How Can Marketers Use Artificial Intelligence

Key Takeaways

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 has 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 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 he 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 after 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 a 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 the 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 focused 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 sense 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 communicated 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 because they have a massive team but even like Microsoft has like 3,000 machine learning engineers not working on solving things so yes I think at 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 [Music]