Most of the things we hear about, todayand marketing are just in our general lives, are really uses of machinelearning, which you could probably define better than me, but the definition wealways go with is making predictions of future outcomes based on historical data is Paul rates. Her 100 copr 2020 hubspots first partner agency back in oh seven, we start in the partner programand also more recently, the founder and CEO of the marketing artificialintelligence Institute. So Im here with Kevin Walsh. Today, product manager, forhub, spot machine learning, proc metric
Russia Larry hop spot to talk about theapplications of artificial intelligence and digital marketing, so be sure to likeor, subscribe to the HubSpot Academy YouTube channel and that throw it overto Kevin lets start at a really high level. Can you tell us if you know whatis artificial intelligence and what is machine learning yeah, I mean to me. Thethe thing I would say about artificial intelligence is if you search on Googlefor, what is AI you’re gon na get 10
Different definitions from 10 differentexperts, so what I always try and talk about is just at a very high level, itskind of the umbrella term to encompass technologies and algorithms designed tomake machines, smart and then, within that machine, learning being the primarysubset most of the things we hear About today and marketing are just in ourgeneral, lives are really uses of machine learning, which you could probably definebetter than me, but the definition we always go with is making predictions offuture outcomes based on historical data.
But the key with machine learning is themachines, get smarter and the predictions get better without human interventionnecessarily thinking again about do you have any sort of specific consumerexamples that people might be familiar with that are using machine learningday-to-day in their lives yeah. We always look at like the bestexample I always give, of AI and machine learning in. Particular is Google Maps sowhen? We try and make it less abstract for, people we just talk about the. Factthat you’re using it dozens if not
Hundreds of times every day in your lifeso, if you think about the apps on your iPhone from Facebook, to Instagram toGoogle Maps to Amazon, to Spotify like every one of them, is using data to tryand predict your behaviors predict what you’re likely to do next. Orwhat you’re going to be interested in or what you’re going to buy and so GoogleMaps to me is one of the best examples.
Of what were trying to get to withmarketing so Google Maps makes. Predictions about the most efficient wayto get from point A to point B, but the human at the end of the day, still makesthe decision – and I think that’s where were trying to go with marketingsoftware, is to make it smarter to make. It predict a little better. What mighthappen or the best route to take, but the
human still takes that makes the finaldecision so there’s a book called prediction machines that I really likethis written by three economists and they look at the future of jobs and saythat itll basically be telling machines what predictions to make and thenfiguring out what to do with those predictions like using human humanjudgment on those predictions and so.
kind of what is the state of artificialintelligence and within marketing sales and services as we think of them I lookat it and think its very beginner level now again you may have and as someonewhos overseen the building of some of this stuff within hub spots platform maylook it a little differently but I generally think that the technology isat the very early stages of development and application to marketing now there’sother industries where its raced.
forward so you could look at just WallStreet for example in finance this stuffs 30-plus years old they’vebeen trying to do this stuff to figure out when to make trades and how much toinvest so looking at the applications to marketing its its just early the moneyhas started to move in we track about 1,100 a I powered sales and marketingtechnologies and about 500 of them have.
funding combined over five billion but alot of that money is funding companies that when you dig deeper they honestlydont really have much proof that it works so they talk about a and they talkabout machine learning and they claimed have been building smarter technologybut if you press for case studies of how it works its often you find that theydont really have them and that really the machine learning adage is part of aroad map and theyre just very beginning.
staging testing so our experience hasbeen its very early but that that’s actually a good thing because it meansthere’s opportunities for both the tech companies and for the marketers thatfigure out what this stuff is and are proactive in tryingfind smarter more efficient ways to do things yeah I tend to agree I hear atHubSpot you know weve been doing machine learning seriously Im goodseriously I would say for about two.
years a little more than two years andweve grown that team tremendously its now a full-fledged group here withinproducts and we do take a lot of inspiration from those bigger brandslike Google and like Spotify and like Netflix that have pretty successfullyimplemented you know recommender and predictor systems that people know andlove like Netflix I mean who hasnt benched Netflix over and over again justbecause you would like this its getting almost spookily yeah accurate yeah yeahand I think the challenge that we face.
is you know you’re trying to as brandseven if you’re a b2b brand consumer experiences have changed and theirexpectation of the simplicity and personalization has changed and sowhether it is like a Spotify or a Netflix or Amazon like theyre so usedto that predictive capability that when you start getting into having to buy anddeal with chat BOTS that arent.
intelligent at our human based logic andgoing through what we try and manufacture as the customer journey asmarketers its there’s a lot of things other big SAS world loves the wordfriction there’s lots of friction in the buying process today that doesnt existon a consumer side and so I think that’s the challenge a lot of big brands haveand especially in the b2b side is trying to find a way to remove that friction bybuilding more intelligence.
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