Category Archives: Corporate Strategy

Marketing in the “Middle”

First published August 1, 2013 in Mediapost’s Search Insider

In case you haven’t heard, email is dead. In fact, it’s died several times. You could call it the cat of digital marketing, working it’s way through its nine lives. And it’s not alone. Search has died more than a few times. Display was DOA over a decade ago, and has resurrected itself, only to suffer several more untimely demises. In fact, for any digital channel you might care to mention, I can probably find an obituary.

For some reason, we love to declare things dead. We like clarity and finality, and there’s nothing like death for getting an unequivocal point across. Death, by its very nature, should be the final word – except that, in these cases, it almost never is. These channels, like Mark Twain, have had “the rumors of their deaths greatly exaggerated.”

It’s yet another example of how we hate ambiguity. We don’t like being in the middle, drifting between two far off anchor points. It feels uncertain and “mushy”. Humans don’t do well with “mushy”. We prefer predictability. We like to know where we stand, which requires knowing what’s under our feet. The middle represents “terra incognito” – undiscovered and unstable. We know, if we stand here, we have to be prepared to be nimble and fleet of foot.

This tendency comes down to an unfortunate human fragility – we like predictable outcomes, but we suck at making predictions. Not just some of us suck at it – we all suck at it. Philip Tetlock conducted a two-decade study looking at the success rate of “experts” in making predictions in a wide variety of subjects, especially politics. The outcome? Experts come out slightly ahead of coin tosses and chimps throwing darts. Tetlock’s long list of blundered predictions is staggering. Expertise does not lead to accuracy in divining the future. Yet, we still cling to this false hope. We crave a universe that unfolds as it should, or, at least, as we expect it to.

The messiness comes from the complexity of real life. There’s just too much “stuff” happening for us to make sense of it with our limited intellectual horsepower. Evolution, in its blind wisdom, has allowed for that by building in some natural defenses against complexity. We refer to them as instincts, emotions and beliefs. The nasty “gotch ya” in this is that the more we accumulate experience and knowledge, the more inflexible those beliefs and instincts become. We tend to adopt “big ideas” or “macro-beliefs” as guiding principles and philosophical anchors, which become the lens through which we see the world. We trade off open mindedness for expertise. Tetlock calls these “hedgehogs”, from Isiah Berlin’s essay. “Foxes”, on the other hand, draw on a wide variety of experiences to shape their views. They, by their nature, tend to live in the middle.  Tetlock found that foxes have much better track records when it comes to prediction. So, if you want to know what might happen, don’t ask an expert, especially one who is regularly seen on TV. Ask a dilettante – who is much more comfortable with “mushy.”

Ironically, Jim Collins, of Good to Great and Built to Last fame, also taps Berlin for the hedgehog and fox analogies, but he believed that “hedgehogs” are what makes great companies great, because they provide a single objective to focus on – the “hedgehog” concept.

So, who’s right – Tetlock or Collins? The answer, as you would expect in a column on this theme, is that they’re both right. The world is neither a place exclusively for foxes nor hedgehogs. The sweet spot is in the middle.

Nowhere is this truer than in marketing – which has to mirror all the irrationality of human behavior. There are no absolutes in marketing; there is just a lot of mushiness in the middle.  We need hedgehogs for the “big ideas” that make great marketing great. But we also need foxes to help us navigate through middle successfully.  In fact, the more time I’ve spent in marketing (trying assiduously to avoid becoming an “expert”) the more I’ve realized that the middle is where all the action is: between quantitative and qualitative, between strategy and big data, between creative branding and direct marketing, between science and art.

And here, in the middle, we hate to call anything “dead,” because you just never know what might happen.

The Problem with Corporate Org-charts

First published July 25, 2013 in Mediapost’s Search Insider

Last week, I talked about the chasm between marketing and sales in most organizations, with the customer left to perish in the middle. One of the responses to that column was that there’s an even bigger divide between marketing and IT (a subject my friend Scott Brinker has written extensively and eloquently about).  The systemic problem that underlies this is that organizational org charts aren’t built in consideration of a customer’s requirements. The structure of the vast majority of companies is decidedly un-customer-centric.

Typically, the dividing lines of a company are decided by competitive profit and loss requirements, arbitrary product categorizations or regional segments. Within these corporate fiefdoms, the hierarchy of management is dictated by corporate traditions that are at least three decades out of date.  They were designed to run a company where economies of scale and sheer mass were the goals. Corporate strategy was aimed at mass producing and distributing as much product as possible to as many markets as possible. The focus was internal, with management’s eyes focused on productivity and profitability. Marketing was largely unidirectional, from the marketer out to the market. There was little in the way of feedback loops.

The typical corporate quadrumvirate is the CEO, CFO, COO and CMO.  All of these traditionally focus their gaze inwards. The “outside-in” perspective is not explicitly outlined in any of these job descriptions. Theoretically, CMOs should be on top of their market, but in practice this view is largely provided through traditional market research, which is usually several degrees removed from the reality of the market.  Even if a truly honest view of a company is captured in a research report, by the time it is digested internally, it’s spun into a form that bears no resemblance to the original.  Alarming findings are ignored or downplayed. Positive findings are exaggerated to bolster reputations and protect pet initiatives.  Corporate BS is in full-flow. Management typically has no idea how an actual customer perceives the business and its products.

Lately, an alphabet soup of new corporate office titles has been trotted out, paying lip service to the idea of customer-centricity: CXO (X=Experience),  CRO (R=Relationship), CIO (I=Innovation), CCO (C=Customer) or CAO (A=Agility). I know of a company that recently appointed a CXO. She was given the title and office, but nothing more. She had no resources, no budget, no mandate and no authority. She was literally stapled on the side of the org chart, with no lines of connection to anyone else, save for a single line running from the CEO down to her.  She soon found out that this was a one-way line. None of her frustrated feedback was taken into consideration.

I suspect the same is true for the majority of these new “designer-labeled” executives. The lesson? You can’t put a Sierra Club bumper sticker on a ’88 Buick, hoping to end up with a Prius.

In response to last week’s column, one reader asked the excellent question, “How do we bridge the gap?” The answer, based on my experience, goes far beyond including it in this quarter’s list of strategic initiatives. This is a foundational problem, and you’re not going to truly fix it without ripping apart the structure of your organization and rebuilding from the ground up, ensuring that the customer’s requirements dictate the reconstruction blueprints.

Let’s face it, that’s just not going to happen in an organization that has several years invested in doing business the same old way. There are handfuls of new companies, however, who “get” the importance of understanding their customers right out of the starting gate. They are re-engineering the org chart away from the traditional practice of simply being “big & profitable” to becoming “intimate and responsive,” knowing that profitability will come from that. They are eschewing typical titles, and all the political baggage that comes with them, in favor of creating new customer-aware roles with real authority.

In the end, I don’t think this is a “problem” that can be “fixed.” It’s one species of corporation, which will eventually be supplanted by another, better adapted to a new market environment.

Marketplace Chasm: The Divide Between Marketing and Sales

First published July 18, 2013 in Mediapost’s Search Insider

Marketing people and salespeople don’t like each other very much. Oh sure, to be politically correct, they will pay lip service to the ideal that they’re all part of one big happy family, working for the common good of the company. But deep down, you know what I say to be true.  Salespeople don’t trust the sneaky and manipulative ways of marketing people. And marketing people think salespeople are a bunch of type A prima donnas. I know –I’ve heard the backstabbing begin when one or the other leave the room. These are two tribes that are uncomfortable sharing the same teepee.

I believe it’s because they don’t think alike. Marketers are long-range operators. They are more comfortable at a distance from their target – sort of like a bomber pilot in WWII. There, they can objectify the campaign, thinking in abstract strategies. They like the 30,000-foot view. Up there, you can see the big picture and plan accordingly. It’s also a lot less messy.

Salespeople like trench warfare. They prefer rolling up their sleeves and battling it out on the front lines, where rulebooks routinely get tossed in favor of whatever works. Ask any office administrator whom the worst culprits are when it comes to filling out forms and filing reports –an accusatory finger will be pointing directly at the sales department door.  The salesperson’s philosophy is that the rules don’t apply to them, as long as they get results.

Look at one common point of confrontation between the two: the lead-gen form. Marketing people want to gain as much information as possible to plug into the prospect database, so they can slice and dice the data to their heart’s content. They’re not completely happy until they can segment and profile based on height, weight, religious affiliation, shoe size, ethnic background, educational level, blood type, current mood, number of relevant sites visited in the past 72 hours, underwear worn, pets owned and brand of toothpaste preferred. All a salesperson wants is a name, some form of contact and a semi-regular pulse. They’ll take it from there.

So, what’s the big deal? Who cares if salespeople and marketers don’t play nice together?  Well, I care. And I say that as a customer. The problem here is that as the sales department and the marketing department have their little turf skirmish, I’m caught in the war zone. All I want is the smoothest possible path to my eventual purchase, on my terms, at my speed. Your job, Ms. Salesperson and Mr. Marketer, is to help me get there. I don’t really care who got blindingly drunk at the last corporate retreat, or who is consistently a pedantic ass at the weekly sales and marketing “huddle.” I just want to buy the best stuff at the best price — period.

What inevitably happens when marketers and salespeople feud is that the path to purchase gets dictated by them, rather than by the customer. Hand-offs from one department to the other can be unnecessarily bumpy, due to internal problems that have nothing to do with the customer. Sometimes, the lack of communication between the two sides requires customers to do all the heavy lifting to keep the sale on track — including supplying information multiple times, constantly explaining their requirements and having to sit through redundant sales pitches.

In the old days where a disconnected, asymmetrical market was the norm, the divide between marketing and sales was less noticeable. We didn’t really start interacting with a vendor until we were in front of a sales rep. Marketing just primed the pump, so to speak. But today, in a more interactive, symmetrical market, we expect a seamless journey from the world of the marketer to the world of the salesperson. We make no distinction between the two. Unfortunately, the same is not true within the walls of the vendor’s organization. As long as these departments continue to feud, the customer will be the ultimate loser.

The Ill Defined Problem of Attribution

First published July 11, 2013 in Mediapost’s Search Insider

For the past few years, I’ve sat on the board of a company that audits audience for various publications. One of the challenges the entire audience measurement industry has faced is the explosion of channels traditional publishers have been forced to use. It’s one thing to tally up the audience of a single newspaper, magazine or radio station. It’s quite another to try to get an aggregate view of an audience of publishers that, in addition to their magazines, have a website, several blogs, various email newsletters, a full slate of webinars, a YouTube channel, multiple Twitter accounts, Facebook pages, other social destinations, digital versions of magazines and an ever-growing collection of tablet and smartphone apps. Consider, for instance, how you would estimate the size of MediaPost’s total audience.

The problem, one quickly realizes, is how you find a common denominator across all these various points of audience engagement. It’s the classic “apples and oranges” challenge, multiplied several times over.

This is the opposite side of the attribution problem. How do you attribute value, whether it’s in terms of persuading a single prospect, or the degree of engagement across an entire audience, when there are so many variables at play?

Usually, when you talk about attribution, someone in the room volunteers that the answer to the problem can be found by coming up with the right algorithm, with the usual caveat something like this: “I don’t know how to do it, but I’m sure someone far smarter than I could figure it out.” The assumption is that if the data is there, there should be a solution hiding in there somewhere.

No disrespect to these hypothetical “smart” data-crunchers out there, but I believe there is a fundamental flaw in that assumption. The problem behind that assumption is that we’re accepting the problem as a “well defined” one – when in fact it’s an “ill-defined” problem.

We would like to believe that this is a solvable problem that could be reduced to a simplified and predictable model. This is especially true for media buyers (who use the audience measurement services) and marketers (who would like to find a usable attribution model). The right model, driven by the right algorithm, would make everyone’s job much easier. So, let’s quit complaining and just hire one of those really smart people to figure it out!

However, if we’re talking about an ill-defined problem, as I believe we are, then we have a significantly bigger challenge. Ill-defined problems defy clear solutions because of their complexity and unpredictability. They usually involve human elements impossible to account for. They are nuanced and “grey” as opposed to clear-cut “blacks and white.” If you try to capture an ill-defined problem in a model, you are forced to make heuristic assumptions that may be based on extraneous noise rather than true signals. This can lead to “overfitting.”

Let me give you an example. Let’s take that essential human goal: finding a life partner. Our task is to build an attribution model for successful courtship. Let us assume that we met our own livelong love in a bar. We would assume, then, that bars should have a relatively generous attribution of value in the partnership “conversion” funnel. But we’re ignoring all the “ill-defined” variables that went into that single conversion event: our current availability, the availability of the prospect, our moods, our level of intoxication, the friends we were with, the song that happened to be playing, the time of night, the necessity to get up early the next morning to go to work, etc.

In any human activity, the list of variables that must be considered to truly “define” the problem quickly becomes impossible. If we assume that bars are good places to find a partner, we must simplify to the point of “over-fitting.”  It may turn out that a grocery store, ATM or dentist’s waiting room would have served the purpose equally well.

Of course, you could take a purely statistical view, based on backwards-looking data. For example, we could say that of all couples, 23.7% of them met in bars. That may give us some very high level indications of “what” is happening, but it does little to help us understand the “why” of those numbers. Why do bars act as a good meeting ground?

In the end, audience measurement and attribution, being ill-defined problems, may end up as rough approximations at best. And that’s OK. It’s better than nothing. But I feel it’s only fair to warn those who believe there’s a “smarter” whiz out there who can figure all this out: Human nature is notoriously tough to predict.

The Story of the Underwood Typewriter Company

Underwood_Typewriter_Company_exhibit_in_the_Palace_of_Liberal_Arts_at_the_1904_World's_Fair

Underwood Typewriter Company exhibit in the Palace of Liberal Arts at the 1904 World’s Fair – Wikimedia Commons

First published June 20 in Mediapost’s Search Insider

A few weeks ago, I introduced you to my Underwood No. 5. Today, I’d like to tell you a little bit about the company that created the iconic No. 5. The story may be over a hundred years old, but that doesn’t mean that the businesses of today can’t learn from it.

The first designs for the typewriter started showing up in the 1870s. After some rather imaginative designs, including one that looked like a pincushion, the Sholes and Glidden Type Writer (1873) was the first writer to introduce the QWERTY keyboard (which I’m still using today). The QWERTY design was (supposedly) introduced to overcome the physical limitations of the machine, which tended to become jammed if frequently used keys were located next to each other. The reason we still use it? Well, suffice to say, habits are a tough thing to break.

The S&G design, and all the other variations that followed for the next two decades, tried various approaches, but all had one thing in common: They were all “understroke” or “blind” writers. The keys hit the paper on the bottom of the platen so users couldn’t see what they were typing.

In the mid-1890s, John T. Underwood was trying to figure what to do with his company, a fairly significant provider of ribbons and carbon paper to then-industry-dominant typewriter manufacturer Remington.  That company had spun off its typewriter division from the sewing machine division, which in turn had evolved from its main business, making guns. But Underwood had heard that Remington had plans to start making its own consumables. He countered by declaring, “All right, then, we’ll just build our own typewriter.” Fate upped the ante by bringing together Underwood and German-American inventor Franz X. Wagner.

Wagner had designed a better typewriter. Or, at least, he had reached an acceptable compromise by combining many of the best innovations of the competitors, together with a few twists of his own, and putting them together into a new package (Does this sound a little like a precursor to the iPhone?). The result was a design that would define what almost every typewriter would look like for the next six decades, until the electrified IBM Selectric.

Underwood quickly locked down Wagner’s design by purchasing his company – and then made Underwood the biggest manufacturer of typewriters in the world. To say Underwood dominated was an understatement. The No. 5 outsold all other competitors combined for the first two decades of the 20th century.

In 1927, Underwood merged with Elliot-Fischer to consolidate market share, with the goal of ensuring dominance. But the decline had begun. One of the problems was that Underwoods seemed to last forever, so replacement sometimes took decades. The other challenge came in the form of a minor distraction known as World War II. During the war, Underwood cranked out carbines for the troops.

By the ‘40s, at the end of the war, Underwood struggled to regain relevance and dominance. But it was saddled by technology that was almost half a century old – all the company knew how to build and sell. There was no “next big thing” to open up new markets. Underwood was also held back by inertia. It was hard for company strategists  to understand why the thing that made them so successful was no longer potent enough to enable survival (Microsoft?).

Eventually, Underwood was gobbled up by Olivetti (1959) and the Underwood name last appeared on a portable Olivetti built in Spain in the 1980s.

There are a few relevant lessons here.  The more dominant your technology, the more likely it is that your company will be limited by it. Dominant technologies, no matter how innovative they are when they first appear, tend to build inertia in organizations as they ride a long winning streak. Struggle is good for the corporate soul, and the simple fact is, competitors are forced to struggle, which makes them sharper, more nimble and more aggressive.

Secondly, along with inertia, successful companies also become complacent. They generally don’t start looking for the next big thing until the existing product line (or lines) begins to falter — and by then, it’s too late. The competition, which is hungrier and moves more quickly, has too much of a lead on them.

And thirdly, it seems that the more reliant companies are on a single product or business line, the more susceptible they are to the inertia and complacency that comes with dominance. They are restricted by a single product cycle, rather than spreading their chances for survival over multiple bets, each at different stages of market maturity.

Underwood is no more. But for four decades, it had a good run. It’s difficult to say whether this is a sad thing, or it’s just the inevitable life cycle of a company.

The Straw that Broke the Market’s Back

First published May 9, 2013 in Mediapost’s Search Insider

Customers are fickle — and I suspect they’re getting more fickle.  Perhaps they’re even feeling a little entitled.A recent survey shows that customers tend to bail on a company not because of a big time screw-up, but because of the accumulation of a lot of little annoyances. Soon, their frustration reaches a tipping point and they look elsewhere.

It would be easy to point the finger at the companies and demand that they get their collective acts together. But I suspect there’s more at play here. It would be my guess that customers are getting harder to please.  And I would further guess that the Web is largely to blame. I think it comes down to a constant rise in our collective expectations, while the reality of our experiences fall behind.

The balance between our expectations and the actual experience determines our loyalty to any course of action. If we have low expectations and a poor experience, we aren’t really surprised, which dampens our subsequent disappointment and leaves us more willing to forgive and forget.  If we have low expectations but a good experience, we’re pleasantly surprised, making us more apt to return. If we have high expectations and a good experience, we get a double hit of happiness. First, we enjoy the anticipation, then we appreciate that the experience actually lives up to our expectations. For a vendor, the scariest scenario is the last of the four: high expectations but a poor experience. In this case, we walk away disappointed and frustrated.

Now, balancing expectations and experience wouldn’t be that difficult for any moderately competent company if those expectations were realistic. But I suspect that more and more of us are entering into our respective experiences with unrealistic expectations. We’re setting our vendors up to fail.

Expectations are set partly based on our past experiences, but they’re also set by the experiences of others. We create our expectation set points based, in part, on what we hear from others.

The Web has created an open, accessible market of experiences and hearsay. We hear about the bad, a feedback loop that increasingly is calling out poor customer service. But we also hear about the good.  Correction – we hear about the exceptional. The “good” is not remarkable. It generally falls within our expectations and so goes without comment. But either the very good or the very bad is exceptional, and we are more apt to comment on it online. Not only do we comment, we also embellish, accentuating the plusses and minuses to make it a better story. Therefore, what we hear from others sets either a very low or very high bar. We steer clear of the low bars, but the high bars stick with us, contributing to the setting of future expectations.

The other thing the Web has done is create expectations that overlap domains.  Previously, when our expectations were set based on our own experiences, they tended to stay domain-specific. We had an expectation of what it would be like to buy a car, stay at a hotel, eat at a restaurant or purchase a new pair of shoes. With the Web, cross-pollination between domains is increasingly common. A head marketer for a well-known industrial manufacturer once said to me, “When it comes to online experience, my competitors are not the traditional ones. I’m competing against Amazon and eBay. That type of experience is what people expect.”

This “nudging up” of expectations is done without much rational consideration. We don’t care much for the reality of operational logistics in any particular domain. We just want our expectations to be met, no matter where those expectations might come from. And when they’re not, we pull the plug on that particular vendor, assuming another vendor can do better in meeting our inflated expectations. The Web has also engendered a virulent “grass is always greener” view of the world. We know a competitor is just a click away (whether or not that vendor is any better than the incumbent).

I’ll be the first to call out a bad customer experience, but when it comes to the increasing fickleness of customers, we should remember that there are two sides to this particular story.

Don’t Use Technology as an Excuse for Bad Customer Service

First published April 11, 2013 in Mediapost’s Search Insider

We all have our horror stories about online customer service. Just in the past two weeks, I added two more to my collection.

After placing an online order with Costco, I’ve had to wait (at this point) a week after the promised delivery date to get the stuff I bought and paid for. Three separate attempts to contact the shipper have been unsuccessful – the first two were simply ignored and the last one resulted in the shifting of blame to the local agent, who was supposed to call me to resolve the issue. That was 48 hours ago, and still no call. I suppose I could invest more of my time to harass them until they actually respond, but frankly, at this point, I just want to wash my hands of the whole transaction.

With the other example, the damage was done before I ever made the purchase, thank heavens. I was planning a trip using Kayak and sorted my booking options according to price. There, in the same format as the search results, was an ad from a well-known travel brand. I assumed the ad would offer me a rate that was comparable to the other results above and below it. After all, I had sorted by rate, so position should equate to price.  In fact, the ad offered a lower rate than the search result immediately above it. The ad worked – kind of. I did click it, only to find the promised offer evaporated and my actual rate was four times the price of the competitor. I quickly clicked back to Kayak to book with one of the competitors, having learned to ignore any further ads from this particular company.

Here’s the troubling thing. Most of you will say, “So what?” These two stories are not that unusual. We’ve come to accept this level of service online as the norm. The online market place is SNAFU – in it ‘s most literal sense. My question is, why? Why do sellers feel they can get away with this, and, what’s more important, why do we, the customer, accept it as the new normal?

Here’s my hypothesis. We accept it because we can’t look the offending party in the eye. They do it because they don’t have to answer for it face to face. Anonymity and arm’s length transactions prevent crappy business-people and their practices from being held accountable.

We humans have a long list of subtle and not so subtle things we can do to ensure fairness in transactions – but they all evolved to work face to face. Over our history, we have evolved many social “governors” that play on our emotions. In general, they work pretty well, as long as we’re all in the same room, tent, hut, tribal circle or canoe. But these governors, 10 thousand generations in the making, are being rendered ineffective by technology in the space of just one generation. We’re hiding behind a computer screen because we can.

I’m sure the customer service agent at the courier company would think twice about promising me a shipment on a certain day – a promise she had no intention of keeping – if she was making that promise to my face and she knew I’d be back the day after the parcel failed to show up.

And I find it hard to imagine that a hotel, airline or car rental firm would offer me a rate that was totally fictitious if they knew the actual cost was going to be three or four times what they offered. At least, I find it hard to imagine they’d do that if I was standing across the counter from them at the time.

So why, I ask again, do we settle for less in our arm’s length transactions? I believe every online company should use the BIP rule of thumb – do business as if you’re doing Business In Person. Assume you’re looking at the person you’re dealing with in the eye. Treat them as if they’re your next-door neighbor. Before you screw them over, assume you’ll have to say “Good morning” every day as you hop in your car and go to work.

You have a conscience for a reason – use it for what it was intended for.

Seperating the Strategic Signal from the Tactical Noise in Marketing

First published April 4, 2013 in Mediapost’s Search Insider

It’s somewhat ironic that, as a die-hard Darwinist, I find myself in the position of defending strategy against the onslaught of Big Data. Since my initial column on this subject a few months ago, I’ve been diving deeper into this topic.

Here’s the irony.

Embracing Big Data is essentially embracing a Darwinist approach to marketing.  It resists taking a top-down approach (aka strategy) by using data feedback to enforce evolution of your marketing program. It makes marketing “antifragile,” in the words of Nassim Nicholas Taleb. In theory, it uses disorder, mistakes and unexpected events to continually improve marketing.

Embracing strategy — at least my suggested Bayesian approach to strategy — would be akin to embracing intelligent design. It defines what an expected outcome should be, then starts defining paths to get there. But it does this in the full realization that those paths will continually shift and change. In fact, it sets up the framework to enable this strategic fluidity. It still uses “Big Data,” but puts it in the context of “Big Testing” (courtesy Scott Brinker).

To remove the strategy from the equation, as some suggest, would be to leave your marketing subject to random chance. Undoubtedly, given perfect feedback and the ability to quickly adapt using that feedback, marketing could improve continually. After all, we evolved in just such an environment and we’re pretty complex organisms.  But it’s hard to argue that a designer would have designed such flaws as our pharynx, which is used both for eating and breathing, leading to a drastically higher risk of choking; our spinal column, which tends to become misaligned in a significant portion of the population; or the fact that our retinas are “inside out.”

Big Data also requires separating “signal” from “noise” in the data. But without a strategic framework, what is the signal and what is the noise? Which of the datum do you pay attention to, and which do you ignore?

Here’s an even bigger question. What constitutes success and failure in your marketing program? Who sets these criteria? In nature, it’s pretty simple. Success is defined by genetic propagation. But it’s not so clear-cut in marketing. Success needs to align to some commonly understood objectives, and these objectives should be enshrined in — you guessed it, your strategy.

I believe that if  “intelligent designers” are available, why not use them? And I would hope that most marketing executives should fit the bill. As long as strategy includes a rigorous testing methodology and honest feedback does not fall victim to egotistical opinions and “yes speak” (which is a huge caveat, and a topic too big to tackle here), a program infused with strategy should outperform one left to chance.

But what about Taleb’s “Black Swans”? He argues that by providing “top down” direction, leading to interventionism, you tend to make systems fragile. In trying to smooth out the ups and downs of the environment, you build in limitations and inflexibility. You lose the ability to deal with a Black Swan, that unexpected occurrence that falls outside of your predictive horizon.

It’s a valid point. I believe that Black Swans have to be expected, but should not dictate your strategy. By their very nature, they may never happen. And if they do, they will be infrequent. If your strategy meets a Black Swan head on, a Bayesian approach should come with the humility to realize that the rules have changed, necessitating a corresponding change in strategy. But it would be a mistake to abandon strategy completely based on a “what-if.”

Evolving on the Fly: Growth Hackers, Agile Marketers, Bayesian Strategists and CMTs

First published January 10, 2013 in Mediapost’s Search Insider

If you are a Darwinist, one of the questions you may have asked yourself is, on what timescale does evolution play out? Is it a long, gradual development of new and differentiated species? Or, as Stephen Jay Gould and Niles Eldridge believe, does evolution happen in short spurts, separated by long periods of stasis (their theory is called Punctuated Equilibrium)?

The next question you might ask is, what does this have to do with marketing?

I venture to say: everything. Bear with me.

If you believe, as I believe, that evolution happens in spurts, then it’s important to understand what causes those spurts. Among many contentious alternatives, one that seems to be more commonly accepted is a sudden dramatic change in what evolutionists call the adaptive landscape.  This is the real world that species must adapt to in order to survive. “Flat” landscapes create an even playing field for all species to survive, resulting in relative stasis. “Rugged” landscapes significantly favor some species over others, accelerating evolution dramatically. “Rugged” landscapes generally emerge after some big event, like a catastrophe.

I propose that marketing is currently a very rugged adaptive landscape. Some marketers are going to thrive, and others are going to disappear from the face of the earth. We’re already seeing exciting new species emerge.

Growth Hackers

If you haven’t heard about them, Growth Hackers are “the next big thing,” at least, according to Fast Company.  A post by Andrew Chen is referenced, where he explains, “Growth hackers are a hybrid of marketer and coder, one who looks at the traditional question of ‘How do I get customers for my product?’ and answers with A/B tests, landing pages, viral factor, email deliverability, and Open Graph.” Think of hackers as tech-savvy marketing guerillas. They move fast, exploit technical opportunities, and track and test everything.

Agile Marketers

According to the Agile Marketing Manifesto, this offshoot of Agile Development enshrines customer focus, validated learning, iterative approaches, flexibility and learning from our mistakes. In the words of my friend Mike Moran, it’s learning how to “Do It Wrong Quickly.” As opposed to Growth Hackers, which is more of a job description, Agile Marketing is a corporate philosophy that encourages (demands) rapid evolution. It embraces the realities of a “rugged” adaptive landscape.

Bayesian Strategists

This was top of mind after my last column, so I added this in as my contribution. As stated last week, I envision strategic thinking to become less of a “shot in the dark” and more of a “testable hypothesis.”  I would never want to see “Big Thinking” give way to “Big Data,” but I believe the two can co-exist, and co-evolve, quite nicely.

Chief Marketing Technologist

Finally, under whose watch does all of this fall? If you believe Scott Brinker (which I invariably do — he’s from Boston and he’s “wicked smaaht”) it falls quit nicely into the job description of the Chief Marketing Technologist. I’ll let him explain in his own words: “A chief marketing technologist (CMT) is the person responsible for leading an organization’s marketing technology.”

A CMT sits astride the rapidly colliding worlds of marketing and technology and makes sure an organization does not fall prey to the all-too-common trap of having these overseen by two completely separate (and often outrightly hostile) departments.

A CMT understands the following realities:

Everything is Marketing

Everything is Changing

Everyone Must Be Agile

In the words of Peter Drucker, “Business has only two basic functions: marketing and innovation.” In today’s world, those two functions are inextricably linked. As a marketer, you have two choices: adapt and survive, or stand still and die. The ones who do the first the best will emerge at the top of the marketing food chain.

The Evolution of Strategy

First published January 3, 2013 in Mediapost’s Search Insider

Last week I asked the question, “Will Big Data Replace Strategic Thinking?”  Many of you answered, with a ratio splitting approximately two for one on the side of thinking. But, said fellow Search Insider Ryan Deshazer, “Not so fast! Go beyond the rebuttal!”

I agree with my friend Ryan. This is not a simple either/or answer. We  (or at least 66% of us) may agree that models and datasets, no matter how good they are, can’t replace thinking. But we can’t dismiss the importance of them,either. Strategy will change, and data will be a massive driver in that change.

Both the Harvard Business Review and the New York Times have recent posts on the subject. In HBR, Justin Fox tells of a presentation by Vivek Ranadive, who said, “I believe that math is trumping science. What I mean by that is you don’t really have to know why, you just have to know that if a and b happen, c will happen.”

He further speculates that U.S. monetary policy might do better being guided by an algorithm rather than bankers: “The fact is, you can look at information in real time, and you can make minute adjustments, and you can build a closed-loop system, where you continuously change and adjust, and you make no mistakes, because you’re picking up signals all the time, and you can adjust.”

The Times’ Steve Lohr also talks about the recent enthusiasm for a quantitative approach to management, evangelized by Erik Brynjolfsson, Director of the MIT Center for Digital Business, who says Big Data will “replace ideas, paradigms, organizations and ways of thinking about the world.”

However, Lohr and Fox (who wrote the excellent book, “The Myth of the Rational Market”) caution about the oversimplifications inherent in modeling. Take, for example, some of the potentially flawed assumptions in Ranadive’s version of an algorithmically driven monetary policy:

–       Something as complex as monetary policy can be contained in a closed loop system

–       The past can reliably predict the future

–       If it doesn’t — and things do head into uncharted territory, — you’ll be able to “tweak” things into place as new information becomes available.

Fox uses the analogy of a Landing Page A/B (or multivariate) test as an example of the new quantitative approach to the world. In theory, page design could be left to a totally automated and testable process, where real-time feedback from users eventually decides the optimal layout. It sounds good in theory, but here’s the problem with this approach to marketing: You can’t test what you don’t think of. The efficacy of testing depends on the variables you choose to test. And that requires some thinking. Without a solid hypothesis based on a strategic view of the situation, you can quickly go down a rabbit hole of optimizing for the wrong things.

For example, most heavily tested landing pages I’ve seen all reach the same eventual destination: a page optimized for one definition of a conversion. Typically this would be the placement of an order or the submission of a form. There will be reams of data showing why this is the optimal variation. But what about all the prospects that hit that page for which the one offered conversion wasn’t the right choice? How do they get captured in the data? Did anyone even think to include them in the things to test for?

Fox offers a hybrid view of strategic management that more closely aligns with where I see this all going — call it Bayesian Strategic management. Traditional qualitative strategic thinking is required to set the hypothetical view of possible outcomes, but then we apply a quantitative rigor to measure, test and adjust based on the data we collect. This treads the line between the polarities of responses gathered by last week’s column – it puts the “strategic” horse before the “big data” cart. More importantly, it holds our strategic view accountable to the data. A strategy becomes a hypothesis to be tested.

One final thought. Whether we’re talking about Ranadive’s utopian (or dystopian?) vision of a data driven world or any of the other Big Data evangelists, there seems to be one assumption that I believe is fundamentally flawed, or at least, overly optimistic: that human behaviors can be adequately contained in a predictable, rational, controlled closed loop system. When it comes to understanding human behavior, the capabilities of our own brain far outstrip any algorithmically driven model ever created — yet we still get it wrong all the time.

If Big Data could really reliably predict human behaviors, do you think we’d be in financial situation we are now?