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

In today's fast-paced business landscape, staying competitive requires not only innovative products but also a deep understanding of customer needs. The integration of artificial intelligence (AI) into product marketing has opened up new opportunities for business growth and improved decision-making. In this article, we will explore how AI is transforming the way companies analyze data, understand customer behavior, and effectively market their products.

Harnessing the Power of Generative AI:

One significant aspect of AI that is revolutionizing product marketing is generative AI. By utilizing advanced algorithms, generative AI enables companies to analyze vast amounts of data and generate insights that were previously unattainable. This technology empowers businesses with the ability to research faster and gain a deeper understanding of their market, providing a competitive edge.

Understanding the Product-Market Fit:

AI-driven analysis is not limited to evaluating market challenges but also extends to understanding the effectiveness of product features. Traditional methods may fall short in identifying the connections between high-performing individuals and specific processes. However, AI can uncover patterns and correlations that are not evident to humans, shedding light on what truly drives success within teams.

Empowering Employees for Enhanced Productivity:

To make work more efficient, AI can automate repetitive tasks, allowing employees to focus on higher-value activities. By leveraging generative AI, marketers can speed up market research, analyze customer sentiments from multiple sources, and identify emerging trends to tailor their product marketing strategies accordingly. This enables businesses to optimize employee time and resources, ultimately boosting productivity.

Expanding AI Adoption:

While large corporations have successfully integrated AI into their operations, many small and medium-sized enterprises (SMEs) have yet to tap into its full potential. Generative AI, such as Microsoft's Copilot, presents exciting possibilities for SMEs to streamline operations, improve employee engagement, and access valuable insights.

Prioritizing Features with Data-Driven Insights:

AI is not only instrumental in understanding customers' buying habits but also in helping companies prioritize features based on market demand. Combining quantitative data, such as user usage patterns, with qualitative feedback from customer journey interviews ensures that product marketing efforts align with customer expectations. By leveraging the power of generative AI, this process can be significantly sped up, saving precious time and resources.

Conclusion:

As AI continues to advance and reshape the business landscape, companies that embrace and harness its power in product marketing will gain a significant competitive advantage. By leveraging generative AI, businesses can unlock valuable insights, streamline operations, optimize workforce productivity, and fuel growth. Actively incorporating AI-driven strategies into product marketing will not only benefit enterprises but also contribute to their ability to adapt and thrive in an ever-evolving marketplace.

Topics Covered in This Episode

1. The interest in generative AI and analyzing data
2. AI in product usage and marketing
3. Bottom-up approach in product marketing and the use of bots
4. Implementing AI in the workplace and its impact on employee effectiveness

Podcast Transcript

Jordan Wilson [00:00:17]:

How can AI Change the way that we use products in 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 gonna talk about that a little bit more today and a lot more On everyday AI. This is your daily livestream 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, Uh-uh. 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.

Daily AI news

Jordan Wilson [00:01:02]:

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. Alright. 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.

Jordan Wilson [00:01:26]:

Here we go. So Mr. Beast, yes, there's Mr. Beast news that has to do with AI. So Mr. Beast is calling out TikTok And AI. So 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 Houston 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 used without their permission in advertising. Not not not a good look, advertisers.

Jordan Wilson [00:02:11]:

What are we doing there? Alright. Next piece of news, which isn't surprising, but Jobs in AI are growing at a very fast rate. So new LinkedIn data shows that job postings mentioning AI have more than doubled in 2 years, which is fascinating because it's not just jobs in AI and working in AI, but jobs in completely, You 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 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 in Cloud, so they have cloud too. But the AI startup, Anthropic, is looking to raise at least $2,000,000,000 from Google and other investors. So they did recently announce a very large multibillion dollar raise from Amazon, but they are, currently Seeking a valuation of between $20,000,000,000 and $30,000,000,000.

Jordan Wilson [00:03:21]:

And the company is already generating revenue, so they're already generating a $100,000,000 and annual revenue and projecting to get 200,000,000, 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 gonna connect, anthropic to the Internet, which y'all have heard me talk about this before. You know? Google bard is is is making those strides. BingChat is is making those strides. Chat 2BT is the leader in that space, so I'm interested to see what anthropic is gonna do there. But let's Let's talk about products.

About Daniel Glickman and ActivTrak

Jordan Wilson [00:03:57]:

Speaking of products. Right? Let's talk about products and services. And I'm I'm very excited, to bring on today's guest 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 ActivTrak. Daniel, thank you for joining us.

Daniel Glickman [00:04:15]:

Thanks, Jordan. Long time listener, first time caller.

Jordan Wilson [00:04:18]:

Oh. Oh, I love that. Yeah. We're going old school in the radio days with that. Love it. Love it. Well, hey, Daniel. Quick.

Jordan Wilson [00:04:25]:

Just tell us tell us, you know, real quick just, first about ActivTrak. You know, what is ActivTrak, and and what do y'all do?

Daniel Glickman [00:04:32]:

Oh, yeah. So ActivTrak is, 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? What, are people working remotely better than people who are working in the office? Should your remote work policies, adapt to, to better productivity. And it's really interesting.

Daniel Glickman [00:05:14]:

Sometimes some companies, they employees work better at home. Some of them, they work better at the office. Right? It's it's it's a new and interesting era, and we collect the data that powers it.

Jordan Wilson [00:05:24]:

Yeah. And that's really interesting talking about worker productivity. I do wanna get to that here in, here in a couple minutes. But first, I wanna 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.

Daniel Glickman [00:05:50]:

Okay. So I have 2 hats at ActivTrak as many people do these days. Right? So one is I lead the traditional product marketing, which basically means we help design and ship out, better pro a product to the better compete in the marketplace. Right? We ask what exactly are the market challenges, and how do our products fit into those? How do we 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 the transformation team that that is shifting the company from a small business approach to a mid market approach. So we're, we're reorganizing how sales and marketing work together, and we're shifting towards more of a sales led and an ABM led, approaching the company.

Jordan Wilson [00:06:40]:

Yeah. And and it's it's super interesting and kind of like what, like what Brian said. So everyone, thank you for joining us. If if you are here live and you wanna know more about product marketing and how AI fits into the fold, please drop a comment and 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, what you know, developing a product, developing a software, people are working and spending a lot of time on saying, okay.

Using AI to track employee activity

Jordan Wilson [00:07:11]:

How is this going to affect our customers? How can, you know, 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, a process. People are actually people like yourselves are spending their careers in 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 gonna 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 you know, they they will use active track technology to gain, you know, better insights into employee productivity, employee engagement, you 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, ActivTrak, be able to show, on the back end to to say, like, yes.

Jordan Wilson [00:08:09]:

Like, if a company implements generative AI in a big way top to bottom, you know, how would ActivTrak kind of track that?

Daniel Glickman [00:08:17]:

Right. So ActivTrak 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 a large team, it adds up to a big difference in productivity. So for example or it the pick it it can translate a lot of money. So for example, if they're you're paying for a 1,000 Salesforce licenses and only 300 people are using them, right, there's a huge immediate savings there. That's easy. That's simple.

Daniel Glickman [00:08:46]:

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 tea this team is spending about 30% of our time in Outlook when they shouldn't. There's no and this is true. Right? Company's shocked, and they had to they 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 in Outlook? Well, yes. Yeah.

Daniel Glickman [00:09:12]:

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 onetime thing. You find them, right, and you deal with it, and that's that. And, unfortunately, that's that's part of a very small part of the 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.

Daniel Glickman [00:09:56]:

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 and the managers don't quite understand how to connect the dots And see, and they all know what questions to ask to be able to find, the answers. Right? And that's when AI really comes into play is that is 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 1 SDR outperforming the other? And what I found was that That one was spending more time in actionable taking action actionable or inactionable solutions rather than in research. Right? And there were implicates, implications around it. And so then there's coaching that you know.

Daniel Glickman [00:10:51]:

Now, okay, we just need to do some coaching around there. Yeah. Go ahead.

How will AI change employee efficiency?

Jordan Wilson [00:10:56]:

That's no. It's it's it's extremely interesting, and I gosh. I would I would I would love to just sit down and and look at all this data because I think that, you know, especially when we talk about generative AI and and and that example, right, that, you know, when you were looking at 2 different, sales, reps and and, You know, 1 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. But even when we talk about, You know, companies implementing generative AI because I think one of the most looked over, you you know, aspects of of large language models, even something Simple that most of us can leverage like ChatCpT is the ability to to research faster.

Jordan Wilson [00:11:44]:

Right? Because it is something that employees spend, So much time on. So, I'm gonna ask you here to to just project some something that may or may not be, in in in your field at all. But do you think that As companies, you 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 gonna change the way that 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 in November for Microsoft, how do you think Work is is is going to change in, employee engagement and effectiveness as well.

Daniel Glickman [00:12:28]:

Well, it's hard to know how it's gonna change. But what we know in terms of employee effectiveness. We know there that there are huge redundancies and huge inefficiencies in how people work. This is the biggest cost of business And or depends on the industry you're in. If you're with that in in the kinds of businesses we we in our audience here are talking about, Typically, the employees are the biggest cost or the huge cost, and and they're the most inefficient. Right? Not and like you said, not because they're necessarily Misusing the time, other equipment, but because it just we don't 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 or generative AI is used mostly in and that most easily adopted.

Daniel Glickman [00:13:16]:

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 g two reviews. Here's the link. Look at my competitor And tell me how which of the top features that their, employee that their customers are are rating. Now tell me, how does that compare to mine? Right? And so that's a work that would take me maybe 2, 3 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 gonna happen in in a few years from now is very hard to say because when 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 gonna get very, very interesting. So right now, I know what's slowing me down Is this takes time to do the research.

Daniel Glickman [00:14:14]:

Right? Takes time to listen into customer interviews, transcribe them, read through the transcription, summarize all of that. Can do the same with Chat GPD or Bob. 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

Jordan Wilson [00:14:48]:

done.

Daniel Glickman [00:14:48]:

Yeah. But whether 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 that generative AI will have on productivity 10 years from now.

Jordan Wilson [00:15:00]:

Yeah. It's it's hard. Yeah. Even even when you say, I'm even curious what's gonna happen in 6 months. Right? Like, yeah, talking years in the future is is so hard to predict. And and and, hey, Ben Ben, thank you for your questions, and We're gonna get to those here in just a second. And if you do have a question for Daniel, whether about ActivTrak, whether it's about product marketing, please get it in. But I do wanna follow-up on on something that that you said here, Daniel.

How Daniel and ActivTrak use AI

Jordan Wilson [00:15:24]:

Like, that example. Right? Hey. Based on this interview with a customer, You 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, their entrepreneurs, they're struggling to say, how can we use generative AI? Right? And I think you you use it in examples like you just said right there. Such a great, easy example that anyone can use. But I'm I'm curious, you know, how, even at ActivTrak, you know, how are You or your team or others, you know, using AI right now because, you you know, it seems like, it's it's only the the largest of the large companies that have, you know, kind of company wide, you know, generative AI, approaches.

Jordan Wilson [00:16:09]:

But how are you, your colleagues, your company using AI even to your own advantage right now?

Daniel Glickman [00:16:14]:

Right. So a footnote, for 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. ActiveTrack does all kinds of creates all kinds of suggestions for productivity for you. We use some AI. You don't know.

Daniel Glickman [00:16:39]:

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 Bob. We use sales, LinkedIn Sales Navigator.

Daniel Glickman [00:16:59]:

We use Rytr. We use Grammarly. We use ChatGPT. Right? And by the way, chat, g p t and valve, they keep competing. It's like Home Depot and Lowe's. It's always like one. 1 is slightly better than the other. You kind of switch between them.

Daniel Glickman [00:17:10]:

Right? And each tool is slightly better than the other one, right, and so for different things. And so you might create maybe use, Bob for in Internet research, Throw it in and then maybe also for ideation a bit, but you would never use it for writing. And Chat GPT also, tends to drift away and just introduce its own ideas, so I don't wanna 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 could use them for free right now. Yeah. They're very, very cheap. So it's it's it's really heyday for it.

Daniel Glickman [00:17:44]:

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 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.

Daniel Glickman [00:18:21]:

Like, I need to write an intro email to a prospect at such and such a company. I wanna high make sure to highlight that our product is better than the competitors of these following bullet points. Can you write a personalized email to them that, that to to get them interested in our product? Now most most, SDRs or BDLs, what do they do all day? Copy, paste, copy,

Jordan Wilson [00:18:48]:

paste.

Daniel Glickman [00:18:48]:

Right. And that's why their position will disappear very soon. Yes. 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 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 it's completely wrong. It starts with, I noticed that, and then they remove the eye.

Daniel Glickman [00:19:12]:

So it just says notice that or seen that, seen that you are, Guess that, you know, like, it's just variations of the same thing. Really silly. Right? What but we can use the same technology in in a in in In a positive way to be customer centric. And so you have to give it the instructions to say, I want to get the the person interested. I wanna 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.

Daniel Glickman [00:19:42]:

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 in in in your industry, and what and results. Yeah. It has no connection to the results at all.

Jordan Wilson [00:19:59]:

I love that I love that, Daniel. You know? And 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.

Jordan Wilson [00:20:16]:

Just saying like, hey. Different different Gen AI tools for different purposes depending on your needs. You know? Chat g p t for this, barred for this. Kind of like you might go to Home Depot for these products. You might go to Lowe's for for for these products. That's such such a great, I I think use case that a lot of people can learn from. But I I do have a couple questions I wanna get to. So, Cecilia here.

Using AI and ActiveTrack for small teams

Jordan Wilson [00:20:37]:

So, Cecilia, thank you for joining us. So saying, you know, we're talking about large teams, You know, using ActivTrak. But she's saying, how well does using AI or ActivTrak 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 ActivTrak maybe be used to show for, A small team's productivity and, you know, engagement.

Daniel Glickman [00:21:01]:

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. Right? And the basic questions that we that The people use us to answer are are people working remotely? Are they when they're at home, what are they doing? Right? Are they 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. They're mandated to go back to the office, it turns out that they sit in a room and they actually don't collaborate.

Daniel Glickman [00:21:36]:

They collaborate better right there at home. It really depends on the company and the culture. And so, a, to find out okay. To make sure that, You know, peep the the that without harming the culture, people are actually doing what they're 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.

Daniel Glickman [00:22:01]:

What is our capacity within the product team at large to handle that? Do we need to 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 we have, the more insight. The more big the bigger the team, the bigger the economic impact, Of course. Right.

Jordan Wilson [00:22:19]:

Yeah. Makes makes perfect sense. So thank yeah. Daniel, thanks for that one. Cecilia, great question. Wanted to get to 1 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.

Jordan Wilson [00:22:37]:

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?

Daniel Glickman [00:22:53]:

That is a great question. I don't have an answer to I've been asking inside the company. We have a, a research team that answers these kind of questions. And what they're telling me is that and 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 they'll definitely trade, but it's really, really depends on the company culture and the type of team. So it's really not so much the type of company, but the type of team. And, and most of the teams that are interested in ActivTrak are teams that have fixed units of outcome, meaning they produce widgets, then so they're Sort of, so, you know, either contact center, service center, sales teams, you know, different, where you can measure units of output.

Daniel Glickman [00:23:37]:

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 measure it. And so those teams tend to work better, anywhere as long as there's a 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 the the and so then it becomes a question of balance. How often should people be in the office? What what 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.

Jordan Wilson [00:24:22]:

Yeah. Yeah. It's a great it's a great point. And just yeah. It's The way we work. You you know? I I think between, you know, the pandemic and shutdowns and remote, and then you you you throw into the mix now and Generative AI. You you know? And I keep I keep thinking, Daniel, of of your example of, you know, the 2 employees similarly that are both working very hard, but very different results. Yeah.

Data collection for product marketing

Jordan Wilson [00:24:45]:

It is it is changing. And, you know, data. I do, real quick, wanna 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 ActivTrak. Yeah.

Daniel Glickman [00:25:17]:

So we have so on the product level, We collect data on usage, who 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. Other there's there's qualitative data Alongside that, which is which is customer journey interviews, and asking people what exactly or asking our best customers, what was your 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 associate with that. So we need to connect the 2 of them. So there's the qualitative, 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 what will cause people to pay more.

Daniel Glickman [00:26:29]:

We want people to pay more because we drive more value to them.

Jordan Wilson [00:26:32]:

Right? Yeah.

Daniel Glickman [00:26:33]:

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 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 know the 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, request. And those are many companies will use tools like this.

Daniel Glickman [00:27:07]:

Right? And so so we we look at so So when it comes to so I'm not track of the original question. I'm sorry.

Daniel's advice on AI in product marketing

Jordan Wilson [00:27:16]:

No. 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, Danielle. You know, we've talked a little bit about just product and Marketing, what y'all at ActiveTrack doing, you know, sales and marketing, customer success, different gen AI tools. So so we have been all over the place. But I do wanna end with this, you know, Because we have talked about a lot.

Jordan Wilson [00:27:39]:

But if you look at product marketing and, how generative AI and and AI systems are being used in product right now. What is the one takeaway? So maybe, you know, someone listening right now is in product marketing at another company, and, you you know, we've thrown out all these great ideas. In What's that 1 piece of advice that maybe you would give to someone that is interested in product marketing and how AI is is used in that space? What's what's kinda your your your big takeaway here

Daniel Glickman [00:28:07]:

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 and recent years through things like review sites, websites, different, company websites, YouTube, etcetera, the 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 com together, 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.

Daniel Glickman [00:29:10]:

How is it positioned, and how should I position myself? Right? This is becoming very, very easy now compared to before. And, and then rewriting messaging based on the particular positioning is also much faster. Before we had Sit down and and think about, okay, what are the exact words we need? Now you can tell generative AI. Hey. Here's my, here are my bullet points around feature description. I want you to re rewrite a to rewrite a, paragraph for me for this very particular audience. Make sure to Highlight this differentiation against the competitors. Go.

Daniel Glickman [00:29:47]:

Right? And this is something would take you maybe half a day before because you'd have to sit down and obsess, and And it does it for you. Take a look at it and say, hey. I'll just change this there, and Yeah. Here we go. Right? This is the big evolution, and I think this allows for much fewer people to work on, the same same

Jordan Wilson [00:30:03]:

problems. Yeah. Absolutely. Just great tips top to bottom, great insights. Daniel, thank you So much for joining the Everyday AI Show and 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 gonna share a lot more about ActivTrak and and what y'all are doing in the newsletter.

Jordan Wilson [00:30:26]:

So thank you, Daniel.

Daniel Glickman [00:30:27]:

Thank you, everybody.

Jordan Wilson [00:30:28]:

Alright. And just yeah. I'm gonna I'm 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 .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.

Jordan Wilson [00:30:45]:

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, Nusany, 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 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.

Jordan Wilson [00:31:18]:

So thank you all for joining us, and we hope to see you back on another edition of Everyday AI. Thanks, y'all.