Operationalizing AI for the Marketing Funnel
Learn how GTM teams can use AI right now in their workflows to drive more efficient outcomes.
Welcome to Future of AI.
We're going to talk about Operation Lacking AI.
And you know, if you're in this room and AI trust,
there's some of us that probably are AI skeptics,
some that are AI survivors.
What we typically hear is that most people are waiting for a day in the future
where AI can actually do things for that.
And the reality is that most people don't actually realize
what AI is capable of.
And you know, unfortunately, there's a big aspect of related education
in the lead curve that's been eroded, I think,
by people's first experience in the AI.
Your sales reps went to chat to be taken and said,
"Right, me, I see, I'll be now."
They probably didn't get a great result, right?
And that puts us in a place, right,
where it's really difficult to be able to win what that journey should look
like.
And so, you know, ultimately, the purpose of today's presentation
is not a sales page.
It's to help you think of how go-to-market teams can actually leverage AI
to drive all of the outcomes that they want,
and how this is really possible today.
So, before I jump too far ahead of it,
probably should introduce myself and my co-presenter, Nathan, over here.
Shakar saying I lead solutions at Copy AI.
Now, I know that you might think we have a lot in common
in that we support the sales team,
but solutions is actually a really unique role at Copy AI.
It actually encompasses customer success, too.
We think about org design very differently due to the investments that we're
making,
but, you know, in a sense, half my team is responsible for making empty
promises
to customers, and the other half of my team has to cash the check.
So, it's really important for us to stay in lockstep,
and, of course, I spend a good day of my, you know, my time
thinking about how we can leverage it, how our customers can leverage it,
how to make their dreams come true.
Hi, I am head of content strategy here at Copy AI.
I'll tell a little bit more about my background with AI in a minute.
The biggest thing, though, is I've walked around telling people I work in AI,
and I feel like public enemy number one at this conference.
So, just bear with me.
I'm happy to talk about any objections afterwards,
but we do have some really cool stuff that will walk you through
that can make marketing, I believe, cheaper, faster,
but also higher quality, and that last part is the most important, so.
Love it.
Uphill battles is what I fight best.
So, let's talk about Copy AI for a second.
Again, promise is not a sales pitch.
There's three numbers that I want to use to tell the story.
15 million, 37 and 40.
Now, the best part about the story is I actually don't even want to complete it
right now.
Just keep these numbers in the back of your head.
We'll talk about what they actually mean and represent later.
What's more relevant to this audience is the problem that we solve at Copy AI,
which is really all around what I think, you know, the mandate is in 2024.
Time and time again, we talk to dozens of go-to-market leaders,
and this comes up almost every single time.
The go-to-market team is simultaneously the most expensive
and the least efficient part of every single organization.
I see a lot of people nodding their heads because you've probably experienced
this
in your own day-to-day.
And, you know, whereas it's easy to point fingers at someone that, you know,
maybe isn't working as hard as they could, the reality is that's not really
what the problem is.
When you zoom out, there's hundreds and thousands of to-dos in typical
organizations,
and the team needs to get these things done every single day.
As things continue to evolve, we continue to layer on new pieces of the tech
stack.
They continue to add up and tasks begin to, you know, constantly pile up.
And the unfortunate reality for marketing teams is that
whereas sales is a little bit more discreet, and it's easy to say,
"Look at how many accounts I'm working."
It's really hard to say, "Hey, remember these initiatives that I've been
working on for three years that have been continuing on an ongoing basis?"
Yeah, I'm still responsible for doing all of that,
and now we're adding more layers to it.
And so, you know, at the end of the day, what we've really done is invest in a
lot of tools, amass these tools and say they're solving a problem for us.
When really we're creating more button-clicking work, more pain for the people
that have to use it day in and day out,
and we've built huge tech stacks and, you know, ultimately not really seeing
the results that would allow us to invest more.
This is the problem that the industry is calling Go to Market Bloat,
and it is the sum total of all of the investments that you've made in the Go to
Market engine.
And as I said, the big problem, right, is that they're not driving the results
that we had all hoped.
And, of course, you know, to add insult to injury, we're in a very different
place than we were a few years ago.
I thought Anthony actually put it best at Screen Capt. his post from a week ago
, right?
Ultimately, we're exhausted.
There's a lot that -- there's a lot of pain that's happened over the last few
years,
and a lot of change of the whiplash of the market correction that has really
pushed us to think differently about how we can actually drive the impact that we need.
And, again, this is to say that, you know, it's okay to be exhausted.
It's okay to feel like there has to be another way, because as we all discussed
this morning,
that is really what innovation is founded on.
And in hindsight, the zero interest rate phenomena is actually something that
shouldn't have ever been
a reality in the first place.
And I'll give you a simple example, right?
Who thinks that their head of engineering or engineering department would have
signed up for continuous
deployment six times a day in a world where they didn't have the ability to do
automated testing?
No, right?
Like, who would sign up for opening -- deploying the app and then having to pay
someone to go click on every
text box, every button, every dialogue modal, and determine that your latest
release didn't break it.
And so, in that world, technology kind of led us to decide to adopt a new way
of working.
The zero interest rate phenomena that we've all experienced has really just
said,
hey, data is important, it's an asset, you're a person, you can analyze it,
why don't you take on this task as well?
And so, what's really happened over the last several years is we're tired
because we've become the tools of our tools.
We are effectively data rich but very information poor.
And in a lot of instances, this actually drives us to not really be able to act
And when we do act, as we shared in the last session, right, it's really hard
to attribute your actions to
actually driving the impact that we wish is in the world.
And that is a glim-grim reality.
And, you know, I hope that I'm not belaboring the pain too much.
I think I took Anthony's message to heart this morning that it's important to
dwell on it so that we can actually
think about, you know, what good really looks like.
And then enter 2024, you know, again, not a surprise, do more with less is
literally the mandate.
And that line should actually be even more crooked, that's not a UI error, we
're getting less resources
and we need to actually drive more results.
But if we're spending all of our time keeping the lights on, how can we
possibly innovate in the long run?
How do we change the way that we go to market and how do we actually work
better together as a team?
So here's a hot take for you, right?
AI is supposed to solve this problem. I think we've all heard that before.
We've also probably heard this one.
AI is a calculator for words.
Anyone here this, believe it, maybe, like I'm hoping, you know, I'm not the
only person that's so deep in AI news that
this is, you know, an analogy that only makes sense to me.
But, you know, I hate to say this is actually one of my least favorite analog
ies for AI
because a hot take on is actually chat GPT is a calculator for words.
And that, don't get me wrong, right? There's a ton of value in having a
calculator.
If I didn't have a TA89, I probably couldn't graph anything other than basic
trig functions.
However, calculators are a tool designed to do a specific job, which, you know,
in this case is helping me solve
an ad hoc math problem. In the case of chat GPT is helping you to solve an ad
hoc work problem.
Calculators are great, they just don't run businesses.
And so when you hear AI is a solution in all of your problems and then contrast
that to a world in which you have to
talk to a chat bot for, you know, a couple hours in order to get something
basic done,
it's pretty challenging to be like, yes, it's going to drastically transform
the way that we go to work.
It'll make you 10, 20, 50% more efficient, but that's not really the outcome
that is going to help you do more with less.
And so, you know, a better take on this, we think, is that AI is much more than
a calculator.
It's actually a second, third, fourth brain that you can actually use in order
to be able to scale your own impact
across the organization. And ultimately, we have to get over this idea that the
only way to use AI is by
interacting with a chat bot back and forth. And, you know, unfortunately, this
is pretty common, right?
We just heard three, you know, amazing marketing leaders all say that chat GPT
is like the end-all-be-all of leveraging
AI. I'm sure we've all also used other AI tools out there. But the reality is
AI tools also don't really hit the
mark, right? In many ways, they have a, you know, buried logic. There's no way
to actually change the way
that you're using it. I'm sure a lot of the folks in here probably use GONG and
review GONG transcripts all the time.
GONG meeting summaries convince me that meeting summaries are valuable. That's
not actually what I'm looking for out of
a meeting. And so, there's this nexus of recognizing that AI is a second, third
, and fourth brain, but really it's
driven by your brain. And that is the foundation of actually how you can get AI
to work for you. And, you know,
hopefully that should be something that's exciting to everyone in the room. And
so the question, right, how can we use
AI systematically to actually scale our own impact? You know, what does it mean
to actually even do that?
How can we actually do that in a way that drives impact and results in
production and not just, you know,
do things a little bit faster? And so, I want to come back to the three numbers
I told you, told the copy AI story.
15 million customers in 37 months powered by 40 employees. Our marketing team
is four people. Four people, total.
The entire company is, you know, 40 employees. We're proud to call customers
like Siemens, Lenovo, Thermo Fisher,
Crow, Rubric, Urban, our largest enterprise customers. Obviously, we're a
premium platform, so this is counting
literally all of our customer base. What's interesting to know is that we don't
compete with Jasper on content.
We compete with HubSpot on content. And Jasper is a company that's, you know, I
would argue they're not our competitor,
and hopefully agree after we show you what our product can do. But, you know,
ultimately, that's very different
because we think differently in scarcity at Copy AI is really what drives
innovation. When I joined Copy AI a year and a half ago,
it was me and one other person that was responsible for every single customer
interaction from every part of the sales cycle
to actually deploying the customer, getting them live, training them, teaching
them how to use the platform.
And in that year, we closed 37 logos, which I'm pretty proud of. It was a lot
of work, but what we realized was all of the things
that we didn't have time for were an invitation to actually think differently
about how we could use our own platform.
And in many ways, that's really driven the entire story of Copy AI. If we can
unlock what it takes to get 100 times the value
out of each person and allow those people to continue being happy and thinking
about their next problem,
and ultimately leaving some of the load behind, we can really drive some really
incredible outcomes.
But what it takes to achieve this is shedding a lie that we've all been told.
There's one thing that you take from today's session. It's that you've all been
likely sold to lie if you believe that chat
GPT or chat modalities are the only way of thinking about using AI. And this is
controversial. I know a lot of people are afraid of, you know,
automation. But the reality is autopilots are greater than copilots. Autopilots
actually allow us to completely shed a
responsibility and leverage our own brain to come up with an idea that we can
encode into AI to follow our best practices
in order to be able to drive the outcomes that we want, repeatably, reliably at
scale.
And if you think about most marketing tasks, right, it's not like you need to
just write one blog post. It's not like you just
need to write one nurture email. It's not like you need to just write one sale
as the email that's personalized.
Practically, everything needs to happen at scale. But the critical part of this
is actually thinking about your role in it
and thinking about your ability to actually instruct AI to drive the outcomes
that you want and scale your own impact in an
organization. So I know that's a lot of waxing, right, we haven't even told you
how to pragmatically use AI.
I wanted to set this up so you have a good understanding of really where we're
coming from here. But really, what we'll
jump to is actually a live discussion and demo of exactly what this means and
how we at Copy AI are leveraging our own platform.
And so Nathan, I'd love to, you know, just have you answer a few questions for
the audience. Can you just tell us a bit about your
AI journey, so far? >> Yeah. And can you guys hear me without the microphone?
Is that okay? >> Can't record without.
Then I'll use the microphone. That's just fine. That's good. My AI journey has
not been a super technical one. I talked to some
of the engineers and the founders. They tell me about how they started coding
at eight years old after school for 12 hours
a day in the library. I was watching The Simpsons, if I'm being totally honest.
I was watching The Simpsons. I was eating junk
food. And that was kind of my childhood. That was my background. When I got
into marketing as an adult, all of that pop culture
really played off. It was great. But I don't have the technical stuff. I don't
code. That's just not who I am. So I've always been
kind of confined to buy how well I can use the tools, how technical those tools
are. When I came, I was at an agency
when I first started writing for Copy AI. I was writing the landing pages. And
at that point, it was really just the chat feature.
And the chat feature is very cool. We'll talk about that and how we use it. But
I was using it differently back then. How many people
are familiar with Copy AI? How many people have tried it? Okay. How many people
have tried chat GPT? Yeah.
You'd use chat about the same way. Our chat function. It uses the same language
model. So you'll get about the same quality results
out of that. Soon you'll be able to use different language models, which is
neat. But I was using it about the same way
you're probably using chat GPT now. You'd want to write a LinkedIn post. You'd
write a LinkedIn post. You'd want to write a blog post.
You'd want to write a blog post. I don't use chat so much for that anymore
since we got workflows. So at first, as I was coming into
AI, it was very technical. Chat, Copy AI broke that down. Made it accessible to
me. As Shakar said, Nathan, you are the perfect
simpleton to be presenting on why this is so easy to use. And it's true. Not a
big technical background. What I loved
the big shift in the company was once we took chat and transitioned into work
flows. When we went to B2B
and we became a GTMAI platform, not just a chat tool, but a GTMAI platform,
that was when the big shift was for me. Because what we did was have
workflows, which was different than anything I'd used before. And as a content
marketer, it really helped me out, scale, and keep quality
very high. That was important for me. So I'll tell you quickly about how I use
chat, and then I'll tell you about workflows and how those are
different. Now I use chat as like a little brainstorm buddy. It's really good
at bouncing ideas back. I don't expect them all to be
great ideas. But me as the human can go through with my creativity, look at the
ideas it's giving me as a launch pad and start
coming up with something better. So we got a CMO last November. His name's Kyle
Coleman. You might know him on LinkedIn. And we sat down, I went
in person to Denver. And he said, Nathan, I want to see how you make a content
calendar. I want to see what you do. So no pressure, new CMO, I'm
sitting down. And I told him, we'll do this together in real time. We'll make
six weeks worth of planned out content. We won't make the
content, but we'll make the plan. And we did it in about an hour. And the
process was simple with chat. I knew we wanted 20 SEO plays. I knew we
wanted 10 thought leadership pieces. So I asked for 70 ideas. I asked for way
more than I needed so that we could go through with our human insight
and pick out the ones that were most interesting. That probably took 20, 30, 40
minutes. Then I took what we agreed on. All the titles, the
topics, just general ideas that excited us. I'm not looking at keywords yet.
Looking at ideas that excite us. Then I could go through
and ask it for, hey, what are some likely keywords that would be good for this?
And I would get, I'd say, give me five or ten per topic. Then I could take
that, head over to AHRF's, check for any volume opportunities, and do all of
that grunt work, but really efficiently and really
quickly. This is very important because I'm going to share about what I do with
the time savings later. That part's really important. Sure,
you're saving time. But what are you doing with it when you get it back? It's
really important. So that's how I started using chat. At the
end of it, like I said, in about an hour, hour and a half, we had six weeks'
worth of content planned out, mapped out. He was excited
about that. All we had to do now was find the thought leaders for our thought
leadership articles and get these drafts over to the
content writers. So that's how we use chat. It was great for one-off tasks, ad-
hask tasks. It can give you some insight
via brainstorming, but awesome. Then we have workflows. And that's when we
became a platform. So workflows are different
than chat. The best way I can think to describe it is workflows create an end-
to-end process, and it completes the little
tasks along the way. So I'll give you an example. I promised myself I wouldn't
teach you how to use AI to write a blog post. I'm not
going to. You will have access to a workflow that's pretty cool that we built.
So we'll talk in a simple way in a second. But the
workflow breaks things down into separate actions is what they're called. And
like I said, it builds a process, a
process that you can run at scale. So for example, we're going to start with
the most simple form of workflow. It's not on the
screen just yet. This is about as simple as it gets. There we go. All right. So
we're going to start with something very simple. One of the things
that we were doing is coordinate LinkedIn posts across our leadership team. And
one thing that I was doing as I was
looking at these LinkedIn posts was we would try to draw attention back to an
article that that thought leader had written or as you'll see
collaborated on together. What I wanted to do was make those better because a
lot of the stuff -- a lot of the LinkedIn posts
that I said, hey, go check out this blog post. It was trying to put in way too
much information. Because one blog post might
have 10, 12 points in it. Each one a little nugget, at least they should. And I
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if we refine this and we get to the point where that's the best possible social
media content, I would imagine you go
from 10% edits to 5% edits to 3% edits to maybe 1% or QA review. And so if we
think about how we can make that even smarter, we
can actually connect it to another process that does this for us. It's going to
take the blog post, identify interesting
interactions, and then pipe it through the exact same workflow that we already
know delivers good final product. And this
ultimately allows us to get a whole lot more posts at once. And you can simply
QA and go through and post the ones
that are most interesting or potentially have the ones that are really going to
drive your audience at the right time. And
so what I'm trying to communicate here is that this is a very different way
about thinking, about utilizing AI. You
are the process architect. Your process is actually what has been driven
results. And as we all agreed today, that is
literally what we need to do best in the next, I don't know, call it five years
, to change the things that aren't working and
figure out the things that are working. And if you go back to the marketing
problem, right, unfortunately content has
always been the bottleneck or the production of that content has always been
the bottleneck. So what happens in a world where
you can actually scale that content production and actually think about it more
from a hypothesis testing paradigm, as opposed to
say, okay, well, I've got $3,000, I can get 10 LinkedIn posts written for that.
I'll post one of them. That's not
efficient. It takes God way too long. And it's never going to drive the
outcomes that you actually want.
All right. Well, we're going to take it now a little more complex. You've seen
this is really another task. We can't
expand it. That's awesome. But where to come up with the original posts? Now, I
view the kind of posts that we're creating
intentionally right now as we switch from B to C to a more B to B platform in
two ways. We have to have the standard
SEO plays. We got a lot of ground to cover. I know SEO is dying. It's going to
be dead, whatever you want to say. But it's
still important. And it is one of those keep the lights on. However, it is one
of the most expensive and time consuming
effects in content marketing, if you want to do it well. Takes a lot to hire
writers and editors and then to get
everything in the right places. So we did something a little bit different. I
built a workflow that started with
just -- actually, yeah, it's not this. Don't worry about that. We'll show the
output of that. Basically, it starts
with the keyword. And this is another workflow that I built. So it starts with
the keyword. And that's the only
thing that we enter at first. From that keyword, what it does -- and this is
when I said that workflows create a
process, but accomplish the tasks along the way, an end-to-end process. The
workflow will take a keyword and it
will actually run that keyword through Google. And it will take the top five
results. Then it will scan those
results and look at all the H2 and H3 headings. Trying to figure out what's
important information relative to
that keyword and what does the reader want to get back. Then it will come up
with some ideas, well, how can we make
that better? Then it will come up with some related keywords. It will
ultimately create a content outline, a
content brief, because we do work with human writers and we want to send them
that context as well. Once that's done
generating, what you get is the result, it automatically kicks off this
workflow to create a long-form blog post. And
the tools that we use -- we use Surfer SEO. It's not a magic bullet solution,
but we use it as kind of a
gauge. Out of the box, these were scoring 60 to 70 on a -- most scales were
between 75 and 85 at the top scores. So we
can hand these off to our human writers and say, hey, make sure this is not
garbage. Make sure there are no
hallucinations. Go in, add some insights. But they have a draft that they can
work through way more quickly. And my
goal was with our writers with the boring SEO stuff. It's my least favorite
part of content marketing, our blog. It's
my least favorite part, but it's very important. And I wanted to find a way,
how can we get more content back without
increasing the budget and get our writers earning more per hour based on the
campaigns that they're working? I
didn't want to rip them off either. And I'm happy to say with this -- with that
version, what we did is we give our
writers something like this, and they're able to do it in maybe 25% of the time
, so they're earning the same. But we
get two or three articles back in the time it used to take them to make one, if
that makes sense. So in that
way, I do believe we're scaling very responsibly. >> So I'm going to, you know,
keep with the same punch line. What does
this actually look like at scale? And one of the things -- for those of you
that were in the last session, you
probably heard me ask about transcript data. That's because that has been a
goldmine and a very fascinating topic
for me. We use it a lot of different ways. But imagine a slightly more complex
workflow. What if we could take a
meeting transcript, automatically identify all of the -- and extract all the
topics that are discussed, look at
all the topics and identify is it a how-to article, is it a best practices
article, is it a thought leadership
article, like what could we write with this, go back and scan to extract all
the supporting details from the
original article, then generate an outline, then use that outline to actually
write a full form article, then
identify of all the people that work at your company, who is the most prolific
person and knowledgeable person
about this, adapt it to their own brand voice using writing samples that they
've provided us, generate social
promo content, and then ultimately publish the blog article directly to Notion
and ultimately to Webflow, and
then just send the person a slack saying, you know, you wrote something, right?
That's actually what they say. And here's some social media posts, LinkedIn posts to promote it, written in your
voice. That sounds like a pipe
dream. I'm sure I lost a lot of people that are like, no way, AI can do that.
>> Are you just telling us a dream? >> No.
I'm about to show you that this is reality. Thank you. That was a great tea out
. >> I did say it sounds like a
flower. >> Your work look credits will be in your account. Don't worry. What
you're looking at here is an
experiment we ran for six months. >> Right. The stupid. >> Here. >> Don't curse
the computer. >> Yeah, they're
listening. So what you're looking at here is a Notion database that contains
content that has been published for
six months. The reason we're having all this content is because what my team
does on a day-to-day basis, it
talks to customers about their most prolific problems in the market space.
Every customer is different. Some of
these articles are super long tail. We had a customer that is using AI to try
to write radio ad copy. And
that's the most long tail thing in the world. They only have one customer that
has ever asked me about that. We
teased that out as a topic. It extracted all of the things we discussed on the
call, the considerations that we
mentioned as to what model they use, why it would be hard, what kinds of
challenges you might need to overcome, and
ultimately wrote some really high quality content from it. I don't want to look
for a specific article here, but let
me just open this guy up. You can get a sense of the quality of content. It's
well formatted. It actually
follows the overall logical order of how we're thinking about things. And of
course has a general outline in
mind. We want to talk about impact. We want to talk about a new way for AI-
powered content repurposing. I
didn't even look at the title of the article. The content repurposing super
power, creating tailor variations for every persona. That might as well be something
that is based on the conversation we're
having right now. And so here's the thing, right? The old process is something
that was broken in the first
place. You would have an interview with someone in the field or a founder or a
CEO. You would ultimately use
that interview to translate that with your own brain into your understanding of
the topic. You would then
have them probably QA the topic. Maybe then it would go to a writing agency and
you'd pay them, you know, countless
amount of dollars to give you back a piece of content from someone that is
further removed from their writing
style than you are. Ultimately, great content in many cases, right? People are
really good at what they do. But
it's a really inefficient process. Whereas when we're actually using the
details of what happened on the call,
letting AI process that and then telling it to go right just like you do, we
can actually drive some
incredible outcomes. And you know, the impact of this is actually we don't
publish all of it. We've actually come up
with a couple of things. The author has been predicted. Nathan, you're prolific
and clearly an expert in a lot of
different topics. It's tagged it. The only thing we haven't automated is this
publish flag. And that is because
Google guidelines and not wanting to get dinged. But what this means is that
Nathan gets to sit on a treasure
trope of more content that you could publish in two, three years. >> I'm
exhausted thinking about it. I'm going to be
talking about it a little bit. When we were given this, I was told Nathan, go.
Publish. It's absolutely not. I don't know if you can see this number.
How long has this been up? >> We started this in October of last year. >> 6,000
full-length articles written
from customer calls with real pain points. Can I see this for a second? >> The
reason that this is really important to me is as I'm
writing a blog post or an article or anything, if I find a use case that I want
to explain, I can pop in here and I can
search it. It's already written step by step. I have to verify. I don't have to
go through it. Again, the emphasis on verify. Stay the human in the loop. Make sure it's accurate. Make sure it's working.
But there's a second benefit that I have found
with this. Sales bugs me a lot less throughout the week for things that they
need to send customers. They can take this. They
have access. They pop in. And if a customer has a similar question to a call
they had last week, they can say, hey, let me
send you a quick one pager for that. They have it ready. They have 6,000
options if they want. Which is incredible.