In today’s episode, I look at how AI will reshape marketing as it transforms language and imagery. With its skill for generating content, AI brings unlimited scaling to communicate and persuade. For analytics, AI coding workarounds compensate for poor math skills with excellent language abilities. Join me as we explore the huge potential of AI to truly personalize the customer journey.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, Mark asks in your perspective, how do you foresee AI reshaping the future of marketing, especially in areas like content marketing, customer journey, mapping and analytics? This is always a challenging question.
I get for literally every professional discipline, how is going to reshape finance and HR and sales and marketing, etc, etc.
And the reason is challenging is because of the nature of generative AI, because of the nature of things like diffusers and transformers.
These, these tools are based on things like language and imagery.
Look at your own profession.
What percentage of your tasks every day are image based, or language based? When you think about that, when you think about the customer journey, for example, as a real simple example, how much of the customer journey is impacted by my language, all of it, the whole thing.
At no point in the customer journey, do you communicate with anybody as a customer who in some way is not using language, right? Except maybe like music videos, right? Where there’s no words is just music and a video.
But that’s pretty rare.
Most of the time, we’re communicating with language, and we are sending emails, we are in online chat, we’re in face to face meetings, we are in zoom calls, we’re using language all the time.
And because we’re using language all the time, it can be very challenging then to say like, well, what is what is generative AI not going to impact? If it uses language, a large language model can impact it, it can change it, it can alter it.
Today’s language models can generate very, very skillful language, right? That’s a great way of saying they are skillful at language, they can write content that can inform that can persuade that can convert, and they can do it at scale at scale that we can’t match as humans.
We are not as fast.
We are not as prolific.
And we’re limited by our physical selves, right? I can sit down my keyboard and write and if I’m super caffeinated, whatever, I can write a whole bunch.
But I can’t clone myself.
I can’t spin up another version of Chris and write some more stuff.
Machines can machines can do that machines can spin up another version or versions of themselves and just go and go and go.
And so if you have a model to maybe trained on how you write, that’s very likely to be a very successful way to generate a lot of content, a lot of language.
If you have a chat bot, that chat bot can handle as many customers as you have compute power for.
You can’t hire that many people.
If you’re a big company, you can’t hire that many people to cover what a language model could do in your call center.
When we talk about things like analytics, machines are large language models are bad at math.
They’re really bad at math.
But what they’re not bad at is language.
They’re very good at language.
And what we’re seeing now, particularly with tools like chat GPT is advanced data analysis feature is that they know they’re not good at math, but they’re good at language.
And so they can write in language aka code to have the code do the math, right.
So that’s sort of the workaround that these tools have discovered is they can create code in Python or R or the language of your choice, execute that code.
And get math solutions that they know they can’t solve.
And so we’re seeing tremendous usage of these advanced data analysis features in chat GPT to process data to look for anomalies in our data to to create algorithms and analyses that maybe we can’t do or we certainly can’t do fast enough.
The other day, I trust insights Katie and I have been working on trying to get reporting out of HubSpot for quite some time, right, we’re not paying the premier, like most expensive fee.
And so there’s a lot of stuff that in the interface isn’t available.
But there’s certain it’s in the API.
And so what we’ve been doing for a while is just trying to write the code necessary to basically build our own version of the reporting.
It was taking a really long time.
The other day, thanks to the GPT form model, and me knowing what I wanted it to do knowing what I wanted to ask it.
I knocked out like eight major reporting tasks that previously were going to take me four to eight hours at a time to do I got all eight done in a day, because the tools generate the language needed to make things happen.
And the last way that for marketing is going to be huge is true, actual personalization.
We have been giving lip service to personalization for 20 years, right? We’ve all gotten the email saying, you know, hi, for F name or high first name.
And the reality is, we don’t have the human scale to personalize not beyond real basic stuff, right? Hey, here’s a here’s an email creator for this segment or this persona in our database.
It’s not personalized, right? It’s it’s cohort based.
And yes, that can improve things.
Certainly, if you are someone who maybe you’re, you’re black, or you’re female, or you’re queer, or whatever, and you get communications that are at least addressing your segment of the market, it feels more personalized than Hey, F name, but it’s still not actually personalized because it’s not a one to one communication, except in the most personal businesses, right? When you go to the local diner, and the server recognizes you as a Bob, good to see you again.
That’s sort of the dream that marketers have had for personalization that is just not coming true.
But AI gives us that ability.
AI has the ability to with enough scale with enough compute power to do real personalization have a language model that remembers who you are that can pull your customer data record up and talk to you as you the person, not the customer cohort, not the group of the category, but but you.
It’s still very early days that a lot of companies have not figured out how to do that well, or at all yet.
But the potential is there.
The potential is there for you to log on to a chatbot on a company’s website, and have that chatbot say, Hey, Chris, it’s great to see you again.
Last time we talked, you were thinking about this new microphone system.
Are you still thinking about it? Because there’s other one that’s coming out now that maybe you might like better.
Or, hey, I remember you’re having that tech support issue to get resolved because if not, it might be easier just to buy a new thing, right? You can imagine that level of personalization in our marketing.
That’s where this stuff can go.
If we let it if we are careful about it, if we are thoughtful about it, and we are willing to do the experimentation to make it happen.
That’s the potential that we see in these systems.
So really good question.
There is no place where language or images are not used that generative AI cannot provide serious transformative capabilities.
So thanks for watching.
Talk to you next time.
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