This episode of the Marketing Smarts Live Show features a fascinating conversation with Christopher Penn, an authority in analytics, digital marketing, and AI.
Penn and host George B. Thomas delve into the ever-evolving landscape of AI in marketing, especially in the B2B sector.
Key Takeaways: Harnessing AI for Efficient and Ethical Marketing
- Ethical use of AI. Penn emphasizes the ethical use of AI tools, urging businesses to review AI outputs critically. He suggests integrating diversity, equity, and inclusion (DEI) committees into AI and marketing teams to ensure content is not problematic. For example, he mentioned the case of a vendor’s AI tool that inadvertently engaged in the discriminatory practice of redlining in Boston, highlighting certain geographic areas based on residents’ race or ethnicity.
- Generative AI. The conversation turned to generative AI, which is transforming how we create content. Unlike traditional AI focused on classification and regression, generative AI can produce new, unseen outputs from given inputs. Tools, such as ChatGPT and DALL-E 2, exemplify that capability, allowing the creation of original text and images.
- Practical applications. Penn shared practical ways AI can be used in marketing. For repetitive, moderate-value content, AI can produce first drafts for promotional tweets or blog post outlines, for example. He also shared a personal example of using AI to turn conference calls into coherent meeting notes and action items.
- Training AI. An interesting aspect discussed was how to train AI with specific prompts, similar to instructing an intern. For instance, Penn described how he directed AI to create social media strategies and content ideas.
- AI in production. The real game-changer, according to Penn, is integrating AI into production environments through APIs. Doing so allows for scaling up content creation and refining marketing strategies based on AI-generated insights.
- AI’s impact on careers. Penn starkly noted that AI won’t replace B2B marketers, but marketers who effectively use AI will outperform those who don’t. It’s a tool that enhances, rather than replaces, human capabilities, he said.
* * *
As we navigate this new AI-driven landscape, it’s clear that its ethical use, combined with human oversight, can significantly enhance marketing strategies. The key lies in understanding AI’s capabilities and limitations and integrating it thoughtfully into business—and marketing—practices.
Check out the video for more details, and the full podcast (link below the video) for the entire insightful conversation.
Make sure you don’t miss any future episodes: Subscribe to the Marketing Smarts Live Show on YouTube. And to catch up on all previous episodes, check out the full playlist on YouTube.
Episode Details, Guest Information, and Referenced Links
Episode No. 36
Guest’s social media profiles:
MarketingProfs resources referenced in the show:
“From the #mpb2b Community” links referenced in the show:
Transcript: A B2B Marketing Deep Dive on AI Foundations, the Future, and More, With Christopher Penn
Welcome to the “Marketing Smarts Live” show by MarketingProfs and the Marketing Smarts Podcast. Where we dive into B2B news, resources, valuable guest content, and much more each week.
If you’re a B2B marketer looking for a place to learn, keep up to date, and have some fun along the way, grab a beverage, a notepad, or at least some style of writing utensil, and welcome to the show!
There’s been a lot of chat about what AI can do—from image generation to automated social media copy, and then there’s the inescapable mantra of “AI is going to take our jobs!” (Spoiler: it won’t.)
But not many people have bothered to think about what it can’t do, or what problems could arise from the technology.
That and more in today’s Marketing Smarts Live show.
Hello to all my Marketing Smarts Live viewers today. I’m super excited to bring you EPISODE 36 of the Marketing Smarts Live show.
This week’s topic is all about A B2B Marketing Deep Dive on AI Foundations, the Future, and More .
So, if your ready to get your learn on, buckle up and let’s get ready to rock a nd roll.
Hey, I’m your boy George B. Thomas speaker, trainer, catalyst, and host of this hear show, the Marketing Smarts Live show as well as the Marketing Smarts podcast found on your favorite podcast app.
Our guest clips today are brought to you by none other than Christopher Penn.
Christopher S. Penn is an authority on analytics, digital marketing, marketing technology, data science, and machine learning.
A recognized thought leader, best-selling author, and internationally renowned keynote speaker, he has shaped five key fields in the marketing industry: Google Analytics adoption, data-driven marketing and PR, modern email marketing, marketing data science, and artificial intelligence/machine learning in marketing.
As co-founder and Chief Data Scientist of Trust Insights, he is responsible for the creation of products and services, creation and maintenance of all code and intellectual property, technology and marketing strategy, brand awareness, and research & development.
Now, remember, the clips of Christopher Penn today are pulled from the full marketing smarts podcast episode and, if you want to listen to the full interview with Christopher Penn and myself, make sure to tune into the Marketing Smarts podcast, link to the full show will be in the description below after the live show ends.
Now, in this episode, again, I’m talking with Christopher Penn about A B2B Marketing Deep Dive on AI Foundations, the Future, and More.
Now, I have to be honest with you. I had a plan going into interviewing Christopher but by the first question, I threw away my plan and just kept diving into amazing things that Christopher was saying.
So, that being said, this episode of the Marketing Smarts Live show might be a little different than other weeks’ you’ve tuned into.
Also, I have to throw in a huge disclaimer. AI is moving at a rapid pace. We have pulled in clips for today’s show that we feel will help no matter the date but, for an updated video on where we are now, I’ll be sharing a bonus piece of content from Christopher S. Penn at the end of today’s show.
OK, let’s get after this AI topic!
In this first clip, I asked Christopher, should we stop using AI for marketing?
Christopher then shared his thoughts on using AI, what it has to do with DEI, how DEI committees and marketing teams may need to work closer together as well as a couple examples of where AI went terribly wrong.
Let’s take a listen.
Christopher: It is perfectly okay to use these tools, as long as you, a human, are reviewing its output and going, “I’m looking for problems.” Ideally, within your organization, you have.
Everyone and their cousin has been ramping up DEI initiatives—diversity, equity, and inclusion. Aside from workshops and all the usual activities, games and role playing, your DEI committee should be on your content marketing committee to look at the content you’re producing and saying, “This is problematic.” Your DEI committee should be involved with your AI team, with your machine-learning team, with your data science team, to say, “Look at that. This doesn’t look like it’s working right.”
I’ll give you an example from the martech show I was at a couple of years ago. I saw this one vendor that said, “We will find you the perfect customers, the ideal customers. You just give us your data, and we’ll put it on a map, visualize it, and there’s your customers.” This is a B2C example. They put up a map of Boston, they put up Dunkin’ Donuts, and showed the city of Boston and said, “These red dots are your ideal customers, go get them. These black dots are not.” If you don’t know the layout of Boston, the southern part of the city are historically black parts of the city and historically less wealthy parts of the city. There were no ideal customers there. They were all in Cambridge, in the financial district, etcetera.
For our international listeners, if you’re not familiar, Dunkin’ Donuts is an American brand of coffee that I would mostly describe as milky weak coffee. The thing that is true about Dunkin’ Donuts in the city of Boston and surrounding areas is that the only people who don’t drink Dunkin’ Donuts are dead. Everybody drinks Dunkin’ because it’s cheap, it’s everywhere, and it’s good enough. For this company to have created this map saying there were no ideal customers in the black parts of the city is a load of what our Spanish friends would call excremento de toro, this is just completely untrue. Everybody drinks Dunkin’ Donuts.
So, this software which was built with statistical data, census data, stuff like that, machine-learning based, produced a phenomenon called red lining. First coined in the 1930s in the real estate industry and the insurance industry where people would take maps of the city and draw red lines around the parts where they didn’t want to do any business. Again, historically black or minority, historically poorer parts of the city. No one stopped to say, “That looks weird, that doesn’t look right.” Someone on a functioning DEI committee would look at it and say, “You just reinvented red lining. This is really bad. Maybe we should turn this software off.”
Another classic example, back in 2018, Amazon created a predictive algorithm to screen LinkedIn profiles for ideal candidates to reduce the delay in hiring engineers. They turned it on and stopped hiring women immediately. Just stopped. Why? Because they trained it on all-male developers, and as a result, it learned that characteristic. Of course, Amazon got a big black eye for this because it was really obvious immediately. Nobody stopped to ask, “What could go wrong?”
To answer your question, no, we shouldn’t stop using these systems, but we absolutely need human beings throughout saying, “What could go wrong?” You need to leverage that investment you’ve made in DEI to have those folks in particular saying, “What could go wrong? It looks like that went wrong.”
Holy cow, those examples are crazy.
For you, I hope you heard that very first part…
It’s perfectly OK to use these AI tools as long as you the human are review the outputs!
Please, make sure you are doing that so, you and your company do not become the next AI horror story in the future.
Are you using AI in your marketing yet?
Put the answer to that in the chat pane or, let me know on Twitter using the hashtag #mpb2b and of course, tag me using @georgebthomas.
We’ll get back to Christopher Penn and his thoughts on B2B Marketing Deep Dive on AI Foundations, the Future, and More But first, I have to ask…
Are you part of the MarketingProfs community? If not, become part of the MarketingProfs community by heading over to mprofs.com/mptoday – That’s mprofs.com/mptoday.
Now, it’s time for one of my favorite sections…
In The B2B News – Where we talk about breaking B2B news or really important tips we find on the google news tab related to you and your B2B business. This week, the title is…
What do AI and ChatGPT mean for humanity and for B2B Marketers – short and longer-term? (Part I) by Sue Mizera.
Promethean moment? Apocalypse? The latest over-hype? Sue Mizera reflects on the ever-changing tech landscape and if these new developments are a scifi dream, dystopian nightmare, or the latest bandwagon.
This article is a stop and make you think type article.
With statements like…
Will it ever discern truth from falsity? Not currently, but theoretically, yes, with new cognitive developments, hybrid systems, and greater integration with other systems.
(The more data we have, the more diverse the sources, the better the model becomes; and the more emergent behaviours develop, e.g., its learning how to code –which still mystifies researchers.)
or even statements like…
Eventually, could AI systems be hacked, causing wild disruptions in businesses, countries, financial and energy systems? A terrifying possibility. Will it change things—life, work, leisure, study, companionship and relationships, as we know them? Almost certainly.
Take the simplest example—as ChatGPT can integrate with other systems, like TaskRabbit, we reportedly can all soon have personal assistants to handle our correspondence, make our appointments, maybe even do our taxes.
So if you want to dig in and read this article, check out the link below when the live show is over.
So let’s get back to Christopher Penn and his Marketing Smarts podcast episode.
I asked Christopher Penn, how the heck did we get here so quick?
Fun facts, I learned that we got here rather slowly but, Chris shares what the heck changes and why it now seems that we got here fast.
Get your notepad ready because he shares some history and tips to use as well.
Christopher: We haven’t gotten here so fast. We’ve been talking about AI for 70 years. Many of the algorithms and things that are in use today were developed in the 1950s. What’s different today is the compute power that we all have. We carry these literal supercomputers in our pockets that allow for a lot of these capabilities.
The conversation has changed, in the last four years especially, because of an architecture called Transformers. Not the awesome 1980s toys, but these AI algorithms. Without getting into the technical bloody guts of it all, essentially these Transformer-based models, which incorporate things like large language models, like ChatGPT for example, allow us to do what’s called generative AI.
There are three different basic classes of AI, three use cases.
There’s regression, which is I have a whole bunch of data, find me something with an outcome, find me things that look like the outcome. This is the principle behind things like recommendation engines. When you fire up Netflix and it says, “You might also enjoy,” these eight shows that are exactly like these other eight shows. When you’re on TikTok and you look at dogs in ballerina outfits, for some reason your entire TikTok feed is all dogs in ballerina outfits, that’s a recommendation engine, that’s regression.
The second major category of AI is classification. This is where you see a lot of companies doing stuff with voice-of-customer things. Bringing in billions of social media updates, bringing in phone calls, interviews, call centers, just classifying what’s the in box. Of the last 20,000 calls we’ve gotten to our call center, what are the five main topics that people are complaining about, and having machines be able to digest that.
Those two things, regression and classification, have been part of AI and have been deployed in production for years now. When you look at your marketing automation system and your CRM, and you see things like automated lead scoring, that’s what is going on there. When you use a piece of software like Demandbase that’s making recommendations about content somebody should see, that’s regression algorithms. It’s very straightforward. I had a chance to chat with a weather chief data scientist, and again we got into the bloody guts of which algorithm they should use, and it boils down to regression stuff, and the software is very good.
What has changed in the last five years is generative AI. This is when you say I want AI to start transforming inputs that I give it into outputs that maybe have not been seen before. In the last six months when people start to catch on and start to understand that generative AI is capable. This started with things like DALL E-2, which is an image generator, and then Stable Diffusion, an open source model. Suddenly, you saw an explosion of people making computer generated images of dogs on skateboards in outer space and fun stuff like that. Generative AI.
Then in the last three months, the rollout of ChatGPT, which is a chat-based interface to a language model called GPT 3, which the current version has been on the market for about two and a half years. People who are in the know have been using it very successfully for the last two and a half years. A lot of these big companies that are generating content in an automated fashion are using that. We wrote some software to connect to it and we do predictive blog posts stuff with it.
The chat version is something that the completely nontechnical user can get behind and say, “I know how to chat, I don’t know what temperatures or P scores of soft max layers are, but I know how to chat.” Now with DALL E-2 and Stable Diffusion people go, “I can make pictures of my dog’s breed in knight’s armor on a horse.” Now they can say I can have this thing write me blog posts, or social media updates, or rephrase the lyrics to Gangster’s Paradise to be about B2B marketing. You can do things with these large language models. That’s what has changed.
What’s coming in the next few months is enhancements to these models. The models are getting larger. Open AI has said on their roadmap for 2023 is GPT 4, which will be about eight times the size in terms of what GPT 3 is, which means more natural conversation, more realistic outputs, harder to detect machine generated versions. All that is coming.
The use cases for these things are going to dramatically multiply. We just did a whole livestream on some of the different use cases. I’m gathering a whole collection of them for the fourth edition of my book because what you can do now is incredible.
I’ll give you a real simple example that’s a huge time saver for me. I record our conference calls, as many people do, with the disclosure that the call is being recorded. Then I have one AI, Otter.ai, transcribe it. It’s full of ums and uhs and all his filler talk. Then I feed that to Open AI and say summarize this into meeting notes and action items, and it gives me two paragraphs, and we’re done. I don’t need a VA, I don’t need anything else, I just have meeting notes and action items. The action items go right into my to-do list and every client call, I don’t miss a thing. That is one of the simplest use cases. It saves time, it saves money, happier clients.
Did you hear that?
Generative AI – Transformers! That’s what has changed.
ChatGPT making it easy for normal humans to easily harness the power without knowing any code!
The nontechnical user can get behind the use of AI. And BOOM here we go!
Also, who else wants to go play with the B2B gangster paradise lyrics?
Sounds like fun to me!
In all seriousness, we’ll get back to Christopher Penn in a few minutes but first it’s time for some…
Dope B2B Learnings From The Vault of MarketingProfs Articles
That’s right, it’s time to dig into the treasure trove of valuable information and pull out two pieces of gold to help you be a better B2B marketer.
Article one this week is: Marketers Know AI Is the Future, But Do They Understand AI Today? by Tod Loofbourrow
Here’s a quick reality-check for the next artificial intelligence (AI) pitch you hear: Ask what the company’s solution optimizes for. If the answer is along the lines of “anything you need,” that should raise a red flag.
AI doesn’t work that way, but it’s ad tech’s favorite new buzzword, so you can understand why marketers say they’re prioritizing a technology that few understand.
Article two this week is: Marketing at the Speed of Thought: AI Use Cases for Four Content Types by May Habib
Your marketing team’s ability to dream up a campaign—or, more accurately, type it up—and automatically create first drafts of copy, images, video, and audio, is no longer science fiction. Thanks to generative AI, marketing teams across all sorts of industries are transforming how they do business.
Two major characteristics of AI are dramatically faster speed of execution and the substantial potential for deep personalization across four main media types: text, imagery, video, and audio.
Want to take a look at each one? Want to keep learning more? If so, check the links in the description below after the live show to get access to both amazing MarketingProfs articles.
OK, back to Christopher Penn… Let’s dive back into this conversation of A B2B Marketing Deep Dive on AI Foundations, the Future, and More
In this next clip, I asked Christopher what type of content or work should we get AI to use? He shares right up front the type of content and then shares some great examples!
Pro Tip: Listen to how he trains the AI on what he wants!! It’s so important in my personal opinion. #ThinkInternInstructions
Take a listen.
Christopher: Any content that is repetitive and of moderate value is stuff that you can hand off to an AI, at least for the first draft. I’ll give you a real simple example. Again, in the large language models. I have a prompt that is prewritten. In fact, let me pull it up here just so I can read it out loud to you. It’s pretty straightforward.
It says, speaking to the machine, “You are an expert social media manager, you are skilled at crafting social media posts that garner high engagement on services like Twitter, TikTok, Instagram, and LinkedIn. In your capacity as a social media creator, you will create promotional tweets enticing audience members on Twitter to download our new e-book. Here are the details of the e-book. Here’s the URL. Here’s what the e-book is about. Here’s an abridged table of contents. Write 10 tweets using the above details, promoting the e-book and encouraging people to download it. Use the details provided for content and benefits as reasons why people should download it. Follow the technical specifications carefully.”
I put this into the large language model and it spits out 10 tweets and a hashtag that has the kind of language that gets engagement, that avoids things I tell it to avoid. I copy and paste this, then take it over to AgoraPulse, drop the CSV file in, and my promotional tweets for the week are done.
I then say to the language model, “Give me 10 Instagram ideas. Here’s the format, suggested photo, accompanying caption.” It spits out a photo of the e-book cover, caption, “Have you tried downloading this?” So on and so forth. Again, this is all language models.
The way that I think people should be thinking about this if you had a new intern on staff, just got them from the temp agency or whatever, what instructions would you give them to do a fairly simply marketing task? Write those out. That is the prompt that goes to the machine. Then the machine does it, and you QA the results and say it’s ready to go.
I was doing an experiment with some fiction writing last night. There’s this one writing group that I’m part of where they have a monthly contest, and this month’s contest the prompt was dream. There’s three restrictions and three bonuses if you do this. I wrote all that out as a prompt and said, “We’re going to write this story in four parts, 750 words each. I want you to write the outline for the story first, title each part, and then write each of the parts.” In about 15 minutes or so, I had a 3,000-word story, and I submitted it.
It was coherent. Was it great? No, it wasn’t great. It was stuff that you’d see in a lot of very similar stories. But I didn’t have to spend three hours writing it, I got it done in 15 minutes. Think about that for your blog. If you have a blog post that you know you have to write a good first draft, you say, “I want you to outline this. I want you to write me a social media strategy for this. I want you to write me a TikTok strategy.”
I did one the other day, because Katie and I are often talking about we’re B2B marketers, what do we do with TikTok. I said, “Here’s info about our company on all these things. Build me a TikTok strategy, give me 10 TikTok video ideas appropriate for a B2B marketer.” It came up with these 10 ideas. Four of these are actually good ideas. Six of them not so much. Guess what? We’re going to start trying these things out.
So, for things where you have questions like, “What should I do with this thing,” these are all good starting points. I think for B2B marketers that are sitting there, you’re stressed, you have 82 things on your to-do list and another 20 are going to come on tomorrow, it’s a way to speed things up for the ideation phase and for the refinement phase.
One other thing that I love to do, and I’m almost hesitant to tell you this… I go to a lot of conferences and things, and because of the pandemic and stuff, when I can I just drive if it’s within driving distance. I have these fantastic P100 masks that are biowarfare masks and they work great, but if I can spend seven hours driving and be in the comfort of my own car, or three hours on a plane packed in like a sardine, I’m going to take the seven hours. I have a little audio recorder, I just plug it in, and I dictate freeform while I’m in the car just thinking out loud, or I listen to a podcast and yell out loud into the recorder.
Then I take the transcript and put it through one of the large language models and say, “Rewrite this with correct grammar, punctuation, spelling, syntax, and formatting.” These language models are OK at creating, but they are fantastic at transforming, at rewriting, at distilling. I can take a one-hour conversation I’ve had with myself and turn it into 10-15 pages of clear coherent content that is me. It’s still me, it’s not the machine. It’s my words, but refined, coherent, logical.
Suddenly, I’ve solved my content marketing problem because I’m using the time that I have available to me. When you are going to the grocery store, you have 10 minutes in the car. Fire up your audio recorder. Feed it to a machine and clean it up, and boom, you have more content than you ever knew what to do with.
Oh my gosh! The fiction writing example made me chuckle.
But, the power to take that and add great to what was good in 15 minutes makes me wonder about AI as starting lines to get to great finish lines faster.
Twitter, Instagram, TikTok, AI ideas to get you going! Are you going to test these for your own company later today?
We’re going to get some last words for today’s show from Christopher Penn here in a few minutes but right now it’s time to turn the spotlight on you, the MarketingProfs community. Yep, time for…
From The #MPB2B Community
We searched far and wide in the #MPB2B universe to find amazing information and conversation to bring to the masses.
So, first, make sure you are using the hashtag, and second, make sure you have fun and add value to the community.
Then, we’ll spotlight you or your crew on the show. This week, it’s…
Stephanie Totty – SaaS Marketing Leader | Advocate for the Oxford Comma | Startups are my jam ??
Her LinkedIn post goes a little something like this…
Really thrilled to announce that I’ll be speaking at MarketingProfs B2B Forum this with a breakout session on: Creating a Content Map: Key Audiences, Messaging, and Your Go-To-Market Strategy ??
Who’s joining me for lobstah in bah-ston this October?!
You need to check out the description and click that link to check out the post and the B2B Forum Speaker lineup read or learn more by clicking the links in the description below after the live show is over!
Marketing Smarts viewer, I have to ask… are you going to be next to get the spotlight?
Remember community, use the hashtag #mpb2b on Facebook, LinkedIn, or Twitter and get the light shined on your awesomeness in the next episode or a future episode of the Marketing Smarts Live show!
Pro tip, it won’t hurt if you tag me into your post as well I’m @georgebthomas on LinkedIn and Twitter.
OK, let’s kick it back to Christopher Penn and some final words for today around this topic of B2B Marketing Deep Dive on AI Foundations, the Future, and More.
I asked Christopher, what should marketers be thinking about as they head back to their regular day?
Here’s what Christopher Penn wanted to share with you!
Christopher: The thing to think about with all this stuff is right now people are experimenting, they’re playing with it, which is awesome and what we want. You have to think about how you put it into production. All these tools have APIs, application programming interfaces, that software can talk to.
I’m going to give you a simple example. I have an SEO keyword list. I can take the search volume for that and I can use predictive analytics, machine-learning, to forecast when in the next 52 weeks that term is going to be searched for the most. Pretty straightforward. This is old math, this is not new stuff. If I have a keyword list of 800 keywords, which I do for my company, I forecast all 800 to figure out which each week are going to be the top five keywords for that week. That used to be how we would figure out our content strategy.
Then ChatGPT and the GPT models came along. We played with it, and then we looked at the little code button and said now we can put this into production. Now what we do is we take the top five keywords every week and feed that to the AI and say, “Write me five blog outlines for the week.” Now I have the top content for that week in prewritten first drafts that I can then hand off to a writer to clean up.
I’ve gone from write a cool prompt and make the thing do something to putting it into production where now it scales. Instead of one blog posts or 20 tweets, it’s 200 tweets or 2,000 tweets. That is what is going to set apart the winners from the losers in this.
AI is not going to take your job. If you’re a B2B marketer, AI is not going to be a B2B marketer. But a B2B marketer who uses AI is going to take the job of a B2B marketer who does not. That is the end of the game. If you are a marketer who is not using these AI tools, you are in danger, your career is in danger, because other people who are using these tools are operating better, faster, maybe even cheaper, and can get more done than you can just by the nature of these tools.
That is my parting words of wisdom. A marketer who uses AI will beat out a marketer who does not use AI. It is like the first time that a basketball player put on sneakers, suddenly the game has changed.
So many great examples, so many things we can do. What will you choose?
Make sure to let us know!
Think about what Christopher said. AI won’t take your job but other B2B marketers who use AI just might!
The game has changed, ladies and gentlemen, the game has changed.
Have you enjoyed today’s journey? Let us know, use that hashtag #mpb2b on whatever platform you’re joining us on.
Also, remember that big fat disclaimer about things changing so fast? Make sure you look in the description as we have added an update from Chris to the resources so you can stay up to date!
