Statcast might have on baseball’s challenged popularity in the US.

In, short, I think that the data won’t hurt, but they might not help.

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