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In this audio recording from the MinneAnalytics MinneFRAMA finance conference, listen as Trust Insights co-founder delivers a technical talk on the applications of AI for marketing.

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Machine-Generated Transcript

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Good afternoon, everyone. I appreciate the fact that this is really sort of like the the food coma slash my coffee. Caffeine ran out of my bloodstream an hour ago. slot. So we’ll try and keep this interesting. Before we begin. This has been, by the way, I want to give a big hand please, to Graham and to the mini for analytics community, please do yes. And

I love how it is a blend of technical and business. So out of curiosity, how many of you raise your hands, see yourself as a technical user, meaning you write our Python or data or process data? Okay, a few of you. So when we get to some of the techniques, which I’ll be happy to explain at least a little bit about what’s going on behind the scenes so that you get some technical value out of it for everybody else. Just check your text messages during those points. This session is being recorded, the video will be available on the aptly named Where can I get the slides.com. So in about a day and a half, you’ll you’ll be able to get the full video in the slides. The reason we’re here is because we have been asked by our superiors by our stakeholders to deliver three things better, faster, cheaper, as marketers and marketers were nice people. But we’re not especially good at optimizing processes. We’ve been asked for better and in fact, instead of getting better, we’re getting worse. And the reason for it is that we’re being overwhelmed by data. This year, according to IDC, we’re going to generate about 30 zettabytes of data as a civilization. Now out of curiosity, how many people have an idea of how large zettabytes is okay, nobody, nobody has any idea. Here’s why it’s such a ridiculously large number. How many of you watch Netflix? Okay, cool. So like everybody,

if you look at a Netflix film an hour is a gigabyte, give or take, if you start watching Netflix and binge watch, I mean, no stopping binge watch started 55 million years ago in the EEOC era, you would just get through one zettabytes today, right? That’s how much zettabytes is we’re creating 30 of these fun fact in about five years, we’re going to be creating about hundred 20, just from our devices, all these little things that we’ve got connected to us and wires sticking out of us all over and sensors, we’re gonna be creating a massive amount of data. And the data we’re generating isn’t necessarily good. How many of you have this person in your CRM,

right?

So our data isn’t great. So can we deliver on better? Well, our CMOS say, No, in fact, the number of CMOS, the percentage of CMOS using marketing analytics for decision making has declined to about 35% year over year this is from cmo survey.org By the way, which if you have not tried it out, it’s a fantastic survey of what CMOS think basically the think that we’re full of crap.

Well, think about it, how much are you spending on analytics. And then to find out that two thirds of it basically is thrown out the window,

we’re being asked to go faster. But we’re not going faster. Our customers going faster. In 2017, this is what 60 seconds on the internet with like, 452,000 tweets, 46,000 posts, 70,000 hours and Netflix in 2018, 266,000

hours and Netflix. Look at the number of Tinder swipes and 990,000 there, 1.1 million there, I’m guessing mostly left.

Those who left know why.

But this is a tremendous amount of data. So the data is going faster. If you and your company were on the front page of The Wall Street Journal, New York Times you today will be one story out of what will be 100 million stories this year. Right. That’s a tremendous number of new stories. When you are when people who are graduating this year we’re born down here you’re talking like the in in the two to 3 million stories a year. And in that year, their birth in perspective, new stories per day, about 200,000. So if you are a front page story, you are one of 200,000. So we’re not getting any better being faster. In fact, we’re getting we’re getting worse at the world is getting faster than we can get faster. And of course, the third thing will always ask for is can we make things cheaper? The answer is no.

We are spending more as marketers than we ever have. We’re spending more on CRM, we’re spending more on brand. We’re spending more on services. But I’m thinking about doing doing this is product. But think about just how much it is that we are spending to try and make marketing better. So are we getting to better faster, cheaper now.

So how do we get to better faster, cheaper the answer, which is the reason I presume you’re sitting in this room, and not because the chairs are comfortable is artificial intelligence. Specifically, the three benefits of artificial intelligence which acceleration accuracy and automation acceleration. We want to make things go faster, we can’t go much faster. As people we really can’t. We are terrible at math, especially marketers, marketers and not known for their mathematical prowess. That’s part of the reason they went into marketing.

And we certainly have more to do every day that we frankly, we’re not very good at. But we can turn over to machines. So for the business folks, let’s set some ground rules about what we’re going to call artificial intelligence. When you look at how a human develops, human beings develop along very specific pathways. Harvard calls this sort of the developing child first you have sensory inputs, then you have language, and then higher cognitive function. Those of you who like me are parents of teenagers appreciate the fact that cognitive function drops off right when they hit there

are machines are evolving the same way from algorithms and statistics to machine learning deep learning to general purpose AI, when the machines realize that fact that we’re really good at screwing things up, and we probably shouldn’t be on the planet. So all machine learning all AI begins with math with statistics and probability. One of the things that is so important, especially for your stakeholders, for the technical folks here is that AI is not magic. It is math, which means that anything you cannot currently do with math, you cannot do with machine learning, right? You simply cannot create something out of nothing. Once you understand, have math in place, then we moved algorithms, algorithms are nothing more than repeatable patterns,

your business users algorithms every single day, you use algorithms every single day, there’s a good chance that I’m not going to ask you, but you probably put the same article of clothing on every day first, right? Some people put the top on first, other people at the bottom on first few weirdos put their socks on first.

But the pattern is the same. The algorithm is the same. Now where things go different is that traditionally when in the past, we were create software and the software create outputs. So when you fire up your word processor, your fire up your Tinder app, you’re doing something with a piece of software that pre exists. Machine learning turns that on its head by saying, Let’s feed data to a set of very complex algorithms and tell the machine write the code for yourself. We can’t do it, you write your code yourself and let us know how it goes. That’s what’s so different about machine learning. Because the machines can learn from what they’re given, they can adapt to massive fast data sets. So let’s say we had a table a block a table full of blocks like this. Now, this is a pretty easy set of data to characterize. But if we had a billion of these, that would be a much harder problem to solve in machine learning. This two broad categories, supervised learning and unsupervised supervised learning is nothing more than telling the machine Hey, look for something. So in this case, we would tell the machine over and over and over again, what’s called training data. This is the color red and eventually it would figure out how to find the color red. The most famous non marketing example of this is IBM, using its Watson service to diagnose or there’s a woman in Tokyo who was diagnosed with a form of leukemia, and she wasn’t getting better. She wasn’t responding to treatment. And what IBM did in partnership with Tokyo University is sequence, studied her genome and then fed watts, 233,000 oncology journals and studies and said, figure out what’s wrong supervised learning, figure out what her symptoms actually looked like she was she was being treated by the wrong for the wrong kind of cancer. So they changed the treatment, and she got better now, that’s pretty cool. But the coolest part is Watson didn’t in 11 minutes.

The second type of machine learning is called unsupervised learning. It’s great to know what the color red looks like. But suppose you had a billion of these blocks, how else would you want to sort through them, you could have I guess, a million internet sort them. But the easiest way is to have a camera pointed at them and sort by colors, shapes by side this and that’s called unsupervised learning what’s in the box of a massive amount of data, categorize, tag and collected so that we can analyze it and then make something useful of it. So for example, I was doing a client project recently as 15 minutes before meeting and they want to know what 2600 articles about their brand said. So we fed it to what’s called a topic modeler, very strict for the technical folks, this was using the quantitative library and our which is a fantastic library and spit out one topic model in about five minutes.

The articles, by the way, is all said that the client was an idiot. So that didn’t go so well. But we got the answer very, very quickly for them.

When you start to glue together, all these different techniques, machine learning, supervised, and unsupervised and stack. So the data flows from one to the next, like pancakes and syrup has moved from one layer to the next, you get what’s called Deep Learning, where you are creating machines that can do things better, faster and cheaper than we can, by a significant margin. How many people here use Google Translate how many people using for at least two years, okay, you know, then about two years ago, it got substantially better. And the reason why is that instead of trying to do a one to one, so take butterfly and make that equal Mariposa, instead, it’s they took all hundred and three languages they had indexed and told deep mind, figure it out, figure out how these languages relate. And through a fairly complex process, what DeepMind found was there sort of a proto language underneath all the human languages. So now, when you ask Google to translate, it goes to English to Google, to Japanese, or a Dutch to Google to Swahili, which also means that if you put sentence fragments in translate it and tell it to come out with a target language, it can actually assembling a reasonably coherent language. That’s the power of deep learning to be able to do solve very, very large data problems. So when we’re talking about this, the universe of AI, these are sort of the story the hierarchy of how it looks. Now, why do you need to know this, especially if you’re a marketer. The reason you need to know this is because an awful lot of vendors like to claim that every single thing from their toothbrush to their their SAS platform has AI so you need something of a of a fact checker

and knowing how these technologies work and how they map to the different processes in your company is important if you’re familiar with Gartner’s hierarchy of analytics from 2011 descriptive, diagnostic, predictive and prescriptive when we start looking at the different machine learning technologies and how they apply, it becomes apparent how you should be applying these technologies. Descriptive analytics is the stuff what happened and that is all pure quantitative basics. Can you even find your data by the way, this for 95% of companies are stuck, then there’s diagnostic, why did it happen, that’s when you develop your qualitative research capabilities. Then you start moving into the interesting stuff predictive, what will happen next met statistical prescriptive, what should we do is where you can use machine learning to solve all those problems. And then proactive CAN MACHINE do it for me. And that’s deep learning. There are very, very, very few companies that can do this about the only one that doesn’t on a regular basis well, and impacts your life is Amazon.

So let’s talk briefly about what problems you can solve with artificial intelligence. In 2010, then Secretary of Defense Donald Rumsfeld was supposed appropriately marked for giving a briefing talking about the known knowns and the unknown knowns, no one had any idea what you’re talking about. But his matrix is a really good way to approach how AI is useful when you have known knowns, we know what we know, we know what the problems are trying to solve. We know what data we have artificial intelligence is fantastic for helping us go better, faster, cheaper, when we don’t know what we know, which is this case, in a lot of companies where you have silos where data is siloed and blocked, you have these unknown knowns, that is a governance problem, AI will not fix the government’s problem, technology will never fix the people or process probably will just make it worse and more expensive.

When you know what you don’t know you have, you have an idea of what you’re trying to solve. We don’t have the data that’s a data science problem. That’s a problem we need to do at the actual scientific method to resolve those unknowns and turn them into knowns and then the unknown unknowns, we have no idea what’s going on

is actually a good thing. Because why we all still have jobs. This is where things like domain expertise are so important, because this is where our life experience comes in. And that’s really where AI is terrible right now, and will be for a while it AI can’t do empathy. It can simulate emotions, but machines can’t feel AI is not great at judgment, particularly very complex judgments that can do decisions and probabilities, but not complex judgment. Like should you date this person, it can’t do multi domain very well at all. And it can’t, for the most part, the place human relationships. The exception is, if your customer experience is so terrible, that machine will be better

Is there anyone from the Department of Motor Vehicles here

that is an example where you know what I would rather deal with a machine that deal with the certainly human behind the counter. So these are the things where AI is not as applicable. So let’s look at a few applications. How do we apply this as marketers five major areas The first is brand than engagement, conversion, loyalty and measurement in brand artificial intelligence is useful to day in assessing where a brand stands and this is a techniques one which is called vector ization, where we can take a massive amount of data and say, Okay, what are the words and phrases that are most closely mathematically related to a target phrase like your brand name, this is what I did for marketing company. And they want to know, when people talk about them, what do people most say they’re leading and helping and packaging, understanding digital marketers building if you didn’t know this about your brand, this is a very helpful mirror to understand what people actually say about you. Which, by the way, most of the time for marketers is very different from what you think your brand is about.

For the technically minded, the fast text library from Facebook is a fantastic factorization library, it is so damn fast,

you can reverse engineer Google using very similar technique. If you were to take the contents of the top five or six pages for any given set of search terms, search keywords, put them into database and run a vector ization library across that you will figure out what are the terms and phrases that are most common for those keywords across the corpus of text. So you can figure out okay, if I want to rank number one for Minneapolis analytics, what are the other top ranking pages all have in common, and you can feature engineer this, that set as wide as you want, but the very basic just suck in the text vector eyes and figure out what are they all have in common, these are the things I should probably have on my page. The reason is, nobody knows how Google works, including Google, Google uses deep learning, it’s a big black box, which means that no one knows what’s in the algorithm anymore. All they know is that it kinda sorta mostly works. So in order for us to counter that we have to at least approach it with similar technology,

we can do prediction as well to find out to figure out brand if you were to do a search forecast is a search forecast for a bunch of these brands and take what trend there are existing can get 10 years of data out of Google Trends, match it with keyword data, you can forecast forward when will searches for a particular brand spike over the next 52 weeks for the technically minded This is Facebook’s profit library, which is

there’s some disagreement about whether a remote is technically machine learning, or it’s just statistics. Either way, it works really well. I tested it last week against a multi layer precept Tron and lm, and both them did much worse than than profit. That profit is really good at understanding seasonality. I think it’s how Facebook knows to just keep showing ads. So imagine being able to forecast when are people going to search most for my brand in the next 52 weeks, or for the product category or for our competitors? How can we use that to time ad budgets or content or social media posts or events,

we can use machine learning for engagement, if go visit the folks up at the Neo for Jay booth. For example, graphing databases are incredible ways to understand and boil down very complex networks. I was at a conference about a month ago called marketingprofs b2b forum. There were 30,000 posts a day from this conference on social media. And I want to know who was most talked about so did some pre processing stuffed into a graphing database and asked it show me who is most talked about and they’ve just gives me an influencer list of people that I can go and target or in this case, we were at the events as walk over and say hi to them, we know who they are, and get them to talk about us. Because if you’ve got the people who are most talked about talking about you and your products and services on stage, you’ll have a successful event, you can use the same technology for any kind of influence where you want to be in front of other people, you and you want to identify who is most talked about, or who can make the best connections for you within a crowd

when it comes to conversion. Again, forecasting is so important. Being able to do predictive analytics. Here’s a fun one, when are people going to be reading their email the most

when people search for terms like outlook out of office or out of office template or out of office settings, there’s a very good chance that in a very short amount of time, they’re going on vacation, and they don’t want to be around the reading email. So if you take that you forecast to Florida, then you inverted when are people searching for that the least these are the times of year that when people will be searching for the least. So in the next year, it’s going to be for second week of January 3 week of April 2 week of September, third or fourth week of October. This is when people will be reading their email almost. And therefore if you are sending out marketing campaign materials, this would be the time to do it. equally true, don’t send it these weeks of each quarter. These are very practical applications of using forecasting and predictive analytics. One of the most important things about AI and machine learning as it applies to marketing is taking away some of the magic and saying look, all you’re doing is planning better, right? People can wrap their brains around with this helps us plan better. Another example on the conversion front are using chat bots. Everybody in the cousins using a chat bot of some kind. They’re not bad.

They’re not magic, but they’re not bad. And there’s something that are definitely worth looking into, particularly from allow the CRM vendors because it’s relatively easy to get started. The fourth application is loyalty. I was just in the Polaris presentation I thought they were very very very right and saying that marketing’s job does not stop with the purchase it stops when the customer quits being interested in you. This also applies to employees to we did a fun thing we analyzed. We did a natural language processing, again using a combination of quantitative and the UD pipe library and our have 2700 glass door reviews of Olive Garden. The for those of you who don’t know what all garden is sort of a fake Italian restaurant chain,

they have a dish that most Americans love called unlimited soup salad and breadsticks for 1299. I think it’s 1299

people love it. Employees hated employees hate it with a passion in fact, other than things like bad pay, and my manager that drunk

happens a lot

the breadsticks what employees hate the most of all the things the endless breadsticks You are a breadstick slave

and the reason for this is in 2014, an activist investor firm took over Olive Garden said our number one goal is to reduce food waste. Now they may have succeeded in truck chip trimming off like I think $60 million a year and food waste. But the question is how much of that also damages the customer experience because now the server hates you the moment best for the next bread basket, breadsticks the service custard spitting in your food.

And finally, of course measurement there is no better place to start using AI then in measurement. So attributes analysis, particularly digital attribution analysis is one of the most powerful easy ways to show the value of AI This is taking we use Markov chains to pull in analytics data path analysis and Google Analytics and basically digest down what are the most impactful channels if you’re not familiar with Markov chains is kind of like digital Jenga where you read you simulate conversions over and over and and keep pulling a block out at a time you do that hundred thousand times then you figure out this channel causes the How to fall down the most which means it’s the most important and what’s really interesting is that when we did this for a client they thought Facebook was the thing but in fact it actually is not the thing for them it’s Facebook isn’t for a little ways down Twitter was their thing so they had immediate insight oh we should change our strategy maybe try that using Twitter more healthfully comments on their blog matter a lot. And they weren’t giving it the love that they deserved. So being able to do this attribution analysis is essential. This is this actually for people who use our channel attribution package in our that let’s plug right and you have to connect to Google Analytics with the RG a package but then you can do this Google charges 30,000 a month for that. So if you can do it yourself, save yourself 30 grand a month.

The most important things to start now with this stuff. And the reason for that is machine learning is based on data. The sooner you collect data, the sooner you have data, the sooner you can get modeling and the sooner you can get that competitive advantage Have you get started seven part journey you begin at the bottom with that data foundation to you even have the stuff it what condition is it in, you’re going to spend a lot of time here hopefully that you can get out of the space as quickly as possible. Then you move to measurement analytics. Do you have KPIs? Do you have KPIs connected to reality? Are they are they useful for making data driven decisions, build your quantitative qualitative capabilities, be able to tell data stories and do market research effectively. Because no matter how good the machine learning is, the machine learning is still cannot extract out why something happened. There is no substitute for you asking customer, hey, why did you buy that thing? or Why didn’t you buy that thing. After that process automation, start to automate the known knowns finding efficiencies, you might use machine learning, it might not. But just by freeing up as much time as you can, will give you the time to build in data science capabilities to learn how to explore the unknown. Build your stats and math capabilities, your code and your engineering that’s when you start using machine learning in production, and then put AI first across the enterprise,

there’s a very common question, which is as a company should we buy, or should we built? And the answer is, maybe it depends on three things. It depends on time, money and strategy. If you have no time and you want time, but but and you got lots of money hire vendor to do it for you. Because you can get up and running faster. If on the other hand, you don’t have a ton of money, but you got lots of time, build it in house. But the really big thing is the strategy. There’s this concept right now in the business world, it’s a little overhyped called digital transformation that promises rainbows and puppies and hugs

the few nerds in the crowd who play Diablo three know exactly what this is.

But digital transformation funding, please about making a company, a digital first company. One of the most important things then is that balance sheet, they make that bigger.

When you start embarking on on your AI journey, you’re taking data you’re refining, cleaning, and building processes around that data, intellectual property methods. And then you’re building models. On top of that those are things that are assets unto themselves. Those are things that will help transform your company. The most famous example this is actually not a machine learning example, at all American Airlines in the 80s built a reservation system that was so powerful, they would license it to every other airline, it was called the Sabre booking system. And it was a whole company unto itself, I think it eventually spun off. Think about your company. If you’re growing coffee trees, for example. And you collect great data, you can license that data. But then if you build a model on top of that data, you could move that model to no corn or soybeans or spam the horn Mel’s in town. I think

you’re really good low spam. But

but it’s an important considerations and important that strategic consideration if all you want or the efficiencies better, faster, cheaper by if you want to transform your organization you have to build there’s no getting around it with Who do you get it from. I mean, take your pick of the magic Microsoft, Amazon, Google, IBM, all of them have great platforms and those platforms start off free and easy to use and become egregious expensive voice You know what,

when it comes to learning the code, the two main choices are in Python I I am older I have a lot more gray hairs. And so I like are better because I can understand syntax I dont still don’t understand pythons in dense but your it will depend on who you have in your shop. The best thing that you can do personally is to take something and analyze the crap out of it. So this is a fun one I just did

taking a look at 1.6 million medium posts. And this was thinking use extra boost for this bias is what matters for a medium post to be successful. And it turns out, it just has to be long. Literally nothing else matters. Not the topics. Not the sentiment number that all the people like on medium is verbosity. So if you want to market on medium, just really long posts, don’t say that’s something in five pages, you can say. And 20,

how do you get your company ready for AI, you’re going to need three kinds of people. There’s a great expression data is the new oil, which is a fantastic expression. Because crude oil is useless, it’s stinky, doesn’t really do anything you need to refine, extracted, refine it and and deliver to market. The same thing is true of AI, you need to extract the data, clean it and then bring it to market. So you need developers who are the people who can get to the data, wherever it lives

at Steve Ballmer sweating profusely at Microsoft and screaming at people, you need data scientists who can refine and help you build these models. But mostly critically, the part where every night every many companies go completely wrong is on the marketing technology front. Because companies generate tons of insights that then find the way into binders that stay on shelves, and no one ever uses them. So this part, which is by the way, the part that you probably play the most in your organization is the most valuable, how do you take those insights and actually put them to use in terms of your own career, how you prepare for AI? First, you need multi disciplinary skills. When we look at what’s happening, machines can automate the boring stuff in almost any profession, without a doubt. But when you have multiple skills across different domains, different disciplines, it’s much, much harder to automate. So if you’re good at data, presentation, and stats and SEO, you’re a very difficult person to replace. So if you’re not a developer, you’re not a coder, you’re not a data scientist, at the very least look at building extra domain expertise into the work that you already do learn to think like a machine every time you face a problem at work, are you thinking, do I solve this problem? Or do I build a process that solves this problem perpetually, that type of thinking is very, very powerful thinking, learn to oversee the machines. One of the biggest problems we have in machine learning right now is bias our albums advice, our data is biased. We’re having a conversation last night of the reception about one of the contest that many analytics did people using social media data to try and predict the election in Minnesota? Well, if you don’t know that on Twitter, for example, it’s skews democratic skews younger, skews minority excuse Lower, lower income, you’re building a bottle that’s biased. So being able to oversee the machines is super important. This comes to life and a whole bunch of really frightening ways in

police department in Atlanta, attempted to predict who would refund they built a machine learning model that’s predict refund spike by criminals, the model is 20%, right? You would have been better off flipping a coin. But it flagged African Americans as five times more likely to reoffend than they actually did. Someone put their thumb on the scale and made the model perform badly. More recently, Amazon had to throw out its AI based hiring system. Because it turns out that when you spend 10 years hiring nothing, but guys, the model is going to predict you should hire more guys,

we know that’s not the outcome we want. So being able to supervise machines and say, Nope, that output is not okay. This is especially true if you’re in a highly regulated industry. Because if there are certain criteria, certain features you are not permitted to use for decisions, you also have to look out for inferred combinations if I say gender is not allowed, but I use movies, books, and, and music that you like, I can nail gender about 99.9% of the time, right? So you have to be able to supervise machines to say, No, the expert will follow the letter law, but will also follow the the content as well

be outcome focused. For those of you who are on the coding side, you probably know this chart, I was trying my hardest to learn how to use a keras and TensorFlow I got to about here in 12 months of reading and researching and testing out code. And then I went to IBM think, this year, and they said, Oh, we made that drag and drop.

Thank you.

But instead of having to write the code now, all I have to do is know what the pieces do and know the logical sequence in the strategy for deploying them. I don’t have to worry about like, did I screw up my Python code and put four spaces instead of a tab in it,

the tools are getting easier to use. I was doing something this past weekend I took was using the visual recognition system to look at solar panels and flew a little drone over my house. I said, Hey, learn what solar panels look like. And then I took a bunch of other photos and I fed it in and said, score it and it said, Okay, so ice cream pretty clearly not solar panels, this room for for voting, not solar panels, my dog with a hat, not solar panels, but a photo is very different than this one. Yes, solar panels, zero lines of code. So as marketers, we have the ability we will have the ability to start using these tools without needing to be coders as long as we understand what the tools do in the future box. It’s not even the future, but it’s mostly as there’s gonna be two kinds of jobs either you’re going to manage the machines, the machines are going to manage you. When I was at the supermarket. Recently, I saw guy pushing the maintenance cart and he had the mop and stuff like that. He had the one hand scanners it normally used for like shopping to do self checkout, he gets the top of our zaps the aisle, walks down the aisle, picks up some random stuff or whatever, and goes to the other in the aisle, scans the top of the aisle, like, What is he doing that I read lies is being managed by a machine. They are measuring how long it takes him to get down the aisle. If he goes too fast. He’s not doing his job. He goes to slow he’s not doing his job. He’s already being managed by a machine. You are to have curiosity. How many people check their mobile phone in the first hour of the day? So anybody who didn’t, right, we’re all being managed by machines. The question is, how intent how conscious are we have that how much of a conscious choice is it? So with that, thank you very much for your attention.

10 minutes to take questions, give or take. anyone have a question to this personal microphone is going to run up here. So

in general, do you think that it’s also become too easy? We are going to learn to take shortcuts? Or do you think people should skip the shortcuts learn how to do it themselves. Once Where’s your stance on that now, I believe there is still valuable is having the experience just as they’re still value, teaching your kids how to do multiplication and division of the long way. And then you can introduce them to the wonders of a calculator. But knowing the mechanics is important, like, for example, when you doing text mining, knowing how vector ization works is a good thing to know. Because that will help you understand the biases because there’s four broad the four types of bias and machine learning data, right there is intentional, like, hey, I want this to say a certain result, there is target bias where the population that you’re analyzing has the data itself is corrupted. So African American healthcare data, there’s zero useful healthcare data for African Americans in the world. Not because there’s such a systemic bias against African Americans that healthcare is a long you have to actually build models from scratch, there’s source bias, which is where the data itself is not correct. That’s like, for example, using Twitter to try and predict an election. And then this tool bias, some limits to what the tools can do. And that’s weird. We’re learning to do it the hard way can come in handy. There’s a whole bunch of folks in the social media marketing space are trying to build all these social analytics tools. And I saw one that was really terrible. It’s like, Hey, we can predict, you know, Instagram things. But they were predicting the text on Instagram. Now, how can you have this friend was like, Hey, you know, they posted pictures, there might have been in various states of undress. There’s a beverage and you got the idea. And then the captain’s Well, this sucks. Now, we all know what that means, right? This is pure sarcasm, but software doesn’t understand that the software things that’s a negative posts, right? So being able to do it the hard way and understand what goes into the algorithm is, this is important for being able to fact

so maybe it’s not a question, but an observation. Maybe you want to comment on it. So that simple fly and I think you’re kind of looking at attribution and various channels, yes, whatever. So myself, and as a marketing agency owner, as well over the years, many times having the conversation with Grandmaster Stapleton a brand

without first looking at the data and getting those insights what I hear most of them saying, I just want to drop blog comments because I don’t want to devote any time to it. Or I don’t want to manage that situation. That was a major contributor, yes, their conversion or whatever would be

what’s your comment we started looking at, maybe it’s an obvious thing. But this particular data first before you just jump in, to make note

is one of the hardest things to do is to have to try and encourage somebody who is not data driven to change their their decision making process to being data driven. And that’s still going to be a human thing. If you know, that person is motivated by greed, then you can say, Hey, this is going to make a lot more money. You know, that person’s motivated by fear, like, hey, if you don’t want to get fired next square for another quarter shitty results need to do it this way. But whatever it is, that makes that person tick, that’s how you have to approach that person. You know, there’s this one for fundamental ways to motivate somebody, there’s, there’s fear, greed, sex and anchor. And that’s about it. So figuring out what it is that makes them tick, and then appealing to that within the limits of ethics

is how you get around that. But it is very difficult to convince somebody with the data alone to make a change in decision, you have to convince them that the outcome of something they either want or they’re afraid of

other questions over there,

according to you, is the major challenge in terms of technical standpoint of personalized marketing by marketing has evolved, adding a group of people right now, it is time to target individuals, right? So what are the challenges?

The number one challenge that market is going to face in the next two to five years? Is there over reliance of personally identifiable information, right, we saw a lot of this this past year with GDPR, everyone in their cousin got it, hey, we’re updating our privacy policy email in May, right, your inbox, little companies you’ve never heard up like, I think I might have done this will be once 10 years ago, but is going away at some point where we release this is becoming extremely regulated. And for good reason. The major tech companies have proven that that for the most part, they’re completely irresponsible with our data,

our friend Mary.

And so we have to pivot to things that are not API, but behavioral nature, hey, how did you spend on the site, what things you click on, where it come from, where you’re going, and what can we infer this behavior,

I don’t care who you are, I don’t care what color your skin

is, I don’t care how many kids you have. I do care if you browse services page, the team page, the about page on the contact page, because you’re probably going to buy it something most systems and most Alex are not set up for that most systems and Alex are still looking for someone to fill out a form and say, Okay, now based on the the domain name of your, your email address, you’re probably this kind of buyer, which is a really bad decision to make. But that is a big technical challenge. And there’s a big strategic challenge to get people to be thinking anonymized behavioral

questions,

Going once, going twice. Thank you very much.

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