Category Archives: Data

Book Review: The Fires

I’m going to recommend that you read a book I hated.

More accurately, I want you to read one chapter of the book I hated: The Fires by Joe Flood.  So don’t buy it; instead, borrow it from the library or find it in a Barnes & Noble and just read chapter 12, “Quantifying the Unquantifiable.”  Apart from the awfulness of the rest of the book, chapter 12 gives a master class in how not to use data.  The lessons therein pertain to anyone using data, although of course I find it most useful to apply those lessons to marketing data.

The book covers an interesting point in New York City history, the late 60s through the mid 70s, when fires ravaged poor neighborhoods despite the city’s best efforts to stop them with better management courtesy of management consultants (what could possibly go wrong?).  Yes, the book covers the “Bronx is Burning” era (a much finer book with a broader perspective).

Dino fire engine

Today, FDNY is well-integrated with the neighborhood.  Note the dinosaur skull on the truck that serves the American Museum of Natural History.

Why I hated the book

Before getting to the good part, I should explain why I hated the book overall.  Author Flood rehashes mid-20th-century New York City through a highly distorted lens.  How distorted?  Let me put it this way: he attributes the failure to the efforts of arch-liberals Mayor John V. Lindsay (who was, in fact, a liberal) and the RAND corporation (which grew out of the Douglas Aircraft Corporation, those filthy hippies who built the AC-47 gunship for the Vietnam War).  He also intimates that Tammany Hall maintained a strong grip on city politics into the 1960s, which would have come as a surprise to Tammany Hall, had it existed in more than name only at the time.

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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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The World’s Most Advanced Mathematical Model: You

A few days ago, I had the privilege of recording an episode of the Inspiring Action podcast with my old fellow traveler Mark DiMassimo (I’ll share a link when it’s published).  Among other things, I discussed simple ways to bring data-centered thinking into marketing without making yourself or your team crazy.  Then Mark asked me a simple yet insightful question that honestly had never occurred to me: what did I mean by mathematical model?

OK, I would have preferred “what can you tell us about the rumors of your hook up with Sofia Vergara?” or “what was it like crushing a grand slam to win the World Series?” but the question forced me to articulate something most people gloss over.  We often talk about “the model,” but what does it actually entail?  If I wrote more clickbait headlines, I’d say “the answer will astonish you.”

Heavy duty mathematical modeling requires a sophisticated statistical approach backed by computing power and software know-how.  However, everyone reading this post has access to his or her own surprisingly effective model: your own brain.

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Groannnnn….mathematical modelsssssss…

In short, you think in math even when you don’t think you do.  Surfacing this sub-conscious math can make you a better marketer.

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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.

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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:

Vizzini: HE DIDN’T FALL? INCONCEIVABLE.

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.

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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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What if We Started With Metrics?

You don’t have to like glam/prog rock to appreciate Brian Eno.  In addition to such classics as “Music for Airports” (which is exactly what it sounds like) and “Baby’s On Fire” (which I hope to God isn’t what it sounds like), Eno created a wonderful tool for getting your head unstuck: Oblique Strategies.

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Screengrab from http://stoney.sb.org/eno/oblique.html

Eno, also a prolific music producer, created a deck of cards with suggestions like the one above to help him out when he encountered dead ends in his work.  He instructed users to draw a card when they felt stuck and follow the directions as they wished to interpret them.  I’ve used them too many times to count to help me solve nagging client problems.

So I created my own Oblique Strategies card:

What if we started with metrics?

As in, what if we started a new marketing project not by asking about business objectives nor by asking about marketing objectives and instead by asking “what can we measure?”

Maybe it’s the 70s synthesizer music talking, but it helped me develop a framework I’d like to run by you all.

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Maybe Marketing Data is Like College Sex

We’ve all heard the joke about big data’s resembling teenage sex: “Everyone thinks everyone else is doing it.  Almost nobody is actually doing it.  And those who are doing it aren’t doing it well.”

A recent conversation I had suggested that we marketing folks maybe have started doing it (big data) like college kids.

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Spring Break is really just like a giant, tequila-driven database

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RT, RU kidding me?

Oh, Vladimir Vladimirovich, what shall we do with you, you latter-day Ivan the Terrible?

Here’s a headline from Russia Today (RT) last year:

20% of American workers have lost their job during last 5 years

That sounds terrible!  Rough times all around, but still…wait, what’s this?  There’s a link to the original study?  By the decidedly non-Red (OK, Scarlet) Rutgers University (RU)?  Whatever does it say?

20 percent of workers laid off five years are still unemployed, seeking jobs

I’ve written extensively about why you should always look for an original source.  I’m just going to point to this article now.

From my warm, caffeinated hands!

So I nearly tried to punch the Financial Times.

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Back off, bird.  I will cut you.

While conducting secondary research for a new project, I found a useful article on the site.  Before I could read the piece, the site served up a one-question survey: had I tried to cut down on my coffee consumption in the past year?

OK, I realize that FT probably wanted to a) recruit visitors for an awareness survey or b) simply build a profile on their visitors.  But first goddam thing in the morning, they want to ask me whether I’m thinking of giving up coffee?

[SHAKES FIST EPICALLY]

Maybe this is the flip side to “taboo data,” the idea that some data are too sensitive to use.  Maybe some are too sensitive–or obnoxious–to ask.

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.

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Not in front of the monkey!

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