Replacing Unskilled Data Marketers With AI

People react to words like “machine learning” or “artificial intelligence” very differently, depending on their interests and levels of understanding of technology. Some get scared, and among them are smart people like Elon Musk or the late Stephen Hawking. Others, including data marketers who lack strategic skills, may react based on a vague fear of becoming irrelevant, thinking that a machine will replace them in the job market soon.

People react to words like “machine learning” or “artificial intelligence” very differently, depending on their interests and levels of understanding of technology. Some get scared, and among them are smart people like Elon Musk or the late Stephen Hawking. Others, including data marketers who lack strategic skills, may react based on a vague fear of becoming irrelevant, thinking that a machine will replace them in the job market soon.

On the contrary, I find that most marketers welcome terms like machine learning. Many think that, in the near future, computers will automatically perform all the number-crunching and just tell them what to do. In marketing environments where “Do more with less” is the norm, the idea of machines making decisions for them may sound attractive to many marketers. How great it would be if some super-duper-computer would do all of the hard work for us? The trouble is that the folks who think like that will be the first ones to be replaced by the machines.

Modern marketing is closely tied into the world of data and analytics (the operative word being “modern,” as there are plenty of marketers still going with their gut feelings). There are countless types of data and analytics applications influencing operations management, R&D or even training programs for world-class athletes, but most of the funding for analytical activities is indeed related to marketing. I’d go even further and claim that most of data-related work is profit-driven; either to make more money for organizations or to cut costs in running businesses. In other words, without the bottom-line profit, why bother with any of this geeky stuff?

Yet, many marketers aren’t interested in analytics and some even have fears of lots of numbers being thrown at them. A set of numbers that would excite analytical minds would scare off many marketers. For the record, I blame such an attitude on school systems and jock cultures that have been devaluing the importance of mathematics. It is no accident that most “nerdy” analysts nowadays are from foreign places, where people who are really good at math are not ridiculed among other teenage students but praised or even worshiped.

The joke is that those geeky analysts will be replaced by machines first, as any semi-complex analytical work is delegated to them already. Or will they?

I find it ironic that marketers who have a strong aversion to words like “advanced analytics” or “modeling” would freely embrace machine learning or AI. Because that is like saying you don’t like music, unless it is played by machines. What do they think machine learning is? Some “thinking-slave” that will do all of the work without complaint or asking too many questions?

Machine learning is one of many ways of modeling, whether it is for prediction or pattern recognition. It just became more attractive to the business community as computing power increased over time to accommodate heavy iterations of calculations, and because words like neural net models were replaced by easier sounding “machine learning.”

To wield such machines, nonetheless, one must possess “some” idea about how they work and what they require. Otherwise, it would be like a musically illiterate person trying to produce a piece of music all automatically. Yes, I’ve heard that now there are algorithms that can compose music or write novels on their own, but I would argue that such formulaic music will be a filler in a hotel elevator, at best. If emotionally moving another human being is the goal, one can’t eliminate all human factors out of the equation.

Machines are to automate things that humans already know how to do. And it takes ample amounts of “man-hours” to train the machine, even for the relatively simple task of telling the difference between dogs and cats in pictures. And some other human would have decided that such a task would be meaningful for other humans. Of course, once the machines are set up to learn on their own, a huge momentum will kick in and millions of pictures will be sorted out automatically.

And as such evolution goes on, a whole lot of people may lose their jobs. But not the ones who know how to set the machines up and give them purposes for such work.

Let’s Take a Breath Here

Dialing back to something much simpler: Operations. In automating reports and creating custom messages for target audiences, the goals must be set by stakeholders and machines must be tweaked for such purposes at the beginning. Someday soon, AI will reach the level where it can operate with very general guidelines; but at least for now, requesters must provide logical instructions.

Let’s say a set of reports come out of the computer for the use of marketing analysis. “What reports to show”-type decisions are still being made by humans, but producing useful intelligence in an automated fashion isn’t a difficult task these days. Then what? The users still have to make sense out of all of those reports. Then they must decide what to do about the findings.

There are folks who hope that machine will tell them exactly what to do out of such intel. The first part may come close to their expectation sometime soon, if not already for some. Producing tidbits like “Hi, human: It looks like over 80% of your customers who shopped last year never came back,” or “The top 10% of your customers, in terms of lifetime spending level, account for over 70% your yearly revenue, but about half of them show days between transactions far longer than a year.” By the way, mimicking human speech isn’t easy, but if all these numbers are sitting somewhere in the computer, yes, it is possible to expect something like this out of machines.

The hard part for the machines would be picking five to six of the most important tidbits out of hundreds, if not thousands of other “facts,” as that requires understanding of business goals. But we can fake even that type of decision-making by assuming most businesses are about “increasing revenue by acquiring new valuable customers, and retaining them for as long as possible.”

Then the really hard part would be deciding what to do about it. What should you do to make your valuable customers come back? Answering that type of question requires not only an analytical mindset, but a deep understanding in human psychology and business acumen. Analytics consultants are generally multi-dimensional thinkers, and the one-trick ponies who just spit out formulaic answers do not last too long. The same rule would apply to machines, and we may call those one-dimensional machines “posers” too (refer to “Don’t Hire Data Posers”).

But let’s say that by entering thousands business cases with final solutions and results as a training set into machines, we finally get to have such machine intelligence. Would we be free from having to “think” even a bit?

The short answer is that, like I said in the beginning, such folks who don’t want to analyze anything will become irrelevant even sooner. Why would we need illogical people when the machines are much cheaper and smarter? Besides, even future computers shown in science fiction movies will require “logical” inquiries to function properly. “Asking the right question” will remain a human function, even in a faraway future. And the logical mindset is a result of mathematical training with some aptitude for it, much like musical abilities.

The word “illiterate” used to mean folks who didn’t know how to read and write. In the age of machines, “logic” is the new language. So, dear humans, do not give up on math, if self-preservation is an instinct that you possess. I am not asking everyone to get a degree in mathematics, but I am insisting that we all must learn about ways of scientific approaches to problem-solving and logical methods of defining inquiries. In the future, people who can wield machines will be in secure places — whether they are coders or not — while new breeds of logically illiterate people will be replaced by the machines, one-by-one.

So, before you freely invite advanced thinking machines into your marketing operations, think carefully if you are either the one who gives purpose to such machines (by understanding what’s at stake, and what those numbers all mean), or one who can train machines to solve those pre-defined (by humans) problems.

I am not talking about some doomsday scenario of machines killing people to take over the world; but like any historical events that are described as “revolutions,” this machine revolution will have real impact on our lives. And like anything, it will be good for some, and bad for others. I am saying that data illiterates who would say things like, “I don’t understand what all those numbers mean,” may be ignored by machines — just like they are by smartass analysts. (But maybe without the annoying attitudes.)

AI: Why Marketers Need to Look Beyond the Buzzword

Tech trends such as artificial intelligence, machine learning and blockchain have become unavoidable on our news feeds. However, if we move away from the buzzwords, there are an increasing number of real-world examples of how AI is transforming marketing and living up to its game-changer promise.

Here in 2018, it is almost impossible to avoid tech trends such as artificial intelligence, machine learning and blockchain in our newsfeeds. Every headline promises that one of these latest buzzwords will either transform your business, industry or lead us to some sort of tech dystopia where the machines will rule the earth.

In a digital world where everything is exaggerated or sensationalized to get quick hits, it’s easy to see why so many of us are beginning to suffer from tech fatigue. Every new solution promises to be faster and smarter than the one before, but can these technologies really transform marketing? If we look closer, early indicators suggest they already are. But first, we need to clear up a few things.

What Is AI and How Did We Get Here?

Much of the confusion and tech fatigue is caused by artificial intelligence being used as an umbrella term for other technologies such as machine learning, natural language processing, and deep learning, which are a subset of AI.

There is also a great deal of misinformation online about machines thinking and making decisions as humans do, which is incredibly misleading and ultimately untrue. The reality is that machines learn from systems and processes that are programmed by humans, so our destiny is still very much in our own hands.

In marketing, our love affair with buzzwords began with big data where businesses captured as much information as they could, only to discover that they didn’t know what to do with it all. This evolved into predictive analytics, and eventually, we mixed it all together, and new solutions appeared run by those that refer to themselves as “AI” companies.

However, the reason marketers and industry experts are getting so excited about AI is that it’s paving the way for the industry to progress beyond data analysis and advance into data generation. Marketers no longer have to endure the time-consuming task of manually categorizing or describing various types of data-rich media such as voice and video.

For these reasons alone, AI and the subset of technologies it relies on are much more than buzzwords; they are game changers in every sense of the word. Enhanced analytics are already helping marketers to adopt a proactive rather than reactive mindset. How they analyze real-time data from a variety of platforms and devices enables them to target audiences with unique personalized experiences.

Looking to the future, these customer experiences will elevate their expectations to an unprecedented level and become the standard. Amazon’s one-click basket was only the beginning, and we can now order an Uber, secure a Tinder date, Netflix movie, and romantic soundtrack with a couple of swipes. It’s easy to see how those AI-driven experiences makers will quickly gain a competitive advantage.

Email Marketing

There is already a wealth of tools such as Bluecore and Custora that enable marketers to learn from their customers’ past behavior and anticipate what they will buy both now and in the future. But capturing the attention of consumers has never been more difficult.

Many of us awake in the morning and reach for our smartphone to see how many emails we can delete before starting our day. Email marketers across the globe are turning to AI to answer some of their most significant questions; for example, when they should send an email, how they should personalize it for the recipient, and how they can get consumers to not only open the message but respond to the campaign.

According to Constant Contact, the average open rate for retail emails is 12%, and the clickthrough rate is 8%. Unsurprisingly, AI can drastically improve email marketing results by interpreting consumer data and treating a consumer as the unique individual that each is, rather than just a demographic or job title.

Content Marketing

Consumers are creating more data points than ever before across a myriad of online devices. This raw data reveals behavior and engagement trends that enable marketers to deliver relevant content that resonates with their target audience.

Sophisticated technology provides straightforward answers to exactly where loyal customers and brand advocates are engaging the most. The answers to these questions make it much easier to deliver relevant content in the right place, on the right device, and at the right time.

AI tools such as Lucy, which is powered by IBM Watson, are already helping brands transform their content strategies. A combination of cognitive computing and natural- language processing gives marketers more effective analyses to form revolutionary content strategies. Forget buzzwords; these tools are already providing real business results and applications for 21st-century marketers.

The Rise of Voice Search

Although we have invested our time and resources into perfecting the SEO on our website, our digital habits are changing how we interact with brands. The smart speakers in our homes and the smartphone in our pockets are beginning to set us free from the screen to find a company and buy a product using our voice rather than our fingers.

Welcome to the world of conversational AI that is powered by an increasing list of digital assistants such as Alexa, Siri, Google, and Cortana. The shifts in user behavior prompted ComScore to predict that by 2020, 50 percent of all searches will be voice searches.

Food for thought?

As users get more comfortable with interacting with branded content using their voice, businesses looking to regain their competitive advantage will need to rise to the challenge of creating new experiences for their customers on AI devices.

Get on the Bus, or Get Left Behind

As AI and its subset technologies continue to evolve at breakneck speed, marketers should be focusing on the art of the possible and meaningfully engaging with their customers.

There are already countless real-world examples of how legacy companies are leveraging AI with fantastic results. For instance, TGI Friday’s used AI marketing to increase its revenue by $150 million in only 12 months and tripled customer engagement without breaking a sweat.

Elsewhere, eBay has been using machine learning (ML) for more than a decade but has now added AI, and as a result, has boosted its sales volume by over than $1 billion per quarter. These are real-world examples of brands taking an early competitive advantage; how long can you choose to ignore the signs before getting left behind?

Our brief online history is already littered with hard-luck stories from household names such as Blockbuster video, Kodak, and Polaroid that failed to adapt to embrace digital disruption and changes in consumer behavior.

Maybe it’s time for marketers to take AI seriously after all.

 

What Machine Learning Means for Your SEO

Brace yourselves. We’re facing a time of change in the SEO world, and machine learning is leading the way. SEO is going to be changing greatly in the next few years, and as long as you stay abreast of the changes, you’ll be able to adjust your strategies to stay ahead.

Brace yourselves. We’re facing a time of change in the SEO world, and machine learning is leading the way.

SEO is going to be changing greatly in the next few years, and as long as you stay abreast of the changes, you’ll be able to adjust your strategies to stay ahead.

Before we begin with what to expect, let’s explain machine learning.

About Machine Learning

Machine learning is an advanced level of computer science that allows computers to learn without being programmed. It’s artificial intelligence (AI).

This new technology runs on data from users. It’s why many large corporations have been working so hard to collect large amounts of data about their consumers. It’s all being put to use by the machines that will be able to identify what those types of consumers want online.

What Machine Learning Will Do to SEO

While there is still some time before machine learning runs the show when it comes to rankings and what is shown to Internet users, there are some aspects of SEO that will surely change.

Improve Content

Content will continue to run the roost. That’s what people want when they turn to the Internet. This content will have to be relevant, informative, and interesting to users. If not, it will get left behind.

Publishers need to know their target audience more than ever, so they can tailor their content directly to them. This will be the only way to drive conversions and get their sites to rank highly on search engine results pages.

User experience will trump everything.

Rely Less on On-Page Optimization and Link Building

Title tags, meta descriptions, headers, and alt text will always play a role in SEO.  However, with machine learning, they will likely be less of a competitive advantage. That’s because AI algorithms will not only read the words on the webpages, but also monitor user experience and usability.  In other words, to get on the first page of Google in the future, knowledge of SEO is not nearly enough; You’ll need to be an expert in SEO, UX, and usability.

Plus, as much as links show Google how great a website is now, that’s all going to change. AI is going to be able to know when links are really relevant by tracking whether users click on the links and if they stay on that page long enough for it to be valuable.

Technical SEO

Technical SEO, which includes canonicalization, site speed, broken links, and structured data will continue to be important even with advances in AI.

In fact, structured data may be even more important in the future due to the growing trend of voice search on mobile and devices like the Alexa and Google Home.  Voice search results answers are often found by the search engine reading structured data from websites.

What’s Most Important — User Experience

The future is all about the user experience, as it should have always been. Over the last few years, SEOs have focused on what they need to do to show Google they have the best site worthy of top rankings for their target keywords. What SEOs should have focused on was what they need to do to show their users they have the best site. That’s how things will change with the new SEO.

Even though we’re not quite there yet with AI, we can all start implementing these tips now. Content should be tailored to the right audience – the audience that would be most interested in the topic.

The strict keyword enhanced title tags, meta descriptions, and on-page optimization is going to become even less important, and it’s going to be more about user experience and usability.  The user experience means everything now and well into the future. If you continue to focus on what your audience wants and provide it, you will be well on your way to staying on track with SEO.

Want more tips to improve your SEO?  Click here to grab a copy of our Ultimate SEO Checklist.

Why Modeling Beats Rule-Based Segmentation

I have been talking about “employing all available data” for targeting and customer insights for some time now. So allow me to pick a different bone today. Let’s forget the data part, and talk about the methodology. When machines can build models super-fast, aversion to modeling only limits the users. After all, I am not asking any marketers to get a degree in statistics. I am just asking them to consider modeling techniques.

I cringe when I hear “rule-based” segments are sitting on top of so-called state-of-the-art campaign engines. This is year 2018 A.D. It’s the age of abundant data with an ample number of tools and options to harness their true powers. And marketers are still making up rules now? It’s time for marketers to embrace modeling.

I wonder what most of the rules marketers are using are made of. Recency? Certainly, but how recent is recent enough?

Frequency? Sure, why not? The more the merrier, right? But in what timeframe? Are you counting transactions, orders or items? Or just some “events”?

Monetary? Hmm, that’s tricky. Are we using an individual-level lifetime total amount, value of the last transaction, average spending per transaction, average spending amount per year, or what? Don’t tell me you don’t even have individual-level summary data. No customer is just a reflection of her last transaction.

Actually, if a company is using some RFM (Recency, Frequency, Monetary Value) data for targeting, that is not so bad. At least it’s taking a look at what actually happened in terms of monetary transactions, not just clicks and page views, along with basic demographic data.

I have been talking about “employing all available data” for targeting and customer insights for some time now. So allow me to pick a different bone today. Let’s forget the data part, and talk about the methodology. When machines can build models super-fast, aversion to modeling only limits the users. After all, I am not asking any marketers to get a degree in statistics. I am just asking them to consider modeling techniques, as this data industry has moved forward from the days when some basic RFM rule sets used to get a passing grade.

Let’s look at the specific reasons why marketers should consider modeling techniques more seriously and ditch rule-based segmentation.

Reason No. 1: Variable Selection

We are surrounded by data, as every move that anyone makes is digitized now. When you describe a buyer, you may need to evaluate hundreds, if not thousands, of data points. Even if you are just using simple set of demographic data without any behavioral data, we are talking about over 100 variables to consider out of the gate.

Let’s say you want to build a rule to find a good segment for the sale of luxury cruises. How would you pick the most predictable variable for that one purpose? Income and age? That is not a bad start, but that is like using just two colors out of a crayon box containing 80 colors.

Case in point: Do you really believe that the main difference between luxury cruisers and luxury car buyers is “income”? Guess what, those buyers are all rich. You must dig much deeper than that.

Marketers often choose variables that they can easily understand and visualize. Unfortunately, the goal of the targeting exercise should be effectiveness of targeting, not easy comprehension by the marketer.

We often find obscure variables in models. They may “seem” obscure, as a human being would never have instinctively picked them. But mathematics doesn’t care for our opinions. In modeling, variables are picked for their predictive power, nothing else. The bonus is that this is exactly how new patterns are discovered.

We hear tidbits such as “People who tend to watch more romantic comedies are more likely to rent cars over the weekend,” “Aggressive investors are less likely to visit family restaurants” or “High-value customers for a certain teenage apparel company are more likely to be seasonal buyers with high item counts per customer, but relatively lower transaction counts.”

These are the contributing factors found through vigorous mathematical exercises, not someone’s imagination or intuition. But they should always make sense in the end (unless of course, there were errors). Picking the right predictor is indeed the most important step in modeling.

Reason No. 2: Weight Factor

Let’s say that by chance, a user stumbled upon a set of useful predictors of certain customer behavior. Let’s go back to the last example of the teenage apparel company’s high-value customer model. In that one sentence, I listed: seasonality (expressed in number of transactions by month, regardless of year), number of item counts per customer (with time limits, such as past 36 months), and number of transactions per customer.

In real life, there would be a far greater number of variables that would pass the initial variable selection process. But for simplicity’s sake, let’s just review these three variables.

Now tell me, which one of these three variables is the most important predictor of this high-value customer model? (Please don’t say they are all equally important.) Model scores are made of selected variables multiplied by the weight of each, as not all predictors carry the same level of predictability. Some may even be “negatively” correlated to the ideal behavior that we are going after. In this example alone, we saw that the number of items was positively related to the high value, while the number of transactions are negatively related. When investigating further about this “strange” correlation, we found out that most of the high-value customers are trained by the marketer to wait for a big sale, and then buy lots of items in one transaction.

The main trouble with the “rule-based” segmentation or targeting exercise is that human beings put arbitrary weight (or importance) on each variable, even if “the right” variables were picked — mostly by chance — in the first place.

The modeling process reveals the actual balance among all important predictors, with the sole purpose of maximizing predictability. Conversely, I have never met a person who can “imagine” the dynamics of two or three variables, let alone 10 to 20 (the typical number of variables in models).

Forget about the recent emergence of machine learning; with or without human statisticians, modeling techniques have been beating rudimentary rules by end-users for decades. If solely left to humans, the No. 1 predictor of any human behavior would be the income of the target. But that is just a reflection of human perception and a one-dimensional way of looking at a complex composition of human behavior. You don’t believe you can explain the difference between a Lexus buyer and a Mercedes buyer with just income, do you?

Reason No. 3: Banding

Much of data are composed of numbers and figures. The rest of them are called categorical variables (i.e., data that cannot be added or subtracted, such as product category or channel description).

Let’s assume that income — not my first pick, as you can see — is found to be predictable for mid- to high-scale female accessory buyers. Surely, different ranges of income would behave differently in such models. If the income is too low, they won’t be able to afford such items. Too high, then the buyer may have moved on to even more expensive handbags. So, the middle ground may seem to be the ideal target. The trouble is that now you have to describe that middle group in terms of actual dollars. Exactly where does that ideal range begin and end? To make it even more complicated, what about regional biases in buying power? Can one set of banding explain the whole thing? We’ve gone way past any intuitive grouping.

Moving onto categorical variables, one of the most predictable variables in any B2B modeling is the SIC code. There are thousands of variations in any one field, and they are definitely not numbers (although they look like them). How would one go about putting them into ideal groups to describe the target (such as “loyal customers”)?

If you are selling expensive computer servers, one may put “Agricultural, Fishing and Mining” as a low priority group. Then, how about all those variations in huge groups, such as “Retail,” “Business Service” or “Manufacturing,” with hundreds of sub-categories? Let’s just say that I’ve never met a human being who went beyond the initial two-digit SIC code in their heads. Good luck creating an effective group with that one variable with rudimentary methods.

Grouping “values” that move together in terms of predictability is not simple. In fact, that is exactly why computers were invented. Don’t struggle with such jobs.

These are just a few reasons why we must rely on advanced modeling techniques to navigate through complex data. The benefits of modeling are plenty (refer to “Why Model?”). Compared to our gut feelings, statistical models are much more accurate and consistent. They also reveal previously unseen patterns in data. Because they are summarized answers to specific questions, users do not have to consider hundreds of factors, but just one model score at a time. In the current marketing environment, when things move at a light speed, who has time to consider hundreds of data points in real-time? Machine learning — leading to full automation — is just a natural extension of modeling.

Each model score is a summary of hundreds of contributing factors. “Responsiveness to email campaigns for a European cruise vacation” is a complex question to answer, especially when we all go through daily data overload. But if the answer is in the form of a simple score (say, one through 10), any user who understands “high is good, low is bad” can make a sound decision at the time of campaign execution.

Marketers already have ample amounts of data and advanced campaign tools. Running such machines with some man-made segmentation rules from the last century is a real shame. No one is asking marketers to become seasoned data scientists; they just need to be more open to advanced techniques. With firm commitments, we can always hire experts, or in the near future, machines that will do the mathematical jobs for us. But marketers must move out of old fashioned rule-based marketing first.

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.

machine
“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?

The Marketer’s Job in an AI Future

Whether you’re talking about cognitive computing, machine learning, artificial intelligence or its more common acronym, AI, the real topic is machines doing jobs humans used to. What does that mean for marketers in an AI-dominated future? How will the human role change? Are robots going to steal marketing jobs, or elevate them?

Whether you’re talking about cognitive computing, machine learning, artificial intelligence or its more common acronym, AI, the real topic is machines doing jobs humans used to. What does that mean for marketers in an AI-dominated future? How will the human role change? Are robots going to steal marketing jobs, or elevate them?

Let’s think it through.

Luddites and Automation

Automation has always been seen as a threat to human employment. In fact, one of the first uses of sabotage against automation happened back in the 1810s. “Luddite” textile workers destroyed weaving machines that were poised to take their jobs. (Yes, that is where the term “Luddite” comes from.)

Today the alarm may be less destructive, but it’s still ringing. For example, a few months ago, PWC projected that the U.S. stands to lose 38 percent of its jobs to automation in the next 15 years. And the New York Times’s Claire Cain Miller has built her column on cataloging the negative impacts automation will have on jobs.

But these analyses focus just on job losses, and that’s not the best way to think about automation. After all, the Luddite movement was 100 years ago. While hand-weaving may not be a growth field today, the textile industry employs far more people now than it did then.

While automation changes the tasks employers will pay people to do, in the past it has not put populations truly out of work. The jobs change, but they’re still there.

Analysts are starting to see hope in the AI future on our horizon as well.

USA Today recently ran a special report on the impact of automation across the U.S. economy. And while some of the stats in it are eye-popping — PWC believes 45 percent of work activities can be automated (PDF), potentially “saving” $2 trillion in labor costs; McKinsey identified 70 jobs that could have 90 percent of their tasks handled by automation — the overall takeaway is that the economy is not collapsing, it’s changing.

How Jobs Will Change With AI

Quartz is one publication that’s taken a positive view of the impact AI will have on humans and our careers. A recent Quartz article by Dennis R. Mortensen argued that AI will elevate our jobs and “restore our humanity.”

“Each time technology ate one type of jobs, new ones appeared to take their place,” says Mortensen. “Human ingenuity did its thing, we adapted, and we survived to live (and work) another century.”

His big takeaway: “Automation will take away the parts of our jobs we don’t like and leave room for more meaningful work.

Engagement, Not Keywords: The Merging of SEO and Content Marketing

SEO used to be pure science. Earning high search rankings meant striking the right keyword density, maximizing backlinks and making sure each element of your website was as optimized as possible. Recently, though, the nature of SEO has shifted. Content relevance is in, and keyword density is out. What does this paradigm shift mean for business owners? That’s what we’ll review in this post.

Check out even more about personalization and artificial intelligence with FUSE Enterprise.

SEO used to be pure science. Earning high search rankings meant striking the right keyword density, maximizing backlinks and making sure each element of your website was as optimized as possible. Google’s algorithms, which have set the course of SEO for years, were tuned to recognize websites that provided good user experiences.

Recently, though, the nature of SEO has shifted. It hasn’t completely changed — the goal of SEO is still to convince a computer algorithm that your site is worthy of a top ranking — but human elements are more prominent in the equation. For example, content relevance is in, and keyword density is out. Backlink quality is also in; sheer backlink volume is out. The list goes on.

What does this paradigm shift mean for business owners? That’s what we’ll review in this post.

The Role of Machine Learning

Before delving deeper into the what, let’s first focus on the how.

During the last decade, Google’s data analytics gurus were the driving forces behind the company’s SEO-defining search algorithm. However, that started to change. By 2012, machine learning was contributing to quality scores in Google AdWords, and now machine learning plays an integral role in shaping the company’s search engine algorithm updates.

What is machine learning? Simply put, it’s artificial intelligence — programs making predictions or determinations based on a wide range of signals or parameters.

Google’s human analytics experts still have the final say over how their algorithm is changed. However, their adjustments are now based on tests and predictions from untold volumes of data processed via machine learning. Google also uses a machine learning system called RankBrain to interpret what people are looking for.

Thanks to machine learning, Google can look deeper into the signals that make websites worthwhile. And as a result, superficial factors such as keyword density and link volume are giving way to meatier, more organic flags.

Content Marketing: Giving People What They Need

Producing SEO-friendly content is as easy today as it was 10 years ago. In some ways, it’s actually easier. A unique, straightforward article is more valuable than a similar article that’s awkwardly stuffed with several keyword variations.

But good content in 2017 isn’t about single blog posts or articles – rather, it’s about content marketing. In this arm of marketing, content is created and distributed with the goals of building your audience, promoting your brand or satisfying customers at all levels of the research and buying processes. Under the old rules of SEO, marketers tuned their content to directly correspond with users’ search queries.

Content marketing is all about connecting with people’s wants and needs — that’s a substantial difference.

How Machine Learning Is Changing the SEO Rules

More than 40 updates in four years — that’s how often Google updates its search engine algorithms. And while most of these updates only caused ripples, others made waves that left digital marketers scrambling for solid ground. What if search engine algorithms evolved seamlessly without updates?

Google Panda Penguin ConceptMore than 40 updates in four years — that’s how often Google updates its search engine algorithms. And while most of these updates only caused ripples, others made waves that left digital marketers scrambling for solid ground.

What if search engine algorithms evolved seamlessly without updates?

Thanks to machine learning, the days of potentially jarring updates could someday be behind us. Machine learning occurs when programs can make predictions or determinations based on a wide range of signals or parameters. Uber, Auto Trader and Expedia are among the many large companies that employ machine learning; the technology is also proving useful in the fields of fraud detection, data security and financial trading. And yes, machine learning is already commonplace within Google and Microsoft, two of the world’s largest search and technology giants.

Don’t expect Google’s programmers to bow down to artificial intelligence anytime soon. However, there’s no denying that machine learning will play a big role in SEO.

Machine Learning’s Place in Google

You don’t need to travel far back in history to find Google casting doubt on the quality of machine learning.

Back in 2008, Google officials still believed their human programmers were more capable and less error-prone than the artificial intelligence available at the time, according to the marketing analysis blog Datawocky. In a 2011 discussion on Quora, a poster who claimed to work at Google from 2008 through 2010 said the company’s search team preferred a rule-based system over a machine-learning system because it could implement faster and more definitive algorithm changes.

However, machine learning was a core component of Google AdWords by 2012. The platform’s machine learning system – referred to as SmartASS — could determine whether users would be interested in ads enough to click them. One year later, Google officials were speaking publicly about working machine learning into their search engine algorithms.

Today, Google uses machine learning with its search algorithms mostly for “coming up with new signals and signal aggregations,” Gary Illyes of Google told Search Engine Land in October. He explained how Google’s search team uses machine learning to predict which algorithm adjustments are most worthwhile.

Illyes also talked about RankBrain, a machine-learning system implemented by Google in 2015.

RankBrain plays a vital role in Google’s ability to interpret long-tail search terms – like those often spoken into smartphones — and return relevant search results. In a Bloomberg article published in October 2015, Google senior research scientist Greg Corrado said the machine-learning system had become the third-most important page-ranking factor out of roughly 200 signals that impact the search algorithm. RankBrain was rolled out after a year of programming and testing, and it’s regularly fed loads of new data to improve its capabilities, Corrado said.

So, we know Google uses machine-learning to test and shape its algorithms. We also know Google is much more open now to embracing this technology. That begs the question: What’s next?

What Machine Learning Means for SEO

The more machine learning plays a role in search engine algorithms, the more digital markers will need to be proactive about maximizing the user experiences of their websites and landing pages. Machine-learning systems will result in more fluid search algorithms that make real-time determinations based on positive and negative reactions to content.

With that in mind, SEO experts can prepare for the machine-learning revolution by focusing on the following questions.

  1. Is your landing page relevant?
    Visitors who arrive at your site on the most appropriate landing pages are much less likely to bounce back to the search engine results page (SERP), and high bounce rates are easily detectible red flags of a poor user experience.
  2. Could my landing pages be more engaging?
    You’re halfway there if your visitors are arriving on the right pages. Now, think of new ways to capture their attention. Can you add videos, guides or additional products that add value for your visitors and make each visit more compelling?

Why Buzzwords Suck

Let’s talk about why buzzwords are bad for the data and analytics business. I don’t entirely deny that there are some benefits of buzzwords. Sometimes buzzwords summarize a long list of complex concepts in one easy-to-understand phrase.

bees-44507_640In my previous column, “Don’t Hire Data Posers“, I wrote that one of the first signs of a poser is excessive use of buzzwords. This month, let’s talk about why such buzzwords are bad for the data and analytics business — besides the obvious annoyance of overuse.

I don’t deny that there are some benefits of buzzwords. Sometimes buzzwords summarize a long list of complex concepts in one easy-to-understand phrase. Big data, CRM (in the past), customer 360, personalizationcustomer experiencereal-time modeling or in-database scoring are some examples.

For instance, the term big data acts as an umbrella for many different ideas that not-so-technical people may not be familiar with. But by saying that magic term, we can cut to the chase much faster. Marketers and decision-makers often interpret the term as “all data and analytics activities that enable data-based decision-making processes,” regardless of the actual data sets and processes in question. So data players like me no longer have to take 15 minutes to explain what we do for a living, and data geeks have more succint voices in executive meetings nowadays.

Similarly, creation of a single customer view or a 360degree customer view may include many intricate steps, but who has time to list them all in a planning meeting? Just drop the term customer 360, and people will get the general idea.

But there are definite downsides to these over-simplifications. So, let’s list the harmful effects of abusing buzzwords:

  • Over-simplification in itself is bad already, as it undervalues the efforts. Just because it takes less than a second to say it, doesn’t mean the actual steps are just as quick and easy. Executors still have to sift through painstaking details to get anything done. I’ve seen marketers who actually thought that properly executing personalization would be simple and easy, when the reality of it is that even the very definition of the word deserves a lengthy consideration. Is it about content, delivery, data or analytics? The answer is all of the above, and one must plan for every aspect separately. Calling personalization simple is like saying, “Why don’t we make more movies like ‘Star Wars’ and make tons of money?” Well, can you make that lightsaber look real in someone’s hand?

Marketing in the In-Between of an AI Revolution

“To be good at the digital and physical is what the future’s about. … Get used to living in the in-between.” That was something Beth Comstock, vice chair of GE, said during her keynote at &THEN16. And it really got me thinking: Marketers are living in a whole lot of in-betweens. It’s not just the in-between of physical and digital. We’re also on the cusp of an AI revolution.

“To be good at the digital and physical is what the future’s about. … Get used to living in the in-between.”

That was something Beth Comstock, vice chair of GE, said during her keynote at &THEN16. And it really got me thinking: Marketers are living in a whole lot of in-betweens.

It’s not just the in-between of physical and digital. We’re also on the cusp of an AI revolution. A few weeks ago I was at Dreamforce where I saw this slide from Wired’s Kevin Kelly:

At that same show, Salesforce announced its Einstein cloud-based AI, which can do some pretty cool things. For example, Einstein can analyze a sales person’s email string and determine if it’s likely to convert. It may notice there’s no one with purchasing power at the appropriate level on it, and recommend the email for you to send and who to send it to to fix that.

Yes, Einstein will write the email for you too.

Meanwhile, at &THEN, Adobe just announced that they’re bringing aspects of their own AI-facilitated product, Adobe Analytics, more directly into its marketing tools. That includes adding the analysis workspace directly into Adobe Campaign, which will allow users to analyze customer segments and campaigns with real-time visualizations and AI insights.

Adobe is also adding predictive remarketing to its suite. Predictive remarketing will look at your website visitors, for instance, individually and identify ones who are less likely to return. It then automatically creates and deploys a remarketing trigger to try to re-engage that customer through email, SMS or other channels.

We’re essentially automating the automation. And as we begin to add a flood of passive data from the Internet of Things to that analysis, the automation is going to get very, very smart.

It’s all part of a revolution in what humans can accomplish with data and machines.

Kelly made an analogy: You could think of the Industrial Revolution as the introduction and mastery of artificial power — steam and electricity replacing human, animal or other forms of natural muscle. That power became controllable; it enabled many, many times the output of the old natural power; and it became cheap enough that we soon put electricity in every household.

He said that’s exactly what we’re about to see in the revolution of artificial intelligence. Hence the next 10,000 startups.

The AI Revolution Will Not be TelevisedWe’re collectively and spontaneously reorganizing our culture around digital information structures, according to Comstock. I think the acceleration of machine learning tools is a testament to that.

The fact it’s happening here in marketing pretty early in the lifecycle of true AI is testament to another thing she said: “Marketers are behaviorists, we recognize and anticipate change.”

And the role of shaping, communicating, and promoting (or discouraging) that change also lies in the hands of marketers. “Communications is where change happens,” Comstock said. “Communications is how you structure a culture.”

Turns out it’s also how you structure a machine culture. And perhaps how a machine culture will continue to restructure us.

So we are all very much navigating the in-between: Of physical and digital customer interactions, of human and machine intelligence, of a revolution that will not be televised because it’s already streaming live on a dozen social networks to audiences carefully chosen by algorithms to be most likely to engage.

Get used to it.