Statistics’ Greatest Hits, Part I

How many times have you given a data-based presentation that made people burst into applause or made people weep? Have you ever given a talk that changed the world?

By my reckoning this has happened more than once to Hans Rosling, a professor of global health at Sweden’s Karolinska Institute. There are two TED talks by Swedish Professor Hans Rosling that stand out in my mind as outstanding examples of how statistical analysis can be:
a. Presented in a spectacularly effective way
b. Used for good in the world

In the first, referred to by many of my friends as the “Gapminder” TED talk, Dr. Rosling uses animated bubble charts to make (stunningly well) some points about worldwide trends in infant mortality, life expectancy, and distribution of wealth and productivity. The talk shows with shocking clarity how the world has been changing over time, in a form which can explode through ignorance and prejudice and create understanding. If you haven’t seen it, you should (see below).

You can use the Gapminder software to create your own animated Bubble Charts:
Gapminder Desktop download

You can also keep up with the organization that has grown up out of this effort on the Gapminder site:

The second TED talk I refer to is the “Washing Machine” TED talk. This talk really showcases Rosling’s storytelling and showmanship, but is still at its heart a presentation based on data. I believe, if I could learn to present like this, there is no limit to what I could accomplish.

If you have some other nominees for Statistics Greatest Hits, please leave them in a comment.

Measurement is No Substitute for Thinking BIG

Whether you are doing SEO for a site or running paid search for one, running display ad campaigns or social media, everyone is trying to measure the same thing – they are trying to find evidence that what they are doing is worth the money and time it costs.

There is a point of view (not necessarily mine) that says: If you have to do a complicated analysis to see the effect of a marketing initiative, then it wasn’t very effective. There is some truth there. It is easier, statistically speaking, to measure a BIG marketing impact than it is to measure a small one.

Reality is complicated, though. If you are engaged in “filling the funnel”, you want to know how how that translates – in the long run – into actual sales. However, unless you can wait until there is data for the whole decision and purchase process to make any decisions about how to manage the campaign and the channel, then you will have to go with measuring some intermediate impact.

It is almost a certainty that enough other things will happen to impact sales in the time between your funnel-filling campaign and the sales it ultimately leads to. Enough things to muddy the waters about how much of of your success (or lack of it) came from the lagged effect of your funnel-filling efforts. Unless the effect is big.

This is not to say that measurement is not necessary for early-stage marketing activities, but to say that you have to apply some common sense to your measurement problems, and one bit of marketing common sense is this: think BIG. Now think BIGGER. You should always be aiming to have a big effect – you won’t always succeed in a huge way, but it should not be for lack of trying.

Here’s an idea: Every time you create a campaign, a marketing tactic, an ad, you should at least TRY to do some creative thinking that taps into one or more sources of disproportionate (on the BIG side) response. What well-defined and targetable group would have a peculiar affiinity with your message and your product? What would make them want to know more NOW, click NOW, buy NOW?

What does that have to do with measurement? Two things:
1. Even if your BIG idea doesn’t work, you are actually testing a hypoethesis and so you have gotten just a little smarter.
2. Your goal is to produce impact so BIG that you don’t even really need to measure it to know that the effort was ROI-positive. (But you are measuring it anyway, so you can explain why the next one needs to have a bigger budget!) And – falling short of a really BIG goal will get you to positive business results more often than falling short of modest goals.

Statistics: the New Plastics? – Steve Lohr/NY Times

Can you “make” yourself a statistician or do you have to be a bit of an oddball to begin with?

On August 5, 2009, the Technology section of the New York Times ran an article headlined: “For Today‚Äôs Graduate, Just One Word: Statistics ” In the article, author Steve Lohr cited interviews and research to declare Statistics to be the glamor career of the near future. I have been a practitioner of marketing analytics and modeling since the 1980s, and I have mixed feelings about this news: the smug sense that I was right all along about the future of business is tempered by my equally strong desire for my competition to remain sparse and disorganized.

Citing The Graduate in an article plugging a career in statistics is more than a little weird. The quoted screen conversation was meant to underscore the vapidity of a career choice based solely on what might make one a lot of money. “Plastics” was supposed to sound ridiculous:

Mr. McGuire: I want to say one word to you. Just one word.
Benjamin: Yes, sir.
Mr. McGuire: Are you listening?
Benjamin: Yes, I am.
Mr. McGuire: Plastics.
Benjamin: Just how do you mean that, sir?

Our values as a society have changed enough since that scene ran in theaters that the exchange might now read less like a siren song for emerging sellouts, and more like sincere advice about a great career opportunity. No matter – statistics is not easy to fake an interest in. I doubt that those who might flock into a statistics course because it was a “hot” field would stay with it long enough to get a degree much less stomach it long enough to make a pot of money.

Perhaps those in the business of training statisticians will reap a windfall over the next few years if a flood of newbies rushed headlong into the field on the promise of high-paying careers. Be that as it may, I have seen too many square pegs suffer in fruitless attempts to enter this particular round hole to believe that many could “will” themselves to master statistics and analytics. Being a “quant” has a lot more to do with your innate cognitive style than your desire for a certain salary.

There is no question that business is increasingly awash in floods of data that are being under-understood and under-leveraged, and that the skills are in short supply required to extract useful meaning and patterns from data to guide decision-making and strategy.