EP 115: How To Make AI Work For Your Product Marketing

This is the Everyday AI show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business and everyday life.

How can AI change the way that we use products and the way that they're marketed? You know, we don't really see how products are built until we really use them or, you know, until we experience them. But we're going to talk about that a little bit more today and a lot more on everyday AI. This is your daily live stream podcast and free daily newsletter helping everyday people like me and you not just keep up with what's going on in the world of AI, because there's always a lot, but how we can actually use it to understand what's going on in our world, to grow our companies, to grow our careers. That's what everyday AI is all about. Thank you for joining us. If you are joining us live, maybe we'll have fewer hiccups than yesterday. We'll see, you know, a lot of connection issues, but hopefully this is great. If you're listening on the podcast, check your show notes. Come join us live. It's a great time to be able to ask questions of the experts that we bring on in different categories all across the business spectrum. All right, before we bring in our guests, let's first take a quick look at what is going on in the world of AI news.

There's a lot.

Here we go. So Mr. Beast. Yes, there's Mr. Beast news that has to do with AI. So Mr. Beast is calling out tick tock and AI so mix. Mr. Beast is probably the world's most famous content creator on YouTube and he alleged that TikTok allowed an AI deep fake version of himself in an ad. So a different company used an AI deep fake version of Mr. Beast ran it in an ad. And Mr. Beast is the latest in the line of celebrities even this week that have been warning against this technology as multiple other actors have seen their AI deepfakes use without their permission in advertising.

Not.

Not a good look. Advertisers, what are we doing there? All right, next piece of news, which isn't surprising, but jobs and AI are growing at a very fast rate. So new LinkedIn data shows that job postings mentioning AI have more than doubled in two years. Which is fascinating because it's not just jobs in AI and working in AI, but jobs in completely, you know, different, different categories that are needing and requiring AI skills. So if you're listening, it's probably a good thing that you're listening because we are building skills to, to make us, you know, better employees and to help build us better companies in the future. Last but definitely not least, Anthropic making more news and looking to raise more money. So Anthropic, you've probably heard of their large language model called cloud. So they have Cloud 2. But the AI startup Anthropic is looking to raise at least $2 billion from Google and other investors. So they did recently announce a very large multi billion dollar raise from Amazon, but they are currently seeking a valuation of between 20 billion and $30 billion and the company is already generating revenue. So they're already generating $100 million in annual revenue and projecting to get 200 million a year by the end of the year. Wow. Anthropic is really making a splash. I'm, I'm excited to see what Anthropic is going to do, how they're more than anything, how they're going to connect Anthropic to the Internet, which y' all have heard me talk about this before. You know, Google Bard is, is making those strides. Bing Chat is, is making those strides. Chat GPT is the leader in that space. So I'm interested to see what Anthropic is going to do there. But let's, let's talk about products. Speaking of products, right, let's talk about products and services and I'm very excited to bring on today's guests so we can talk a little bit about us. So please help me. And welcoming to the everyday AI show, Daniel Glickman. He is the senior director of product marketing at ActiveTrack. Daniel, thank you for joining us.

Thanks Jordan. Long time listener, first time caller.

Oh, I love that. Yeah, we're going old school in the radio days with that. Love it, love it. Well, hey Daniel, quick, just tell us, tell us real quick. Just first about ActiveTrack. What is ActiveTrack and what do y' all do?

Oh yeah, so ActiveTrack comes from the traditional world of employee monitoring or. Oh, thanks for showing. Yeah, or productivity monitoring. So we collect the data or rather the metadata of what different team members are doing, employees are doing or what devices are doing within your company. Right. And we analyze it to give you better understanding and create impact of productivity within your organization. Who needs coaching? What processes work better? Are people working remotely better than people who are working in the office? Should your remote work policies adapt to better productivity? And it's really interesting, some companies employees work better at home, some of them, they work better in the office. It's a new and Interesting era. And we collect the data that powers it. Yeah.

And that's really interesting. Talking about worker productivity, I do want to get to that here, in here in a couple minutes. But first I want to also just because it seems that people, if they don't work in product and they hear, oh, this person's in product marketing or product development and sometimes people are left scratching their heads. So before we dive in deeper into the AI side of this, Daniel, maybe explain a little bit even what you do in your role as, you know, senior director of Product marketing.

Okay, so I have two hats at Active Track, as many people do these days. Right. So one is I lead a traditional product marketing which basically means we help design and ship out better product to the better compete in the marketplace. We ask what exactly are the market challenges and how do our products fit into those and how do we package them in a way that better sells? So we work closely with the product developers to design these features and we work closely with the sales team to explain how to sell them. Yeah, I also lead the transformation team that is shifting the company from a small business approach to a mid market approach. So we're reorganizing how sales and marketing work together and we're shifting towards more of a sales led and an ABM led approach in the company.

Yeah. And it's super interesting and kind of like what Brian said. So everyone thank, thank you for joining us. If you are here live and you want to know more about product marketing and how AI fits into the fold, please drop a comment, let us know. But like Brian said here, he said, I always think this episode doesn't apply to me yet. It always does. Like absolutely right. Like people don't know that there's multiple teams when you know, developing a product, developing the software. People are working and spending a lot of time on saying, okay, how is this going to affect our customers? How can marketing take this new product feature and explain it to people? So it's, it's, it's not a, a random haphazard, you know, process. People are actually people like yourselves are spending their careers and big teams to build better products and explain them to us all as well. You know, let's just jump right into it, Daniel. I was, I was going to weave all the way around but you know, one thing that, you know, Active Track does and I, I threw it up on the screen here is it helps companies so you know, they, they will use Active Track technology to gain, you know, better insights into employee productivity, employee engagement, you know, with, with AI Right. So we're just going straight, straight for some hot takes here with AI. How, how do we think that employee productivity and employee engagement is, is going to change? And then maybe what insights might as an example Active Track be able to show on the back end to say like, yes, like if a company implements generative AI in a big way, top to bottom, you know, how would ActiveTrack kind of track that?

Right. So Active Track right now tracks the metadata, meaning we know how much time somebody spends in Zoom or how much time somebody spends say in Salesforce. And when you amplify that across a large team, it adds up to a big difference in productivity. So for example, or the pick, it can translate a lot of money. So for example, if they're, you're paying for a thousand Salesforce licenses and only 300 people are using them, right. There's a huge immediate savings there. That's easy, that's simple. And these. Yet we see lots and lots of people all the time surprised by this. Right? Simple processes in an organization just analyzing the data and saying, hey, you're spending your team, this team is spending about 30% of our time in Outlook when they shouldn't. There's no. And this is true, right? Company shocked. And they had to, they couldn't believe the data. They said this doesn't make any sense. How could this be that we're spending 30% of our time? And I was like, well, yes, yeah. And so there. So these are the easy stuff that where AI comes into play is where the connections that are not obvious for a human to dig in. So right now we're surfacing many reports to people where they can dig into the data. And the biggest questions that people have are a, who are those who are essentially not working? And those are not very interesting questions to answer because that's a one time thing. You find them, right? Right. And you deal with it and that's that. And unfortunately that's, that's part of a very small part of a story of a big company. But the interesting questions are, hey, I got a sales team for example, or I've got a call center team and I see that some people are outperforming the others. Everybody's working, everybody's hustling. But why are some outperforming others? And this has to do with process. They're following some process that they may not even understand themselves. They can. And the managers don't quite understand how to connect the dots and see and they don't know what questions to ask to be Able to find the answers. Right. And that's when I really comes into play is that is to make those connections that we as humans have a hard time realizing. Right. And so for example, in the sales team, I was, I was looking at my own sales team the other day and asking the same question. Why is one SDR outperforming the other? And what I found was that that one was spending more time in actionable, taking action actionable or in actionable solutions rather than in research. Right. And there were implicates, implications around it. And so that known as coaching that, you know, now. Okay, we just need to do some coaching around there. Yeah, go ahead. That's.

No, it's, it's, it's extremely interesting and I, gosh, I would, I would love to just sit down and look at all this data because I think that, you know, especially when we talk about generative AI and that example, right. That you know, when you were looking at two different sales reps and you know, one is more productive, they're both working hard, one's working on more actionable items, another is spending more time researching. So it's not that any, that either person was, was necessarily had a flawed approach or that anyone was misusing their time either. Right. Which, which is very fascinating.

Fascinating.

But even when we talk about, you know, companies implementing generative AI, because I think one of the most looked over, you know, aspects of large language models, even something simple that most of us can leverage like ChatGPT, is the ability to, to research faster. Right. Because it is something that employees spend so much time on. So I'm going to ask you here to just project something that may or may not be in your field at all. But do you think that as companies, you know, implement generative AI, you know, more top to bottom? Because I feel so many, you know, even small and medium sized businesses haven't yet. You know, how is that going to change the way that we work? Because someone like yourself, you are able to look at all this data, this employee engagement where employees are spending their time. How do you think that generative AI, once it is, you know, maybe when Copilot is released in November for Microsoft, how do you think work is going to change? And employee engagement and effectiveness as well.

Are you still running in circles trying to figure out how to actually grow.

Your business with AI?

Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on gen AI. Hey, this is Jordan Wilson, host of this very Podcast companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too. Just like some of the biggest companies.

In the world do.

Go to your everyday AI.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on gen.

Well, it's hard to know how it's going to change, but what we know in terms of employee effectiveness, we note there that there are huge redundancies and huge inefficiencies in how people work. This is the biggest cost to a business and or depends on the industry you're in. The kinds of businesses we and our audience here are talking about, typically the employees are the biggest cost or they're huge cost and they're the most inefficient. Right. And like you said, not because they're necessarily misusing their time or their equipment, but because it's just we're not machines, we're humans. Right. And so how do we make that more efficient? And so some of it is finding is is speeding up our work and taking repetitive tasks. And I think that's where AI right now generative AI is used mostly in organizations and most easily adopted. And that's why it's oftentimes a bottom up approach is when in product marketing for example I can take, I can go to Bob, ask Bob. Hey, go to G2 Reviews, here's the link. Look at my competitor and tell me which of the top features that their employee that their customers are rating. Now tell me how does that compare to mine? Right. And so that's a work that will take me maybe two, three days to sit down and put together. It's just repetitive. So that's very obvious for me, right. As a product marketer, oh, I can have a bot that can automate some of these repetitive tasks. What's going to happen in a few years from now is very hard to say because it's when we're able to connect those dots from things that right now we cannot see, we don't know to look for. That's when it's going to get very, very interesting. So right now I know what's slowing me down is this takes time to do the research, takes time to listen into customer interviews, transcribe them, read through the transcription, summarize out of that. I can do the same with ChatGPT or Bold. I just give them the whole transcription and say, hey, give me 10 Google Google AdWord titles based on this interview with the customer. And you know what, it works like a charm. It's beautiful. And I just hand them over to my ads manage campaign manager. Right. I can see that it's very intuitive for me and I can see the connection there between my productivity and the work that needs to be done. Yeah, but where we don't know is what we don't know. And we, and that's, and that's, and that's why we cannot predict the big impact that generative AI will have on productivity 10 years from now.

Yeah, it's, it's hard. Yeah. Even, even when you say, I'm even curious what's going to happen in six months. Right. Like, yeah, talking years in the future is so hard to predict. And, and hey, Ben. Ben, thank you for your question, Cecilia. We're going to get to those here in just a second. And if you do have a question for Daniel, whether about Active Track, whether it's about product marketing, please get it in. But I do want to follow up on something that you said here, Daniel, like that example, right? Hey, based on this interview with the customer, you know, hey, large language model, give me 10 different ideas for Google Ads. Fantastic use case there, right? Because I think so oftentimes people, whether it's in their, their roles, their departments, they're entrepreneurs, they're struggling to say, how can we use generative AI, Right? And I think you, you use it and examples like you just said right there, such a great easy example that anyone can use. But I'm curious, you know, how even at active Track, you know, how are you or your team or others, you know, using AI right now? Because, you know, it seems like it's only the largest of the large companies that have, you know, kind of company wide, you know, generative AI approaches. But how are you, your colleagues, your company using AI even to your own advantage right now?

Right. So a footnote for mo, most of the time when we're using AI, we don't know we're using AI. For example, in LinkedIn Sales Navigator, LinkedIn Sales Navigator will suggest target accounts and new leads for me based on AI, right. I Don't think of it as AI. Active Track does all kinds of, creates all kinds of suggestions for productivity for you. We use some A.I. you don't know, right? It seems to you very obvious here. You're outperforming compared to last week with productivity. How do we know? We run some analysis and the data, right? So a lot of the AI we use, we don't. We're not even aware. But here's some simple things that we use. We use simple tools. We use bald, we use sales, LinkedIn, sales navigator, we use Writer, we use Grammarly, we use chat GPT. Right? And by the way, chatGPT and well, they keep competing. It's like Home Depot and Lowe's. It's always like one versus one is slightly better than the other. You kind of switch between them, right? And each tool is slightly better than the other one.

Right.

And so for different things and so you might create, maybe use bald for Internet research, throw it in and then maybe also for ideation a bit. But you would never use it for writing. And chatgpt also chatgpt tends to drift away and just introduce its own ideas. So I don't want to use that for an. For analysis and writing, right. I'll use something like Writer, which is very focused. And most of these tools, by the way, you can use them for free right now. They're very, very cheap. So it's really heyday for it now at this point. Always have a human inspect the work and have a human manage the work. Think of it as an intern. These are interns, right? They're running and doing all the work for you. So we're using these tools for a market research. Go collect. Collect the data, suggest improvements, analyze. Here's my, here's my latest customer interview. Look for repetitive key phrases. Look for what are the top messages that you would that you hear this customer say what would you I need? And then you would ask questions like I need to write an intro email to a prospect at such and such a company. I want to make sure to highlight that our product is better than the competitors in these following bullet points. Can you write a personalized email to them that to get them interested in our product? Now most, most SDLs or videos, what do they do all day? Copy paste, copy paste. And that's why their position will disappear very soon and completely disappear. And if you look at the, at the cold emails that you're getting in your inbox, every single one of them starts the same exact sentence in different variations. Why they use some kind of AI to rewrite the same thing over and over again. It's basically, it's completely wrong. It starts with I noticed that. And then they remove the eye. So it just says notice that or seen that. Seen that you are. Guess that. You know, it's just variations of the same thing. Really silly, right? But we can use the same technology in a positive way to be customer centric. And so you have to give it the instructions to say, want to get the person interested? I want to ask the person about so and so. Get very prescriptive, like you're explaining to an intern, this is the proper way of doing it. Don't let it tell you how to do things. Right. And so it's right. And so it doesn't know. It's. It has no context. It doesn't know what are the best practices in your industry and what drives results. Yeah, it has no connection to the results at all.

I love that. I love that. Daniel, you know, even what you said, a couple great points there. You know, treating generative AI systems like they're your intern. Right? Like that's, that's what we teach people too. It's like, hey, teach, teach a generative AI system like it's a new employee. You know, don't just copy and paste something. I love your analogy too. Just saying like, hey, different, different gen AI tools for different purposes depending on your needs. You know, chat GBT for this bard for this. Kind of like you might go to Home Depot for these products. You might go to Lowe's for, for these products. That' such a great, I think use case that a lot of people can learn from. But I do have a couple questions I want to get to. So Cecilia here. So Cecilia, thank you for joining us. So saying, you know, we're talking about large teams, you know, using active track. But she's saying how well does using AI or active track work for smaller sales teams or companies? How does it kind of show the processes? So great, great question, Daniel. What's your take? How can AI or even Active Track maybe be used to show for small teams, productivity and engagement?

Yeah, ActiveTrack always starts with a small team, whether it's in a large company or a small business. Always starts with small team. We usually start with some kind of pilot program. And in fact, most of our customers are small businesses. And the basic questions that people use us to answer are people working remotely when they're at home, what are they doing? I'll be collaborating when they're in the office or when they're at home. And you'd be very, very much surprised to know the answers. So sometimes people come back to the office or mandated to go back to the office and it turns out that they sit in the room and they actually don't collaborate. They could be better when they're at home. It really depends on the company and the culture. And so a to find out, okay. To make sure that you know that without harming the culture, people are actually doing what they're supposed to. What they're supposed to. Very simple. We use it in ourselves. And I love it. It helps for capacity planning. We have one of my employees is going out on maternity leave next, in early next year. And we're thinking, okay, what is our capacity within the product team at large to handle that? Do we need to get a replacement? Can we manage without all of these questions? We can see the data right there. And so, yeah, small teams, larger teams. The more data you have, the more insight. The more big, the bigger the team, the bigger the economic impact. Of course.

Like, yeah, makes, makes perfect sense. So thank, yeah, Daniel, thanks for that one. Cecilia, great question. Wanted to get to one more question here quick from Ben. So, Ben, asking any trends in which types of companies or employees work better remotely or in the field. That's. I love that question. I wasn't even thinking that. So thanks for that, Ben, because I'm sure, you know, active track, you know, during the pandemic and we have these work from home hybrid work environments, you know. Yeah. What is, what are you all seeing in terms of just the state of work and productivity, you know, between remote in person hybrid teams?

That is a great question I don't have an answer to. I've been asking inside the company we have a research team that answers these kind of questions. And what they're telling me is that we have some research papers around these topics and you can see it's anonymized. It's sort of bigger, bigger data. You can see that there are definitely trends, but it really, really depends on the company culture and the type of team. So it's really not so much the type of company, but a type of team. Most of the teams that are interested in active track are teams that have fixed units of outcome, meaning they produce widgets. So they're sort of either contact center, service center, sales teams, different where you can measure units of output. When it comes to knowledge based teams, it's a bit more difficult to measure the output. Like how do we, you know, what is my output? I don't really know exactly right. We can't measure it. And so those teams tend to work better anywhere as long as there's a clear understanding of what is the process. It is very difficult sometimes to create brainstorming and different sort of the different little things when you're at home and when you're isolated. And so then it becomes a question of balance. How often should people be in their office, how often should they be in meetings? It becomes a question of how do you manage these teams rather than should these teams exist remotely or in the office?

Yeah, yeah, it's a great point.

And just.

Yeah, it's the way we work. You know, I think between, you know, the pandemic and shutdowns and remote, and then you throw into the mix now generative AI, you know, and I keep thinking, Daniel, of your example of, you know, the two employees similarly, that are both working very hard, but very different results. Yeah, it is. It is changing. And, you know, data. I do real quick want to ask you about data because it's. It's one of the most important thing when we talk about, you know, not just leveraging AI, but even improving everything in the workplace. It all comes down to having good data. And, you know, even when we're working with AI models, same thing, we have to make sure we're giving it great inputs. So can you talk real quick just about the importance of. Of data collection for product, you know, product marketing and even how y' all are using data at ActiveTrack?

Yeah, so we have. So on the product level, we collect data on usage, which cohorts are using different features, for example, and how often. And so we want features that most customers use most of the time. Right. And we're less interested in features that some people use some of the times. Right. And so it helps us prioritize the features and calibrate against what is the market demand. There's qualitative data alongside that, which is customer journey interviews and asking people what exactly are asking our best customers? What was your buying journey like? What was the problem you were looking to solve? Right. What did you call that problem at the time? Right. And did we meet your expectations? How so what surprised you for the better when you found us? What was the trigger that made you know, this is the right solution to you and which features did you associate with that? So we need to connect the two of them. So there's the qualitative, quantitative and the qualitative. We have over 10,000 customers, so we have a lot of data, sometimes too much about what, you know, what are people using? But it doesn't necessarily correlate to what will people buy or what will cause people to pay more. We want people to pay more because we drive more value to them, right? Yeah, yeah. And so to drive more value, we have to identify what are the problems we're solving and that's by intimately knowing the customer. And then of course there's the data about what are people requesting. So we have, we use tools like product board to categorize requests, we bring in requests from the entire company. Everybody is welcome in the company to post and say, hey, I heard a customer mention this, I heard a customer mentioned that, and we just dump it into product vault and then sold it out and surface the highest value or high most common requests. And those are many companies will use tools like this. Right? And so, so we, we look at. So when it comes to lost track of the original question.

No, it's, it's, it's all good. You know what, because actually it's, it's a great transition point because we have covered so much. Right. Like we've talked top to bottom, Daniel, you know, we've talked a little bit about just product marketing, what y' all at ActiveTrack doing, you know, sales and marketing, customer success, different gen AI tools. So we have been all over the place. But I do want to end with this, you know, because we have talked about a lot. But if you look at product marketing and how generative AI and AI systems are being used in product marketing right now, what is the one takeaway? So maybe, you know, someone listening right now is in product marketing at another company and you know, we've thrown out all these great ideas. What's that one piece of advice that maybe you would give to someone that is interested in product marketing and how AI is used in that space. What's, what's kind of your big takeaway here for them?

Yeah, I would say that, that now we have the ability to, to have generative AI do market research for you in a speed and ease that wasn't available before. The data, the public data has been accumulating over the last recent years through things like review sites, websites, different company websites, YouTube etc. There are lots of different pieces of data about competitors out there and it just took a lot of time to accumulate it, put it together and analyze it. And this is the bigger evolution that you're able to very quickly collect that together, have a bot go out there on the web for you and you can tell it, hey, please analyze based on third Party data only not looking at the company website. Right. Exclude what are your sources. Run the same analysis without excluding company website. Right. Things like that. So make your training an intern and collect that data and find out what are people saying and what are people thinking about the competition, how is it positioned and how should I position myself? This is becoming very, very easy now compared to before. And then rewriting messaging based on the particular positioning is also much faster. Before we had to sit down and think about, okay, what are the exact words we need now you can tell Generative AI. Hey, here are my bullet points around feature description. I want you to rewrite a paragraph for me for this very particular audience. Make sure to highlight this differentiation against the competitors. Go. Right. And this is something that would take you maybe half a day before because you'd have to sit down and obsess and it does it for you. Then you take a look at it and say, hey, I'll just change this there. And here we go. This is the big revolution. I think this allows for much fewer people to work on the same problems.

Yeah, absolutely. Just great tips top to bottom. Great insights. Daniel, thank you so much for joining the everyday AI know and sharing, sharing your experience with us all. And hey, make sure to sign up, sign up for the daily newsletter. But, but Daniel, thank you for joining us. We're going to share a lot more about ActiveTrack and what y' all are doing in the newsletter. So thank you, Daniel.

Thank you everybody.

All right. And just, yeah, I'm gonna, I'm gonna throw that up there real quick because we did go over a lot. We did go over a lot. So don't worry. Sign up for our daily newsletter. Go to your everyday AI dot com. We're gonna have a lot more on what Daniel just broke down because he gave us so much great information. So we're gonna have more on active track also. You know, I do, I do have to shout this out quick. I was noticing in the comments there was just a lot of good old fashioned networking going on. I'd love to see this.

Michael.

Asking questions about AI image generation Nissani, you know, answering them, you know, which, which brings up this point. Hey, we're creating a little something called the AI inner circle. So if you're listening on the podcast, if you're a longtime listener, first time caller like Daniel was, reach out to me in the show notes. We have a little AI inner circle going on today and Friday. It's a free event to network with other like minded AI people. So thank you all for joining us. And we hope to see you back on another edition of Everyday AI. Thanks, y'.

All.

And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.