The Human Factor (in the Age of Machines)

All of this hype about machine learning must be addressed somehow. This blog post is about how marketers can coexist with machines, and not to leave full control to them. Too many human users are doing that already.

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“machine,” Creative Commons license. | Credit: Flickr by Jonas Tegnerud

No matter how far AI evolves in the future, for as long as humans remain as the dominant species on this planet, machines will exist to serve the benefit of human collectives, in some form or another. That is an optimistic view and possibly the best-case scenario.

Now, if we imagine the dark path as kindly illustrated in movies like “Terminator” or the “Matrix” series, AI may one day decide to eliminate humans as we are merely nuisances to them (the worst case scenario), or convert us into living, breathing battery packs to power them with our body heat (the next-worst-case scenario).

Even without such doomsday predictions, it is quite feasible that machines will take jobs away from most of us, starting with menial and repetitive ones and moving on to so-called white-collar positions with thinking involved. Not quite the end-of-the-world case, but definitely the end-of-the-world-as-we-know-it situation, as the cognitive process won’t remain as a uniquely human function.

Not too long ago, it was big news that AI decisively defeated one of the smartest human beings on Earth in the game of Go. It was quite an achievement — not necessarily for the machine, but for the humans who designed it. The machine, less than one year after that achievement, is now up to the level that its older version won’t able to match. The latest is that it doesn’t even play Go anymore, after having played the game by itself millions of times.

Here is my take on that event: First, why is that so surprising? Yes, the game of Go is far more complex than chess, with a virtually unlimited number of outcomes. But everything happens on a game board and the rules are quite simple. Machines and humans can observe and predict events within that set boundary. If machine does nothing but “1” task within the rule set for an unlimited amount of time without being bored or getting tired, of course it will beat humans who easily get distracted or grow tired.

So can we even call such a match fair? At some point in the distant past, a car passed the speed of the fastest human runner or even a man on a horse (with exactly 1 horse-power). But other than the fact that we still continue to humiliate horses by measuring the engine power in terms of “horsepower,” who cares about that? We don’t have runners compete against cars in the Olympic Games, do we?

The second point is that, yes, it is newsworthy that an AI beat one of the best Go players in the world. But so what? The history of computers has been a series of human defeats in terms of speed and accuracy since the very invention of the thinking machine. Computers have been outperforming humans in many ways all along, so why does everyone get so scared them all of a sudden? Is it fear of the unknown or loss of control?

We have learned how to coexist with clunky mainframes in the past, and we will learn how to live — and live well — with AI with or without cute faces. And that’s if, and only if, we maintain the “human factor” in the evolution of thinking machines.

So let’s stop thinking about how smart machines have become, and let’s think about what that word “smart” means.

What ‘Smart’ Means

Does it mean that it remembers things better than us? Undoubtedly. The best use of a computer is to have it remember what we don’t want to remember. Just because I can’t even remember my work number without my “smart” phone, that doesn’t mean that I became dumber. I will use the remaining memory space in my brain to store some other useless information, like the average driving distance of an old golfer or a name of an actor in some obscure movie. Then again, why even bother with all of that when I can just Google them anytime?

Creating a Persona Menu (for You)

Personas are like menu items, each representing key characteristics of target customers that marketers need to know to push their products.

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“93H,” Public Domain license. | Credit: Flickr by saul saulete

I have been writing about the importance of using modeling techniques for personalization for some time now (refer to “Personalization Is About the Person” and “Segments vs. Personas”). If I may summarize the whole idea down to a 15-second pitch:

  • We need modeling because we will never know everything about everybody, and;
  • Selfishly for marketers, it is much simpler to assign personas to product groups and related contents than to have to deal with an obscene amount of customer data and a long list of content details at the same time.

Simply, personas are like menu items, each representing key characteristics of target customers that marketers need to know to push their products.

One may say, “Hey, I just put in SKU-level data into some personalization engine!” To which, I must ask, “Do you also put in unrefined oil into your beloved automobile?” I didn’t think so. Not that ruining some personalization engine will break anyone’s heart. But it may annoy the heck out of your customers by treating them as extensions of their immediate purchases, not as living, breathing human beings.

I’ve actually met someone from a software company at a conference who claimed to be able to create hundreds of thousands of combinations of SKU-level transaction data and content data. If you have a few hundred thousand SKUs and tens of thousands of pictures and creative items, well, the number of combinations will be quite large. Not exactly the number of stars in the universe, but quite unmanageable, enough for marketers to just “let go” and leave it all to the machine on a default setting. So, even if someone automated the process of combining such data (with some built-in rules, I’m sure), how would any marketer – and recipients of messages – make sense out of it all?

That type of shotgun approach is the mother of all of those annoying “personalizations,” like offers of the very same items that you just purchased. For such rudimentary methods, it might actually be a great achievement to offer a yoga mat to someone who just bought a yoga mat. Hey, they are in the same category after all, categorically speaking, right?

The key to humanization of marketing messages is to make them about the customers, not about marketers, products or channels. And that kind of high-level personalization requires, well, a real human touch. That means, each block of information must be bite-sized so that human beings – i.e., marketers – can process and consume it easily.

When I first came to America (a long time ago), it wasn’t so easy to go through menu items in a typical diner. Too many items! How can I pick just “one” of those items that matches my appetite and mood of the day? Now imagine a menu that goes on for hundreds of thousands of lines. And you have to act fast on it, too.

Personas, or architypes as some may call them, are the bridges between obscene amounts of data points and yet another large set of pictures and content. The idea is to have a manageable number of personas to make it easier for us to match the right content to the right target.

I bet most content libraries are not crazy big, but large enough. But on that side, it is what it is. You will not cut out some valuable digital assets just because the inventory got big. So, we have to make the personal data – especially behavioral and transactional data – more compact to facilitate easy assignment, as in “Show this picture of a glass of red wine next to a juicy steak” to a persona called “Wine Enthusiast” or “Fine Dining.” The assignment itself would be as simple as saving a room for persona designation in the content library (if you don’t even have a content library, we need to talk).

Then, how would you come up with the right list of personas for “you”? Having done this a few times for many companies in various industries on a national level, I have some tips to share.

  1. Be Product-Centric: Anyone who has been reading my articles about personalization will be surprised by this one, as I have been screaming “customer-centric marketing” all along. But, in the end, we are doing all of this to sell more of our products to customers. Think about the products you want to push, then think about the types of characteristics that you would love to know about customers to push those products in a relevant way.

Trying to sell cutting-edge products? Then you may need personas such as “Early adopter.” Selling value-based items? You may want “Bargain-seekers.” Pushing travel items? Try “Frequent business traveler” or “Family vacation” personas. Dealing with high net-worth people? Well, go beyond simple income-select and try “Globetrotter,” “Luxury car,” “Heavy stock investor,” etc., depending on what you are selling. By the way, these luxury personas may or may not be related to one another, as human beings are much more complex than their income levels.

  1. Be Creative: Models can be built if you have data for “some” people who have actually behaved in a certain way to be used as targets. That limitation aside, you can be as creative you want to be.

For example, if you are in the telecommunications industry, expand the typical triple-play offering, and dig deeper into “why” people would need broadband service. Is it because someone is an “Avid gamer,” “Heavy VOIP user,” “Frequent international caller,” part of a “Big family,” “Home office worker” and/or “On-demand movie watcher”? If you can differentiate these traits, you don’t have to push broadband Internet services with brute force. You can now show reasons why they need over 100 megabits per second service.

If you are dealing with mostly female customers (who are, by the way, responsible for the bulk of economic activities on a national level), one can imagine categories that start with various health and beauty items, going all of the way to yoga and fitness personas. In between those, add any persona that is an ideal target for the products you are trying to sell, be it “Fashion enthusiast,” “Children’s interests,” “Gardening enthusiast,” “Organic food,” “Weight watchers,” Gourmet Cooking,” “Family entertainment,” etc., etc. The keys is to describe the buyer, not the product.

  1. Start Small, but be bolder as the list grows: In the beginning, you may have to prove that personalization using model-based personas really works. Yes, building a persona is as simple as building a propensity model (in essence, they are exactly those), but that doesn’t mean that you start the effort with 50 persons. Pick the product that you really want to push, or characteristics that you need to know in order to resonate with your core customers, and build a few personas as a starter (say five to 10). You may find some data limitations along the way, but as you go through the list, your team (or analytics partners) will definitely gain momentum.

Then you can be bold. I’ve seen retailers who routinely maintain over 100 personas for just one major product category. And I’ll bet that list didn’t grow that big overnight, either.

Also, when you are in an expansion mode, just add items when in doubt. Think about the users of those personas, not mathematical differences among models. Do you know the difference between Kung Pao Chicken and Diced Chicken with Hot Peppers? Just peanuts on top. But restaurants have them both because customers expect to see them.

Similarly, there may be only slight differences between “Conservative Investor” and “Annuity Investor” personas. But the users of those personas may grab one or the other because of their targeting need at the moment. Or whatever inspired their marketing spirit. Think in terms of user-friendliness, not mathematical purity.

  1. Do Not Go Out of Control: When I was leading a product development team in a prominent data compiling company in the U.S., our team developed about 140 personas covering the entire country for various behavioral categories, including investment, travel, sports (both active participation and being a fan of), telecomm, donation, politics, etc. One of our competitors tried to copy that idea, and failed miserably. Why? It had built too many models.

For instance, if you are building personas for the cruise industry in general, you may need just “Luxury cruise” and “Family cruise” for starters. Those are good enough for initial prospecting. Then, if you must get deeper into cross-selling for coveted “onboard spending,” then you may get into “Adventure-seeker,” “Family entertainment,” “Gourmet,” “Wine enthusiast,” “Shopping expedition,” “Luxury entertainment,” “Silver years,” “Young parents,” etc., for customization of offers.

My old copycats with too many models had developed separate models for “each” cruise fleet and brand. How were they going to use all of that? One brand at a time, with one company as a user group? Why not build a custom model as needed, then? Surely that would be more effective if the model is to target a specific brand or fleet. Anyway, my competitors ended up building a few thousand models, for any known brand out there in every industry, seriously limiting the chance those personas would be used by marketers.

As I mentioned in the beginning, this is about matching offers (or content) to the right people at the right time. If you go out of control, it will be very difficult to do that kind of match-making. If your persona list is just big for the sake of being big, well, how is that any different from using the raw data? You’ve got to know when to stop, too. The key is “not too small, and not too big,” for humans and machines alike.

  1. Update Periodically: Like any menu, persona lists go out of date. Some items may not have been used actively. Some may become obsolete as business models and core product lines go through changes. And models do go stale, as well. You may not have to review this all of the time, and there will be staple menu items, like spaghetti with meatballs in a restaurant. But it will be prudent to go through the menu once in awhile. If not because of the product, then because of people’s attitudes about it changing.
  2. Evangelize: It would be a shame if the data and analytics people did all of this work and marketers didn’t use it fully. These personas are in essence mathematical summaries of “lots of” data in compact forms. They can be used in targeting (for selecting the right target for specific product offers), and for personalization of offers and messages based on dominant characteristic of the target (e.g., show different pictures to “Adventure-seeker” and “Family entertainment” personas, even if they are about to board the same ship). Continuously educate your fellow marketers that using personas is as easy as using any other type of data, except that they are compressed model scores with no missing values.

The personalization game is complex. It may look easy if you just buy an off-the-shelf personalization engine, set up some rules with unrefined data and let it run. While it’s better than sending uniform message to everyone, that kind of rudimentary approach is far less than ideal, not to mention the annoyance factor.

To maximize the power of all available data and the personalization engine itself, we must compress the data in forms of personas. Resultant messaging will be far more relevant to your target audience as, for one, a persona is a built-in mechanism for the personal touch. If you set the menu up as a bridge between data and people, that is.

Telemarketing: The Impossible Tradeoff

One of my Brazilian colleagues, Roberto Silva (not his real name), was a frequent traveler to the U.S. on business and for pleasure. He had a daughter at an American university and he visited her whenever he could. He also liked buying things at specialist outlets and, a few years back, had bought some trousers (which became his favorites) from Lands’ End.

Call center agentOne of my Brazilian colleagues, Roberto Silva (not his real name), was a frequent traveler to the U.S. on business and for pleasure. He had a daughter at an American university and he visited her whenever he could. He also liked buying things at specialist outlets and, a few years back, had bought some trousers (which became his favorites) from Lands’ End.

With his wife reminding him regularly that these favorite trousers were wearing out, he decided to buy some new ones on his next trip. From his New York hotel, he telephoned Lands’ End and introduced himself to the cheery telephonist who welcomed him back to Lands’ End. A moment later, she asked him about his daughter, how she was doing and if she had graduated from college? Stunned, he asked how she knew about his daughter and she said that the last time he had called, he had mentioned that as the reason for his visit. What could she do to help him?

She asked how he liked the trousers he had bought before. He replied that if they still had them in stock, he’d order two more pair. “Can we ship them to the same hotel you stayed at last time? We can have them to you by tomorrow evening,” she said. Of course he purchased them and some other items and when he told me the story he said emphatically: “I’ll never buy trousers like these anywhere else. There are warm, friendly people who work there, not a telephone bullpen staffed by bored and underpaid, out-of-work actors. These people obviously enjoy talking to customers and seem in no hurry to get you off the phone and you don’t have to listen to endless menu options and punch in some numbers to get someone to talk to you.”

Perhaps that’s a rather long way around to introduce the “impossible tradeoff,” the obvious cost-saving of having an automated system interact with the customer up to or beyond the point where he or she either needs or demands to talk with a human being, vs. a totally human interface which may be less efficient in terms of costs, but is more likely to have customers become “advocates,” as my friend Roberto had. Can you imagine someone saying how happy they were only having to make four menu options instead of 10?

Banks and credit card companies seem to be in competition with mobile phone operators to win some prize for making it difficult to talk with anyone (and making you wait the longest time if you want to). Internet sellers are often even worse, hiding their telephone numbers in the most secluded nooks of their websites. The recent United Airlines disaster of dragging a passenger off of a flight to free up some oversold seats is a horrible example of how a focus on efficiency (in this case, maximum passenger loads created by intentional over-booking) can undermine customer loyalty. After that incident, it will take a long time before anyone is ever “loyal” to United again.

The ultimate question is one of relative value. And despite all of the big data in the world, there really is no way of gauging accurately the relative value of the tradeoff. How strongly the customer feels about the transaction must be an important if unquantifiable (soft) data point.

The bean-counters will assure you of the obvious saving; machines are, in the long run, cheaper than people. They work 24/7, they don’t demand raises and they don’t need pregnancy leave. Then they will argue that customers are better-served, get to speak to the right knowledgeable person faster than explaining their problem over and over again — or better, have it dealt with without human intervention. Not so for my friend Roberto, who will counter that his loyalty and the loyalty of many like-minded customers will more than make up for the savings in long-term revenue and insulation against “efficient” competition.

So where do you draw the bottom line?

It’s always a tradeoff compromise (the best solution or the worst). But I would opt for an automated answer which, first, thanked the caller for calling and second, offered a choice of:

  1. Immediately talking to a warm, friendly and knowledgeable human, or
  2. Hearing a short menu, which may speed you to the answer you are looking for.

Unfortunately, a “right” answer is impossible.

 

Endit …

Business IS Personal, and Other Leadership Rules

“Business is one of the most human things in the world,” Simon Sinek said early on in his presentation during &THEN. He shared that when he hears someone say, “It’s not personal, it’s business” he just laughs to himself. No, no it’s not … business is personal. It’s human.

I have a new marketing crush. It’s Simon Sinek.

Simon SinekHe was the Monday morning inspirational keynote speaker during DMA’s &THEN event last week and I’m still running over in my head all the things he discussed in under an hour, a week later, because he gave us that much to chew on.

His wonderfully dynamic speaking skills aside, Simon was able to be upfront and frank with a hall full of marketers.

“Business is one of the most human things in the world,” he said early on in his presentation. Then he commented on that when he hears someone say, “It’s not personal, it’s business” he just laughs to himself. No, no it’s not … business is personal. It’s human.

business_personalAnd human is something we could all stand to do a little better, and a bit more often. Especially in leadership roles.

Simon spoke about how in this ever-connected world, technology shouldn’t replace human contact. Instead, it should bring humans together. And leaders need to take the charge.

Certificates Don’t Make a Leader

“[There’s an] incredible lack of leadership across the world today in every industry,” Simon said. It may seem harsh, but hang on before you brush off his point.

As humans, we like intensity because its easy to measure, and this is how leadership is often taught:

  1. Attend a leadership seminar
  2. Earn a certificate
  3. ”I’m a leader now!”

It’s the intensity we crave, but that’s not how it works. Consistency matters more than intensity. Good leaders are built over time, energy and actions.

Another point of his I really liked was that good leaders create an environment of vulnerability, which allows people to speak up and honestly ask for help and feel safe. If you know you can ask for help with a project and not fear a layoff or something else, employees will do so. This builds trust and stronger teams (trust me, THIS WORKS).

Put the Phone Down

We’re all saying this, but Simon both reinforced points and made some new ones.

When someone’s smartphone is out — whether in their hand, on a table or anywhere else visible — it makes the other person in the conversation feel less important. Why? Because at any moment it’s understood that a notification can go off, and attention gets transferred directly to the phone.

During a meeting, a smartphone on the table announces to all “you’re not important.” And yes, Simon let us all know that flipping the phone over in an attempt to be polite is still just as bad. And it’s true! How many meetings have you sat through with all the buzzing from phones being set to vibrate … or the phone with the ringer still on?

It’s distracting, but we all do it … and probably because a fair number of the people in leaderships roles are doing it. Not to be jerks, but because of this need to constantly be connected. Here, the tech gets in the way of the relationships.

Toward the end of his presentation, Simon said, “Whoever understands people the best wins.” “People” are our prospects, customers and even our fellow employees. Make it personal … because that’s just what good business is.

There will probably be a couple more blog posts in the future that will reference Simon’s presentation at &THEN 2016 … he gave me a lot to think about.

The Purpose-Driven Brand

Since the beginning of time to this very moment, we humans have been driven by purpose. Consciously and unconsciously, we seek meaning in our lives and the need to actively make a difference and leave a personal legacy of good when we move on from this existence. Jung addresses this in his Individuation process and so, too, do modern and past psychologists and researchers of human behavior drivers.

Since the beginning of time to this very moment, we humans have been driven by purpose. Consciously and unconsciously, we seek meaning in our lives and the need to actively make a difference and leave a personal legacy of good when we move on from this existence. Jung addresses this in his Individuation process and so, too, do modern and past psychologists and researchers of human behavior drivers.

Rick Warren, founder of The Saddleback Ministries, and best-selling author, discovered just how powerful our need and drive for purpose is when he wrote, “The Purpose-Driven Life: What on Earth Am I Here For?” Written in 2003, this book became the bestselling hardback non-fiction book in history, and is the second most-translated book in the world, after the Bible.

Today’s consumer seeks purpose outside of the traditional methods of religion, volunteerism, and random acts of kindness toward friends and strangers. Many of us, in fact most of us, seek to further our sense of purpose with our choices at the grocery store, online shopping carts and more. According to research by Cone Communications and Edelman, consumers in the U.S. are more likely to trust a brand that shows its direct impact on society (opens as a PDF). Others, upwards of 80 percent, are more likely to purchase from a company that can quantifiably show how it makes a difference in people’s lives—beyond just adding to the investment portfolio of a very select few.

According to the Merriam Webster dictionary, purpose is defined as:

: the reason why something is done or used
: the aim or intention of something
: the feeling of being determined to do or achieve something

Consumers are not just expecting big business to define a social purpose for the brand, they are demanding it by how they are making purchasing and loyalty choices. Edelman’s “Good Purpose Study,” released in 2012 and covering a five-year study of consumers worldwide shows:

  • 47 percent of global consumers buy brands that support a good cause atleast monthly, a 47 percent increase in just two years.
  • 72 percent of consumers wouldrecommend a brand that supports a good cause over one that doesn’t, a 39 percent increase since 2008
  • 71 percent of consumers would help a brand promote its products or services if there is a good cause behind them, representing a growth of 34 percent since 2008
  • 73 percent ofconsumers would switch brands ifa different brand of similar quality supported a good cause, which is a 9 percent increase since 2009

Another research group, Cone Communications, showed that 89 percent of consumers are likely to switch brands to one that is associated with a good cause if price and quality are similar; and 88 percent want to hear what brands are doing to have a real impact, not just that they are spending resources toward a cause.

This new state of consumerism doesn’t just show people still have a heart and soul, it is a big flag to brands in all industries to integrate CSR or Corporate Social Responsibility into their brand fiber, customer experience and marketing programs.

I interviewed William L. “Toby” Usnik, Chief CSR Officer for Christie’s in New York City, who maintains that CSR has moved far beyond writing a check and then emotionally moving on from a cause or community in need. It is about a brand’s purpose being bigger than developing its return to shareholders. Validating Usnik is a recent article published in the March 21, 2015, edition of The Economist, quoting Jack Welch of GE fame as saying “pursuing shareholder value as a strategy was ‘the dumbest idea ever.’ ” While that might be debatable, it is becoming less and less debatable, per the statistics above that show how defining a brand’s purpose in terms of the social good it delivers to communities related to its business is anything but “dumbest”—and rather, is getting smarter and smarter by the day.

Charting new territory in his role as Chief CSR Officer for Christie’s, Usnik’s first step was to define CSR as it relates to human psychology and the values of the Christie’s brand. For Usnik, it starts with building a brand’s purpose around Maslow’s hierarchy of needs and helping your constituents get closer to self-actualization, or that state of reaching a higher purpose for a greater good.

“Moving customers upwards through Maslow’s hierarchy of needs is critical to address,” says Usnik. “Customers of all ages, and especially Millennials, are moving toward a state of self-actualization and looking to define their purpose and place in communities and the world. They seek relationships with brands that are doing the same within their own value set. As a result, any business today needs to ask itself, ‘What is the impact of our activities on each other, the community, the workplace, customers and the planet?’ “

Defining your brand’s purpose and corresponding CSR efforts is the first step to developing emotional and psychological bonds with internal and external customers. When you make your CSR actionable by engaging others in your cause, you can build passion and loyalty that not only define your brand, but also your profitability. Coke defines its brand through its happiness campaign that involves delivering free Coke and other items, like sports equipment and toys, to villages around the world, and through water sanitization programs.

Tom’s Shoes, an example that is known to most as one of the pioneers in philanthropic branding, went from $9 million to $21 million in revenue in just three years by being a “purpose-driven brand” that enables people to give back to others simply by making a purchase. With a cost of goods sold of $9 and a sale price of more than $60, that is not hard to do.

At Christie’s CSR, is a big part of CRM. According to Usnik, Christie’s helps many of its customers sell high-value works of art. Many customers then donate the proceeds to social causes that align with their personal values or passions. By helping customers turn wealth into support for charitable causes, they actually create strong emotional bonds with customers, rooted in empathy and understanding—which is far more critical for securing lifetime value than points and reward programs.

In just 2014, $300 million in sales were facilitated through Christie’s that benefited non-profit organizations. Additionally, Christie’s regularly volunteers its charity auctioneers to nonprofit events. And in 2014, he estimates they’ve raised $58 million for 300 organizations.

The key to successful branding via CSR programs and purpose-driven strategies that transcend all levels of an organization and penetrate the psyche of we humans striving to define our role in this world is sincerity. Anything less simply backfires. Brands must be sincere about caring to support worthwhile causes related to their field, and they must be sincere when involving customers in charitable giving.

Concludes Usnik, “You can’t fake caring. If you pretend to care about a cause you align with, or a cause that is important to your customer, [you] won’t succeed. Caring to make a difference must be part of your culture, your drive and your passion at all levels. If you and your employees spend time and personal energy to work closely with your customers to make a difference for your selected causes and those of your customers, you are far more likely to secure long-term business and loyalty and overall profitable client relationships.”

Takeaway: The five primary drivers of human behavior, according to psychologist Jon Haidt of the University of Virginia and author of “The Happiness Hypothesis,” are centered around our innate need to nurture others, further worthy causes, make a difference in the world, align with good and help others. When brands can define themselves around these needs, we not only influence human behavior for the greater good, we can influence purchasing behavior for the long-term good of our individual brands. And per the Edelman research, 76 percent of customers around the world say its okay for brands to support good causes and make money at the same time. So define your purpose, build your plan, engage your customers and shine on!

Don’t Do It Just Because You Can

Don’t do it just because you can. No kidding. … Any geek with moderate coding skills or any overzealous marketer with access to some data can do real damage to real human beings without any superpowers to speak of. Largely, we wouldn’t go so far as calling them permanent damages, but I must say that some marketing messages and practices are really annoying and invasive. Enough to classify them as “junk mail” or “spam.” Yeah, I said that, knowing full-well that those words are forbidden in the industry in which I built my career.

Don’t do it just because you can. No kidding. By the way, I could have gone with Ben Parker’s “With great power comes great responsibility” line, but I didn’t, as it has become an over-quoted cliché. Plus, I’m not much of a fan of “Spiderman.” Actually, I’m kidding this time. (Not the “Spiderman” part, as I’m more of a fan of “Thor.”) But the real reason is any geek with moderate coding skills or any overzealous marketer with access to some data can do real damage to real human beings without any superpowers to speak of. Largely, we wouldn’t go so far as calling them permanent damages, but I must say that some marketing messages and practices are really annoying and invasive. Enough to classify them as “junk mail” or “spam.” Yeah, I said that, knowing full-well that those words are forbidden in the industry in which I built my career.

All jokes aside, I received a call from my mother a few years ago asking me if this “urgent” letter that says her car warranty will expire if she does not act “right now” (along with a few exclamation marks) is something to which she must respond immediately. Many of us by now are impervious to such fake urgencies or outrageous claims (like “You’ve just won $10,000,000!!!”). But I then realized that there still are plenty of folks who would spend their hard-earned dollars based on such misleading messages. What really made me mad, other than the fact that my own mother was involved in that case, was that someone must have actually targeted her based on her age, ethnicity, housing value and, of course, the make and model of her automobile. I’ve been doing this job for too long to be unaware of potential data variables and techniques that must have played a part so that my mother to receive a series of such letters. Basically, some jerk must have created a segment that could be named as “old and gullible.” Without a doubt, this is a classic example of what should not be done just because one can.

One might dismiss it as an isolated case of a questionable practice done by questionable individuals with questionable moral integrity, but can we honestly say that? I, who knows the ins and outs of direct marketing practices quite well, fell into traps more than a few times, where supposedly a one-time order mysteriously turns into a continuity program without my consent, followed by an extremely cumbersome canceling process. Further, when I receive calls or emails from shady merchants with dubious offers, I can very well assume my information changed hands in very suspicious ways, if not through outright illegal routes.

Even without the criminal elements, as data become more ubiquitous and targeting techniques become more precise, an accumulation of seemingly inoffensive actions by innocuous data geeks can cause a big ripple in the offline (i.e., “real”) world. I am sure many of my fellow marketers remember the news about this reputable retail chain a few years ago; that they accurately predicted pregnancy in households based on their product purchase patterns and sent customized marketing messages featuring pregnancy-related products accordingly. Subsequently it became a big controversy, as such a targeted message was the way one particular head of household found out his teenage daughter was indeed pregnant. An unintended consequence? You bet.

I actually saw the presentation of the instigating statisticians in a predictive analytics conference before the whole incident hit the wire. At the time, the presenters were unaware of the consequences of their actions, so they proudly shared employed methodologies with the audience. But when I heard about what they were actually trying to predict, I immediately turned my head to look at the lead statistician in my then-analytical team sitting next to me, and saw that she had a concerned look that I must have had on my face, as well. And our concern was definitely not about the techniques, as we knew how to do the same when provided with similar sets of data. It was about the human consequences that such a prediction could bring, not just to the eventual targets, but also to the predictors and their fellow analysts in the industry who would all be lumped together as evil scientists by the outsiders. In predictive analytics, there is a price for being wrong; and at times, there is a price to pay for being right, too. Like I said, we shouldn’t do things just because we can.

Analysts do not have superpowers individually, but when technology and ample amounts of data are conjoined, the results can be quite influential and powerful, much like the way bombs can be built with common materials available at any hardware store. Ironically, I have been evangelizing that the data and technology should be wielded together to make big and dumb data smaller and smarter all this time. But providing answers to decision-makers in ready-to-be used formats, hence “humanizing” the data, may have its downside, too. Simply, “easy to use” can easily be “easy to abuse.” After all, humans are fallible creatures with ample amounts of greed and ambition. Even without any obvious bad intentions, it is sometimes very difficult to contemplate all angles, especially about those sensitive and squeamish humans.

I talked about the social consequences of the data business last month (refer to “How to Be a Good Data Scientist“), and that is why I emphasized that anyone who is about to get into this data field must possess deep understandings of both technology and human nature. That little sensor in your stomach that tells you “Oh, I have a bad feeling about this” may not come to everyone naturally, but we all need to be equipped with those safeguards like angels on our shoulders.

Hindsight is always 20/20, but apparently, those smart analysts who did that pregnancy prediction only thought about the techniques and the bottom line, but did not consider all the human factors. And they should have. Or, if not them, their manager should have. Or their partners in the marketing department should have. Or their public relations people should have. Heck, “someone” in their organization should have, alright? Just like we do not casually approach a woman on the street who “seems” pregnant and say “You must be pregnant.” Only socially inept people would do that.

People consider certain matters extremely private, in case some data geeks didn’t realize that. If I might add, the same goes for ailments such as erectile dysfunction or constipation, or any other personal business related to body parts that are considered private. Unless you are a doctor in an examining room, don’t say things like “You look old, so you must have hard time having sex, right?” It is already bad enough that we can’t even watch golf tournaments on TV without those commercials that assume that golf fans need help in that department. (By the way, having “two” bathtubs “outside” the house at dusk don’t make any sense either, when the effect of the drug can last for hours for heaven’s sake. Maybe the man lost interest because the tubs were too damn heavy?)

While it may vary from culture to culture, we all have some understanding of social boundaries in casual settings. When you are talking to a complete stranger on a plane ride, for example, you know exactly how much information that you would feel comfortable sharing with that person. And when someone crosses the line, we call that person inappropriate, or “creepy.” Unfortunately, that creepy line is set differently for each person who we encounter (I am sure people like George Clooney or Scarlett Johansson have a really high threshold for what might be considered creepy), but I think we can all agree that such a shady area can be loosely defined at the least. Therefore, when we deal with large amounts of data affecting a great many people, imagine a rather large common area of such creepiness/shadiness, and do not ever cross it. In other words, when in doubt, don’t go for it.

Now, as a lifelong database marketer, I am not advocating some over-the-top privacy zealots either, as most of them do not understand the nature of data work and can’t tell the difference between informed (and mutually beneficial) messages and Big Brother-like nosiness. This targeting business is never about looking up an individual’s record one at a time, but more about finding correlations between users and products and doing some good match-making in mass numbers. In other words, we don’t care what questionable sites anyone visits, and honest data players would not steal or abuse information with bad intent. I heard about waiters who steal credit card numbers from their customers with some swiping devices, but would you condemn the entire restaurant industry for that? Yes, there are thieves in any part of the society, but not all data players are hackers, just like not all waiters are thieves. Statistically speaking, much like flying being the safest from of travel, I can even argue that handing over your physical credit card to a stranger is even more dangerous than entering the credit card number on a website. It looks much worse when things go wrong, as incidents like that affect a great many all at once, just like when a plane crashes.

Years back, I used to frequent a Japanese Restaurant near my office. The owner, who doubled as the head sushi chef, was not a nosy type. So he waited for more than a year to ask me what I did for living. He had never heard anything about database marketing, direct marketing or CRM (no “Big Data” on the horizon at that time). So I had to find a simple way to explain what I do. As a sushi chef with some local reputation, I presumed that he would know personal preferences of many frequently visiting customers (or “high-value customers,” as marketers call them). He may know exactly who likes what kind of fish and types of cuts, who doesn’t like raw shellfish, who is allergic to what, who has less of a tolerance for wasabi or who would indulge in exotic fish roes. When I asked this question, his answer was a simple “yes.” Any diligent sushi chef would care for his or her customers that much. And I said, “Now imagine that you can provide such customized services to millions of people, with the help of computers and collected data.” He immediately understood the benefits of using data and analytics, and murmured “Ah so …”

Now let’s turn the table for a second here. From the customer’s point of view, yes, it is very convenient for me that my favorite sushi chef knows exactly how I like my sushi. Same goes for the local coffee barista who knows how you take your coffee every morning. Such knowledge is clearly mutually beneficial. But what if those business owners or service providers start asking about my personal finances or about my grown daughter in a “creepy” way? I wouldn’t care if they carried the best yellowtail in town or served the best cup of coffee in the world. I would cease all my interaction with them immediately. Sorry, they’ve just crossed that creepy line.

Years ago, I had more than a few chances to sit closely with Lester Wunderman, widely known as “The Father of Direct Marketing,” as the venture called I-Behavior in which I participated as one of the founders actually originated from an idea on a napkin from Lester and his friends. Having previously worked in an agency that still bears his name, and having only seen him behind a podium until I was introduced to him on one cool autumn afternoon in 1999, meeting him at a small round table and exchanging ideas with the master was like an unknown guitar enthusiast having a jam session with Eric Clapton. What was most amazing was that, at the beginning of the dot.com boom, he was completely unfazed about all those new ideas that were flying around at that time, and he was precisely pointing out why most of them would not succeed at all. I do not need to quote the early 21st century history to point out that his prediction was indeed accurate. When everyone was chasing the latest bit of technology for quick bucks, he was at least a decade ahead of all of those young bucks, already thinking about the human side of the equation. Now, I would not reveal his age out of respect, but let’s just say that almost all of the people in his age group would describe occupations of their offspring as “Oh, she just works on a computer all the time …” I can only wish that I will remain that sharp when I am his age.

One day, Wunderman very casually shared a draft of the “Consumer Bill of Rights for Online Engagement” with a small group of people who happened to be in his office. I was one of the lucky souls who heard about his idea firsthand, and I remember feeling that he was spot-on with every point, as usual. I read it again recently just as this Big Data hype is reaching its peak, just like the dot.com boom was moving with a force that could change the world back then. In many ways, such tidal waves do end up changing the world. But lest we forget, such shifts inevitably affect living, breathing human beings along the way. And for any movement guided by technology to sustain its velocity, people who are at the helm of the enabling technology must stay sensitive toward the needs of the rest of the human collective. In short, there is not much to gain by annoying and frustrating the masses.

Allow me to share Lester Wunderman’s “Consumer Bill of Rights for Online Engagement” verbatim, as it appeared in the second edition of his book “Being Direct”:

  1. Tell me clearly who you are and why you are contacting me.
  2. Tell me clearly what you are—or are not—going to do with the information I give.
  3. Don’t pretend that you know me personally. You don’t know me; you know some things about me.
  4. Don’t assume that we have a relationship.
  5. Don’t assume that I want to have a relationship with you.
  6. Make it easy for me to say “yes” and “no.”
  7. When I say “no,” accept that I mean not this, not now.
  8. Help me budget not only my money, but also my TIME.
  9. My time is valuable, don’t waste it.
  10. Make my shopping experience easier.
  11. Don’t communicate with me just because you can.
  12. If you do all of that, maybe we will then have the basis for a relationship!

So, after more than 15 years of the so-called digital revolution, how many of these are we violating almost routinely? Based on the look of my inboxes and sites that I visit, quite a lot and all the time. As I mentioned in my earlier article “The Future of Online is Offline,” I really get offended when even seasoned marketers use terms like “online person.” I do not become an online person simply because I happen to stumble onto some stupid website and forget to uncheck some pre-checked boxes. I am not some casual object at which some email division of a company can shoot to meet their top-down sales projections.

Oh, and good luck with that kind of mindless mass emailing; your base will soon be saturated and you will learn that irrelevant messages are bad for the senders, too. Proof? How is it that the conversion rate of a typical campaign did not increase dramatically during the past 40 years or so? Forget about open or click-through rate, but pay attention to the good-old conversion rate. You know, the one that measures actual sales. Don’t we have superior databases and technologies now? Why is anyone still bragging about mailing “more” in this century? Have you heard about “targeted” or “personalized” messages? Aren’t there lots and lots of toolsets for that?

As the technology advances, it becomes that much easier and faster to offend people. If the majority of data handlers continue to abuse their power, stemming from the data in their custody, the communication channels will soon run dry. Or worse, if abusive practices continue, the whole channel could be shut down by some legislation, as we have witnessed in the downfall of the outbound telemarketing channel. Unfortunately, a few bad apples will make things a lot worse a lot faster, but I see that even reputable companies do things just because they can. All the time, repeatedly.

Furthermore, in this day and age of abundant data, not offending someone or not violating rules aren’t good enough. In fact, to paraphrase comedian Chris Rock, only losers brag about doing things that they are supposed to do in the first place. The direct marketing industry has long been bragging about the self-governing nature of its tightly knit (and often incestuous) network, but as tools get cheaper and sharper by the day, we all need to be even more careful wielding this data weaponry. Because someday soon, we as consumers will be seeing messages everywhere around us, maybe through our retina directly, not just in our inboxes. Personal touch? Yes, in the creepiest way, if done wrong.

Visionaries like Lester Wunderman were concerned about the abusive nature of online communication from the very beginning. We should all read his words again, and think twice about social and human consequences of our actions. Google from its inception encapsulated a similar idea by simply stating its organizational objective as “Don’t be evil.” That does not mean that it will stop pursuing profit or cease to collect data. I think it means that Google will always try to be mindful about the influences of its actions on real people, who may not be in positions to control the data, but instead are on the side of being the subject of data collection.

I am not saying all of this out of some romantic altruism; rather, I am emphasizing the human side of the data business to preserve the forward-momentum of the Big Data movement, while I do not even care for its name. Because I still believe, even from a consumer’s point of view, that a great amount of efficiency could be achieved by using data and technology properly. No one can deny that modern life in general is much more convenient thanks to them. We do not get lost on streets often, we can translate foreign languages on the fly, we can talk to people on the other side of the globe while looking at their faces. We are much better informed about products and services that we care about, we can look up and order anything we want while walking on the street. And heck, we get suggestions before we even think about what we need.

But we can think of many negative effects of data, as well. It goes without saying that the data handlers must protect the data from falling into the wrong hands, which may have criminal intentions. Absolutely. That is like banks having to protect their vaults. Going a few steps further, if marketers want to retain the privilege of having ample amounts of consumer information and use such knowledge for their benefit, do not ever cross that creepy line. If the Consumer’s Bill of Rights is too much for you to retain, just remember this one line: “Don’t be creepy.”

Who’s Your Scapegoat?

I find it interesting that machines and procedures often become scapegoats for “human” errors. Remember the time when the word “mainframe” was a dirty word? As if those pieces of hardware were contaminated by some failure-inducing agents. Yeah, sure. All your worries will disappear along with those darn mainframes. Or did they?

I find it interesting that machines and procedures often become scapegoats for “human” errors. Remember the time when the word “mainframe” was a dirty word? As if those pieces of hardware were contaminated by some failure-inducing agents. Yeah, sure. All your worries will disappear along with those darn mainframes. Or did they? I don’t know what specific hardware is running behind those intangible “clouds” nowadays, but in the age when anyone can run any operating system on any type of hardware, the fact that such distinctions made so much mayhem in organizations is just ridiculous. I mean really, when most of computing and storage are taken care of in the big cloud, how is the screen that you’re looking at any different than a dummy terminal from the old days? Well, of course they are in (or near) retina display now, but I mean conceptually. The machines were just doing the work that they were designed to do. Someone started blaming the hardware for their own shortcomings, and soon, another dirty word was created.

In some circles of marketers, you don’t want to utter “CRM” either. I wasn’t a big fan of that word even when it was indeed popular. For a while, everything was CRM this or CRM that. Companies spent seven-figure sums on some automated CRM solution packages, or hired a whole bunch of specialists whose titles included the word CRM. Evidently, not every company broke even on that investment, and the very concept “CRM” became the scapegoat in many places. When the procedure itself is the bad guy, I guess fewer heads will roll—unless, of course, one’s title includes that dirty word. But really, how is that “Customer Relationship Management” could be all that bad? Delivering the right products and offers to the right person through the right channel can’t be that wrong, can it? Isn’t that the whole premise of one-to-one marketing, after all?

Now, if someone overinvested on some it-can-walk-on-the-water automated system, or just poorly managed the whole thing, let’s get the record straight. Someone just messed it all up. But the concept of taking care of customers with data-based marketing and sales programs was never the problem. If an unqualified driver creates a major car accident, is that the car’s fault? It would be easier to blame the internal combustion engine for human errors, but it just ain’t fair. Fair or not, however, over-investment or blind investment on anything will inevitably call for a scapegoat. If not now, in the near future. My prediction? The next scapegoat will be “Big Data” if that concept doesn’t create steady revenue streams for investors soon. But more on that later.

I’ve seen some folks who think “analytics” is bad, too. That one is tricky, as the word “analytics” doesn’t mean just one thing. It could be about knowing what is going on around us (like having a dashboard in a car). Or it could be about describing the target (where are the customers and what do they look like?). Or it could be about predicting the future (who is going to buy what and where?). So, when I hear that “analytics” didn’t work out for them, I am immediately thinking someone screwed things up dearly after overspending on that thing called “analytics,” and then started blaming everything else but themselves. But come on, if you bought a $30,000 grand piano for your kids to play chopsticks on it, is that the piano’s fault?

In the field of predictive analytics for marketing, the main goals come down to these two:

  1. To whom should you be talking, and
  2. If you decided to talk to someone, what are you going to offer? (Please don’t tell me “the same thing for everyone”.)

And that’s really it. Sure, we can talk about products and channels too, but those are all part of No. 2.

No. 1 is relatively simple. Let’s say you have an opportunity to talk to 1 million people, and let’s assume it will cost about $1 to talk to each of them. Now, if you can figure out who is more likely to respond to your offer “before” you start talking to them, you can obviously save a lot of money. Even with a rudimentary model with some clunky data, we can safely cut that list down to 1/10 without giving up much opportunity and save you $900,000. Even if your cost is a fraction of that figure, there still is a thing called “opportunity cost,” and you really don’t want to annoy people by over-communicating (as in “You’re spamming me!”). This has been the No. 1 reason why marketers have been employing predictive models, going back to the punchcard age of the ’60s. Of course, there have been carpet-bombers like AOL, but we can agree that such a practice calls for a really deep pocket.

No. 2 gets more interesting. In the age of ubiquitous data and communication channels, it must become the center of attention. Analytics are no longer about marketers deciding to whom to talk, as marketers are no longer the sole dictators of the communication. Now that it is driven by the person behind the screen in real-time, marketers don’t even get to decide whether they should talk to them or not. Yes, in traditional direct marketing or email channels, “selection” may still matter, but the age of “marketers ranking the list of prospects” is being rapidly replaced by “marketers having to match the right product and offer to the person behind the screen in real-time.” If someone is giving you about half a second for you to respond, then you’d best find the most suitable offer in that time, too. It’s all about the buyers now, not the marketers or the channels. And analytics drive such personalization. Without the analytics, everyone who lands on some website or passes by some screen will get the same offer. That is so “1984,” isn’t it?

Furthermore, the analytics that truly drive personalization at this level are not some simple segmentation techniques either. By design, segmentation techniques put millions of people in the same bucket, if a few commonalities are found among them. And such common variables could be as basic as age, income, region and number of children—hardly the whole picture of a person. The trouble with that type of simplistic approach is also very simple: Nobody is one-dimensional. Just because a few million other people in the same segment to which I happen to be assigned are more “likely” to be into outdoor sports, should I be getting camping equipment offers whenever I go to ESPN.com? No siree. Someone can be a green product user, avid golfer, gun owner, children’s product buyer, foreign traveler, frequent family restaurant visitor and conservative investor, all at the same time. And no, he may not even have multiple personalities; and no, don’t label him with this “one” segment name, no matter how cute that name may be.

To deal with this reality, marketers must embrace analytics even more. Yes, we can estimate the likelihood measures of all these human characteristics, and start customizing our products and offers accordingly. Once complex data variables are summarized into the form of “personas” based on model scores, one doesn’t have to be a math genius to know this particular guy would appreciate the discount offer for cruise tickets more than a 10 percent-off coupon for home theater systems.

Often people are afraid of the unknowns. But that’s OK. We all watch TV without really understanding how HD quality pictures show up on it. Let’s embrace the analytics that way, too. Let’s not worry about all the complex techniques and mystiques behind it. Making it easy for the users should be the job of analysts and data scientists, anyway. The only thing that the technical folks would want from the marketers is asking the right questions. That still is the human element in all this, and no one can provide a right answer to a wrong question. Then again, is that how analytics became a dirty word?

Building Customer-Centric, Trust-Based Relationships

More than a buzzword, “being human,” especially in brand-building and leveraging customer relationships, has become a buzz-phrase or buzz-concept. But, there is little that is new or trailblazing in this idea. To understand customers, the enterprise needs to think in human, emotional terms. To make the brand or company more attractive, and have more impact on customer decision-making, there must be an emphasis on creating more perceived value and more personalization. Much of this is, culturally, operationally, and from a communications perspective, what we have been describing as “inside-out advocacy” for years.

More than a buzzword, “being human,” especially in brand-building and leveraging customer relationships, has become a buzz-phrase or buzz-concept. But, there is little that is new or trailblazing in this idea. To understand customers, the enterprise needs to think in human, emotional terms. To make the brand or company more attractive, and have more impact on customer decision-making, there must be an emphasis on creating more perceived value and more personalization. Much of this is, culturally, operationally, and from a communications perspective, what we have been describing as “inside-out advocacy” for years.

Most brands and corporations get by on transactional approaches to customer relationships. These might include customer service speed, occasional price promotions, merchandising gimmicks, new product offerings, and the like. In most instances, the customers see no brand “personality” or brand-to-brand differentiation, and their experience of the brand is one-dimensional, easily capable of replacement. Moreover, the customer has no personal investment in choosing, and staying with, one brand or supplier over another.

A key opportunity for companies to become stronger and more viable to customers is creation of branded experiences. Beyond simply selling a product or service, these “experiential brands” connect with their customers. They understand that delivering on the tangible and functional elements of value are just tablestakes, and that connecting and having an emotionally based relationship with customers is the key to leveraging loyalty and advocacy behavior.

These companies are also invariably quite disciplined. Every aspect of a company’s offering—customer service, advertising, packaging, billing, products, etc.—are all thought out for consistency. They market, and create experiences, within the branded vision. IKEA might get away with selling super-expensive furniture, but it doesn’t. Starbucks might make more money selling Pepsi, but it doesn’t. Every function that delivers experience is “closed-loop,” carefully maintaining balance between customer expectations and what is actually executed.

In his 2010 book, “Marketing 3.0: From Products to Customers to the Human Spirit,” noted marketing scholar Philip Kotler recognized that the new model for organizations was to treat customers not as mere consumers, but as the complex, multi-dimensional human beings they are. Customers, in turn, have been choosing companies and products that satisfy deeper needs for participation, creativity, community and idealism.

This sea change is why, according to Kotler, the future of marketing lies in creating products, services and company cultures that inspire, include and reflect the values of target customers. It also meant that every transaction and touchpoint interaction, and the long-term relationship, needed to carry the organization’s unique stamp, a reflection of the perceived value represented to the customer.

Kotler picked up a theme that was articulated in the 2007 book, “Firms of Endearment.” Authors Jagdish N. Sheth, Rajendra S. Sisodia and David B. Wolfe called such organizations “humanistic” companies, i.e. those which seek to maximize their value to each group of stakeholders, not just to shareholders. As they state, right up front (Chapter 1, Page 4):

“What we call a humanistic company is run in such a way that its stakeholders—customers, employees, suppliers, business partners, society, and many investors—develop an emotional connection with it, an affectionate regard not unlike the way many people feel about their favorite sports teams. Humanistic companies—or firms of endearment (FoEs)—seek to maximize their value to society as a whole, not just to their shareholders. They are the ultimate value creators: They create emotional value, experiential value, social value, and, of course, financial value. People who interact with such companies feel safe, secure, and pleased in their dealings. They enjoy working with or for the company, buying from it, investing in it, and having it as a neighbor.”

For these authors, a truly great company is one that makes the world a better place because it exists. It’s as simple as that. In the book, they have identified about 30 companies, from multiple industries, that met their criteria. They included CarMax, BMW, Costco, Harley-Davidson, IKEA, JetBlue, Johnson & Johnson, New Balance, Patagonia, Timberland, Trader Joe’s, UPS, Wegmans and Southwest Airlines. Had the book been written a bit later, it’s likely that Zappos would have made their list, as well.

The authors compared financial performance of their selections with the 11 public companies identified by Jim Collins in “Good to Great” as superior in terms of investor return over an extended period of time. Here’s what they learned:

  • Over a 10-year horizon, their selected companies outperformed the “Good to Greatcompanies by 1,028 percent to 331 percent (a 3.1 to 1 ratio)
  • Over five years, their selected companies outperformed the “Good to Great companies by 128 percent to 77 percent (a 1.7 to 1 ratio)

Just on the basis of comparison to the Standard & Poor’s 500 index, the public companies singled out by “Firms of Endearment” returned 1,026 percent for investors during the 10 years ending June 30, 2006, compared to 122 percent for the S&P 500—more than an 8 to 1 ratio. Over 5 years, it was even higher—128 percent compared to 13 percent, about a 10 to 1 ratio. Bottom line: Being human is good for the balance sheet, as well as the stakeholders.

Exemplars of branded customer experience also understand that there is a “journey” for customers in relationships with preferred companies. It begins with awareness, how the brand is introduced, i.e. the promise. Then, promise and created expectations must at least equal—and, ideally, exceed—real-world touchpoint results (such as through service), initially and sustained over time, with a minimum of disappointment.

As noted, there is a strong recognition that customer service is especially important in the branded experience. Service is one of the few times that companies will directly interact with their customers. This interaction helps the company understand customers’ needs while, at the same time, shaping customers’ overall perception of the company and influencing both downstream communication and future purchase.

And, branding the customer experience requires that the brand’s image, its personality if you will, is sustained and reinforced in communications and in every point of contact. Advanced companies map and plan this out, recognizing that experiences are actually a form of branding architecture, brought to life through excellent engineering. Companies need to focus on the touchpoints which are most influential.

Also, how much influence do your employees have on customer value perceptions and loyalty behavior through their day-to-day interactions? All employees, whether they are customer-facing or not, are the key common denominator in delivering optimized branded customer experiences. Making the experience for customers positive and attractive at each point where the company interacts with them requires an in-depth understanding of both customer needs and what the company currently does to achieve that goal, particularly through the employees. That means companies must fully comprehend, and leverage, the impact employees have on customer behavior.

So, is your company “human”? Does it understand customers and their individual journeys? Are customer experiences “human” and branded? Is communication, and are marketing efforts, micro-segmented and even personalized? Does the company create emotional, trust-based connections and relationships with customers? If the answer to these questions is “YES,” then “being human” becomes a reality, the value of which has been recognized for some time, and not merely as a buzz-concept.