Know Your Sources

I received an email earlier this week from Jim Geurts titled 13 Stats to Convince Your Boss to Invest in Mobile in 2013. It was a great read – I’d highly encourage you to spend a few minutes with it. A sampling: 25.85% of all emails are opened on mobile phones, and 10.16% are opened on tablets.

The content was great. But what I appreciated even more were the sources. (The stat above comes from Knotice).

In the Digital Age, facts can lose context easily. And, with an ever-increasing supply of “fact,” there is danger in Googling for answers. The numbers that seem too good to be true? They can be just that.

If a number is important, follow it to its source. A recent non-marketing example drives the point home as clearly as any I’ve seen. It comes from an article in this month’s Atlantic titled How Long Can You Wait to Have a Baby?:

The widely cited statistic that one in three women ages 35 to 39 will not be pregnant after a year of trying, for instance, is based on an article published in 2004 in the journal Human Reproduction. Rarely mentioned is the source of the data: French birth records from 1670 to 1830. The chance of remaining childless—30 percent—was also calculated based on historical populations… In other words, millions of women are being told when to get pregnant based on statistics from a time before electricity, antibiotics, or fertility treatment.

It’s not a marketing example, but the author’s point can – and must – be applied to anyone seeking truth online these days: “Most people assume these numbers are based on large, well-conducted studies… but they are not.”

Know your numbers. But, as importantly, know your sources.

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In Praise of Fact

Theories are the bedrock of innovation. They question the status quo. They drive new thinking. They spark change. But where do theories come from?

Whether we can articulate the specifics or not, theories originate from data. From numbers, experiences, results or reactions. And data comes from the past.

But the role of theory is to look into the future.

Facts are the bridge. If data comes from the past and theories look to the future, facts live in the present. Facts are the linchpin.

While theory’s job is to explain facts. It is the job of fact to organize data.

Theories get the credit. And, perhaps, they should. Great theories bring data – and facts – to life. With vision, through storytelling.

But make no mistake, facts do much of the work. Obsessing over facts is time well spent. Doing so is the first step towards your future.

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Putting Context In Context

Numbers may not lie. But, without context, they don’t tell the truth.

Take the recent example of Google’s Flu Tracker which, this flu season, estimated the epidemic to be twice as widespread as it actually was. What happened? The Tracker was looking at the numbers.

According to The New York Times, the Flu Tracker estimates for number of people in the U.S. with the flu are derived from “people’s location + flu-related search queries on Google + some really smart algorithms.” But media coverage + social buzz accelerated the queries. One unaccounted for variable – the context – changing another sends the entire equation out of whack.

The lesson: If it can happen to Google, it can happen to you.

For the most accurate read on your numbers, don’t just look at the numbers.

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How can you be a better predictor? Get foxy.

Nate Silver, statistician, first became noticed in the political world when he correctly guessed the outcome of 49 out of the 50 states in the 2008 presidential election. This year, he topped his election record by predicting outcomes of all 50 states and the District of Columbia. Needless to say, his book released in September 2012 about making predictions, The Signal and the Noise, shot up to the bestseller list for nonfiction and was named’s #1 best nonfiction book of 2012.

There are many different areas of life where accurate predictions come in handy: From the housing markets to political outcomes, from the weather to baseball, there are things that we want to be able to predict so we feel better equipped to handle the future.

When you’re listening to experts on the news, what should you be looking for to know for sure that they are a better predictor? How can you think to become a better predictor? Silver cites Philip Tetlock, a psychology professor in the 1980s, for the answer. Tetlock administered surveys asking experts to predict a variety of events between the 1980s and 1990s, and found he could divide these people into two distinct categories: hedgehogs and foxes.

Tetlock received this analogy from the philosopher Isaiah Berlin, who received the comparison from the poet Archilochus. The fragment of one of his poems says, “The fox knows many things, but the hedgehog knows one big thing.”

Hedgehogs are generally the experts you see when you turn on your television. In politics, they’re interesting to watch because they stick with one candidate, even when the polls appear to be saying something different. They believe in big ideas and governing principles, generally using one or a few of these to explain everything that happens in society. Silver uses the examples of “Karl Marx and class struggle, or Sigmund Freud and the unconscious. Or Malcolm Gladwell and the ‘tipping point’” (Silver, 53). While simplistic might not be the accurate term for them, hedgehogs do strive to tie in all of their guiding principles into one large idea, and reject any notion that doesn’t fit into their big idea of the universe. Because of their bias and mindset, they might ignore clues that would lead their predictions to become more accurate.

Foxes, in contrast, don’t get as much air time. They are more able to see complexities and nuances, and believe in a lot of little ideas and using as many approaches as necessary to solve a problem. Isaiah Berlin uses examples like Aristotle, Shakespeare, Balzac, and Joyce as thinkers that use their many and varied experiences to form conclusions, instead of focusing on one big idea. They are more comfortable adjusting their predictions if there is evidence that they should do so. Because of their thought process, they might raise doubt over political candidates and qualify their predictions with a degree of probability. In politics, this means they aren’t as interesting to watch.

Any time you are working to predict the success of something in your day-to-day work, think about how you’re approaching that forecast. Are you using one technique that you have used for years; one that doesn’t allow room for adjustments if it doesn’t work out as planned, or are you willing to try options, leave room for doubt, and adjust as necessary to more accurately foresee an outcome? If you are a fox, the latter will apply.

So, how should you think? Use information instead of ignoring what doesn’t fit into your main idea, change with that information, and make the best forecast you can today, every day. Just remember to stay foxy.

The Ever Increasing Supply of “Fact”

Facts are everywhere.

There are more available today than we can possibly use. From data to infographics, from survey results to study findings, its hard to spend any significant time online without tripping over something seemingly significant. Powerpoints have become littered with facts. Blog headlines scream about them. Twitter feeds stream them live. Its exhilarating. And maddening.

And increased supply doesn’t mean decreased demand. At least not directly. What it does influence is decreased control. Which leads to the potential for greater danger, because “facts” are no longer as digestible or dependable as they once were. In order to restore order, I believe there are two necessary steps:

TELL MORE STORIES. As facts become universally available, their context disappears. And facts without context are not only less powerful, they can be downright dangerous. If we want facts to be meaningful, we must understand how and when and why they matter – and how and when and why they don’t. Stories provide that important context in a memorable way.

BE MORE CAREFUL. I’ve written about how a fact that seems too good might not be true. But that goes far beyond marketing – to the bastions of science and academia. I read an article yesterday titled “More trial, less error – An effort to improve scientific studies.” Its most disturbing accusation? Most published research results are wrong. I really hope that’s false.

The bottom line: If facts are to remain indisputable, we must take greater care in both their presentation and their definition. That’s a fact.

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Use Your Data As A Horse Shoe. Or A Hand Grenade.

All models are wrong. But some are useful.
– George E.P. Box

The perfect answer is nice and tidy. Predictable and repeatable. And incredibly elusive. That’s why it is impossible to consistently predict the future.

Just don’t let that stop you from trying.

Figure out how to squeeze all the value out of the process itself. Out of knowing more. Out of getting close.

Remember, whether or not you’re exactly right, having the numbers on your side can’t guarantee success. And – conversely – whether or not you’re exactly right, having the numbers on your side can stack the deck heavily in your favor.

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Marketing Frameworks vs. Marketing Formulas

Agency-side, client-side. Senior-level, entry-level. Beginning of a project, recap of a project. We are all seeking nice, tidy answers.

That’s why marketing formulas are such great tools. They have a beginning and an end. If you know the variables that nice, tidy answer is one calculator away. The process is clear. Beginning, middle, end.

But The Digital Age is not linear. Consumers travel through decision making journeys, not sales funnels. So dependable – permanent – formulas are harder to come by. There are too many variables. And those variables are – by definition – changing. That’s why frameworks are such great tools.

A marketing framework is a model. It’s a guide. It provides a system of thinking that allows you to not only visualize your variables, but the potential relationships between them. To be confident that you’ve considered all the pieces of your puzzle. While frameworks can’t provide that nice, tidy numerical answer, they can provide a level of understanding that manages the chaos and provides clear direction.

If you don’t have a formula, start with a framework. Invest enough in your framework and you might even end up with a formula.

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How To Start Simple: Just Start

Two weeks ago, I wrote a post titled “Start Simple.” My focus was on the word “simple.” Equal attention should be given to the word “start.”

Starting is hard. It’s hard because if you haven’t done it, you won’t know what it’s going to take. Or where it’s going to lead.

A case in point comes from the eMarketer webinar I attended earlier this week – “Measuring Social Media Success.” 39% of worldwide retailers don’t measure social media marketing.

I’ve not talked to those 39%, but I would bet they haven’t started because they don’t know the right way.

Here’s the secret: There isn’t one way.

Now, there is a right way for your company. But your organization’s culture, available resources, strategic vision and short-term goals – among other things – must all be considered. And the only way to figure out that “right way?” It’s simple. Start.

Prototyping isn’t just for products. It can be used for research methodologies. For ideas. And even for processes. Processes like “Measuring Social Media Success.”

Start by being clear on goals and objectives. Start by articulating the right questions. And then start figuring out the right answers from there.

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New Year’s Resolution For Your Business: Use More Of Your Brain

Do people really only use 10% of their brain? Scientific American says no. Actually, they report it as “laughable.”

Do businesses use 10% of their brain? It’s a question worth considering.

As I wrote two weeks ago, Data Is A Numbers Game. And as businesses capture more and more data, they need to be careful they aren’t just using a smaller and smaller percentage of it. Remember, 60% of respondents to a MIT study responded that their “organization has more data than it knows how to use effectively.”

So, what are the steps towards tracking your tracking? They aren’t complicated.

Take an inventory. What tools do you have at your disposal? Ask your co-workers / team members what tools they’re using, too. The demands for your attention have increased. You might be surprised at what you’ve actually forgotten.

Prioritize. What questions you are able to answer with those tools? Which matter most to your success? Call the tools that provide these critical answers your business’ “Brain.”

Be honest. How effectively are you are using your Brain? How much attention do you pay to it? How often do you access its insight? As often as appropriate? Or, maybe, sadly, less so?

Make a new year’s resolution to boost your Brain’s power. Feed it. Grow it. And make sure you are paying it its due attention.

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Track Your Tracking: Data Is A Numbers Game

Seeing a story about brands embracing analytics has become nothing more than daily routine. But as Einstein famously said, “information is not knowledge.”

There is a tipping point with data where too much makes it hard to focus on what actually is important. And with 60% of respondents in a MIT Sloan Management Review survey agreeing that their organization has “more data than it knows how to use effectively,” our New Normal has tipped.

What to do? Start by tracking your tracking. We measure in order to optimize. What we measure deserves that same attention. Regular audits should consider whether what you’re measuring matters. Don’t just ask “what do we know?” Ask “what do we care?

Too little data and you’re uninformed. But too much poses its own problems. Data is, itself, a numbers game.

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