#AIForMarketing With Pam Didner
AI for sales is still a bit of a nebulous term. Yet, some companies have undeniably utilized AI for their marketing and even reaping the results they wanted. Chad Burmeister sits down with Scott King, Director of Pipeline at Zimperium, to talk about how Zimperium leverages AI for marketing. In particular, Scott talks about Moneyball marketing, defining for us what it is and how they use it with AI for automation. Taking us into his life, he also shares his journey into the industry and how he stays on top of his game.
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#AIForMarketing With Pam Didner
I'm here with somebody, who is amazing. I think of her as the AI for marketing expert. This is Pam Didner who is the keynote speaker at B2BMX. Pam, welcome to the show.
Thank you for having me. It’s a nice intro. I appreciate that. It made my day.
I am glad you're here. I've shared your name at least a dozen times since I saw you at B2BMX.
I love you. Thank you.
We are going to be talking a lot about AI for marketing. If this is your first here, we wrote the book on AI for Sales. When I met Pam at the conference, she said, “I bought your book in preparation for the keynote.
Yes, I did. I bought your book and I read it. That was nice.
It has 21 chapters. It starts from data and it ends at AI for social selling. Each chapter is dedicated to a specific niche of how companies are using AI in different categories. That's the sales side. I think we should combine it with the marketing side.
I love to talk about the marketing side of AI.
You have a daughter, you said at the conference.
I have a boy. From my perspective, he can be a daughter as well. We shop together and both of us are shoppers. I'm not necessarily a buyer, but he loves to shop so here we are.
Everyone always asks me, “What is AI for sales? What is AI for marketing?” You did such a good job of opening up and saying, “I asked my kids. I asked my son, ‘What do you think of artificial intelligence?’” Share with everybody, what did you find out from that conversation with that group of people?
Chad brought a very good point. I had a party and I have multiple people there. The demographic is from 20 to 50 or 60 years old. I asked them specifically about what comes to mind when I say AI or Artificial Intelligence. The majority of people have a Hollywood version of the AI like Terminator, Black Mirror, which is a very popular Netflix show. Some of them mentioned Janet. She is a virtual bot from a popular Netflix series called The Good Place. Some people mentioned Samantha from the movie, Her.Everybody was mentioning different things and as a B2B marketer, I segment them and give each one of them a persona. What I have realized is multiple people are incredibly pessimistic. When I mentioned AI, they immediately went to the dark side, “Are you talking about the Terminator or the AI eventually is going to take over all jobs?” and that kind of stuff. There are people that are incredibly optimistic. They’re talking about R2-D2. They were talking about Data from Star Trek. I was like, “This is great. They are my tiger.” There were several people mentioned Bender. If you haven't seen Bender, it's a robot from Futurama. He’s incredibly selfish, bitter and self-centered. There are several people I called Bender. It was that genre in terms of when people were talking about artificial intelligence. The things that come to their mind is like, “The Hollywood version of it.” What I did at the keynote was, “That's great, but let’s bring it down one notch and then talk about what AI capabilities can do for marketing.”
The thing that tipped for me, I sat in the front row. Pam went about fifteen minutes overtime and I was like, “This is great. It was like U2 is coming out for an extra song.”
I stressed out Andrew Gaffney, who is the event organizer for B2BMX, during those fifteen minutes. He was ready to kill me.
Nobody walked out of the room. It was highly compelling. There were two big tipping points for me. The first was when you have the picture of the human shaking the hand of the bot and you said, “Who's wearing the pants in the relationship with the AI?” The AI can start to tell you, when do I wake up in the morning? When do I brush my teeth? I didn't hear you mentioned that I keep getting notices on I'm a skier. There's an AI that'll go in the bottom of your boot and it will tell you even while you're skiing, “You need to edge more. You need to lean back.” The AI’s telling you, but I'm ready to relinquish that level of control too. If I can ski better, I'll spend the $250.
That's one thing about artificial intelligence. In a way, it's not necessarily a physical robot. It's software or machine learning algorithms that are embedded in the software. Think about everything that you do can have some software embedded to it to make that device to make that tool a little bit more intelligent. You brought a good example, Chad. It’s basically like, “Boots are boots.” If you add the piece of software and they link that to say you'll phone with an app and to have a machine learning algorithm built into it, all of a sudden, it becomes smart.It's a smart device that will gather information about how you behave and how you act. Hopefully, through the algorithm, assuming that it is written nicely, it can pick up the pattern or the behavior that you do things and help you to optimize it. That is the essence of artificial intelligence in this world. It’s software or the machine learning algorithms that are built into devices and help you make your life better and more productive. It's not about taking over the world. Maybe one day, we don't know, but it’s about the software.
When you walked people through, narrow AI is what we're living in now. Help everybody to understand, what's narrow AI? What's the next wave after that? There were three waves if I remember right.
In general, they are three types of AIs. We loosely categorize it. They have something called a narrow AI or a weak AI. If you see people with say weak AI, narrow AI, or artificial narrow intelligence, it is the AI that can do a specific task competently. An autonomous car, driving the car, that's a specific task. Theories to answer your questions is doing that specific task as well. Google Translate is doing one specific task and do it very well. The machine is intelligent enough to modify their behaviors when situations change. That's on the narrow side.The next level is called the general. It's called artificial general intelligence, strong AI, or human-like AI. These are the AIs that can perform intellectual tasks that humans can. They can understand its environment as humans would. The next level is super. It's called ASI or Artificial Super Intelligence. That goes beyond humans. They exceed humans in every possible way, including scientific, creativities, general wisdom, and social skillset. That sounds far away, but you never know. Technology accelerates and that keeps Elon Musk up at night.
The one thing that I heard you say in there and I can't remember how you phrased it. The thing that always interests me at the AI events that I go to, non-sales and marketing related, just AI events is the ethics panel that they always have. It’s a trolley car dilemma. The trolley car dilemma says you're the conductor on a train. There are two paths to the track. You need to choose left or right. On the right-hand side, there are five people over there. On the left side, there's only one. If you're a human and you're the conductor, you know there's about to be a tragedy if you choose five or one. It’s pretty simple. You then start to throw in pieces of information.
The policy and the law always come after the technologies develop.
The five people are wearing orange jumpsuits and it's outside of prison. The one is a student and they have a cap and gown on. It's a sad conversation, but at the same time, it's like if you start to hand over the keys to these types of decisions that involve ethical decisions, who gets to sit on top of it and program those decisions? There's going to need to be ethical committees and boards. There'll be a chief ethics officer at big companies, Fortune 1000. It's going to be an interesting next decade.
This is not just for AI. If you think about technology in general, the policy-making or the definition of law and how that has changed. Have you noticed that the policy and the law always come after the technologies developed? For example, how Facebook is used during the 2016 election. There was nothing to guide that. Even with the internet, freedom of speech. How do we guide that? For the longest time, if you live in a specific state, you pay sales tax, but what about if you buy stuff on the internet? For the longest time, there was no guidance in terms of how to pay taxes until the technology was developed much further down the road.That also applies to artificial intelligence. As we figured out as a society collectively, when we encounter those technologies, there will be issues that we have to deal with. I don't think that's different than any other technology we encounter in the future. We are creating another bean, but even though we cannot see it, but they are making a decision on behalf and how do we make sure that decision is made with human’s best interest in mind. I don't have an answer to that, to be honest with you. The only thing I know is as we encounter, there will be a discussion. There will be guidance. There will be a law that made it available to it. Unfortunately, it always comes after certain things happen.
One example is if you're interviewing candidates and there's bias involved in that process. I've had experience with a certain type of individual. I interviewed that individual and I make decisions. I go, “This particular name, I know that already.” I don't even need to read the rest of the resume. I made jumps to the conclusion. AI can be positive in that sense because it can bring fairness, read the words on the page and the history, and say, “This is a 98% of everything that you're looking for based on the words on the page.”
It’s yes and no. I do agree with you. If your machine algorithm, also the data that you feed into it can mitigate the bias, yes, they can make a recommendation with the bias being minimized. However, I don't know if you remember, it's probably a few years ago, Amazon scrapped its AI recruiting tool. That's because that tool engenders bias. The thing is they use the resumes that Amazon received in the past several years and use that as the data to feed into it. In the past several years, the majority of people that apply for jobs at Amazon are male. Using that data to feed into the algorithm creates a bias, in terms of AI think the females are not necessarily competent to do the job. They screen out the females at the initial round.
These are complex decisions and this is where the human jobs are not in jeopardy.
In the time being, there still a decision-making process or sorting process that's still needed for us to make a decision, when we get information from AI.
There's a company I heard about that it starts with an M. It ends with AI. They're in stealth mode so I'm not going to name them. They integrate into Eloqua. They will automate A/B testing across using machine learning and natural language processing to go read all the emails and then make the decision on which AB test to run without a human required to make that AB test decision. Subject lines, length of emails, all of the things that go into the email of this company. This is a person who's had three successful exits, multibillion-dollar companies. I have to believe there's a dare there behind what's going to come out to market very soon.
Another thing I mentioned in my keynote is that a lot of media companies, they are building homegrown tools. They call it AI reporter or robot reporters that if you feed them information or you feed them breathe and hook them with the keywords and the key phrases. Build it and program that they are going to source the trusted sources, they can write content. On the B2B side of things, a lot of times when we write a white paper or when we write any earnings report, for example, the outcomes are predictable. You go below the guidelines because of certain wordings that you choose to use for these three different scenarios.Even for elections, this candidate would win or the other candidate. There are specific words associated with each candidate’s potential winning. You can predict or anticipate what that content will look like and feed the keywords into the AI and then have the AI write that report for you. I can see that. For email communication, that same analogy applies. Not necessarily consumer or creative, in the business world, the way we write emails, the way we communicate a lot of time is predictable. We are not going to go, “We are going to see a demo. We are not going to see a demo.” We want to consume this piece of content or this piece of content is maybe interesting because we consumed these seven pieces of content in the past. There is a way to probably predict in terms of what you can do next. Having that entrepreneur to launch a specific platform that can help you try to email, that doesn't surprise me.
I know a guy in Boulder who had a team of eight people doing SEO for companies and now he has himself. He doesn't pay the people. The AI, he feeds the keywords in and it's all automated. He said, “Chad, my results are better for my customers.” They're going to be a $2 million company. They grew from 0 to $2 million in a year.
AI For Marketing: In the business world, the way we communicate a lot of times is very predictable.
At the same time, you still need to continuously monitor the results. You cannot leave it run.
The reader can start to figure out. It's almost like a crossword puzzle. When you get it and you start seeing the keywords, you might be able to start filtering that stuff as a human over time. Let's go through a couple of questions. You consult with organizations. Is AI at the top of the forefront right now or what's your core practice? How do you help companies?
To be honest with you, on the B2B side, it depends on what they do. AI is not necessary a key initiative in the ad driving. Many companies are still struggling to get data right. The prerequisite of AI is you need to have a clean and high-quality data. You need to make sure that's there. Many of the company, they tend to have legacy systems, especially the enterprises. They’re still working through the data. However, they are looking into AI as sourcing third-party platforms like your platform. For example, it has AI built into it. It’s not necessarily doing the homegrown type of a product, but they are using multiple different third-party tools to test and access the AI capability. The perfect example is Einstein in Salesforce. Many companies are using that. The way that the people are using AI is not necessary they create homegrown tools. They are not necessarily there yet, but they are using third-party tools and take advantage of it.
What I gathered from that is that you're saying, “Commercially available products may be where it starts. Let’s dip our toe in the AI water.” As I start seeing that, now I'm the CIO of an organization that sells, maybe it's Merrill Lynch, maybe it's a real estate company. They might try something and say, “If I go build that, that could give me a huge competitive advantage.”
The way I suggest people go about it is not necessarily like built AI for AI's sake and identify a specific audacious question you want to solve and then think it through in terms of what databases that you need to use, and what model you should build to solve that problem. Using a specific question, very audacious questions that your analytics platforms with data cannot help you. You need to integrate different databases. You need to gather the data and feed it into some model building and use that to start.
Think about you're a marketer, you spend $1 million a year on leads. You give it to your SDR team. Maybe they're advanced and they call the lead 5 to 6 times. They have a cadence. They do 3 to 4 emails and they do some LinkedIn. That's still not the norm. It's moving towards the norm. Let's assume that occurs. They might talk to 10% to 15% of their leads. They have a meeting of the X number per year. This company that was at B2BMX, did you get to talk to Kronologic?
I didn't. I missed that one. I tried to talk as many technology vendors as possible.
That's why I like to go to marketing conferences and sales conferences because you never know who you're going to run into. Let's say a lead comes in and it's a white paper download. I'm ready to buy, but I’m a lukewarm hand raiser. The traditional process is, send you an email, thanks for the white paper download, call you and do all that stuff. What would happen if the AI automatically at a certain point in the cadence or maybe even an hour after you download the white paper, what if the AI sent on my behalf a calendar invite to you to me? It was pleasant, “Pam, thanks for downloading this. I'm very interested in talking with you because I want to be there for value.I'm not there to sell you anything. This isn't a sales pitch. This is, ‘Let's talk. I’m curious to talk with you.’” They claim a 3X to 5X lift in meetings because, we as humans, some people are good at inviting people to a calendar meeting. Other people never ask for the meeting. When you can hand that over to AI in a smart, intelligent way, they showed how the coolest part was they monetize the value per meeting. They can do some advanced routing to say, “If you give it to Chad, he's only going to get a $300 average meeting value. If you give it to Bob over here, he's at a $2,400 value. Let's go ahead and put the calendar meeting on Bob's calendar instead of Chad's because Bob's eight times more effective on a dollar per meeting basis.” It’s a pretty advanced level AI built into that platform.
It's lead sorting. They sort the leads based on some of the inquiries or some information that you entered into the system.
They automate the calendar invite. Most of the time, you have to go to your calendar and do it.
There's a sorting process that's still needed to actually make a decision when we get information from AI.
You have to click on it. I do that every day. I need AI to help me out.
By the end of 2020, AI will be to the rescue. Two more questions, since we've talked a lot about the meat of this already. Let's get to know Pam. If you go back to your college days, what did you study in college? What were you passionate about then?
I didn’t study anything I was passionate about, to be honest with you. I was studying and trying to get a job. I graduated when the time was in depression. I was an accounting major. I was a CPA. I am a Certified Public Accountant. I got a job with the Big Six, it's a Big Four now. I was a corporate auditor. I very quickly realized accounting is not something I want to do for the rest of my life and I moved on to different jobs and try different things. I was a late bloomer. I didn't know what I wanted to do when I grow up. I still don't know seriously, but I follow my heart along the way. I was very lucky to join a big company called Intel. At that time, they encourage people to move around. I was able to move on finance and accounting to project management to manufacturing, to operation, to product development, and eventually moved to marketing and then marketing strategy. I was lucky enough to be with one company but probably move around about twenty times. I was very fortunate and grateful to see how a big company runs. That was probably the best experience I had.
This next question changed the trajectory of my life. I went to this conference. It was called Rich Litvin Intensive. He teaches these coaches how to be life coaches. He would do this. It was about an eight-minute conversation. Sometimes it went up to twelve. He goes, “What did you do college,” and then when you go back and he would use the number six, “When you were six, what were you passionate about?” The reason this matter is because in life you get parents and teachers and everyone else puts these filters on you that cause you to believe that accounting's the right path. If you can go back to when you're six and your eyes light up and go, “What I love to do,” what is that?
I can barely remember what happened yesterday and you are asking me what I was passionate about when I was six?
I remember being in my kitchen in Colorado with a glass of water. There was a wood floor and my friend and I was always messing around. We were like 5 or 6. We'd try to spill the water on the wood floor. We were mischievous. I was a rebel at age 5 or 6 but then I became the best rope climber in all of the elementary school, every year for six years running. It’s always stayed on the wall. I need to be competitive in life. No matter what that is, I feel like if I wanted to go be a Tiger Woods golfer, I could have probably done that if you hired the right person and did the right stuff.
I was bossy when I was little. I always tell people what to do. I don't know if that has a lot to do because I was the oldest child. I have two younger brothers. I always like, “No, you guys should do this.” It started even early on. When we play any game, with older children together, I always want to play the teacher. I want everybody to be students. That doesn't mean like I'm cocky and all. I’m like, “You don't know why you are doing.” That's not my point. My point is that I started early young, I see myself as a coach, teaching people. Interesting enough that I left the corporate world and I wrote two blogs and a lot of stuff I do in addition to doing keynotes, that's a separate thing. I have a completely different persona on stage, but what I'm very good at is doing workshops and doing training. I like to take complex ideas and then make it very simple that people can understand. If money is not an issue, I want to be a teacher.
If you can connect that dot, what I found was most of the time people in the room didn't realize that the job they're in now lines up nicely to what they thought they would do when they were a kid. If I were to say your story, here's Pam. She went to school to be an accountant. At age 5 or 6, she was the oldest sister of two. She likes to direct and make everybody get together and people appreciated that because Pam was knowledgeable. She helped make everybody feel good about being part of the Pam train. She goes to be an accountant. She learned a lot, but it wasn't the best thing for her. She got through. She graduates. She went to Intel, a big company. She got to try on a lot of different jobs. What she found her passion was exactly back to when she was younger, teaching people, that's what lights her up and now she's the AI for marketing queen.Thank you for summarizing my story in less than two minutes.It's fun to get to know you, Pam. I've enjoyed the conversation. We need to do a keynote together on the road here.
AI For Marketing: A prerequisite of AI is you need to have a clean and high-quality data.
You do sales. I do marketing. Let’s get on the stage, drive that message to the ground and bring the house down.
Thank you, Pam.
Thank you so much.
We are out. Kronologic, makes sure to check them out. They're a pretty cool tech.
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About Pam Didner
Being in the corporate world for 20+ years and having held various positions from accounting and supply chain management, marketing to sales enablement, she has a holistic view of how a company runs. She thinks strategically and then translates the big picture into actionable plans & tactics.Digital marketing requires marketers to think beyond their roles and responsibilities and connect various online and offline dots. She can not only do that but also help your marketing enable your sales team!
