Psychology And Neuromarketing Can Be Fallible. So what?

There has been plenty of buzz up about the Reproducibility Project, which aimed to validate about 100 psychology science studies by attempting to reproduce the studies. Marketers should take note.

For those who place their faith in scientific-like testing (and big data), the findings of the Reproducibility Project ought to be astonishing. Two-thirds of the original studies tested proved fallible and even those that could be replicated demonstrated irregular statistical variations. Specifically, the magnitude of the effect tested was frequently half as small as the original finding.

Never place too much faith in any marketing formula. 

Sure, there is plenty to be gained by running A/B tests in an attempt to convert your business thinking from "we think" to "we know." There are many successful examples. But just because the results of testing turn out one way or another doesn't ensure success. A/B testing isn't a sure thing in marketing.

The truth is that we must stop treating single studies as unassailable truths, especially when other variables could be influencing the outcome of any finding, outcome, or assumption. True scientific thinking, after all, comes with a critical mindset rather than a yielding one. And we need to be more critical now than ever before, especially as people attempt to manipulate our thinking daily.

You can find evidence everywhere. Journalists are more likely to write attention-grabbing narratives first and then find examples to fill in the blanks than ever before. Scientists are more likely to build studies based upon biased theories than rely on objective observations. And marketers, whether they admit it or not, generally attempt to validate their work more than they produce better outcomes.

And no, it isn't always intentional. Anyone who has ever gone to an eye doctor only to be prescribed an inferior prescription knows how easy it is for mistakes to happen. No matter how meticulous the doctor or technician might be in the office, you eventually have to try it in the real world.

It recently happened to me. In the office, it seemed monovision — wearing a distance vision contact in one eye and a near vision correction in the other — was a suitable option for my slight presbyopia. In the real world, it didn't work at all. Too much of my interaction with the world relies on intermediate vision for monovision to be effective. The same thing can happen in scientific studies.

There are reasons humans are mostly unpredictable. 

If you truly want to understand psychology and sociology as it applies to marketing, you have to make a real effort to understand humans. First and foremost, you have understand that humans are the only creatures on this planet that form flexible and scalable cooperatives based on abstract concepts.

Yuval Noah Harari, author of the international bestseller Sapiens: A Brief History of Humankind, is especially intuitive on this point. As he explains it, bees and ants can form scalable cooperatives but aren't flexible in their ability to change their social structure. Whereas chimps and dolphins are flexible in how they cooperate, they are only able to do so in relatively small numbers.

The reason, it seems to me, it that humans are also the only creatures on this planet to operate with a dual reality, a perceptional concept studied in depth by Donald Hoffman, professor of cognitive science at the University of California, Irvine. In sum, humans perceive an objective reality (what is true) and a conceptual reality (what we accept as truth) at the same time.

For example, the money in your wallet is a piece of paper. The concept that it has value is a fiction that we have collectively agreed to accept as truth. And, to be clear, it is this dual reality often discussed by Hoffman that provides us our unique ability to form flexible and scalable cooperatives.

Marketing and communication, at their core, only has one purpose: to change behavior. And as such, marketers usually try to change behavior by drawing attention to an objective reality or attempting to elevate (or diminish) a conceptual reality. And what makes this especially interesting is that in the last decade, especially with the advent of social media (and likely to become more prevalent with the rise of augmented and virtual reality), is that marketers spend more time targeting the conceptual reality.

So what? The greater the emphasis on conceptual reality, the greater the unpredictability of testing because humans, throughout history, have proven to be consistently inconsistent. And in knowing this, maybe it is time to treat your approach to the science side of marketing as an exercise in adjustment and not in the collection of unassailable truths that will one day be proven false. Good luck.

The Best Time Fallacy For Social Sharing

You can read countless opinions about the best time to share content on social networks and come up with all sorts of conclusions. Some people have even published guides about sharing. And other people claim that there is a science behind sharing. Maybe it is science or maybe it's more random.

If it really was science, one would think big data could decipher it by now. Or who knows? Maybe it already did. If you spend a little time reading these articles, most pros are convinced by their metrics.

Some look for peaks in reach. Others avoid peaks in reach.  Some prefer off hours. Others prefer on hours. Some measure peaks in engagement. Others measure other stuff. Some say do what everyone else does. And others? Well, they say Friday.  Friday? Yes, Friday. 

Take your pick or subscribe to the most common of claims — 1-3 p.m. on Twitter, 1-4 p.m. on Facebook, 5-6 p.m. on Instagram, 8-11 p.m. on Pinterest, etc. — and you will eventually learn one thing. These assumptions are mostly wrong, at least wrong enough that they aren't always right.

Social sharing is largely shaped by three interdependent factors. 

The simple truth is that different social communities consume, engage, and share differently and different content (both in form or function) is consumed, engaged, and shared differently. The very best that anyone can hope for is to assess how their community receives and responds to content.

So where some self-proclaimed data analysts get it wrong is in not considering the entire picture. Ergo, the best time to share isn't necessarily dictated by big data patterns but by three interdependent influencers that established those data patterns. Specifically?

Community Demographics. Demographics do shape some online activity much like they shape broadcast channels, with the exception of increased accessibility at work. Sooner or later, marketers are likely to see age, gender, income level, race and ethnicity as influences (with occupations or interests being big tells too). This is doubly true for brands driving demographics to their accounts.

The point is that musicians and music lovers might be more active between 4 p.m. and 6 p.m., graphic designers between 7 p.m. and 9 p.m., authors and book lovers at around 11 a.m. and again at 5 p.m. This space, by the way, tends to perform better earlier in the day, especially between 6 a.m. and 8 a.m., which corresponds with marketers and communicators getting into work on the East Coast.

Social Media Management. And if you ever wondered why so many social media professionals can make seemingly contradictory claims about the best time, chalk it up to their own design. If a social media manager engages people between 6 a.m. and 8 a.m., 11 a.m. and 1 p.m., and 4 p.m. and 6 p.m. every day, then it's more than likely they will develop an audience around those times.

In fact, it might even make sense to pick times slightly off from some community demographics in an attempt to reach underserved prospects. Or, depending on resources and strategies, it might make sense to weight more activity during other timeframes. In the case of this space even, I'm partly responsible for that 6-8 a.m. timeframe mentioned earlier.

Content Type And Relevancy. Of course, engagement doesn't begin and end with participants. Not all content is created equal at the same time. For example, a social media manager might find that long-form content, studies, and white papers are best delivered when people are fresh while shorter content and timely information feels better late in the day and early morning.

Not all topics are created equal either. Some are predisposed to natural timeframes. People are more receptive to food porn before they eat rather than after they eat whereas recipes are easier to consume mid-morning and a few hours after dinner. And other special interests (such as programs or television shows) have unique timeframes too. Sometimes it can even be as simple as before and after (and sometimes during) the program.

In sum, the best time to share content has nothing to do with data patterns and much more to do with the factors that created those data patterns, with "do what seems to work" coming in a close second. Even the case of this space, all the external data suggests that I'm publishing at the worst time for a communication blog except the evidence that comes with publishing and sharing at other times.

There Is No Such Thing As An Easy A/B Lunch

"It is perhaps an all-too-human frailty to suppose that a favorable wind will blow forever." — Richard Bode

In the context of his book, First You Have To Row A Little Boat, Bode was writing about how almost impossible it is to imagine what it might be like to be caught in a dead calm while there is a breeze blowing hard against your sail or in your face or on your back. It's almost impossible to imagine it because our brains are mostly predisposed to see the most fleeting moments as infinitely constant.

When things are good, we think the honeymoon will never end. When things are bad, we readily embrace the pain as permanent. Never mind that most of us have lived long enough to know that the evidence doesn't bear either infinity out. We're generally inclined to indulge ourselves in deception.

Social media is not a science. It only feels like one.

Sure, some applications of social media seem to fall under the banner of science. Marketers are indeed in the business of observation and experimentation. They do attempt to study the structure of online communities and the behavior of people on a one to one, one to some, and one to many scale.

Some applications even attempt to apply scientific method to the mix, with A/B testing among the most prominent manifestations. There is only one problem with it. While A/B testing sometimes leads to a product development or marketing breakthrough, the operative word is sometimes.

The wind doesn't always blow in a favorable direction and sometimes it doesn't blow at all. Never mind that more and more data scientists are attempting to decipher public manipulation, but they frequently fail to appreciate that data has the propensity to manipulate its handlers too.

The biggest problem today, it seems, is that many data scientists have studied statistics but relatively few are practiced at applying scientific method in the physical or natural world (or psychological and sociological worlds for that matter). If they were, they might better appreciate the incongruity of choice — six studies of which were recently shared in an Econsultancy article by Ben Davis.

While some studies are stronger than others, a fair encapsulation of the research concludes that the choices offered, number of choices offered, order of the choices offered, and order of emotional triggers all influence A/B testing. Or, in other words, if A/B both suck, you prove nothing at all.

If you ask people whether they like big keys or little keys on a cellular phone, no one innovates touch screen technology. If you ask people which cola they like better during an A/B experiment, someone will eventually rediscover the recipe for New Coke. If you always listen to prescreen tests, every movie will have a happy ending.

But those examples are only the most straightforward research failures. Some hiccups are caused by the most subtle changes. The order information is presented (shoes before or/after a new dress). The timing of an interruption (when most people are online or when they are more receptive to share). The influence of the last destination they visited (did they leave feeling elated or aggravated).

There is no such thing as an easy lunch in marketing.

There are plenty of people who will tell you otherwise, but it's simply not true. Marketing is not a science, even if marketers love to sell science. It can be an asset but only if you think and think deep.

A few years ago, I had the privilege of working on franchise collateral for Capriotti's Sandwich Shop. I can't really speak to what they are doing now in terms of marketing, but I still love their sandwiches.

The challenge they had and probably still do, had a lot to do with psychology. Specifically, one of the questions that needed to be asked was how could they become part of the lunchtime decision-making process? The answer isn't as easy as you think.

When most people make decisions about what to have for lunch at the office the first A/B choice they create is fast food or sit down. The primary influencer at this stage is time, but it quickly turns toward taste. If fast food wins the consensus, then most people will run down the big brand list (McDonald's, Burger King, Wendy's, etc.) and make a decision based on preferences, experiences, and proximity.

Interestingly enough, KFC only gets a shot if someone says they don't want a burger. And other alternatives, like Subway, are added to the mix if someone insists on no fast food (a position thanks mostly to their Eat Fresh campaign). So where does Capriotti's fit?

A/B testing convinced some people that it fits everywhere because they consistently win on taste, but it really wasn't true. Sure, it won with loyalists, catering, or as a wild card but not where it needed to. To capture the average lunchtime customer, it comes down to the first round choice. Fast food or sit down? This sandwich shop is neither.

My solution was a bit different from the marketing firm that had contracted me onto their team. While they wanted to push award-winning sandwiches, I wanted to reframe the front end choice that there is lunch or Capriotti's, thereby pre-empting the fast food or sit down decision-making process.

But we didn't then and no one has since. So despite being voted the greatest sandwich in America, it's still niche and not mainstream no matter how many A/B tests they run. Why? As I said. There is no such thing as an easy lunch. Just because the winds of research keep blowing your organization in different directions doesn't mean it will always be there or push you to the destination you want. Someone has to aim for it.

Big Data Will Be The Blind Spot For Marketers

It's almost frightful how big big data will get. It's valuation is expected to reach $47 billion by 2017. It seems to me that estimate is too soft. Big data is like a boom town. I don't mean that figuratively.

The $4 billion Utah Data Center will eventually turn Bluffdale, Utah, and surrounding communities into boom towns. It's not the only place it will happen either. Government isn't the only player in big data.

Everybody wants an inside scoop on how individuals, groups, and mass populations operate for predictive and manipulative reasons. They want to uncover the non-existent philosopher's stone of human behavior so they can tell when someone who scratches their nose has malicious intent. They want to guess the direction of the public like they might plot the expected path of migrating geese. And they, marketers in particular, want to know which 140-character combination will not only get attention but also drive sales or, at least, pick up a follower that might buy a product within the next 100 tweets.

Some people will read that paragraph and feel spooked out. Some people will read it and salivate. It seems to me either might be an overreaction, but sometimes it's hard to tell. What is easy to tell is that big data will eventually lead to more blind spots than spoilers.

Analysts are too busy tracking online activity without concrete outcomes. 

Part of the problem, especially for marketers, is that they measure the wrong information. Forrester, ISTMA, and VisionEdge Marketing recently conducted a study that demonstrated precisely that.

What they found was that marketing performance management is operationally proficient but strategically stalled. The problem is exactly what you might think it is. Marketers are measuring marketing activity and not business outcomes, message effectiveness or predictive insights.

What does that mean? Marketers are too busy trying to prove performance to justify their efforts. They point to CRM and marketing automation to create dashboard reports that show how many people visited, shared and traveled down the sales funnel. They make decisions based on those measures too, and most of them revolve around the numbers they think matter, tying it to things like platform popularity.

It's not enough and I'm not the only one saying it. According to the analysts, only nine percent of CEOs and six percent of CFOs rely on marketing data to make decisions. In other words, most marketers have online clout and not the real stuff.

Big data will be rendered useless unless marketers measure on multiple levels. 

Less face facts. Although online sentiment can be useful, it's doesn't tell the whole story no matter how much money you throw at it. If it did, BP would not have survived the Gulf oil spill. If it did, JC Penney wouldn't be desperate after being right. In both cases, big data was off the mark.

Data needs to happen across every public, not just the public. Data needs to be discovered with multiple methods, not just one method. Even some of the most visible social media crisis events have been largely forgotten. Others were online loud, but many people never heard they happened.

You might find something different when you talk with people as opposed to react with them.

• Interviews. With the right interviewer, nothing beats a series of interviews. It's how some of our major clients have tapped my firm to write white papers. They work in other ways too. Once I interviewed 40 employees at a company that believed nobody saw the company like they did. I found out that they all saw the company the same way despite that belief.

• Focus Groups. Brainstorming sessions and focus groups made up of trusted stakeholders or select demographics can transform reaction captures into think tanks. For example, when I conduct core and strategic sessions, the first 50 responses are often the least important. Once a group hits closer to 100, they start thinking about vision instead of what's expected.

• Vetted Surveys. Instead of self-selected surveys, sometimes slanted with leading questions, try objective surveys (and control groups) with people who are solicited based upon belonging to a specific public or stakeholder group. Find out what they think of an organization, industry, and what's missing from the equation — not only what they expect but what they never thought to expect.

• Big Data. As I mentioned before, big data has a place. Just remember that sometimes you have to distinguish between the public and customers. One example that comes to mind was the initial launch of the iPhone without physical buttons. The quieter majority of customers didn't care. They didn't seem to care that the USB port was left off their iPads either. Never-customers cared much more.

After you're done, don't forget that inside out is just as important as what's being said outside.

• Employees. No matter how great you think your organizational brand might be, it isn't all that if your employees don't believe it and protect it. Most social media crisis events happen because one employee forgets just how important every branded piece of communication can be.

• Stakeholders. When working with the National Emergency Number Association (9-1-1), I was privy to some very intelligent ideas on improving emergency communication because the association's stakeholders included several dozen thought-leadership companies that had glimpses of future tech. Do you need another reason to talk to vendors, partners, shareholders, etc.?

• Customers. There is plenty that can be tracked when it comes to customers and there is much more to consider than a single click. The value of the lifetime customer is more. It's one of the reasons most major companies jumped at affiliate programs. Their marketing jumps in after one buy.

• The Public. Looking at the public makes sense, but with obvious limitations. Listening to the public en masse can sometimes be a good thing because it often serves as a commonsense barometer. Other times, it isn't nearly as good because it can be manipulated by catfish or implied wrongdoing.

Doing all this work takes more time, which means you can't turn on a dime with every decision. But then again, if an organization could turn on a dime then its brand relationship must be pretty thin. Or maybe the better way to say it is: isn't it commonsense to talk to people if you want to understand them?

Catching Catfish: Always Vet The Data

Some people never feel the need to be anonymous, online or off. But other people do, with their intent ranging from noble to malignant or their reasons ranging from convenience to pre-existing community standards (e.g., most people use creative avatars and punchy screen names). It's increasingly accepted.

So, it seems, is lying. As many as 25 percent of people admit they lie online (um, it's higher), citing security as the primary reason (um, it's not), and that doesn't account for the growing number of social network accounts that are partly or completely fabricated.

The phenomenon has grown up enough that it carries a better moniker than when Mackey or Chapel stole the show. Some people refer to fictitious and semi-fictitious accounts as catfish, named after the film-turned-television series. The series premiered on MTV in November 2012. It happens all the time.

The consequence of catfish in communication. 

Catfish are the bane of big data, enough so that some social networks are starting to do the unthinkable while ignoring the more obvious breaches like the one recently shared by Amy Vernon. In creating what is assumed to be a fictional account, someone hijacked Vernon's photos and started using them as his or her own under the name 'Melissa Dugan.'

And much like the new television series, Vernon's recent story sheds some light on the impact of catfish. There are personal and professional consequences. Fortunately, she is reasonably able to cope with it so far. But one can only imagine how long (if ever) Manti Te'o will need to fully reconcile the impact of having an online girlfriend — who died and was later resurrected — who was fabricated.

Much like the documentary Catfish, some people go so far as building an entire network of fabricated profiles to support their primary fabricated account, often grabbing up other people's pictures to do it. In the documentary, for example, an entire network of fake friends validated the fictitious account.

It's one step further than what married people who want an affair do on dating sites. Instead of making up one persona, catfish make up entire communities. What they do isn't limited to individual events.

Beyond individual masquerades and into public opinion. 

While some social media experts are quick to think about how fake accounts game popularity, some catfish are specifically set up to skew public opinion. Sometimes these efforts are harmless (such as casting a few extra votes for a favorite band on a survey). But others might not be harmless, given they are used to literally mask agendas by "washing" content through five or six profiles.

Three years ago, I tracked an unsupported news release that eventually became 'validated' by news. Public opinion catfish operate in much the same way, sharing volatile content across less-volatile social network accounts to create the illusion that whatever news is being shared is credible, sometimes rewritten to appear palatable. Or, in other cases, "washing" away geographical data is sometimes done to affect the perception of public policy (e.g., online politics frequently infuses outside interests).

Organizations are equally susceptible to such campaigns. It's not all that uncommon for some angry consumers to repost singular complaints across dozens of networks and review sites (and sometimes with more than one account) in order to disparage a product or service for whatever reason (justly or unjustly). There have even been cases where black hat competitors have driven up negatives, directly (fake reviews) and indirectly (propping up real negative reviews).

While there is a need to retain anonymity online (much like there is a need to preserve social satire), the rest of it — fraud and identity theft — is the leading unaddressed challenge within digital communication. And the best course of action today, although not foolproof, is to slow down, vet the data, and then vet the data again (even if you recognize the avatar, photo or logo as a trusted source).