Hey everyone my name is Shannon Pavelski, welcome to another student. Expert session today? Our guest speaker is – Jim Stern? Jim has 35 years? Of experience. In sales? Marketing? And? Digital communications? He sold, business computers to companies. That had never owned, one in the 1980s consulted in, keynoted! About online marketing in the 1990s and found a conference and a professional association around digital analytics in the 2000s hes also an author and just published his 12th book artificial intelligence for marketing practical applications, hes going to share what AI and machine learning are
With us today and how to use it to our advantage and stay relevant as marketing professionals, Im excited to have you with us here, Jim so without any further adieu. Jim, take it away hi, Im, Jim Stern and Im here to give you an introduction to artificial intelligence for marketing Ive been in online marketing since 1994, Ive written a slew of books about
online marketing advertising customer service etc and my latest one is artificial intelligence for marketing now Im not here to teach you how to be a data scientist Im here to teach you how to be a better marketing professional artificial intelligence is a new tool for us to use and Im here to explain what it is how useful it will be and how you can up your game so that you will continue to be a valued marketing.
professional heres the outline first of all what is artificial intelligence how does it work what is it good at and then what is it good for first of all lets talk about what it is not it is not science fiction its not what you see on TV its not robots that become self-aware that is so far in the future.
it may not be impossible but its so far in the future its not worth our time worrying about it from a professional perspective as marketers artificial intelligence covers a wide variety of subject matter so natural language processing being able to hear the voice turn voice into text text in the meaning and then respond to that using voice or text natural language processing computer vision the ability to look at a picture or a video and under stand whats going on inside it is that a cat or is it a skunk its also robots and self-driving cars which as a.
marketer were not so interested in immediately but eventually yes we will use robots in the marketplace in the stores self-driving cars will become a platform for advertising if somebody doesnt have to drive you can advertise to them but the most important thing that artificial intelligence provides for us marketers is machine learning so what is machine learning and why is it such a big deal now weve been looking at getting computers to think for more than 50 years the philosophy of Thinking.
Machines the concept of getting machines to understand for themselves has been around for a long time but only now do we have the processing power only now do we have enough data that we can test out these theories and find out which ones work best the other thing that’s happening is that the whole artificial intelligence community was brought up on the concept of open source this idea that were gonna put our ideas out there.
and share them and other people can improve upon them machine learning algorithms are being invented daily and are up on the internet for people to play with whether its from IBM or Microsoft or Amazon or Apple Google theyre making these available for anybody to use so how is machine learning different from the computing that weve been doing for lo these many years if youve done any coding at all you know that a program is specific instructions to the machine on exactly what to do we know exactly what we want we tell the computer exactly what it.
should do if this happens do this if that happens do this other thing if you dont know what to do spit out an error message a mathematical model is well its an Excel spreadsheet this is my marketing budget this is how much Im going to put into each bucket this is how much response I expect to get now what if I put 10% more here or 5% less there what if my return on my investment is 2% higher here and 5% lower there I can play what if all day.
long its a mathematical model a statistical model is a bit more sophisticated I look at all of the data and plot out all the data points and draw a line through them and then project where that line is going to go and I make a prediction on what the past has told me Im dealing in likelihoods and probabilities rather than exact numbers both of these methods require a human to iterate over and over so it doesnt scale very well machine learning on the other hand derive structure from the data it infers rules.
from the data in order to create a model and then when it gets new information it can change its mind this is a really central point instead of giving the Machine exact instructions on what to do we tell it how to interpret and we give it the authority the ability to change its mind when new information comes in now there are three different kinds of.
machine learning and when you’re talking to data scientists this is important to understand the differences supervised unsupervised and reinforcement supervised machine learning is really valuable when you know the answer already yes this is a dog no that is not a cat yes this is a friend of mine the machine needs a lot of labeled data that means you give it a lot of information or you say these are true these are not true now you have a bazillion examples.
so heres an example of getting a machine to recognize cats were going to give it labeled data that means heres a photograph and were going to label all of the cats as cats and its going to look for what is similar how are these things alike well they all have these pointy things on the top of their head they all have these almond shaped eyes theyre all sort of looking in this direction and then it looks at this.
thing and goes I dont know what that is that’s confusing to me so you have to give it lots and lots and lots of cats in order for the machine to understand and when I say a lot of data let me just show an example this is how many data points its necessary for CAPTCHA to understand that its looking at the letter a so that is supervised you know the answer is already unsupervised is when you dont know the answer and want the machine to figure out something.
for you you want the machine to tell you something about the data that you didnt know before so lets look at a bunch of my different customers and tell me what buckets they belong in these are the ones who are most likely to purchase these are the ones with the highest lifetime value these are the ones who are most likely to never buy from me.
again or lets go out and now that you know what my best customers look like go out and find others out in the world who look like that that I should advertise to this is unlabeled data really really good for categorization the third category the third type of machine learning is reinforcement this is when you’re not sure what the answer is we want to put the right message in front of the right person at the right time but there is no exact message to put in.
front of a person at exactly the right time so we give the Machine the authority the capability of trying things out of experimenting and if it gets the right reaction somebody clicked or they engaged or they purchased then the Machine gets a reward a mathematical reward and so it tries to do that better and it tries to optimize over time so.
weve got supervised unsupervised and reinforcement learning okay that’s what it is but how does it work so the amazing thing about this stuff is that it actually delivered on the promise of big data big data was a great idea but its just too much for the human mind to hold too many permutations too many possibilities too many numbers to keep track of fortunately computers are very good at.
keeping track of numbers so that’s great so data scientists created a bunch of algorithms now its not terribly important that you understand what all of these are if somebody talks about decision trees or support vector machines that’s fine just go along with a conversation theyll get around to it being meaningful soon but just for the sake of interest lets dive into what a.
neural network is and what deep learning means this is the simplest of possible neurons weve got three inputs and a decision the three inputs are how much will it cost to go to the movies hows the weather outside and how much work will it be so how much does it cost to go to the movies well if I want to go to a Hollywood premiere I have to get on an airplane and rent a.
hotel room and rent a tux and that’s that’s expensive that’s too expensive Im not gonna go doesnt matter about anything else Im just not going but if Im if its a normal price ticket well the next consideration is oh there’s a blizzard outside or its a hundred and twenty degrees outside Im not going anywhere so it doesnt matter if its free Im not gonna go or finally the amount of effort have to get a babysitter have to stop by and check up on my dad and make sure hes okay got to.
make sure we stop at the grocery store on the way back oh that’s just too many things going on at once that’s any one of these can override the others and be the go no-go decision now neural networks have hidden layers so in this human example the hidden layer is the last time we went to the movies we saw science fiction action-adventure so this time were gonna have to see a romantic comedy hmm last time we went I broke a tooth on an.
unpopped kernels of popcorn bad experience the last time we went to the movies my wife and I got in an argument about where we should park and so that was not a pleasant experience hmm now Im not consciously thinking all of these things theyre just theyre going on back here so when she asks hey honey do you want to go to the movies Im going mmm not really and if she says why then Ive got to.
start sorting through all of my thought processes that’s what the machine is doing its hidden layers of cogitation and it can have thousands of layers it can have many many hidden layers which are not logical theyre not reasonable theyre mathematical and then the machine spits out an answer and lets say that were looking at supervised and it says this is capped and we say no no no that’s not a.
cat and its important for you to know that its not a cat and go back and figure out why you thought it was a cat and fix it so the machine is going to work backwards and is going to find which node overrode the whole decision and which node caused that one to change its mind and which previous one caused it to change its mind and this is called back propagation now this is as much math as Im going to show you back propagation says oh I see why I thought that was a cat Im going to change the.
model the mathematical model so if I see something that looks like that again Ill know its a skunk now heres where things get fun decision trees random forest support vector machines neural networks deep learning what if we put them all together in an ensemble what if we gave the machine the choice over which algorithms to use and had it use all of them to figure out which ones best yeah its a little meta but this is where data scientists are playing these days it gets really interesting really.
quickly so that’s what it is and that’s how it works but whats a good at well two things specifically dimensionality and cardinality and if you’re like me those terms mean nothing so allow me very briefly to explain dimensionality is attributes per object okay whats an object it is a thing in your database lets call it a person so youve got a bunch of people in your database big data lots of people great but if you know a lot of things about them that’s many attributes you know.
their name address phone number when was the last time they called when what were the last 16 pages they looked at on your website whats their phone number those are attributes about them cardinality is options per attribute so lets take phone number that little yellow dot is that individuals phone number how different is it from everybody elses completely the number of options of.
phone numbers is as many people as there are everybody has their own phone number so what is their age with somewhere between one and 120 what is their zip code well there’s 43,000 zip codes in the United States what is their phone number well there are 7 billion people there are 7 billion phone numbers so there are lots of dimensions we know a lot of different things and lots of options per attribute and you put those together and there are so many permutations the human.
mind cant handle it but the machine can the machine is really good at high dimensionality and high cardinality so heres a quick recap of what we covered so far supervised and unsupervised an enforcement learning decision trees yeah I skipped over support vector machines didnt have enough time neural networks putting them all together in ensemble and then what is the given machine good at ok so how is.
it useful well its good for marketing if we go all the way back to the definition of artificial intelligence you remember it included things like robots well yes robots so this is a Japanese hotel that uses a robot to check you in step up to the counter the robot will check you in unless you go to the next counter over and then this robot will check you in and if you’re.
really good this robot will bring you your midnight snack robots are also being tested in stores so the Lowes home improvement stores are using robots to greet people at the door and direct them to what theyre looking for when we get to computer vision heres a company called gum gum that looks at social media to identify whats out there and reports back to you.
on where your image shows up your brand shows up whos talking about you what kind of influence they have whether you’d like to reach out and engage them with further promotions or to use their visuals and of course to track the competition now computer vision is also useful for augmented reality here to find your store or to identify pricing here to bring to 3d life a two-dimensional.
picture or just to help you find recipes off the ketchup bottle Warby Parker is using it to help you identify what kind of glasses would look best on you when it comes to natural language well you talk to your phone right you talk to your Alexa or your hey Google device these things are becoming more ubiquitous and are gonna becoming a.
serious challenge to marketers when you ask amazon to send you more paper towels which Brander is it going to choose and how do we as marketers get on that list now when we bring these together it becomes very compelling natural language processing conversation bots voice recognition Im not sure if you’re familiar with the staples easy button but take a look at this video its just a blue pen right sounds simple but its surprisingly complex what brand what.
type what quantity now magnify that across the chaos of all the stuff you order for all the people in your office what if managing this was as easy as saying hey please tell me what you need blue pugs now it is that’s the magic of the staples easy system using simple spoken words anymore post-it notes the easy button lets you order everything your team needs to be productive fed and caffeinated french vanilla coffee and letter size copy paper its an extra set.
of hands helping you save countless hours manage costs and maintain control of ordering supplies three boxes of sharpies the staples easy button brings the power of on-demand to businesses on any device exactly when something is needed dry erase markers order with voice through the easy button or through the app order with a text with an email with a slack body with a photo or even with facebook Messenger its easy to track shipments I need to check on my order and get in touch with customer service the brain behind the easy system.
is IBM Watson Watsons cognitive intelligence turns what you say into data and then turns that data into answers you need the staples easy button learns over time with every interaction it becomes more personalized and more intuitive please tell me what you need so when you tell the easy button you want blue pens it knows the exact loop ends you want that’s the staples easy system I think youll agree that’s a pretty compelling story and a glimmer of what kinds of opportunities we as marketing people.
have to work with in the future but what about today how do you bring this onboard how do you bring this into your company what but whats the first thing you need to do step one you are responsible as a marketing person to know marketing and to know the industry that you’re marketing for what questions you’re trying to ask what problems you’re trying to solve what business imperatives you are trying to address.
the machine will only do what its told you need to know what to tell it number two know your data where is it collected how is it collected how is it stored how is it cleaned how is it integrated with other data how might it go wrong what how might you bring two datasets together that address different realms but somehow need to find a way to mesh together that’s a human problem so where.
do you begin you start with tasks that are repetitive and sort of boring and have a lot of data available whether its sorting sales leads or coming up with a response to social media questions or creating a chat bot these are the things that there are lots of iterations and an opportunity for a machine learning system to help you the next question is should you build this stuff or buy it and I am strongly.
on the buy side building it means you have to hire data scientists you’re gonna compete with everybody and their brother who is out there building this stuff to find the PhDs who can figure out algorithms but at the same time large companies like Google and IBM and salesforce com are adding machine learning tools into their data systems if you’re a Google Analytics user or an Adobe analytics user those companies are.
building machine learning systems into their marketing platforms but there’s also a lot of venture capital money being spent at startups so startups are coming up with ways of taking what data you have applying their data science and giving you insights and answers you have to decide what problem you want the machine to solve and then you have to decide what data it should consider if you give it too little data doesnt have enough to work with if you give it too much it wont be very confident in its.
answers so there is a correct amount or a correct variety of data to be considered and finally become proficient at the smell test now this is something that’s uniquely human the machine is going to take the question you ask take the data that its been given and come up with an answer and if you change the data it can change its answer that’s fine but it cant tell you whether the answer.
is meaningful there are some things a human can look at and just go nope that is not gonna work I can tell so where does the human come into this what advantage do you have over the machine the machine is crackerjack at large amounts of data it can correlate quickly its very accurate its low-cost it doesnt get tired it doesnt take vacations but it aint human it doesnt understand compassion or empathy or insight it doesnt.
understand ambiguity it cannot take two conflicting concepts and make sense out of them and that’s what the human can do so heres your homework assignment your job will be to stay tuned with what kinds of tools are available and what they can do theyre changing rapidly stay tuned number two train your BOTS this is a very interesting area you’re already using spellcheck right well there’s a system that Google has that will guess the rest of your sentence not just the spelling mistakes but suggest entire sentences for you to just hit.
return and make it faster for you to write up a response to an email if those kinds of tools are available to everybody then your special value comes in training your version of that tool to understand you better train your BOTS because eventually you’re going to walk into a company and you’re gonna have an interview and theyre gonna say what experience do you have what tools do you use well Im very good at Word and PowerPoint in Excel and here are a.
half-a-dozen bots that I have trained to help me do market research to help me do analysis to help me write reports to help me identify good graphics to use and those are part of your skills you’re bringing that trained bot into the picture and that makes you a better catch in the future my name is Jim Stern I run the marketing evolution experience conference Im co-founder of the digital analytics Association and I hope you now have a strong introduction to artificial.
intelligence for marketing thanks for listening.
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