How to Use AI - A Guide for Marketers

Justin Norris: 00:00:24

If you're in marketing, AI made 2023 a lot of things.

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: 00:00:27

Exciting, scary, overwhelming,

maybe all of the above.

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: 00:00:32

If I'm being honest, I've had some

mixed feelings about AI myself, and I'll

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: 00:00:36

talk about why later in the episode.

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: 00:00:38

But I can tell you now what I don't think.

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: 00:00:41

I don't think AI is another one of our

Buzzword y, flash in the pan marketing

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: 00:00:45

trends that we can safely ignore

because everyone will be talking about

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something different in six months.

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Even though, yes, it's overhyped,

yes, it's buzzword y, and yes, we are

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: 00:00:54

ruining it by slapping AI on every

app's homepage, AI is still important.

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Let's say, for a second, you

were a textile weaver on the eve

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of the industrial revolution.

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Your job is going to be

fundamentally changed.

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What the rise of mechanization did

for physical labor and what the rise

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of computers did for computational

labor, AI is doing it now to

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creative and analytical labor.

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Some tasks will disappear,

some jobs will disappear, and

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this will all take some time.

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But I think it's important for all

marketers to become AI literate

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and start understanding it today.

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Now, there is no shortage of AI

content out there, and I don't

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like to just add noise to the room.

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So why am I making this episode?

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Well, everyone is talking about

how transformative AI is, but the

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information is still very piecemeal.

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There are tons of app focused listicles

or cool tricks for ChatGPT, but if

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you're like me, you don't have time to

go looking for ways to put apps to work.

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You have business problems to solve,

and I haven't yet seen anyone take a

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comprehensive look at the business of

marketing, breaking it down into the

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fundamental jobs to be done and creating

guidance for marketers on how they might

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incorporate AI into their daily work

in each area, if that's even possible.

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So that's what I set out to

do, and if you go to revops.

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fm forward slash AI, you can

find an Airtable base I made.

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The main thing that makes it special,

in my point of view, is that it

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starts with the marketing use cases,

rather than with tools, and identifies

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where AI can help with each one.

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It then looks at some apps that can

support each use case and provides

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an analysis of both the potential

feasibility and the potential impact.

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To start with, there are 35

use cases and over 40 apps.

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And to be honest, I could have kept

going, but I do have a day job.

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It's a V1, and I hope to

keep it updated over time.

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I encourage you to go to revops.

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fm forward slash AI once the

episode is done to look at it.

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But in this show, I'm going

to give you the highlights.

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I'll summarize some key

learnings, my points of view on

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where you should be using AI.

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And also where maybe not to use it.

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And talk about the use cases

that have me most excited.

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By the way, I am assuming here that

you have basic familiarity with what

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generative AI and large language

models are and how they work.

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If you need a total 101 primer on

these concepts, that's no problem.

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Just pause here and go

check out the show notes.

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I'll include a link for you there.

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Okay, let's dive in.

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Now this first AI application is

one I've seen relatively few people

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talk about, but it's actually

the one that has me most excited.

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Using AI to analyze unstructured text.

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I'm excited about this because

of the potential impact.

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As marketers we've become increasingly

quantitative in our orientation over

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the last 10 years, but there is gold

in the form of customer insights

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located inside our call transcripts,

our interviews, social media posts.

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It's incredibly time consuming,

however, to analyze this data manually.

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Trust me.

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I've done it.

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It's repetitive work, it relies on

pattern recognition, it's pretty boring.

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This is a perfect application for AI.

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Machines are just better at

this, and they're less biased by

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proximity or by small samples.

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So as an example of a specific use

case in this area, how about processing

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your gong calls to extract things like

common pain points, specific competitors

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mentioned, or answers to questions

like, how did you hear about us?

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You could do this in a few ways.

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One way might be to export all the call

transcripts from Gong or whatever other

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call recording software you're using,

using that tool's API, and then having a

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developer run those transcripts through

an LLM, and summarizing the key insights.

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Alternatively, and probably the way

I do it, is to create a workflow

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in a tool like Zapier or Workato to

process those new calls as they occur.

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So these workflows could be

triggered any time a call matching

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your parameters is logged.

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The workflow would extract the transcript,

send it to an AI model for processing,

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and then the resulting output can be

fed into CRM or other database fields.

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This is something that's

totally achievable today.

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I like this because it keeps the

data accessible to CRM users,

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but it can also be aggregated and

analyzed quantitatively in reports.

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Another use case for unstructured

text analysis is document

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or video summarization.

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So, for example, there's

a tool called PandaChat.

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It lets you submit PDFs, documents,

websites, even video or audio files, and

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then you can just ask it to summarize

those assets for you, using whatever

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prompts make sense for that purpose.

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Now, I don't think this replaces

detailed reading or actually

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consuming the asset yourself.

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But it's a super useful research tool

for when you want to quickly identify

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whether that resource has what you need

and if it's worth exploring further.

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That's just a few of the

potential use cases here.

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Moving on from the analysis of

structured data, the second big

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bucket of applications for AI

I'm interested in is analysis of

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structured or quantitative data.

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The kind of data we typically report on.

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It excites me for kind of similar reasons.

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Here's a few specific use cases,

and a lot of these already have

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relatively mature solutions.

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You might even be doing

some of them already.

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The first one is creating

forecast projections.

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So I'm talking about business

or sales or marketing forecasts.

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Humans are generally

pretty terrible at this.

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Most companies, the forecast is almost

always wrong, it's just a question of by

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how much, and often that number is a lot.

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But if we summarize all the inputs

that go into a forecast, a machine can

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actually forecast pretty accurately.

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And a great example of an app

that does this is Groblox.

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Side note, I actually interviewed

their CEO Tony Holbein in episode 9,

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so you can learn more about it there.

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But one of the things that Groblox

can do is continually track all your

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metrics down to the tiniest KPIs.

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And continually calibrate a forecast

based on your actual business performance.

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So even the most disciplined and rigorous

human is going to have trouble managing

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all those inputs as efficiently, but

for a machine that's something it

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can totally do and produce a forecast

that is much more reliable and much

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more consistently close to reality.

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A system like this can even act as an

early warning system by detecting, for

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example, that the MQL to SQL conversion

rate for hand raisers in your German

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market It's 10 percent lower than

expected this week, and then indicating

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the projected impact on revenue two

to three quarters down the road.

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That sort of thing is magic, and again

is a use case where machines are just

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going to do a better job, or will do

a job that humans couldn't really do

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at all, to that level of granularity.

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Let's talk about recording

and analytics more generally.

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We've long employed analysts to help

turn data into insights, and I don't

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think those jobs are going away.

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But a lot of that work is simultaneously

easy for a machine, because it involves

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some relatively straightforward

trend or pattern recognition, and

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at the same time it can be kind of

intimidating for some business users.

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So this is a great place to bring in

AI and present users with insights

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summarized in plain language.

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For example, rather than giving the

user a report and requiring them to

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do 100 percent of the interpretation,

AI can provide a text based analysis

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or a data story to go along with it.

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Another use case is to actually generate

the report based on a text prompt

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rather than requiring a user to know

SQL or manipulate a reporting interface.

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So you can just say, show me the sales

trend in this geography for the past 12

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months and segment it by lead source.

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And it can just produce that for you.

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ThoughtSpot and Tableau are

two BI tools that already

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incorporate these features today.

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And my friend Grant even has

a startup called Moji that's

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creating these data stories with AI

specifically for your Marketo emails.

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So something that's already

happening and I think this is pretty

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obviously the way of the future

when it comes to data analysis.

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And what about paid media?

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If you work in Dimension, you

probably already use AI in some

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way to optimize your ad spend.

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Most platforms have some sort of

machine learning based goal setting

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that can allow you to optimize for

specific outcomes in your bidding.

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There are also third party platforms

like BrightBid or Albert, and there's

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a few others that I've put in the

Airtable base that do the same thing.

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Now assuming these work as

intended, this makes total sense.

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A computer can understand the millions

of potential variables that affect

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an auction better than a person.

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So why not let AI do that work?

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And what you may not be doing

is using these tools to actually

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generate new ad variants by AI.

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So, new headline, new copy, and playing

with those things to optimize your

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overall performance on that ad platform.

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That's a separate use case from the

data analysis, but it is something

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that you can do in some of these

platforms, even natively in Google

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Ads using their automatically

created assets feature, for example.

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Last but not least in the area

of data analysis, let's think

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about good old fashioned lead

scoring and account scoring.

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This is actually one of the

original AI use cases for marketing,

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going all the way back to when

it was called predictive scoring.

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There were tons of new startups

doing it in the mid to late: 2010

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And unlike traditional rules based

scoring, which uses static, predefined

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values, predictive scoring uses

more sophisticated machine learning

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models to, uh, well, predict which

leads are most likely to buy.

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And there are vendors like SixthSense

or MadKudu doing it today, and you

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might already be using one of them.

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So let's move on to the third big

bucket of AI applications I want to talk

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about, which is visual media generation.

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So this is an interesting one for me, as

I'm actually not a huge fan of generative

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AI when it comes to creative writing

and more on this in a few minutes.

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So, why do I think imagery is different?

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Possibly because I can't draw.

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No, really, I think the main

reason is that most imagery in B2B

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marketing is already pretty bad.

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Stock imagery is usually kind of lame,

and it's hard to find something that's

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super relevant or that's personalized

to a subject that you're working on.

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Now, with AI, you can create imagery

that is way more personalized and

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specific to your subject matter with

a lot of fine grained control and

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just continually revise that image

to get almost exactly what you want.

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And it generally looks

just as professional.

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I'm thinking here about feature images for

blog posts, social media, even websites.

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The advances in AI image generators like

Midjourney or Dolly over the past year

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have been pretty astounding and they're

only going to continue to get better.

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Now I don't think these tools are ever

going to create like truly original

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masterpieces of art, but that's not really

what we're looking for in marketing.

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We just need images that look

good, that are relevant, that

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are effective for the purpose.

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And I think AI can provide that

in most cases, and usually at a

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lower cost than human illustrators.

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The far out thing is that going

forward, the same advancements are

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going to apply to video as well.

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If you look at most of the, you

know, what's ahead for: 2024

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newsletters and lists, AI generated

video is usually the top of them all,

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at least the ones that I've read.

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I still think there's going to be a

place for human produced feature videos

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and marquee pieces of content, but my

expectation is that AI tools are going

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to eat up The commodity bottom end

of this, and it's going to make video

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more accessible to use in places where

we may not have used it at all before

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for cost reasons or for time reasons.

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So if you can spend 20k with an agency

or a few hours with AI and get relatively

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