InfoShare 2019 Keynote: 5 Ways AI is Changing Marketing Forever

Ladies and gentlemen, please bring it together from speaker from Trust and Science, Mr. Christopher S. Good afternoon. That's the extent of my Polish. I apologize in advance, I'm American.

Today's talk in the program is called Cognitive Marketing, How AI Will Be Changing Marketing. As you can see, we're past that. We are now looking at how AI is going to change or is changing marketing right now. Should you be interested in following along, you can get the slides at the aptly named Where Can I Get The Slides.com. Marketing is in a tough place right now.

Everybody wants better, faster, cheaper marketing. How are we doing at that? Um, let's tackle better first. This year, this year, our civilization, humanity as a whole, will create about 40 zettabytes of data. 40 zettabytes is an unimaginably large number.

It's difficult to even conceptualize. How many people here watch Netflix? Okay, like everyone. It's a planetary phenomenon. If you were to start watching Netflix, binge watching, no stopping to eat, to sleep, to use the restroom, and you started 55 million years ago, you'd just be using one zettabyte of data now.

And we're going to create 40 of these this year. So we have more data than ever before. And the data isn't necessarily good. Right? If you have a business, you know this person, test at test.com.

They don't buy very much, but boy, are they omnipresent. So we have a data quality problem. We are also really bad at measuring what we do. In the most recent CMO survey, which if you have not read it, you should go get a copy for free at cmo survey.org tells you global CMOs and what's on their minds. We are using analytics to make decisions only 43% of the time, which means the other more than 50% of the time, we're just guessing.

And yet, when we talk to our stakeholders, our boards of directors, our owners, they want to know the impact of marketing. And we can't prove it. So marketing, not doing better. Okay, how about faster? Are we getting any faster with our marketing?

Well, the world is getting faster. The world's getting significantly faster. This is 60 seconds on the internet. Last year, this year. In 60 seconds, we went from 266,000 hours of Netflix watch to 694,000 hours of Netflix to watch, the equivalent every 60 seconds.

That is a lot of Netflix and Jill. When you look at other things, number of tweets being sent, number of snaps being captured, emails being sent, even Tinder swipes, 1.1 million Tinder swipes to 1.4 million Tinder swipes. The world is getting faster. Even something as traditional as news stories. This year we'll have about 120 million news stories planetarily.

That's 172 news stories per minute. If your company is on the front page of the newspaper, on the local television, or on your favorite publication, guess what? Your window of fame lasts about seven seconds and then it's gone. So marketing isn't getting faster because the world is getting much, much faster. So are we getting at least cheaper?

Are we at least saving some money? Ugh, well, only 36% of CMOs can prove the effects of marketing quantitatively. 50% say they kind of guess, and 13% just said we give up, we're not going to try and measure anything. So we're not getting cheaper. In fact, we're spending more than ever.

We're spending more on brand, more on CRM, more on service, more on products. The net effect of spending when you don't know what you're spending your money on is disaster. So what's the solution to this? Unsurprisingly, it's artificial intelligence, or at least a solution to this problem. Because it promises three things accuracy, promises acceleration, and automation.

AI is much, much faster than we are. Some of the computational things that can happen with artificial intelligence would blow your mind in terms of how fast a machine can think. It's better too. Anyone who's ever copied and pasted something incorrectly knows just how prone all we are to mistakes as humans. So what is this stuff?

What is AI? If you look at how humans develop, in our first years of life, we are develop our basic sensory pathways, our language, our higher cognitive functions. As a parent of a teenager, I appreciate the fact that cognitive function ends at 13 there. Just a harvent thing. Machines follow a similar path.

From algorithms, repeatable processes that we've built and established that generate a guaranteed outcome. We get we transition from traditional software to what is called machine learning, where the machines, instead of us writing the code and the machine spitting out data, now we give the data to the machine, it writes the code and then does the work for us. And then deep learning, where we take multiple machine learning techniques, stack them like pancakes, and the data flows from layer to layer like syrup, and eventually you get outputs that are far better than what humans can do. The far end is something called general purpose AI or artificial general intelligence, when a machine becomes sentient and self-aware. We're a long way away from that.

Depending on who you ask, it could be five to fifty years, but it's not something you need to worry about right now. The state of the art today are machines that are doing some incredible, incredible processes. This past February, IBM put a machine up against a human debater on stage, a human debate champion. They were told 15 minutes before the debate began what the topic was. Should we subsidize kindergarten?

And both the machine and the human had to prepare, they delivered their opening statements. Each contestant debated with the other, offered rebuttals, and a closing summary. And the machine did this in real time. It was talking and interacting with the debate champion. It was an incredible performance that shows just how far machines have come.

Now imagine having that kind of capability for your business. It will be here soon. So all this sounds great, you might be thinking that sounds like a long ways off. It's not. Let's look at a few examples of what you can do with AI today.

We have five problems we want to solve: untapped data, unknown people, unclear analytics, unfocused data, and unpreparedness as marketers. Untapped data is the first problem that we all have as marketers, as business owners. We're sitting on a tremendous amount of data, and we're not doing anything with it because there's too much of it. I took a look using brand 24, which I'm sure anyone from that here? No?

Okay. Um it's a great Polish social media social media marketing company, by the way. Take a look at them. I looked up content about Gdańsk last night. There are 82,000 posts about Gdańsk in the last 30 days, right?

If you were managing social or digital or news for this city, and you wanted to understand what was being talked about, you're dealing with 114 posts an hour. But using techniques like text mining, which work in pretty much every language on the planet, you can digest down those posts and get an understanding of what people are talking about the most and what is most prominent. So these are the Instagram tags from about 30,000 of those posts. The bigger and the redder a topic is, the more important it is. So I don't speak Polish.

I did ask somebody before we began if there was anything obscene on here. They told me no. I hope they're right. But using this, I could then go ahead and start coming up with campaigns of my own to figure out what I need to align my marketing with that is in accord with what people are already talking about. You can do this with any data: emails, paperwork that's laying around, your CRM data, whatever you've got, you can turn it into easy summaries that you can then take action with.

Because analytics without action is meaningless. Second problem, everyone has hopefully heard of social media by now. And the big thing today is influencer marketing. Who is influential? A lot of people look at just who's the loudest or the has the most followers.

Those are overly simple measures. What if you could use machines to help you understand who is being most talked about? That would be super valuable. So again, using that same social data from brand 24 and a technique called network graphing, we can see who is the most referenced within these discussion topics about Ghazansky. And we have, of course, the city's accounts themselves, and a whole bunch of these other accounts up here that are tourism, trade, things like that.

They're doing a good job of helping reinforce the reputation. But there's a whole bunch of other nodes in there that if you worked for the city or you work for the port, you might want to reach out to these folks and say, hey, we should work together. And the nice thing is you don't have to look at the terrible map. You can look at a spreadsheet and hand this to your publicity agency, your tourism bureau, your major businesses, and say, find these accounts, find these people and work with them, create content with them. So that's solving the unknown problem.

Our third problem is not how is having analytics but not knowing what direction to take with them. We're unclear. I was looking last night at uh bank Polski's search terms. So uh in a search engine optimization tool, I want to see what are the terms that bank Polski ranks for. And there's 28,000.

They're they've got a lot of content and it's very popular. I can't make sense of this, right? That's too much data to process. But if we do a technique called clustering, we can turn it into a very easy to understand grid. The terms that in this case, for the if I worked for Bank Polski, I would say the terms that have a lot of traffic but are low difficulty, we need to create more content on those pages to help defend them, to help protect them from competitors taking away market share, taking away search share from us.

So we want to create videos and interviews and podcasts and all these things that would get people to link to the website. On the other hand, what if we looked at the competitors, MBank and uh Intelligo and these other companies that are creating content? What do they rank for that Bank Polski doesn't? Well, there's a whole bunch of uh terms in here. One of them that stuck out was um correct my pronunciation, uh Little Podroge close?

No? Okay. Um it's a apparently say vacation sort of travel booking, and there are these banks have figured out that people want to know more about this topic and are aligning their marketing with it. So if I worked for Bank Polski, I'd want to create content to take advantage of some of these terms. That is how we take unclear data and make it clear what we should take action with.

The fourth problem we're trying to solve is data that's too unfocused. We just have too much of it. Anyone who has ever downloaded Facebook page insights has seen the lovely spreadsheet you get with 14 tabs, 50 columns. There's just too much data. Using a technique called gradient boosting, you can take all of your social data, all of your CRM data, all of your web analytics data, put it in essentially a really big spreadsheet, and then tell the machine: hey, tell me which interactions of variables leads to the outcome we care about, like revenue or sales.

And the machines do that and identify and boil down hundreds or even thousands of variables down to just a few that you then know you need to focus on. And you can do this across different kinds of data sets. You can do it on Instagram and social data itself. You can do it on B2B content data like Facebook data or screen scores or SEO data. But it doesn't matter what kind of data you've got, as long as it's in something that looks kind of like a spreadsheet, you can use this technology to boil things down to actionable steps you can take.

And finally, marketers, because we're so bad at data in general, struggle to figure out what's likely to happen. And so we're always reacting, we're always panicking. This thing had just happened. How do we get to have that? We use machine learning to be prepared, to look ahead.

Again, if you look at, at least in English speaking nations, what people search for about travel and tourism and destinations. In the next 52 weeks, you can forecast forward using a technique called autoregressive integrated moving averages to look at when people will be searching for a term like best cities in Europe or best places to visit in Europe or European cities or best cities in Europe. And if I worked for the tourism board or I worked for a travel company, I would want to time my campaigns, my hiring, my ad budgets, my emails, my podcasts, my media interviews, with when each search term is going to be relevant to my business. That's using predictive analytics to forecast ahead, say this is what's likely to happen. Let's stop guessing, plan ahead with the data, and be able to hit the mark to be popular, have our content about the things that are popular when they are popular.

We did this recently for a company in the US, and we saw within 90 days 44% more traffic and conversions just because they had the terms right, they had the ideas right, they had the time wrong. Once they got the time right, things were great. So those are the five problems that marketing needs to address, and the AI can help us fix today. The important thing is that we need to start now. You need to start today.

Remember that machine learning is all about putting data into the machine and getting the machine to learn from it. So the sooner you start, the sooner the machine can start learning. That also means that if a competitor is late to the game, not only do they have to catch up technologically, they have to catch up from a data perspective too, because they're behind and they have less data that's they can train on than you do. So starting immediately is important. There's a seven-step process for doing this.

It's called the enterprise AI journey. The first step in the journey is to get your data, find it, make sure it's clean, make sure it's usable. This is really hard. 80 to 90% of companies will get stuck here because their data's a hot mess. Once you have your data, the next step is to become measurement and analytics oriented, to be a data-driven company, make decisions using data instead of guessing or experience or intuition using data to make decisions.

The third step in the process is to build qualitative research capabilities. All the analytics in the world don't help you because they don't tell you why somebody made a decision. You have to unfortunately still go out and talk to people in focus groups, surveys, customer advisory boards, one-on-one interviews to get an understanding of why your audience is making the decisions it makes. The analytics help you figure out who to talk to and what to talk to them about, but you have to do the hard work on the why. The fourth step is process automation.

You have to build process automation into your company to free up time, to free up resources, to get as much uh efficiency as possible so that you have room for the next three categories. You need data science capabilities. The data scientists are people who can engineer data, ask great questions of it, study it, and know what gaps there are. They can probably write code. I know there's a whole bunch of folks on the expo floor that are offering that they're trying to hire these folks.

They are very, very scarce, so you need to hire sooner rather than later. From there, you build machine learning techniques to solve very specific tactical domain problems within your company, and eventually you become an AI first company. There are very few of these in the world right now, and you can name almost all of them just by thinking about big technology companies. But this is the way businesses are headed because it is the fastest way to get to great efficiency and greater productivity. So the question of do you buy the stuff or do you build it?

Depends on three things: time, money, and strategy. If you have time and you don't have money, you build it. If you have money, you don't have time, you buy it. But also, if AI is going to be part of the core competency of your company, you definitely want to build it because you want that secret sauce to stay in your house. For those things that you buy, the good news is the stuff is getting easier, faster, and cheaper than ever to access AI-based solutions.

Uh, real silly one that I love, I really like is called Otter. You just load voice files, MP3 files into it and automatically transcribes them. I've got a camera in the back of the room that's transcribing this talk right now so that later on I can just put up the transcript as a blog post. If you use Google Analytics, Google Analytics is already building AI into your system. If you look in the upper right hand corner, there's a little button called insights.

That's Google's AI saying, hey, I noticed an anomaly in your data. Something's going on here. You might want to take a look at it. And you can even ask Google questions in that gray search bar like what caused conversions last week. Sometimes it works, sometimes it gives you a nonsense answer, but it's built in for you.

You don't have to do any extra work. If you want to build this stuff, there's a whole bunch of different vendors that offer these capabilities. One that I use a lot is called IBM Watson Studio. It's a drag and drop environment for being able to assemble machine learning technologies, which is very, very nice. And if you like coding, the two languages you should focus on the most are R and Python.

Both of these are excellent languages to do amazing techniques. Everything in this presentation that you've seen, I did in my hotel room either last night or the night before, using one of these programming languages. So it's not like you need a billion dollar cluster of computers. You can do this stuff right from your laptop. How do you prepare your company for artificial intelligence?

You're going to need three kinds of people. You need developers, people who can take the data from where it lives and extract it out. You need uh then you need data scientists to come and clean it up to prepare it to make it useful. By the way, if anyone's interested in the algorithm for determining what's influential in social media, that's it. And you need marketing technologists, people who can take those data science exports and turn them into usable things that the business can use.

That's who you're gonna need at your company in order to be able to make the most of artificial intelligence. Now let's answer the question that's on everyone's mind. How do you prepare your career for AI? Who's gonna lose their jobs? If you open your calendar and you looked at Monday, the first Monday in July, and you know exactly what's gonna happen at work that day, from the moment you get there to the moment you leave, you will probably lose your job.

The reason why is that repetitive tasks are easy for machines to automate. If, on the other hand, you open your calendar to this coming Monday and you're like, I have no idea what's gonna happen, but I know it's gonna be a mess, your job's probably safe. How do you continue to insulate yourself from these things? You need a few different things. You need multidisciplinary skills.

If you look at the top 10 skills that are valued on LinkedIn this year, any one of these areas has significant tasks that can be automated, but it's very, very difficult to automate things across areas. So if you're good at cloud computing and you're good at UX design and you're good at translation, you are very hard to replace. If you're good at sales leadership and you can produce videos and you can produce audio, you are extremely difficult to replace. So having multidisciplinary backgrounds really helps so you can keep training and keep learning. Learn to think like a machine.

You don't necessarily need to code, but you need to approach your problems to think: how do I not just solve this problem now? Whatever the problem is you have at work, how do I solve it now and build a system to keep solving it in the future? Think that way, and you'll be much more impactful and effective. You need to oversee the machines. This is going to be a career in its own right for the next five to ten years.

In the United States, there's this crazy state we have called Florida, where all sorts of weird stuff happens. And one of the things they did in Florida was they attempted to create an AI that would predict whether people would commit another crime, something called recidivism. The algorithm this police department in Florida came up with was 20% accurate, which is just terrible. They should have stopped right there. But it was biased.

Somebody wanted African Americans to be flagged as five times more likely to re-offend than they actually were. So a big part of our jobs is going to be overseeing the machines, overseeing their outputs and saying, hey, this doesn't look right. Last year, Amazon got in a tremendous amount of trouble when it created a hiring AI that trained on its data and immediately stopped hiring women. Why? Because the entire training data set was all guys.

And nobody thought to think, huh, maybe we should have this be gender balanced or ethnicity balanced or age balanced. So we as the humans have to provide that guidance to the machines. They're not going to get it on their own for the foreseeable future. We have to be outcome focused. Don't worry about the technology.

I used to have this chart hanging up in my office as I was trying to learn this deep learning framework. And I got through like the first column in about a year. It was really tough because it's in a language that I don't particularly write a lot of code in. Then I went to IBM Think in 2018, and they said, Why are you doing that? You got a drag and drop system, just drag and drop the pieces together.

Like, well, dang it. Machines do it for us. This year, they said, don't even bother doing that. Now we have this thing called auto AI. You just put your data in and it'll do everything supernaturally.

You just inspect it and make sure that what's coming out of the machine is good. Alright, so be outcome focused. Know what questions to ask. You need to be the chief questions officer of your company, asking good questions to get the machines to answer them. Where's the future going on this stuff?

It's going to some really interesting places. Last fall, there was a company called OpenAI that released a natural language learning model that is so good, you start typing a sentence and it'll write the rest of the essay for you. The output it comes out with is good about half the time. Now think about what that means for your business, your company to be able to access these models and have it write that white paper that you've been meaning to get around to, or write that advertisement, or write your social media posts for the next decade. When writing a blog post or a book is a button press away.

That's where this stuff is going. That's where this technology is going. In the future, there's gonna be two kinds of jobs. Either you're gonna manage the machines, or the machines are gonna manage you. There's a grocery store near my house, and there used to be this guy who walked about the cleaning cart and would walk to the end of each aisle and scan the aisle and clean the aisle.

And now there's this robot named Marty that just drives around the store very slowly and looks for spills. And it it then tells a human, hey, I think I found a spill, come over here and clean it. By the way, if you lay down in front of it, it thinks you're a spill too. But a machine is managing a person. And guess what?

You're being managed by a machine every single day. How many of you use this when you wake up? Alright? Everybody. We are all being managed by machines.

The question is, how much more control do we want to give to the machines, or how much more control do you want to have over your life? Because I will tell you, in terms of career path and growth, the way you want to go is you want to be the one managing the machines. Thank you very much. Ladies and gentlemen, hands together because of our S Pen. Want help solving your company's data analytics and digital marketing problems?

Visit trustinsights.ai today and let us know how we can help you.