Episode 14

full
Published on:

9th Jan 2024

How to Use AI - A Guide for Marketers

Everyone is talking about how transformative AI is, but the information is still very piecemeal.

There are tons of app focused listicles or cool tricks for ChatGPT, but if you're like me, you don't have time to go looking for ways to put apps to work.

So I set out to take a comprehensive look at the business of marketing, breaking it down into the fundamental jobs to be done and creating practical guidance for marketers on how they might incorporate AI into their daily work in each area.

This episode covers four main applications that have me excited, but if you visit the link below, you can access an Airtable base with over 35 use cases and over 40 apps.

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Key Topics

  • [00:00] - Introduction
  • [01:30] - Why make this episode
  • [03:17] - Unstructured text analysis
  • [05:27] - Structured data analysis
  • [09:47] - Visual media generation
  • [11:37] - AI and workflow automation
  • [15:09] - AI for creative writing
  • [17:15] - Two visions of AI usage

Resource Links

Transcript
Justin Norris:

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

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Exciting, scary, overwhelming,

maybe all of the above.

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

mixed feelings about AI myself, and I'll

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talk about why later in the episode.

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

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I don't think AI is another one of our

Buzzword y, flash in the pan marketing

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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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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:

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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:

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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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similar results, it's going to be

harder and harder to justify that spend.

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The last bucket of use cases

I'm excited about is in the

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area of workflow automation.

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And this is in part because I'm just

an automation geek in general, but when

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you add AI to automation, you see a huge

expansion of what's possible versus just

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using predefined, rules based automation.

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For example, you may use an automation

platform like Zapier, Mercado, or Trey,

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and within those workflows you can now

include action steps that reference

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large language models like ChatGPT.

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You can even call ChatGPT from a

marketing automation platform that

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supports outbound webhooks like Marketo.

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So this opens up a wide range of potential

use cases to leverage AI in a completely

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automated way, rather than needing

a human to interact with a chatbot

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and type prompts into that interface.

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So for example, Within those workflows,

you could generate customized email

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text based on a lead's attributes.

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You could cleanse lead data and

feed it back to a source system.

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You could send an AI generated

custom alert to your internal team

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based on certain sales triggers.

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There's a lot of different use

cases you could think about here.

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In addition to using AI inside

your workflows, all the main

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workflow builders also offer the

ability to generate those workflows

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themselves using AI via text prompts.

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So for example, you could log into

Zapier and you can use a prompt like,

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create a workflow that's triggered

whenever I have a new lead in Marketo

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with a source of partner and add

it to such and such a Google Sheet.

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Now the challenge I have with these

natural language workflow builders is that

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there does seem to be a last mile problem.

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So in my experiments using Zapier,

the workflows were on point.

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for simple requests, but often a

bit wonky for more complicated ones.

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So it's not like you can just hand this

over to a user that doesn't know how

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to use the system and let them at it.

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You still need a sophisticated builder

to understand and debug the output, and

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it may or may not save you a ton of time.

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That being said, even if this is still

half baked today, it won't be long, I

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suspect, until they're more mature and

are able to produce more reliable outputs.

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Here's another completely different

example of workflow automation applied

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to the domain of media production.

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So you can use AI to do things like

generate transcripts of audio and

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video, automatically clean up filler

words like um and uh, shorten word

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gaps, process a longer file to produce

shorter clips for social media that

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are automatically formatted for

different platforms, and so on.

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This is the kind of non value

added work that takes a lot of

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time and doesn't necessarily

require a human's creative input.

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Again, though, the challenge I

found in practice is that a lot of

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the tech is still not fully baked.

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For example, in Descript, which I

use as my podcast editing software,

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removing the filler words often

results in clipping an adjacent word

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or producing an ugly transition.

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So it still requires manual cleanup.

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In practice, I still often

find it's easier to just do

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the whole thing manually.

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Now this really sucks.

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I hope we get to a place where it's

reliable 99 percent of the time because

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it will save me personally a lot of work.

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I think inevitably we will get there.

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Last workflow use case and one that's

near to my heart is documentation.

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It can be a real pain to document a

system by hand, screenshotting each

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step, typing out instructions, and so on.

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With tools like Scribe, you can simply

record your screen while executing a

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process, and it's going to automatically

capture that process and then summarize

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it with both text and screenshot.

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I've used Scribe myself,

and it was pretty magical.

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I still edited the output and

cleaned it up, but what it did was

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get me 90 percent of the way there.

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And the result is something

that's way clearer, and that's

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a delight for people to use.

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So that's it for the use cases

I'm really excited about.

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Obviously there's so many more

examples and apps I could have covered.

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Those are just some of the main

ones that are top of mind for me.

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Something that you might want

to start thinking about as well.

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I now want to just touch lightly

on an area where I haven't actually

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been that impressed with AI.

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Where I'm not even sure, ultimately, how

appropriate or beneficial it is to use.

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It relates to creative writing, and I'm

being somewhat contrary in here, as this

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is where most people seem to get really

excited about AI, and there are lots of

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popular apps focused just on this use

case that people seem to really like.

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So maybe I'm missing the boat, but where

I've experimented with it, and it is

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where I've experimented the most, I've

really found that almost 100 percent

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of the output was not fit for purpose.

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Now to be clear on what I mean, I

think AI can do a fine job at producing

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transactional text, things like Summaries.

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Or anything where the clarity of

communication is the main objective.

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But when we think of text that needs

to persuade, needs to entertain,

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to educate, to build affinity.

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Things like content marketing, longer

form copywriting, social media posts.

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Anything that really requires the

spark and imagination of a human

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being, I just don't think it works.

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What I have found is that AI does a

great job creating middle of the road,

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conventional, generic sounding content.

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So, yeah, if you need to produce

commodity SEO articles and don't care

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about originality or how valuable the

content actually is, Maybe that's fine.

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What it can't do, by its nature,

is produce novel, original, or

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unconventional points of view.

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Something that goes beyond the status

quo, and actually advances the state

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of knowledge in that particular area.

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However, that ability, the

ability to do that, is what makes

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content actually worth reading.

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Now, I suppose you could provide

your original points of view in

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bullet form, and have AI do a

first draft that you then finesse.

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And maybe this is going

to save you some time.

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But for me as a writer, so much of

the communication is also in how

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you choose to present your thoughts.

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The order, the word choice.

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That's what makes it you.

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That's what makes it who it is.

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And I don't want to outsource

those decisions to AI.

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Ultimately, I feel like creative writing

is a fundamentally human activity.

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Like, would you want to listen

to this podcast if you were just

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hearing me read out an AI script that

represented the average of the average

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of what everyone else was saying?

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What would be the point of that?

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So I have strong feelings here,

and if you think I'm missing the

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boat, feel free to reach out and

let me know why you disagree.

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Maybe I'll evolve.

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In conclusion, I want to leave

you with one parting thought.

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I think in some ways we stand at

the crossroads of two potential

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ways of relating to AI as a society,

and I'll express them in terms

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of two popular works of fiction.

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For the positive vision, we have Star

Trek, and like so many developments

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in technology, the Star Trek

universe has really anticipated the

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rise of generative AI decades ago.

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If you're familiar with any of the

series, Star Trek ships have massively

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powerful computers and they can

accept natural language prompts and

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produce almost anything, including

lifelike 3D replicas of people and

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places via holographic generation.

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Now there's still risks to this

technology, but for the most part in the

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shows, humans remain firmly in control.

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They treat the computer like

what it is, a powerful tool that

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can amplify human capabilities.

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And assist them in their tasks.

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Ultimately, it's something that

makes them stronger, not weaker.

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I would argue.

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On the flip side of this, we have

my personal favorite Pixar movie,

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Wally, and in this world we have the

autopilot of a spaceship on which

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a group of humans have escaped an

environmentally ravaged planet earth.

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The autopilot and the ship ecosystem

handles their every need for that

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group of humans, leading them to

become essentially adult babies.

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Completely dependent consumers

who no longer play an active role

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in managing their own destinies.

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So obviously it's pretty clear which

vision of AI use I'd prefer, and I'm

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sure almost everyone listening would

prefer the same in principle, but to

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me the WALL E example is a cautionary

tale about outsourcing too much

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decision making, too much creativity

to the machine, and that's how I aim

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to apply it in my own day to day use.

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I'm going to wrap it up here, but

just a reminder to go to revops.

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fm.

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ai to see the full

Airtable base of use cases.

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I would honestly love your thoughts

and feedback, so please comment on

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the post and share if you agree, if

you disagree, or anything in between.

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Thanks for listening.

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About the Podcast

RevOps FM
Thinking out loud about RevOps and go-to-market strategy.
This podcast is your weekly masterclass on becoming a better revenue operator. We challenge conventional wisdom and dig into what actually works for building predictable revenue at scale.

For show notes and extra resources, visit https://revops.fm/show

Key topics include: marketing technology, sales technology, marketing operations, sales operations, process optimization, team structure, planning, reporting, forecasting, workflow automation, and GTM strategy.

About your host

Profile picture for Justin Norris

Justin Norris

Justin has over 15 years as a marketing, operations, and GTM professional.

He's worked almost exclusively at startups, including a successful exit. As an operations consultant, he's been a trusted partner to numerous SaaS "unicorns" and Fortune 500s.