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Author: Mike Moran

Mike Moran is an expert in digital marketing, especially content marketing areas, such as personalized websites, search, and social media. Mike also has a 40-year background in technology with a dozen US patents, specializing in machine learning, natural language processing (NLP), and other AI techniques, as well as search technology.

Mike serves as a senior strategist for Converseon, a leading social listening technology company based in New York City. He is also a senior strategist for SoloSegment, a site search and customer experience software company. Mike is also an executive consultant for the Consultant's Collective and a Program Director for the Conference Board. Mike currently serves as a member of the Board of Directors of SEMPO, is a Certified Speaking Professional (CSP) in the National Speaker's Association, and a Senior Fellow for the Society of New Communications Research.

Mike spent 30 years at IBM, rising to Distinguished Engineer, an executive-level technical position. Mike held various roles in his IBM career, including eight years at IBM’s customer-facing website, ibm.com, most recently as the Manager of ibm.com Web Experience, where he led 65 information architects, web designers, webmasters, programmers, and technical architects around the world.

Mike's newest book is Outside-In Marketing with James Mathewson, covering Big Data and Content Marketing. He is co-author of the best-selling Search Engine Marketing, Inc. (with fellow search marketing expert Bill Hunt), now in its Third Edition. Mike is also the author of the acclaimed Internet marketing book, Do It Wrong Quickly, named one of best business books of 2007 by the Miami Herald. Mike founded and writes for Biznology®, a daily blog devoted to digital marketing.

In addition to Mike’s broad technical background, he holds an Advanced Certificate in Market Management Practice from the Royal UK Charter Institute of Marketing and is a Visiting Lecturer at the University of Virginia’s Darden School of Business. He also teaches at Rutgers Business School and is a Distinguished Instructor at the University of California Irvine.

 |Mike Moran is an expert in digital marketing, especially content marketing areas, such as personalized websites, search, and social media. Mike also has a 40-year background in technology with a dozen US patents, specializing in machine learning, natural language processing (NLP), and other AI techniques, as well as search technology.

Mike serves as a senior strategist for Converseon, a leading social listening technology company based in New York City. He is also a senior strategist for SoloSegment, a site search and customer experience software company. Mike is also an executive consultant for the Consultant's Collective and a Program Director for the Conference Board. Mike currently serves as a member of the Board of Directors of SEMPO, is a Certified Speaking Professional (CSP) in the National Speaker's Association, and a Senior Fellow for the Society of New Communications Research.

Mike spent 30 years at IBM, rising to Distinguished Engineer, an executive-level technical position. Mike held various roles in his IBM career, including eight years at IBM’s customer-facing website, ibm.com, most recently as the Manager of ibm.com Web Experience, where he led 65 information architects, web designers, webmasters, programmers, and technical architects around the world.

Mike's newest book is Outside-In Marketing with James Mathewson, covering Big Data and Content Marketing. He is co-author of the best-selling Search Engine Marketing, Inc. (with fellow search marketing expert Bill Hunt), now in its Third Edition. Mike is also the author of the acclaimed Internet marketing book, Do It Wrong Quickly, named one of best business books of 2007 by the Miami Herald. Mike founded and writes for Biznology®, a daily blog devoted to digital marketing.

In addition to Mike’s broad technical background, he holds an Advanced Certificate in Market Management Practice from the Royal UK Charter Institute of Marketing and is a Visiting Lecturer at the University of Virginia’s Darden School of Business. He also teaches at Rutgers Business School and is a Distinguished Instructor at the University of California Irvine.

 |Mike Moran is an expert in digital marketing, especially content marketing areas, such as personalized websites, search, and social media. Mike also has a 40-year background in technology with a dozen US patents, specializing in machine learning, natural language processing (NLP), and other AI techniques, as well as search technology.

Mike serves as a senior strategist for Converseon, a leading social listening technology company based in New York City. He is also a senior strategist for SoloSegment, a site search and customer experience software company. Mike is also an executive consultant for the Consultant's Collective and a Program Director for the Conference Board. Mike currently serves as a member of the Board of Directors of SEMPO, is a Certified Speaking Professional (CSP) in the National Speaker's Association, and a Senior Fellow for the Society of New Communications Research.

Mike spent 30 years at IBM, rising to Distinguished Engineer, an executive-level technical position. Mike held various roles in his IBM career, including eight years at IBM’s customer-facing website, ibm.com, most recently as the Manager of ibm.com Web Experience, where he led 65 information architects, web designers, webmasters, programmers, and technical architects around the world.

Mike's newest book is Outside-In Marketing with James Mathewson, covering Big Data and Content Marketing. He is co-author of the best-selling Search Engine Marketing, Inc. (with fellow search marketing expert Bill Hunt), now in its Third Edition. Mike is also the author of the acclaimed Internet marketing book, Do It Wrong Quickly, named one of best business books of 2007 by the Miami Herald. Mike founded and writes for Biznology®, a daily blog devoted to digital marketing.

In addition to Mike’s broad technical background, he holds an Advanced Certificate in Market Management Practice from the Royal UK Charter Institute of Marketing and is a Visiting Lecturer at the University of Virginia’s Darden School of Business. He also teaches at Rutgers Business School and is a Distinguished Instructor at the University of California Irvine.

 

Categories
Marketing Personal Development Technology

How to Build Human Trust in AI

To read the popular press, AI can outdo humans at anything, but the truth is far more complex. AI applications are typically to do just one thing well, such as when Watson took on all comers in the Jeopardy game show. But while many of us are fine with letting computers play games, polls show that many of us are distrustful of self-driving cars. And that trust is a key issue, because otherwise valuable applications will be slowed down or even stopped if people don’t trust the technology. Research also shows that when AI systems give incorrect answers too frequently or answers that make no sense to humans, it reduces their trust in the system.

So, how do you build human trust in AI?

Explain the system’s decisions. There are calls for explainable AI, where the system must provide an explanation of how it came to its decision. This technique is still R&D, because techniques today are notorious for being proverbial black boxes. The problem with the research is that when you force systems to use only those techniques that are explainable, they inevitably work worse. Someday, this might be the answer, but not today.

Improve the system’s accuracy. The reason you want AI to explain itself is so you understand how mistakes happen. If you can make it work well enough, maybe no one needs an explanation. After all, most is us don’t know how our cars work, but we trust when we apply the brakes that it will stop. Hardly anyone knows how Google’s AI works, but we trust that our searches will get us good results, so we keep using it.

Reduce the really big mistakes. Watson once gave a really bad answer to a Final Jeopardy question in the category US Cities, providing the response of “Toronto.” We call that a “howler”–an answer so bad that even if you don’t know the correct answer, you still know that response is wrong. You can actually tune the system to reduce howlers by scoring bad answers as worse responses than wrong answers that are “close.”

Put humans in the loop. This might be the simplest of all. Instead of treating every AI system as one to replace humans, maybe it is easier, safer, and more trustworthy to set up the AI to help the humans do their jobs. Watson is being used to diagnose diseases, but rather than replacing doctors, Watson shows the doctors possible diagnoses based on the symptoms, with the doctor making the final decision. When decisions are so high stakes, this might be the most prudent approach.

AI is no longer science fiction, but people are understandably nervous of this kind of powerful force that works in mysterious ways. We need to pay close attention to building human trust in the system to see AI used in the safest and most valuable ways possible.

Categories
Marketing Personal Development Technology

The Most Powerful AI Needs Human Judgement

I grow weary of reading the simplistic headlines around the impact of AI. Some people say that AI will put many of us into a new leisure class that doesn’t need to work. Others argue that AI will make us all unemployed. They are both saying the same thing, actually, so it is just a personality test to divide optimists from pessimists. But there is no technology that in the past had that kind of impact, so why is this one different? It probably isn’t.

What is much more likely is that as machines do more, we humans will do something else. Something machines can’t do yet. That’s the way it has always been, so I think that is the way to bet.

What fuels my belief that this is true is that the most powerful AI we see today depends on human judgement. No, I don’t mean the highly-paid data scientists and AI engineers that are all the rage these days. Sure, they are important, but I am talking about ordinary people doing ordinary jobs using judgement that computers just don’t have. This technique is called semi-supervised machine learning or active learning.

Here is how it works. Supervised machine learning is what most AI applications use. They need human judgement, too. But they use it only at the beginning. They ask humans to tell the system the right answer to a question–for example, whether a tweet has positive or negative sentiment. You pile up enough tweets with human answers and use that to train the AI system. So, far, so good. But that is where most systems stop.

The most powerful systems keep getting better, using semi-supervised machine learning. The secret is something called the confidence score. Most AI systems can do more than just answer the question. Beyond telling you that they think this tweet is positive or that tweet is negative, they can tell you how confident they are in that opinion. So, the system might be 90% confident that this tweet is positive and just 60% confident that another tweet is negative.  This provides some interesting possibilities for semi-supervision.

You can set up your system so that your system handles automatically any tweet with over 70% confidence. If it is that sure of itself, let it provide that answer on its own. But if it is less than 70% confident, you can refer that tweet to a human being to check its answer. Is that tweet negative–the one with 60% confidence? Checking the answers the system isn’t sure of is semi-supervision, and it has two benefits. The first is that the system is more likely to get the answers right if it can ask a human to check its work.

The second benefit is that each new human answer is new training data that the system can use to improve its model. By constantly asking for help with the answers it is least sure of, the system is improving itself as rapidly as possible. You can add more training data at any time to any machine learning system, but if your new training data is merely adding more examples of what the system is already doing well, it doesn’t cause any improvement. Only by adding new training data in the areas that the system is getting wrong does improvement happen.

So, yes, machine learning is very important. But semi-supervised machine learning is what provides that most rapid way of continuously improving your machine learning application. If your team isn’t using that approach, it might be time to ask why not.

Categories
Marketing Personal Development Technology

Why You Shouldn’t Be Aiming for Viral Marketing

Viral marketing seems like the business success a marketer can strive for–your message is out there, and everyone is sharing it. What could be better? Well, a lot, actually, because going viral says a lot about how many people see your message, but not a whole lot about how many people are buying your product.

Some of you might remember the feel-good stories or days gone by, such as Blendtec, that great blender whose messages went viral when they pulverized an iPod with that powerful little motor. And that viral marketing definitely led to sales–huge increases, in fact. So, why am I so down on viral marketing? A few reasons:

  • You’re not so unique. Nowadays, there is way more content vying for attention. The possibility of any marketer breaking through the way Blendtec did is much less than it was.

 

  • Facebook makes it hard. Facebook used to show any content–now it suppresses marketing content in the free feed to make you pay them for ads.

 

  • You’re not looking for everyone. This is probably the most important reason. If you’re Coca-Cola, then maybe viral is great, because your target market is anyone with a neck. But you’re probably not.

But the biggest reason that viral marketing doesn’t work, is that even if it goes viral, it doesn’t persuade anyone to buy. The best thing about those old Blendtec videos were that they showed off their differentiation. If it can obliterate an iPod, you can bet it won’t leave any lumps in your smoothies. But most viral attempts focus solely on sharing and reach and not at all on persuading all those people that they reach.

Focus on reaching the right people instead of all the people. Maybe that finally give you the boost in sales you really need.

Categories
Marketing Personal Development Technology

Give Me Another Dollop of That AI

You can be forgiven if the way we talk about Artificial Intelligence makes you think you can order it up like a scoop of ice cream. It seems that way because we constantly read that:

  • AI solves all our problems
  • AI experts cost an arm and a leg
  • AI analyzes data than any person can
  • AI will make us all unemployed

While each of these statements might turn out to be true (well, we hope the last one is wrong), they all suffer from the same problem. They act as though all AI is the same. That all AI is one monolithic thing that can be added to any system if you just have enough money.

It’s not true.

First off, there are many different kinds of AI applications and they require different techniques. Voice recognition is not the same as text analytics is not the same as optimizing search results. These applications are different from each other and they use different techniques to perform their “magic.” Most of them use multiple AI techniques. And they usually depend on the existence of data.

I have been phoned up by more than one expectant client who wants to solve this problem or that problem with AI. Often, that is perfectly reasonable, but just as often I have to tell them they need to take several steps first. Often, they need to set up a standard process that collects data in a standard way so that the AI techniques have something to work with. Luckily, even taking these initial steps has business value, if you do it right, so the clients are usually easily persuaded to move forward.

Wanting to use AI is not a problem. Forward-looking organizations are always pushing the envelope and AI is just the latest way to do it. But let’s make sure that we are getting the business value we expect and that we are ready to take the preliminary steps to get there. We shouldn’t make AI a problem looking for a solution.

Categories
Growth Health and Wellness Technology

Digital Transformation Drives Strange Bedfellows

The dust is clearing on the recent announcement that CVS is acquiring Aetna in a deal that surprised many observers. But it shouldn’t surprise you, if you have been paying attention to the way digital transformation is creating new threats and opportunities in formerly staid industries.

Aetna is in the health insurance business and has been trying to get bigger, but regulators have turned down that approach in recent years. So, for Aetna, it makes sense that, if you can’t acquire and you are concerned about competing at your current size, you would agree to be acquired.

What surprised people was who the acquirer was, because people still think of CVS as a retail chain. CVS is indeed a retail chain and it is clearly making this move because Amazon (and to a lesser extent, Walmart) are within striking distance of a broad attack on specialized retailers, such as drug stores. While many retailers are shrinking amidst this onslaught, CVS has an option to pivot from pure retail to healthcare, where it might be a lot easier to compete with a physical presence.

CVS has been sprinkling its MinuteClinic urgent care facilities in many of its retail locations and has become a powerhouse in the drug coverage market with Caremark, so adding Aetna  makes a lot more sense for a healthcare company that happens to have a retail presence. If that, in fact, seems like what Amazon is becoming, with its recent acquisition of Whole Foods, maybe that’s no mistake.

A joke has been circulating in recent years as to whether Amazon can become Walmart faster than Walmart can become Amazon. CVS has evidently heard that joke and beaten Amazon to the punch (line). CVS already has a deal with the Cleveland Clinic to provide a platinum option for the very best care that can be delivered through telemedicine. If Amazon jumps in, don’t be surprised to see clinics in Whole Foods with telemedicine options, too.

It would be one thing if this were all happening just to cut costs, but it is really the patient experience that is driving the changes every bit as much as cost. When you are sick, you don’t want to call the doctor and hope for an appointment during business hours. You want to make an appointment 24×7 as easily as you summon an Uber car and get your prescription at the same place you get your diagnosis. That’s the new experience that is possible already for minor problems. What CVS is betting is that major problems that need more than a nurse practitioner can be handled through in-network doctors and high-end specialist through telemedicine with the nurse practitioner right there to assist. Instead of being referred to a specialist, maybe they can summon a specialist on your first appointment.

Now there is a lot to be worked out, but you can see the direction it is going in. Healthcare is likely to be a very interesting space in the next few years. All the local practices have been bought up by the hospital health care networks who have bet heavily on local providers as though the current model will last forever and they just need to lower prices. My guess is the retailers will bet more on a low-cost MinuteClinic model with in-network doctors (like Aetna’s networks) and a high end telemedicine model. Over time, there should be considerable price pressure on the hospital networks getting squeezed in between.

If you’re not in healthcare or retail, maybe you think you’re off the hook. Guess again. The kinds of pressures causing these cross-industry mergers are the very essence of what digital transformation causes. If you aren’t staring down your customer experience and asking how digital can change the game, you are just waiting for someone else to disrupt you.

Categories
Marketing Personal Development Technology

Does Marketing AI Replace People or Enhance Them?

Blame Hollywood. Blame Elon Musk. Blame whomever you want for the wide perception that Artificial Intelligence will put us all out of work. Others say that AI will lead to a life of leisure. Few are pointing out that those two predictions are the same–it’s just a question if you are optimistic or pessimistic.

But it is overly simplistic, because the all-knowing AI presence–Artificial General Intelligence–is so far a figment of imagination. Today, we are benefiting from Narrow AI–machine’s ability to outdo humans at just one thing, such as chess, or Jeopardy or Go. These AI wonders would be left wondering if applied to any other task.

And most AI in marketing is not even as autonomous as the game-playing types that make the news. By far the most prevalent AI in use is “human-in-the-loop” AI, such as semi-supervised machine learning. Rather than the computer doing it on its own, it is human beings that help shape the computer’s judgement. I work with Converseon, an AI-based social listening company, which uses human-coded data to do its initial training for sentiment analysis. But as it makes predictions, it uses its confidence level to decide which calls it is sure of and which ones it will refer to human beings to check. Any corrections are rolled back into the training data to make it even smarter.

That approach is more likely to be how AI is used today. Rather than eliminating people, it needs people to train it and people to correct it. It can outperform people over time, but its initial usage is to augment the performance of people. If you’ve been waiting for AI to wipe out your marketing team, you likely have a long wait. But if you want to use AI to make better decisions, the future is now.

Categories
Marketing Personal Development Technology

Are You Missing the Third Kind of Search Marketing?

Most marketing teams know about Search Engine Optimization (SEO) and Pay-Per-Click (PPC). You have teams devoted to getting searchers to your site from Google and other search engines. But what happens after they get there? Do you focus just as strongly at getting them to convert? Do you focus on the third kind of search marketing–site search?

Site search–that box in the upper corner of your website–finds pages on your own site. It’s a critical way to convert those searchers who find your site into customers. Here’s why–the folks who search on your site are your most qualified visitors. Think about it. If you land on a website and don’t find what you are looking for, you probably bounce back to Google and search again. But what if you really want to buy from that company? What if you really think that company has what you are looking for? You stick around and perform a site search.

That’s why reports show that site searchers have conversion rates anywhere between 43% and 600% more than other site visitors. So, the question becomes, “What would they find with your site search?” Would they find their answer? If you’ve been ignoring site search, probably not.

If you’re spending precious resources on attracting searchers to your site because you know your customers use search, why would you ignore site search once they get to your site? But most companies do.

Get ahead of your competition by focusing on the third kind of search marketing, site search. Instead of just attracting searchers to your site, you will turn searchers into customers.

 

Categories
Marketing Personal Development Technology

Are You Using the Right Content Marketing Metrics?

Years ago, I took my three year-old to her second dentist appointment. I wasn’t expecting any problems because she had dealt with her first appointment like a champ and I had assumed that the first one would be scarier than the second one. And the second appointment went swimmingly–in fact, she seemed uncommonly cheerful when I told her where we were going. Then. when we got home, she asked, “When do we go to the party?”

She hadn’t been invited to any party, so I had no idea why she was asking that. After some back and forth and some head-scratching conversation with her mom, we realized that she had indeed attended a friend’s birthday party following her first dental appointment, so she had put those events together into one firm (and happy) memory and now was expecting the other shoe to drop after seeing the tooth doctor again.

We were able to explain to her that there was no party for her today, and she understood, but it caused me to recognize something all of us human beings do–and not just when we are three years old. We tend to impute meaning to coincidences. This is deadly when making data-driven marketing decisions.

I heard a story–don’t know if it is true–that back in the summer of 2012, the Sprint social media team was happy when their positive mentions starting increasing dramatically. At least at first. A little digging showed them that the mentions were about the Olympics and that the happy conversations around the word “sprint” in that context was not something they should take personally.

Another time, I showed a set of results to a client and told them we had tested them and that they were 90% accurate. The client took a quick look at the first 10 results on the screen and insisted, “That can’t be true–look, the first one is wrong!” The other nine were correct, which is what 90% means, but he distrusted the system anyway.

These examples probably seem silly to you–because they are mistakes you didn’t make. But I see clients performing unnatural acts with numbers all the time just because no one is really thinking about what they mean.

One former client told me that they use their web analytics to see the conversions related to every piece of content in their system so they know what the best content is. Unfailingly, the “best” content was for their best-selling products. Maybe you think that products are best sellers due to marketing content alone, but I have my doubts.

Instead of using simple correlations of which pages lead to conversions, perhaps they need to dig deeper, as the Sprint team did, to really understand their numbers. If you are ready to dig deeper–to think in a new way–you can use AI analysis to remove a lot of spurious correlations to get to the underlying causes of what is going on. Once you do that, you can really work on improving the right things.

But if you keep thinking the same old way, someone might have to tell you that there is no party for you today.

 

 

Categories
Marketing Personal Development

Do You Trust Your Marketing Metrics or Your Gut?

I have spent some quality time with a new client who is struggling with getting the C-Suite to recognize the value of marketing. Yes, it’s an old-line B2B company–how did you guess? They have a lot of data describing the success of marketing, but the C-Suite at their company would rather ascribe that to superior product or their go-getter sales team.

So, they asked us to do a competitive assessment of how much companies like them are spending on marketing. We found various studies and gleaned data from annual reports and we believe that the average is about eight percent of revenue spent on marketing. This client was spending less than a tenth of that. And we found many studies showing that higher marketing spending is correlated to higher revenue. Does that mean that it causes higher revenue? We can’t prove that, but maybe it is the way to bet.

Still, they weren’t sure that the C-Suite was going to accept the number. Did we compare apples to apples? Couldn’t their situation be different? Were some of those companies a lot bigger? We tried to explain that these were reasonable questions that might explain a 35% or 30% difference, but not a 90% difference.

But they know those are the questions they are going to get, so they are trying to prepare for them. The reason that they will get those questions is that their C-Suite has a history of going with their gut rather than the numbers–at least when it comes to marketing. (That’s the only way that you end up here, frankly.) Now, a new day might be coming, because they did decide to do this study, which provides some hope.

But in the end, every C-Suite executive needs to ask themselves whether they are smarter than the data. Because no one can change your mind on something you are sure you already know. You have to at least be a bit curious. A bit doubting of your own opinion. A bit open to the possibility that the world has changed.

If you are, you can model being data driven for everyone else. You can model changing your mind in the face of new information. You can model getting it right instead of being right. Ask yourself if your team doesn’t think that mere data will change your mind, and then ask yourself if you want them to be right.

 

Categories
Marketing Personal Development

Do You Know Why Your Marketing Content Is Working?

Most large companies have embraced data-driven marketing by now. (If your company hasn’t, I suggest you give it a big hug right now.) But along the way to Big Data Nirvana, a few problems arose. A big one is understanding the difference between correlation and causation. For example, on an e-Commerce site, do you know which web page is most associated with purchase? It’s the Thank You page. You probably guessed that great Thank You pages don’t cause more purchases.

So, yeah, that’s a silly example, but we make similar mistakes all the time. Every time I work with a client to help them figure out their best content, they fall back on the same numbers–which content led to conversion. So, they know to throw out the Thank You page and the shopping cart page, but when they look at the list, guess which pages show up? The best-selling products. OK, but is that because the products are better or the content is better? We don’t know.

Even if we could tease that apart, it’s still unsatisfying, because the reason you want to identify the best content is to make more of it. But how helpful do you think it is to point at a successful page and tell your content writers, “Make more like this one.” One writer slowly raises her hand and quickly asks, “Like that page in what way, exactly?'” At that point you give them all a blank look and start to drool just a little.

You don’t know. You have no idea. You might know that it worked, but you don’t know why. How can you answer this reasonable question? Enter artificial intelligence.

You need one AI technique to tear apart the page–text analytics. You need data analytics to identify which pages have the best outcomes (conversion rates, inbound links, social shares–whatever you think identifies success). You need machine learning to suss out which characteristics seem to be shared by pages that are successful.

Now you can answer the writer’s question because you know exactly in what way the should design the new pages. You know whether pages with bullets work better than ones with streams of text. You know how many images are too many. You know if using brand names at the top of the funnel is a turn off.

You know a lot. And the more pages you look at, and the more characteristics of those pages you look at, and the more activity on your website, the more you learn. Artificial intelligence isn’t the future. It’s now. It isn’t magical–it’s very practical. If you are not doing it, maybe the competitor is answering questions that cause you to drool.