most of the things we hear about today and marketing are just in our general
lives are really uses of machine learning which you could probably define
better than me but the definition we always go with is making predictions of
future outcomes based on historical data
is Paul rates her 100 copr 2020 hub spots first partner agency back in - oh
seven we start in the partner program and also more recently the founder and
CEO of the marketing artificial intelligence Institute so I'm here with
Kevin Walsh today product manager for hub spot machine learning proc metric
Russia Larry hop spot to talk about the applications of artificial intelligence
and digital marketing so be sure to like or subscribe to the HubSpot Academy
YouTube channel and that throw it over to Kevin let's start at a really high
level can you tell us if you know what is artificial intelligence and what is
machine learning yeah I mean to me the the thing I would say about artificial
intelligence is if you search on Google for what is AI you're gonna get 10
different definitions from 10 different experts so what I always try and talk
about is just at a very high level it's kind of the umbrella term to encompass
technologies and algorithms designed to make machines smart and then within that
machine learning being the primary subset most of the things we hear about
today and marketing are just in our general lives are really uses of machine
learning which you could probably define better than me but the definition we
always go with is making predictions of future outcomes based on historical data
but the key with machine learning is the machines get smarter and the predictions
get better without human intervention necessarily thinking again about do you
have any sort of specific consumer examples that people might be familiar
with that are using machine learning day-to-day in their lives
yeah we always look at like the best example I always give of AI and machine
learning in particular is Google Maps so when we try and make it less abstract
for people we just talk about the fact that you're using it dozens if not
hundreds of times every day in your life so if you think about the apps on your
iPhone from Facebook to Instagram to Google Maps to Amazon to Spotify like
every one of them is using data to try and predict your behaviors
predict what you're likely to do next or what you're going to be interested in or
what you're going to buy and so Google Maps to me is one of the best examples
of what we're trying to get to with marketing so Google Maps makes
predictions about the most efficient way to get from point A to point B but the
human at the end of the day still makes the decision and I think that's where
we're trying to go with marketing software is to make it smarter to make
it predict a little better what might happen or the best route to take but the
human still takes that makes the final decision so there's a book called
prediction machines that I really like this written by three economists and
they look at the future of jobs and say that it'll basically be telling machines
what predictions to make and then figuring out what to do with those
predictions like using human human judgment on those predictions and so
kind of what is the state of artificial intelligence and within marketing sales
and services as we think of them I look at it and think it's very beginner level
now again you may have and as someone who's overseen the building of some of
this stuff within hub spots platform may look it a little differently but I
generally think that the technology is at the very early stages of development
and application to marketing now there's other industries where it's raced
forward so you could look at just Wall Street for example in finance
this stuff's 30-plus years old they've been trying to do this stuff to figure
out when to make trades and how much to invest so looking at the applications to
marketing it's it's just early the money has started to move in we track about
1,100 a I powered sales and marketing technologies and about 500 of them have
funding combined over five billion but a lot of that money is funding companies
that when you dig deeper they honestly don't really have much proof that it
works so they talk about a and they talk about machine learning and they claimed
have been building smarter technology but if you press for case studies of how
it works it's often you find that they don't really have them and that really
the machine learning adage is part of a road map and they're just very beginning
staging testing so our experience has been it's very early but that that's
actually a good thing because it means there's opportunities for both the tech
companies and for the marketers that figure out what this stuff is and are
proactive in trying find smarter more efficient ways to do
things yeah I tend to agree I hear at HubSpot you know we've been doing
machine learning seriously I'm good seriously I would say for about two
years a little more than two years and we've grown that team tremendously it's
now a full-fledged group here within products and we do take a lot of
inspiration from those bigger brands like Google and like Spotify and like
Netflix that have pretty successfully implemented you know recommender and
predictor systems that people know and love like Netflix I mean who hasn't
benched Netflix over and over again just because you would like this it's getting
almost spookily yeah accurate yeah yeah and I think the challenge that we face
is you know you're trying to as brands even if you're a b2b brand consumer
experiences have changed and their expectation of the simplicity and
personalization has changed and so whether it is like a Spotify or a
Netflix or Amazon like they're so used to that predictive capability that when
you start getting into having to buy and deal with chat BOTS that aren't
intelligent at our human based logic and going through what we try and
manufacture as the customer journey as marketers it's there's a lot of things
other big SAS world loves the word friction there's lots of friction in the
buying process today that doesn't exist on a consumer side and so I think that's
the challenge a lot of big brands have and especially in the b2b side is trying
to find a way to remove that friction by building more intelligence