you buy things that are a type hour for the same reason you would buy
traditional marketing technology it needs to be solving a business challenge
about where it needs to be helping you get to a goal more efficiently it just
so happens that there's now smarter technology on the market so palki tell
us about how yeah we've seen a number of ways like we've actually had had one
company about a ten billion dollar manufacturing company literally just
came to us and said what would it cost to do AI like to be AI first as a
marketing organization I said what in the world does that mean and they didn't
know and so you realized that because it's sort of the net the buzzword now
and people just hear AI and they seem like well we need that whatever that is
and what we always advise like at the end of the day it's just smarter
marketing technology like you buy things that are AI power for the same reason
you would buy traditional marketing technology it needs to be solving a
business challenge you have or it needs to be helping you get to a goal more
efficiently it just so happens that there's now smarter technology on the
market and so as a marketer you need to just know how to determine is it really
using machine learning and is that machine learning making the product
better and is the product roadmap for that company building more intelligent
software that will do one of two things for you if you're lucky both make you
more efficient as a marketer and as an organization and help you drive revenue
or achieve your goals more efficiently so that's really what it does and so any
way you would use AI you're gonna go through the same buying process you
would go through if you were buying marketing automation software email
marketing software a conversational AI you're you're looking at a business case
for it and you're trying to prove out what the value will be to invest in it I
mean how do you guys when you're road mapping which features in the hub spot
that you're gonna integrate machine learning into like how are you guys
thinking about it yeah there's a few different types of use cases that we
look for recommender system so that could be blog content recommendations we
have a module for that on the CMS to recommend relevant blog posts based on
the current blog posts that you're viewing optimization problems
so if which can often be like personalization so with our lead flows
tool we have a feature called continuous optimization which
is going to adapt how that lead flow school is presented based on what's on
the page and based on other offers that that person has converted on and also
categorization problems so things that can help you identify you know what is
the potential customer problem that's in the support case so in our tickets
object for categorizing types of topics across those tickets so recommendations
optimizations and categorizations are three like broad use cases that you can
apply supervised learning specifically that businesses can start to look at the
supervised learning being giving it data to train it on and then teaching it yes
or no that was like a good action good response good recommendation exactly so
you mentioned earlier historical example so you need to know in the situation
what was the human decision that was taken and you can use those historical
examples to apply that without humans to a wider range so you can take a little
bit of human intuition and scale it very quickly to to identify you know
high-priority deals or high-priority good fit contacts urgent support cases a
lot of email deliverability and it was an early application for yeah it's like
how do you get this through the spam filters is this good email is it a bad
email what are the factors that go into it exactly so we have a team a top spot
focus strictly on deliverability and they they've been manually reviewing
sends that HubSpot customers have put out that have a particularly high bounce
rate and based on those examples we can kind of map those characteristics to
other sends and so as we're sending even more and more email it's helping our
deliverability overall increase one of one of my favorite examples that's
really tangible to most people when they're thinking about AI is Gmail so
the idea of training so if you think back over the last couple years when
you're using Gmail and it would recommend the three to five word
responses which was like the smart reply every time you as a user was saying yes
you were saying this is a good response and you were just choosing that three
word reply we were training their algorithms that that was a good
recommendation and now we see within Gmail you know fast forward a couple
years smart compose where it's now recommending entire sentences to us so
it's looking at the sender probably everything you've ever communicate
with that person the subject line any context within the emails itself and is
trying to make predictions about what you want to write next
so I mean Google is always obviously kind of ahead of where the rest of the
market is getting to a massive team but even like Microsoft has like three
thousand machine learning engineers not working on solving things so yes I think
the end they just it's designed to make our lives as marketers more efficient so
we can maybe focus on more human unique human abilities that cats