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In this episode of In-Ear Insights, we present the audio from our New England Direct Marketing Association opening keynote. How will AI be used by direct marketers? Listen to this episode, and if you’d like to catch the video, it’s available here.

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Machine-Generated Transcript

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This is in your insights, the trust insights podcast. In today’s episode of in your insights, we bring you the audio from our net 2019 keynote to the New England Direct Marketing Association about the uses of artificial intelligence and machine learning in the direct marketing industry enjoyed the talk.

Thank you very much. Logistically just for later, if you’d like to copy the book, you have to go to this URL AI for marketers calm, is just kind of provide and then just receive over that way, we’re not hanging out looking at staring at each other swiping credit cards. All right, let’s talk about artificial intelligence. We use markers want three things? We want better, faster, cheaper, everybody wants better, faster, cheaper? How are we doing as marketers with better, faster and cheaper? On the topic of better marketing? Is it a tough place right now, this year, in 2019, we as a civilization will create 40 zettabytes of data. That’s an unimaginably large number. To give you a sense of how large of it is how many people watch Netflix. If you were to start watching Netflix, without stopping to eat, to drink, to sleep, any of those things, and you started 55 million years ago, you would just get to one zettabytes of data usage now, and we’re going to create 40 of these this year. marketers are not great at keeping up with this much data. And especially for those of you who work in direct mail, you know, you know this version very well test, a test, right? address or bad information. as marketers, we’re not doing a great job with data quality. So marketing, not really getting better. Part of the reason why is because of the use of data or the lack thereof. And the most recent cmo survey, which by the way, if you have not downloaded it is completely free, not even a form to fill out. At cmo survey done the work is released twice a year. And it’s what’s on the minds of some marketing officers. In the most recent cmo survey, marketing analytics use for making decisions is at an all time high of 43% should be sort of what are you doing the other 56% of the time, just guessing. And yet, when you look at what chief marketing officers care about, overwhelmingly they said we want to prove the value of marketing. Two thirds of chief marketing officers need to be able to prove the value. So we’re not doing better as marketers release getting faster Well, the world’s getting faster. In 2019. At the variety and the speed of which data is happening as faster than ever in 2018 there were 266,000 hours of Netflix washing within 60 seconds on a track 2019 that’s 694 about I was a Netflix watch as a lot of Netflix and chill. 74,000 schools on Instagram last year 340,000 schools this year, Snapchat went down a little bit hundred 87,000 million emails comes last year hundred 80 million emails this year. This is within 60 seconds. 60 seconds on the internet. 1 million TechCrunch swipes last year, 1.4 million this year, 60 seconds. If you are looking just at something as simple as new.

This year,

we are on track for about 122 million new stores 272 new stories per minute. If your company was on the front page, your comment on the Wall Street Journal, you’re going to need seconds right because something else has come to this place. So marketing is not keeping up with the world was getting much faster, we are not. Finally are we doing anything to get cheaper. When you ask CMOS, how you quantify the impact of marketing only about 36% can say they can prove the impact of market 50% discount Yes. And 30% said Aquavit are not even going to try. So two thirds of marketers can’t prove quantitatively the impact of the marketing. And we see this in the amount of money that we’re spending, we’re spending more on brand we’re spending more on CRM, we’re spending more on proper spending on service. Marketing is not getting cheaper, by marketing is getting more expensive. The combination of not being better, not being faster and not being cheaper, leads to an unpleasant outcome. It leads to us getting our butts kicked the solution or a solution to this on surprisingly, which is why you’re here is artificial intelligence, which promises three things better, faster, cheaper. Artificial Intelligence is absolutely faster, because it’s computers, it’s at the end of the day, it’s all math. So it’s computers that can do things much faster than we can. Anyone who’s ever made a mistake copying and pasting something knows that machines are much better at data accuracy than we are. And there are so many tasks that we do as marketers, cleaning lists, for example, that machines are just so much better at doing that we are so automation making things any less expensive by taking off boring tasks. So what is this stuff? You’ve heard a lot about it seen in the news, people are afraid of terminators and things like that. Artificial Intelligence is about getting machines to think like humans, if you look at how a human develops, we begin with basic sensory things, right? Our senses, basic elements, touch the soap, it’s hot out and open. But again, we evolved to things like language, being able to communicate and then higher cognitive function, being able to think and reason, by the way, parents of teenagers appreciate the fact that Harvard says cognitive function declines. Machines are no different. We begin with things like algorithms will monitor machine learning, deep learning, and they get to a whole general purpose, or artificial general intelligence, which let machine become sentient. And think for itself. That’s one place all depending on who you ask between 550 years back, but it’s not, it’s not something we need to worry about. All artificial intelligence begins with statistics and probability at the end of the day is is this picture of a hot dog? Or is it not? Is this or the set or not? From stats, machines have algorithm. An algorithm is something you use every single day in market, you can done an ad test, you use an algorithm, use algorithms in your daily life, it’s set up repeatable processes with a guaranteed outcome, I would bet you small Adrian, you tell us that then you probably put on the same journal article clothing, everyone write something from the bottom up versus the top one verse for their socks on first. But whatever the case is, you probably are consistent. That’s an algorithm. Now where things change is when we get into what’s called machine learning, the first

stage artificial intelligence, traditional, used to write software, write the code right from the album, and then the machine would spit out a result, everything from your word processor, mailing label printer to your video games, that is exactly that. Machine learning reverses. We now feed data to machines, and they write their own software. That’s how machine learning works. And there’s two broad categories. Let’s say we had a table full of kids blocks, right. The first type of machine learning is called supervised learning. We want to understand we want to teach the machine to recognize something so we show the machine the color red over and over and over again, until it recognizes the color red, and then everything after that. They can find it all around. supervised learning as most famously known for, in this case from IBM. Back in 2013, IBM on college was working with the university or university Tokyo, there was a woman who had leukemia, he was not getting better, she was not responding to three. So they fed her genome lots, and they fed 233,000 oncology journals, Watson and set figure it out. What’s wrong. Watson sequenced the genome, read the journals and spit back you’re treating the wrong high to leukemia, supervised learning to treat in this kind and said, they changed the dream she may remember, hey, I saved a person’s life. Now what’s not what’s cool about that is a little she did that. But it didn’t 11 minutes. Okay, that’s how fast this stuff works. supervised learning, you will see this most often in the direct marketing Well, it’s simple things like using AI to clean list, right? You know, a proper mailing address looks like you teach machine that and it will clean faster and better than you can then you possibly could. The second category is called unsupervised learning. Instead of just identifying the blocks by by the color red, what do you want to identify by all colors? Or by their shapes? Or by their sizes? When you had a pile of data to machine and saying do some unsupervised learning, it will try to categorize all the different ways to slice and dice that data. A simple example of this is I was getting ready for a client meeting and or 2600 articles that we needed to digest now to tell the client here’s what’s what was in the news about you wrote road we suffer if I just did it down one truck was programming the individual topics. Here’s what all these articles are generally about presented to a client. Now unsupervised learning can be used in direct marketing, just to classify wonder, who are the people who are on your list? For example, what are all the different zip codes where all the different areas where the genders and people who are on their lists being able to classify and categorize audiences and then be able to expand that and do things like our fam analysis to figure out who your most valuable members. The last category is called Deep Learning, which is when you combine many layers of machine learning, deep learning, sack pancakes where each layer is now over the weekly hemorrhaging money, and the data. Not perfect enough syrup, like deep learning lets us create machines that are faster, better and cheaper than any human machines can read live South better than humans can hold higher accuracy, as an example, deep learning. So this is the what AI is. And the reason we need to know this is not because we expect you to become an artificial intelligence engineer, or a data scientist, we expect you to know this because you need a detector of our smash friends called experimental photo. When you hear vendors talking about how we all have AI in their products, being able to ask better questions to those vendors to say, okay, explain what kind of machine learning is, is in your profit. Because in the most recent In a article in The Financial Times recently, they said 35% of vendors insanely I don’t have a limit. So being able to ask these questions that you’re asking. Now, what does this look like when you go to the state of the art this past February, IBM had a debate with that whole project debater between a human debate champion onstage and machine. And they were both given the topic 15 minutes in advance should we subsidize handlebar machine had to go out and use research the human had us research, they both presented opening statements, they both listen to each other’s arguments. They both presented remodels and summer closing articles. Now

in theory on the human one, because the human not more votes on the audience. But the fact that those machines listened, argued and came back with intelligible responses the entire time was groundbreaking. Imagine you will have a customer service representative, for example, and you truly listen to the customer, talk to them and provide them good answers. That’s the state of the art today. Now there are some things as not generally speaking, AI is not whether that machines can understand sentiment and emotion and text, but it can’t act. Right apathy is being able to understand somebody builds them to take action appropriate. machines, a very bad complex judgments that go outside of the walls, for example, has been in business for a while, you know, there’s that friend or that Hollywood, that person you like that like Oh, you know what, we’ll just have your brain on this thing. And what we’re going to do an extra, that’s human judgment that overrides the rules, machines, by definition cannot override the rules they made those machines are very bad, a general life experience, they can also means they can solve problems very narrow, not take into account the big picture. And for the most part, humans, machines can not substitute for demo. I see for the most part because there are some places and some companies where you prefer, for example, the registry of motor vehicles. We’re glad to hear from a few anyway. So let’s look at some practical examples of how this How does this stuff sounds like science fiction, how’s it being used in reality today, solving five kinds of problems, untapped data, unknown influencers, unclear actions to take on focus, marketing, and on prepare for planning, marketing planning. Let’s take a look at untapped data. First, you are sitting on a tremendous, tremendous amount of data, you’re not moving from who’s on your mailing list to what they create on social media to your CRM. And we didn’t example, recently for International Women’s Day, there were 1.6 million social media posts published on that day about that topic, that is the equivalent of $8,960, you cannot read that much. But a machine can. So we’re able to digest down to one chart what all the major topics for that day, and then provide that as guidance to social media management pleasure to say if you want to participate in this conversation, these are the things that you should talk about. And by the way, there’s some things here that you should not talk about unless you have to be female. But being able to digest down that kind of insight is tremendously valuable. If you want to be able to act within the moment machines can help you do this. Another another example, we looked at, we did a project for a recruiting company, looking at all the listings they put up on their in the job boards, and all the words they got their job. And then we took 17,000 calls from their call center. How does she transcribing all the words that people talked about on the phone with a recruiters? Yeah, there’s almost no overlap. And so this company is wondering why we have such a hard time recruiting well, because in your job as is nothing new there that people actually want to talk to you about. Right? People care, these these truck drivers here things like region and job type, and will they be home for Christmas, and none of that is in the job. So being able to take that untapped data, the all those calls in the call center and turn them into useful actionable information is the value. The second type is being able to identify influencers, people who could help your business is representing an event. This is an example from Adobe summit, which is a major mega mega tech conference. And about a month ago, there were 120,000 social posts that occurred on the first day, if you want it to talk to people who everyone else hopped up about using machine learning and a technique called network graphing, you can take all that conversation and find the most influential people. Some of them are speakers, but a whole bunch of our folks, ordinary folks that you can talk to, to get a hold of and present your products and services. And that comes in handy form of a spreadsheet that you can use to say, Here’s who’s the worth talking about this show. Now imagine being able to do that at a major conference or major event. If you’re in b2c, imagine to do something like Coachella and figure out who are the B or C lyst. Folks that you might want to talk to, you’re not going to use the A listers, but you might need to be FC lyst folks, and get them to represent your brand.

The third category is being able to clear up what’s in your data, we have a lot of data, you have a lot of data right now. But a lot of it is very difficult to manage because there’s trackers are so large. And an example recently for an insurance company, we looked at their search engine optimization, the keywords that this company rank for, they had 80,000 keywords that were the SEO manager trying to figure out what words the phrases should we focus on, that’s a very daunting task to redo all 80,000 and figured out what to focus on. But if you were to put it into a machine and have the technical cluster, you can find the things that the pages of someone’s website that they rank for past and help them shore up their listings show up in their their search engine optimization. Conversely, you can then use the exact same technique to figure out what their competitors rank for that they don’t. And go after those, those topics create content around those topics. So by the way, this technique is incredibly valuable for direct marketing, because if you use this information about what page the most valuable and smooth website, guess what that can inform the paper collateral, the real world prints, the bus wraps and things based on what you know people care about. So you can take digital data and bring it into the real world. For example, understanding what drives performance. We have more data than ever, and we have a lot of it. If you were to take every metric that you have in marketing, from locations of billboards, never pieces, direct mail stepped out, search keywords, social media posts, email center, when your marketing automation system on your email list all the way from your CRM to pay pay us money. You put them on a giant spreadsheet, you as a human would find it very, very difficult to figure out which combination of those variables leads to revenue leads to business, technique, operating procedures and machine learning. You can do that the machines can mix and match and understand what is the most impactful things that you can be doing. This is an example. We had a surprising for an automotive. We wanted to understand what things got them feat in showrooms. Oddly enough, it was actually the size of their social media postings, we did not expect that we expected to be something else. But for them, it was about getting to audiences. Right? If you want to prove the value of direct mail, for example, there’s no better way to to add on you’re effectively into all of your other marketing data, running through this have a process and show. Here’s how direct mail contributes to the business outcomes people care about. And you can do this across multiple different types of technology. We looked at Instagram stuff, we looked at b2b content, being able to understand what drives the outcomes we care about is so important. And finally, parent preparing in advance for me marketers are so reactive, because it’s like juggling playing chainsaws mostly is the office how to how to get out that so the answer is the title, Nicole predictive analytics using the same keywords and a technique called or aggressive integrated moving averages. stats, you can take the amount of time someone to search for something in the past and forecasts over and predict for when somebody’s going to do something. So for customer viewers, for example, that example sold life in if you could show them in the next couple of days or weeks or months, when certain terms would be more searchable. indicated interested by the audience. You want to help somebody set it up, send out the most impactful mail, for example, figure out what the audience is going to be searching for. And make sure that it’s in their mailbox that week, when they’re thinking about. That’s predictive analytics. And it’s not something that is actually this complex, you could literally turn it into a sheet that you would enhance your marketing market, hey, this is going to happen the next four weeks Get ready, let’s plan ahead. We’ve been doing this for ourselves with our podcasts trying to make sure that we’re tying the topics we talked about, with what the audience is interested in. And we’ve seen faster than average, we’re about 44% faster growth than before we were doing that.

Predicting and understanding what people are interested in from most of our businesses is a pretty easy thing to do. Because most of our businesses are fairly cyclical in nature, especially b2b. b2b is incredibly cyclical. So those are five examples of using machine learning technology today in market. This is not fictional stuff. This is not navel gazing, these are real things done. And the technology is so powerful, and so mature, they can run on a laptop, you don’t need to have like a billion dollar Supercomputer Center. Most of these techniques run on today’s laptops. The important thing is to start now, remember that machine learning is based on data. So if you’re not collecting the data, you’re not cleaning the Navy not getting the ready, you’re at a disadvantage and bear fruit pattern has. Conversely, if you get started at competitive weights, not only do they have to catch up in general, but they have a data deficit. Because you’ve got run Max, how do you get started? Getting started is a seven step process. The first step and the hardest step by far is that strong data foundation. Does your company know where to say it is? Is it clean? Is it well prepared? Is it optimized for machine learning, artificial intelligence. This will take 80 to 90% of your time when you get started, because it is such a difficult thing to do. We worked with a company last year that their sales department prohibited their marketing department from seeing sales data, like how does that work? Not very well, by the way. It turns out sales was not selling nearly as much as they said they went from there, you must become measurement oriented, become a data driven culture be able to set meaningful key KPIs key performance indicators. My favorite definition of a KPI is this is a number with which you will either get promoted or fire. If your numbers that you’re working with, I’m not going to get promoted or five APS. But building that data driven culture is the second step in this journey, the third step is to be able to do qualitative research, which actually the direct mail was done very, very well. And over the years. And the reason for this is that most of our data is really good at telling us what happened. Very little, our data is good at telling us why something happened. You have to ask somebody, why did you make that decision? Why did you purchase this brand? Why do you not go to this store, even though it’s slightly closer to your house, building the qualitative research capabilities is important. The fourth step is process automation. We’re busy. We are busy as marketers, so we have to figure out ways to automate as much as possible free up our time. So we can focus on things like data science, a really simple example. I used to do social media scheduling for my company, it took a while to put together you know 100, and some odd hosts sleep as a bad local software, and built software to do that for me. So now instead of taking three hours a week, it takes three minutes a week, and we can move on to more guy to work. So finding ways to automate every process you can practically automate is really important. Once you become data driven, you find the data cannot explain your data, you free up your time, that’s when you start getting into the actual machine. With data scientists, people can understand your data and build capabilities for analyzing experimented with your data, implementing machine learning, and eventually becoming an AI first organization. There are very few of these little if you like only have so far behind. Now there’s like 10 companies that truly are AI first, and you can name most of them, facebook, facebook, and Google and Apple and so on and so forth. So this is the journey you need to take in order to become an actor hapa any one of these sessions bring you returns, but taking as many of them as possible will change the way you do business. Now there are a lot of vendors out there, should you buy these capabilities or build them? It’s an interesting question. I used to say this question of time and money, their time, no money, we built it and get money, no time what. But it turns out, there’s a bigger thing to think about here. And it’s a strategy. This is concept that people love to talk about called digital transformation, which promises puppies and unicorns, rainbows. And what’s funny about digital transformation is once you clear we all have there is some legitimacy to it. When you look at how a company is governing its marketing technology properly, it looks kind of like this, let me highlight this row here, the thing that is worth paying attention to is this box.

Part of your marketing is creating value for your company bucks, in a digitally transform how many the data the analyst the models, the machine that we built could be valuable as its own product. For example, let’s say you are popping up. And you build a machine learning model that effectively predicts how well a certain type of coffee tree grows. You can pick up that coffee tree growing model and apply it to soybeans, to rice to stupid crops and video games. Right? You can even do any of those things. And that model itself has value above and beyond the fact that your homies copy comment, your direct mail house and you build a model that is predictive, based on the data you have of which customers are going to respond to a piece of collateral, you can pick that up and sell that itself. So the question of buyer build is not so much time and money, it’s really using artificial intelligence to change or promise you. If the answer is yes, you have to live live, because that’s your secret sauce? And the answer is now by save yourself some time. Some examples, I’m just buying it off the shelf, they’re real simple. One that I love a lot is a tech program called honor. Honor, takes audio files, like those holes, or even like this scheme that we’re doing right now. We’re just recording the audio from him and transcribes it. How much do you talk eat every day, how’s your customers talk physically talking to you, being able to unlock that data freedom is incredibly viable. This cost like 80 bucks a year. For those of you who are hybrids and working digital Google Analytics, adding a Jewish hate to his top work here knows this box in the upper right hand corner called insights. In Google Analytics, you click on that box, and it will tell

you, hey, our AI

was looking at your data, we found some things you might pay attention to is doing the work for you. You can even ask it, ask Google and questions about your data. So in a lot of cases, your vendors will be adding this stuff into your technology already. If you want to build it, if you want to have a core competency that is improved by AI, couple different things to look at. One would be IBM Watson studio, which is a very nice drag and drop environment to build AI without having to write code. And if you or somebody at your company wants to write code, the two languages that are the most popular are an Irish little bar language called Python. So how you prepared your company or the stuff, you’re going to need three kinds of people. This is an expression data is the new oil in 2006. I like this expression a lot. Because if you ever work through those nasty, disgusting stuff doesn’t do much except seeing everything that you need to distract it or find embrace market somehow usable product. This is the same for your data. Need developers to extract your data from we’re all places that lives in your company, through your vendors, your third parties from your customers either and bringing in one place. You need data scientists, people who can help you take that data and finish traffic and turn it into those models into those those pieces of software and you can use to draw useful conclusions. And then you need marketing technologists, people who can take those insights and models and apply them to the business. If you’re a smaller company, you start marketing technologies work backwards, you’re bigger company, you start with developers and workflows. The reason was the marketing technologist sit there and help you incrementally improve things like revenue to afford to hire good folks. After the big question, what about you who’s gonna lose their job today I, I want to change or eliminate 70 to 75% thousand 910 years. The easiest way to tell whether your job is that best is to open up your calendar and look at the first row the second Monday in July. If you know, down to the minute, what’s going to happen that day at work Johnson nation, because it is so routine, so rope, so free program, that what you do can be automated. On the other hand, if you open up your calendar for this coming Monday, and you have no idea what’s going to happen, but you know, it’s gonna be a service. Jobs probably okay.

To further insulate yourself, focus on multidisciplinary sales. Remember, the machines are not good at Gen X rates. If you look at the top 10, most in demand skills. I’m LinkedIn. Any one of these categories has things tasks and like can be automated. audio production has things that can be automated, analytical reasoning, artificial intelligence itself. But if you are good at multiple things, if you’re good at audio, and you’re good at sales, leadership, and you’re good at people management, you are very difficult to replace. Extremely difficult lyst you’re good people management and good at cloud computing, add your good UX, you are extremely difficult to replace, because you’re bringing in insights and things that work from multiple disciplines into your work. That’s tough program. Second thing is learning how to think like a machine. So algorithmic thinking, it doesn’t mean to write code. But one of the things that distinguishes a great Modern Marketing practitioner, from the old days is to look at a problem and say, How can I programmatically or systematically solve this problem? So it never happens again, as opposed to how can I solve this problem today, and I’m Oh, by the way, next Wednesday, but I have exactly the same problem. Right? Being able to think like a machine is so important. Being able to oversee the machines is going to be a profession unto itself in 2016. In Florida, which is where all real things happen. Somebody a police department tried to write an algorithm to predict recidivism, the likelihood that someone would commit another crime, the album was 20%. Correct, which is far less than the focal point. But it predicted African Americans, would we offend five times more than they actually put their thumb on the scale and said, I want this machine to spit out this result. Last year, Amazon got a tremendous amount of because they wrote an album to try and predict which candidates to hire. They trained the algorithm incorrectly no stop hiring women. This ability to oversee machines to understand the elements and say, Hey, machine that’s not okay, is a vital part of our work in the last five years. We have to be outcome focused as marketers worry less about the process and more about the outcomes, the questions you need to ask to get the outcomes you want. Three years ago, I was trying to learn this technique called keras. framework. And it got to about the first column on here. And it worked. It was thing conference and they said Why are you doing that is a drag and drop environment that will do it all for you don’t promote and was like well, this year, I went to bank and they said oh, we don’t do that anymore. Now, what she just does for you is the Lord your data and you’ll figure it out. And then you judge whether the outcome is what you want or not. So instead of worrying about the coding be focused on the outcomes. Your job is to become the chief questions Officer of your company to ask what outcome we after. Is this fair? Is this reasonable? Is this doesn’t will this improve the business? To give you a sense of where things are going? How many of you somewhat regularly use social media?

Okay, pretty much everybody.

natural life generation is going to be the next thing that will drastically change. This is something I was writing last night in my basement because that’s what I do for fun. I took all the social posts from from them. And some of the folks here from the most overdrive and from a couple of obligations. I have this deep learning model. Start writing new social posts and it came up with some really good ones in about 15 minutes time I wrote 20,000 tweets. That’s something that was silly. Some of them are not RMB. I mean, some of these are really, really good. Now if some goofball in his basement, they do this in an hour at zero dollar costs. This is what’s going to change all of your marketing. In the future. They’re going to be two kinds of jobs. How many people are locals is regions in a month. Okay. This avatar latency Marty the robot driving around. Marty didn’t used to exist these to be a cable cleaning supplies are up and down the aisles. And they brought in Margo to detect spills that kids Job Change. Now that kid is being managed by machine. Marty goes up and down the aisles and says hey, there’s a still an aisle six, come over and cleaning by the way he laid out in front and Barney thinks you’re still. But this is the future. There’s two kinds of jobs even if you’re going to manage machines, the machines are going to manage you. That’s it. So the question for you is, who do you want to be? Do you want to be the person manage by the machines? Do you want to be manage?

Thank you very much.

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