I’ve just started reading Blink by Malcolm Gladwell. He examines the mind’s ability to make rapid, intuitive decisions. He asserts that these instinctive reactions are frequently superior to decisions based on deep and careful analysis. Sometimes more information and more time isn’t better.

There are a load of analogies I can make to business analytics. My first reaction (which Gladwell would suggest I trust) is to consider a spectrum of “thinking” that occurs within businesses:

• At one end, there is “meme-driven” decision-making. Memes are not unlike stereotypes, an issue that Gladwell tackles. Reacting to factors like how a person is dressed or their demographics is dangerous and misleading. However, that type of superficial thinking isn’t to be confused with the reaction that an art expert might have in first seeing a fake. It is the combination of experience and subtle details that offer the instant insight.

• At the other end of the specturm is intense statistical analysis. Some companies have found their way to a world where decisions require a pound of data as proof. At these organizations, a gaggle of MBAs are running around making up unknowable assumptions to achieve ROIs that exceed a benchmark. This extreme is no better than making decisions without data. It can be worse in that there is a false sense of security about knowing what will happen.

I suspect there is a happy balance between these extremes. Blink offers some ideas as to what this might be.

I found this site that offers a great articulation of the value of visual data analysis…

Visual statistics, conceptualized as modern successor of epistemology in search for meaning, can help us to separate facts from fiction, has potential to transcend the schism between the quantitative and qualitative approaches to data analysis, and, in general, can contribute to the better understanding of our world. It can offer new algorithms for visualization of data structures rotated in the three-dimensional space, provide us with insights into the hyperspace, and much more.

Quantification and Visualization of Structural Properties

While the primary goal of traditional statistics is to distinguish real differences from random variability, the main goal of visual statistics is not only that, but also the elaboration of these differences into meaningful patterns that can be quantified and visualized. To accomplish this goal, we propose a new conceptualization of variance that encompasses not only the quantification of differences among elements of data matrices into a single index, but also extracts the structural properties of these differences.

Mark Hurst’s bit literacy – an approach to combatting data overload – is valuable reading both for your personal mental health and for its business implications.

Most consumers are aware that their time and attention spans are under attack by endless e-mails, IMs, alerts, advertisements, and digital entertainment. I suspect most businesses are not as aware of this problem. When I see investments to build complex and comprehensive corporate information factories , I wonder if there is true recognition that more is not necessarily better.

Mark’s comments could easily be applied to the businesses I’ve seen…

“when a person becomes bit literate, what remains after all the letting go is valuable. I equate that with meaningful. Because—and here’s the kicker—the bits by themselves aren’t meaningful. Bits are just pointers to meaning, just containers of thoughts, just phantom images of the real item. The meaning is what lies behind the bits, what drives the bits. In their super-abundant quantities, swarming and overwhelming our consciousness, bits obscure the very meaning that created them. It’s only after clearing out a path of emptiness that we can arrive at the meaning behind the bits.”

This perspective should be considered by businesses as they try to decipher the truly important drivers behind a deluge of data.

Most businesses work hard to understand their customers. Many are missing an opportunity by not focusing more on individual customers. Putting the microscope to individual customer behaviors is a fruitful place to gain a more textured and accurate view of the whole.

As James Vornov puts it: ”...things look very different from inside the system as one of the components compared to the view from above. From within, experience can be granular and anecdotal. From without, with a broad or long view and large numbers, there is smoothness and predictability.”

In our fledgling business, we’re having success with developing something we call customer viewers. These viewers are Excel spreadsheets that visually describe usage data for an individual customer – then offer the ability flip to another randomly chosen customer. Imagine flipping through a stack of photographs; before long you can identify important characters, common scenery and features that stand out as unusual. Our customer viewers tap into this powerful and instinctive pattern recognition capability of the mind.

Scientists approach their investigations in this same ground-up way. Biologists had to do the hard work of describing individual species before generalizing into a taxonomy. Likewise, you wouldn’t find a psychiatrist saying: “10 of my 25 patients have obsessive behaviors. We will consider them the obsessive segment.” That is what your business is doing if it is trying to shed light on customer behavior exclusively from the top down.

This worm’s-eye view is rare in the analytics world – and the insights it reveals are amazing. You start to see actions and behaviors that never show up in regressions, distributions, or other statistical approaches.

Does your organization make basic assumptions about its capabilities? Knowing your organizational “memes” can highlight the invisible constraints that may be holding you back. I want to share a recent example I ran across, but first a little on memes.

Meme is a word coined by Richard Dawkins in his book The Selfish Gene. It has been defined as “a contagious information pattern that replicates by parasitically infecting human minds and altering their behavior, causing them to propagate the pattern” (Glenn Grant). Memes are all around us. They are ideas that we catch from others (like a virus) and just as quickly spread to others. Memes compete for survival, and those that gain acceptance live on.

Mitch Ratcliffe’s essay on Invisible Dogma offers an articulation of this concept for organizations:

”...Invisible dogmas – unstudied and facile management fads or simple faith in a particular way of doing business imposed at some stage in an organization’s life – are akin to the religious fervor that laid civilization low in the early fifth century. Rigorous thinking was replaced by faith reinforced by dogma, which became unquestioned truth, very much like the company that, when asked why it does something one way and not another, responds “Because that’s the way we’ve always done it.”...”

Memes are an under-appreciated force in driving business performance. I recently ran across an excellent example.

Recently, I had the good fortune to speak with a top executive at one of the world’s most successful financial processing companies. Toward the end of the discussion, I asked him about the assumptions that are prevalent in his organization. It didn’t take him long to come up with an example that was troubling him. He was concerned about a commonly-held belief among employees that the company was not good at innovation. At the same time, everyone agreed the company was good at “fast following.” Though only a small portion of the company’s revenues came from products developed in the last three years (an innovation metric that falls far behind most leading companies), that wasn’t the part that worried him. In his view, the belief by employees that innovation wasn’t part of what they “do well” had implications beyond product development. If employees don’t try to think creatively about problems and look for opportunities to change for the better, how could the company improve?

In my previous post, I mused on the subject of edge cases and the learning opportunity they provide. I want to touch on how this applies to customer analytics.

This weekend I read a blurb in the Washington Post Food section. A customer by the name of Anne Monahan complained about the “dark blue menus printed in black ink” at a local restaurant. “In dim light, the menus were nearly impossible to read,” she remarked. The restaurant co-owner said that she hadn’t noticed the problem before, and vowed to change the hue of the menus the next day.

It would be easy to dismiss Ms. Monahan as an outlier and a whiner. After all, this complaint was rare. An edge case. But the restaurateur decided to respond to the issue. Perhaps other patrons were bothered by it, but hadn’t commented.

This is not to say that companies should be a slave to the edge case. But don’t throw them out. Listen to what they have to say and be willing to respond, because:

• They may be the canary in the mindshaft – telling you something that others haven’t yet realized

• They may be an extreme case of common behavior that shows up more subtly amongst other customers. Recognizing this behavior can only help you better meet customer needs in the future.

• They may offer new ways to think about the business or customers. As I said in my last post, edge cases help define the boundaries of reality.

• Collectively, outlier customers provide a service: they stress test the product and highlight unrealized strengths or weaknesses.

Of course, there is also much to learn from the ordinary cases – the mainstream customers. I think most companies already understand the ordinary. In fact, the ordinary is already deeply embedded in the business’ assumptions.

Most statistical analysis tells you: watch out for outliers. They are the data points that can screw up your averages. Because of their rarity, they aren’t deemed worth focusing on. I disagree.

I hope to return to this topic as we find ways to apply it at our clients.