In Episode 78 of the Digital Velocity Podcast, Erik Martinez sits down with Kimberly Storin, Chief Marketing Officer at Zoom, to explore how modern marketing teams can strategically integrate artificial intelligence into their workflows without sacrificing the human connection that drives brand affinity.
Kimberly shares insights from her deep background in B2B tech—including roles at Dell, IBM, and AMD—and breaks down what it takes to move beyond AI experimentation and into scalable, impactful implementation. From choosing the right use cases (like content generation) to fostering a culture of calculated risk-taking, Kimberly outlines a roadmap for mid-sized teams to adopt AI tools while staying rooted in customer empathy.
Listeners will learn how to structure an internal AI “center of excellence,” avoid data-driven pitfalls, and balance innovation with authenticity. If you're a marketing leader curious about integrating AI thoughtfully, this episode delivers practical guidance, strategic perspective, and inspiring takeaways for the path ahead.
Kimberly Storin | LinkedIn
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Narrator (00:00):
Welcome to the Digital Velocity Podcast, a podcast covering the intersection between strategy, digital marketing, and emerging trends impacting each of us. In each episode, we interview industry veterans to dive into the best hard hitting analysis of industry news and critical topics facing brand executives.
Erik Martinez (00:01):
So, I'm super excited about AI and everything AI. I probably don't dig into it as much as I really want to. But you seem to have an amazing background in marketing and communications and integrating technology. Could you just take a brief moment and tell us a little bit about your journey ?
That's quite the resume. Awesome. Well, I am excited to talk about this. You know, I think for the vast majority of people, AI is still very new. And I've talked to businesses every day that are adding AI tools to their tech stacks, and yet we're not seeing the gains that one would expect from these tools. And I know it's early but, what do you think the core problem is and how do we go about fixing it?
And then, all of a sudden we woke up and it's AI everywhere. And it's AI at your fingertips in a way that it never has before. So when I first started working with the data scientists that are building and developing these AI platforms and tools, it was really technical people that were building these things, right? It was data scientists, it was the IT practitioners. It was line of business in the sense of, they were the ones kind of starting the use case discussion, but it was these data scientists and IT practitioners and really deep technical folks that were trying to bring these solutions to bear for the use cases that the line of business was developing.
So then we realized that even if you figure out the compute challenges and the software limitations, you then have this problem of garbage in, garbage out. And I would say we're still facing that limitation across the board, right? That our data is not cleansed in the way that it would need to be in order to run great AI models.
It's really democratizing AI in a way that I think we never really expected, right? I, if you had asked me in 2015 when I was working on a GPU accelerated server. I would've told you that you would always need a data scientist to run AI workloads. And that's not the case, right? I planned a vacation two years ago, 100% using chat GPT, and my stepson, is now superpowered by AI in college. And what we're realizing is you have to be comfortable with the technology and we're not at the point now where AI is going to replace you, although maybe, down the road that might be the case. I don't, personally, think it's coming, in the near term future of that replacement, but it is augmenting and you will be left behind if you don't know how to leverage AI.
And that's really what AI, I think can offer as we start to think about. Embedding these tools that we don't have to develop, right? We no longer need the data scientists in order to develop these AI tools because there's teams and companies that are built and, really looking to solve our problems with these AI enabled tools.
Kimberly Storin (2) (00:07):
So for marketing, I think the clearest and cleanest use case right now is content. I think content generation and the ability for AI to superpower your content generation is at our fingertips right now. Our team uses a tool called Jasper and we're, very happy with that tool. We use chat, GPT regularly to solve that problem, I've played around with other, content solutions as well. And it's not an end all, be all. Again, it doesn't replace the human, but can it make your first draft or your outline faster? Absolutely. Can it make your revisions and your editing and your grammar. Faster? Absolutely. And so I think, when I'm stepping back and saying, what's the first use case that I would try to leverage AI in terms of making it. Easier, faster, better. I think content is, one of the first ones to go attack.
I think there's, you know, there's other things. We've been using chatbots for a really long time and so that's another place that I think is, a pretty easy sandbox and a place to really think about, how do you play around with the, power of AI is in the chatbot. In some of the intent-based tools that are being brought to market.
Erik Martinez (00:09):
You've mentioned several times now in our conversation so far, I'm gonna paraphrase here, but you're talking about data gaps and data problems. I'm actually prepping a discussion next week for a summit on multi-touch attribution. And one of my positions on multi-touch attribution is that it's limited by a number of factors. One data collection on the pixel side, even if you're using server side tracking isn't perfect. It's much less than perfect, right? Second thing is, if you are a smaller brand, and you've work with some enterprise brands, but if you're working as a smaller brand, you may not have budget to be in all the places.
Kimberly Storin (2) (00:10):
I think part of it is, picking some use cases where you are not as dependent on the data and use that as the testing ground. That's why I really like content, right? Content is a way like, yes, Jasper absolutely lets us load up our brand voice. It learns from the work that we're doing. It gets better and better over time as we continue to train it on our brand and our voice and our tone, et cetera. So it's absolutely true, but it's also not 100% dependent on the data that's loaded in, like you might see. In a intent based model or an attribution model, right, where that gets a little bit more challenging.
So if you're trying to build a culture of AI adoption and build a culture of experimentation within your team, there are use cases and opportunities for workloads that would give you the chance to start to build that muscle memory and build 'cause so much of it. Is cultural, right? How comfortable are people in your organization with learning these new tools and technologies that will supercharge them?
Erik Martinez (00:12):
Yeah, that makes perfect sense and is excellent advice. I was reading some of your articles and one of the things you mention is the importance of moving beyond the isolated AI experiment. What do you mean by that? And how should marketing teams integrate AI into their strategies holistically, because I think, you're talking about, "Hey, we gotta change our culture", right?
There's a lot of potential bias as well that can come out come to the surface in those little experimentations that happen. Like if somebody's kicking the tires. I used to see this where I would have, IT folks building mini server farms under their desk, literally. With GPU accelerated servers and they would go around in their company and basically challenge them. I can run a campaign faster than your marketing team or I can build a chat bot that looks like this. I wanna prove it out. And it was almost shadow IT in a way, right? There were costs, that were being assumed by these decision makers, and what they were really doing was just kicking the tires under their desk, and there was no control.
But once the organization is purchasing enterprise class AI software for content, for analytics, for product marketing and competitive research you can quickly lose control of, your return on investment. And so that's why I think it's really important that you have a planned and focused approach that is very much based in cultural adaptation in order to move the needle. And I think it starts at the top. It needs an executive mandate with a clear objective and a framework for collaboration across the team.
And then I think it also requires systematic and standardized approach. Almost what I would call a center of excellence model. Where you have a marketing AI champion, sometimes I call them a spiritual leader, right? Who have really invested in understanding the nuances of AI, prompt engineering, the importance of data and the importance of cross organization collaboration of working closely with the IT team, making smart decisions from a security standpoint for the organization, and then really leading with that intent and structure across the board. Stopping those one-off rogue experiments is really important because the team should embrace a mindset of experimentation across the board. Versus just seeing that in pockets. And then once you start to see that agility and curiosity and kind of the adopt and go mindset, if you will, inside the organization, you can really start to pilot those AI initiatives
Where are we comfortable with the progress that we're making? How do we double down and scale that or where do we kill it quickly? And at the end of the day, it is the processes, the tools, the data, and the people. Way more than it is just a, technology in and of itself. And so having those processes and having the ability to really look across the organization and determine, is this working? Am I seeing the results that I expected? And how long should I be waiting for those results to manifest? And sometimes. Some of these tools are easy, plug and play. Some of them require pretty significant integration with your CRM system or your intent data or your website, et cetera. And those things take longer, and they do require patience, and they require collaboration with the customer success team at the vendor that you're working with, in order to truly understand what that integration implementation process might look like, that timeline, and ensuring that you're baking that into your assumptions and your ROI payback as well.
Where are we comfortable with the progress that we're making? How do we double down and scale that or where do we kill it quickly? And at the end of the day, it is the processes, the tools, the data, and the people. Way more than it is just a, technology in and of itself. And so having those processes and having the ability to really look across the organization and determine, is this working? Am I seeing the results that I expected? And how long should I be waiting for those results to manifest? And sometimes. Some of these tools are easy, plug and play. Some of them require pretty significant integration with your CRM system or your intent data or your website, et cetera. And those things take longer, and they do require patience, and they require collaboration with the customer success team at the vendor that you're working with, in order to truly understand what that integration implementation process might look like, that timeline, and ensuring that you're baking that into your assumptions and your ROI payback as well.
So if you were sitting in charge of a mid-size enterprise and you were gonna pick somebody, what are the qualities that person would have and then what would be the first type of project that you would task them with?
And I know, agile marketing obviously. I mean at IBM it was 27 steps to being agile, which is not really that agile, but let's take the capital A out of the equation, right? And thinking about agility as a lowercase A. And I think as you think about, curiosity is kind of that ability to look forward and agility is the ability to look back and to tweak and modify and enhance and optimize what may or may not be working. In that postmortem
Because it's being comfortable with the calculated risk taking themselves, but it's also someone who can work collaboratively across functions and bring that, spirit of agility and curiosity and calculated risk taking to others. And so I do think that there is this underlying attitude that will make or break your AI spiritual leader, within your marketing team.
But you're not suggesting that, you're suggesting the person who can say, "Hey, here's what I think the outcome's gonna be", right? " Here's where we think some of the pitfalls might be, and here's our unknowns. And we just don't know until we run the experiment or we go through the process".
How do you avoid that pitfall? And what are the measures? Because you've got this person in charge who's trying to move the ball down the field, so to speak, and yet, it feels like they're getting sacked behind the line every single time. How do you mitigate for that type of scenario?
Kimberly Storin (2) (00:23):
I think the way to mitigate for that is to have the mandate start at the top. So if you as the CMO right, or you as the VP of marketing are not fully bought in, educated and continually updated on the progress that the team is making, like you can't have their back. And so that bi-directional communication up and down and ensuring that the executive in charge is clearly bought in and supporting the programs and the initiatives, I think takes away that inherent feeling that you're getting sacked from behind, right? There should be no surprises either way. And that requires executive support, executive communication, and constant updates both sides. And that requires collaboration.
Awesome. Let's pivot this conversation a little bit. You have talked about in this day of technology maintaining human connection. Connection with your customers and quite frankly, the people within your own organization. How do you view the role of technology in today's marketing world and trying to maintain that human element as you're going about doing your daily work?
And so again, kind of, going back to the importance of building a system. The processes, tools, data, and people. Like people are such an important piece of that. And when we think about being, in B2B especially, right? Sometimes we forget how critical. It is. This is an emotional decision. People are making a million dollar decision and their job is on the line, their reputation is on the line, and we as B2B marketers often forget how critical that emotional and human connection is across our customers.
Erik Martinez (00:26):
Whether it's B2B or B2C, I think that's the same story, right? At the end of the day, we want to create meaningful connection. What does your team do to ensure that you're maintaining that human connection, even as you're leveraging some of these more powerful technologies.
So we started with the manifesto and that's really critical. And then because our team is distributed, we've got a team that sits in Denver, but we also have a lot of folks that sit across North America. And so we built a culture committee that is a monthly connection point for all of our teams. We also have an internal Gchat Slack channel, right? That is really dedicated to connecting the team. We have a weekly question that we ask the team that is personal or professional, depending on the week. Somebody is assigned to be that chief Vibes Officer, every week to come up with a question that kind of spurs the connection.
And then every year we rotate out the two people that lead our culture committee and they're in charge of planning those monthly activities. It can be anything - like we've done, you know, MTV cribs, show off your workspace. We've done learning and panel discussions with marketing leaders that we've brought in. We've done just a variety, really, of kind of get-to-know-you, team bonding, and learning and development. And they know to look forward to those cultural events every month. People carve the time out, they make the space for it, and really show up to help each other. And I've really seen, that marketing manifesto in particular really manifest itself in terms of the actions and the behaviors, and the team cheers each other on, right? One person's individual win is a team win. And you really see that across the board.
Let's expand out to the customer now. How do you maintain that connection? You know, let's just talk about advertising for a moment. Right. We're out, prospecting for new customers to come into our sphere of influence. And there's a lot of research that says, we can put the best promotion and that may be a trigger for somebody. But more times than not, the trigger really is more emotional than it is rational.
Kimberly Storin (2) (00:30):
So really think about it
And we also know that it takes 15 touches to get anyone to make a decision. We know that people want to do 80% of the research before they talk to a seller and so that makes us think about how do we create the moments that matter? How do we be there in the moments that matter? How are we, first on the mind of a decision maker when it matters most, and so it really comes down to are you effectively leveraging all of the channels at your disposal? To be the voice of, and the voice to the customer means that you have to know your customer and know them inside and out, and understand the psychographics and understand where they get information and be in the relevant places where they do get information. If there is a buying committee. Are you influencing the buying committee at the right time?
Where the right media mix is for that buyer, and every buyer has a different strategy. So you can have a framework that you can generally apply, but there's never a one-size-fits-all, because every buyer group has a different pain point, has a different psychographic and demographic view and spends their time in different places with different influencers and your product can have a variety of product market fit. You know, either great product market fit or some challenges. Or not differentiated. Or differentiated.
So I think sometimes it's easy to kind of get caught up on all the things like we cannot cut through the noise anymore. It's just impossible, right? We can't capture attention anymore. Earned media doesn't have the same value that it used to, paid media absolutely does not have the same value that it used to. So how do you find the right place at the right time and the right mix that influences your people that you're trying to sell to?
I would say it's all the things that we talked about at the beginning, in terms of using controlled experimentation to find the tools that work best for your team. If content is a place that you can see the uptick pretty quickly, like picking those things and leveraging it as a superpower, but not thinking about it at a replacement for human intervention.
Erik Martinez (00:35):
Yeah you're really talking about how do we accelerate the process and not completely overhaul the process, right? You probably have a good process in place, but you can leverage the technologies to enhance it. As we move to close what last piece of advice would you like to leave with the listening audience?
Awesome. Kim, thank you so much for your time today. If somebody wants to reach out to you, what is the best way?
Welcome to the Digital Velocity Podcast, a podcast covering the intersection between strategy, digital marketing, and emerging trends impacting each of us. In each episode, we interview industry veterans to dive into the best hard hitting analysis of industry news and critical topics facing brand executives.
Erik Martinez (00:01):
So, I'm super excited about AI and everything AI. I probably don't dig into it as much as I really want to. But you seem to have an amazing background in marketing and communications and integrating technology. Could you just take a brief moment and tell us a little bit about your journey ?
That's quite the resume. Awesome. Well, I am excited to talk about this. You know, I think for the vast majority of people, AI is still very new. And I've talked to businesses every day that are adding AI tools to their tech stacks, and yet we're not seeing the gains that one would expect from these tools. And I know it's early but, what do you think the core problem is and how do we go about fixing it?
And then, all of a sudden we woke up and it's AI everywhere. And it's AI at your fingertips in a way that it never has before. So when I first started working with the data scientists that are building and developing these AI platforms and tools, it was really technical people that were building these things, right? It was data scientists, it was the IT practitioners. It was line of business in the sense of, they were the ones kind of starting the use case discussion, but it was these data scientists and IT practitioners and really deep technical folks that were trying to bring these solutions to bear for the use cases that the line of business was developing.
So then we realized that even if you figure out the compute challenges and the software limitations, you then have this problem of garbage in, garbage out. And I would say we're still facing that limitation across the board, right? That our data is not cleansed in the way that it would need to be in order to run great AI models.
It's really democratizing AI in a way that I think we never really expected, right? I, if you had asked me in 2015 when I was working on a GPU accelerated server. I would've told you that you would always need a data scientist to run AI workloads. And that's not the case, right? I planned a vacation two years ago, 100% using chat GPT, and my stepson, is now superpowered by AI in college. And what we're realizing is you have to be comfortable with the technology and we're not at the point now where AI is going to replace you, although maybe, down the road that might be the case. I don't, personally, think it's coming, in the near term future of that replacement, but it is augmenting and you will be left behind if you don't know how to leverage AI.
And that's really what AI, I think can offer as we start to think about. Embedding these tools that we don't have to develop, right? We no longer need the data scientists in order to develop these AI tools because there's teams and companies that are built and, really looking to solve our problems with these AI enabled tools.
Kimberly Storin (2) (00:07):
So for marketing, I think the clearest and cleanest use case right now is content. I think content generation and the ability for AI to superpower your content generation is at our fingertips right now. Our team uses a tool called Jasper and we're, very happy with that tool. We use chat, GPT regularly to solve that problem, I've played around with other, content solutions as well. And it's not an end all, be all. Again, it doesn't replace the human, but can it make your first draft or your outline faster? Absolutely. Can it make your revisions and your editing and your grammar. Faster? Absolutely. And so I think, when I'm stepping back and saying, what's the first use case that I would try to leverage AI in terms of making it. Easier, faster, better. I think content is, one of the first ones to go attack.
I think there's, you know, there's other things. We've been using chatbots for a really long time and so that's another place that I think is, a pretty easy sandbox and a place to really think about, how do you play around with the, power of AI is in the chatbot. In some of the intent-based tools that are being brought to market.
Erik Martinez (00:09):
You've mentioned several times now in our conversation so far, I'm gonna paraphrase here, but you're talking about data gaps and data problems. I'm actually prepping a discussion next week for a summit on multi-touch attribution. And one of my positions on multi-touch attribution is that it's limited by a number of factors. One data collection on the pixel side, even if you're using server side tracking isn't perfect. It's much less than perfect, right? Second thing is, if you are a smaller brand, and you've work with some enterprise brands, but if you're working as a smaller brand, you may not have budget to be in all the places.
Kimberly Storin (2) (00:10):
I think part of it is, picking some use cases where you are not as dependent on the data and use that as the testing ground. That's why I really like content, right? Content is a way like, yes, Jasper absolutely lets us load up our brand voice. It learns from the work that we're doing. It gets better and better over time as we continue to train it on our brand and our voice and our tone, et cetera. So it's absolutely true, but it's also not 100% dependent on the data that's loaded in, like you might see. In a intent based model or an attribution model, right, where that gets a little bit more challenging.
So if you're trying to build a culture of AI adoption and build a culture of experimentation within your team, there are use cases and opportunities for workloads that would give you the chance to start to build that muscle memory and build 'cause so much of it. Is cultural, right? How comfortable are people in your organization with learning these new tools and technologies that will supercharge them?
Erik Martinez (00:12):
Yeah, that makes perfect sense and is excellent advice. I was reading some of your articles and one of the things you mention is the importance of moving beyond the isolated AI experiment. What do you mean by that? And how should marketing teams integrate AI into their strategies holistically, because I think, you're talking about, "Hey, we gotta change our culture", right?
There's a lot of potential bias as well that can come out come to the surface in those little experimentations that happen. Like if somebody's kicking the tires. I used to see this where I would have, IT folks building mini server farms under their desk, literally. With GPU accelerated servers and they would go around in their company and basically challenge them. I can run a campaign faster than your marketing team or I can build a chat bot that looks like this. I wanna prove it out. And it was almost shadow IT in a way, right? There were costs, that were being assumed by these decision makers, and what they were really doing was just kicking the tires under their desk, and there was no control.
But once the organization is purchasing enterprise class AI software for content, for analytics, for product marketing and competitive research you can quickly lose control of, your return on investment. And so that's why I think it's really important that you have a planned and focused approach that is very much based in cultural adaptation in order to move the needle. And I think it starts at the top. It needs an executive mandate with a clear objective and a framework for collaboration across the team.
And then I think it also requires systematic and standardized approach. Almost what I would call a center of excellence model. Where you have a marketing AI champion, sometimes I call them a spiritual leader, right? Who have really invested in understanding the nuances of AI, prompt engineering, the importance of data and the importance of cross organization collaboration of working closely with the IT team, making smart decisions from a security standpoint for the organization, and then really leading with that intent and structure across the board. Stopping those one-off rogue experiments is really important because the team should embrace a mindset of experimentation across the board. Versus just seeing that in pockets. And then once you start to see that agility and curiosity and kind of the adopt and go mindset, if you will, inside the organization, you can really start to pilot those AI initiatives
Where are we comfortable with the progress that we're making? How do we double down and scale that or where do we kill it quickly? And at the end of the day, it is the processes, the tools, the data, and the people. Way more than it is just a, technology in and of itself. And so having those processes and having the ability to really look across the organization and determine, is this working? Am I seeing the results that I expected? And how long should I be waiting for those results to manifest? And sometimes. Some of these tools are easy, plug and play. Some of them require pretty significant integration with your CRM system or your intent data or your website, et cetera. And those things take longer, and they do require patience, and they require collaboration with the customer success team at the vendor that you're working with, in order to truly understand what that integration implementation process might look like, that timeline, and ensuring that you're baking that into your assumptions and your ROI payback as well.
Where are we comfortable with the progress that we're making? How do we double down and scale that or where do we kill it quickly? And at the end of the day, it is the processes, the tools, the data, and the people. Way more than it is just a, technology in and of itself. And so having those processes and having the ability to really look across the organization and determine, is this working? Am I seeing the results that I expected? And how long should I be waiting for those results to manifest? And sometimes. Some of these tools are easy, plug and play. Some of them require pretty significant integration with your CRM system or your intent data or your website, et cetera. And those things take longer, and they do require patience, and they require collaboration with the customer success team at the vendor that you're working with, in order to truly understand what that integration implementation process might look like, that timeline, and ensuring that you're baking that into your assumptions and your ROI payback as well.
So if you were sitting in charge of a mid-size enterprise and you were gonna pick somebody, what are the qualities that person would have and then what would be the first type of project that you would task them with?
And I know, agile marketing obviously. I mean at IBM it was 27 steps to being agile, which is not really that agile, but let's take the capital A out of the equation, right? And thinking about agility as a lowercase A. And I think as you think about, curiosity is kind of that ability to look forward and agility is the ability to look back and to tweak and modify and enhance and optimize what may or may not be working. In that postmortem
Because it's being comfortable with the calculated risk taking themselves, but it's also someone who can work collaboratively across functions and bring that, spirit of agility and curiosity and calculated risk taking to others. And so I do think that there is this underlying attitude that will make or break your AI spiritual leader, within your marketing team.
But you're not suggesting that, you're suggesting the person who can say, "Hey, here's what I think the outcome's gonna be", right? " Here's where we think some of the pitfalls might be, and here's our unknowns. And we just don't know until we run the experiment or we go through the process".
How do you avoid that pitfall? And what are the measures? Because you've got this person in charge who's trying to move the ball down the field, so to speak, and yet, it feels like they're getting sacked behind the line every single time. How do you mitigate for that type of scenario?
Kimberly Storin (2) (00:23):
I think the way to mitigate for that is to have the mandate start at the top. So if you as the CMO right, or you as the VP of marketing are not fully bought in, educated and continually updated on the progress that the team is making, like you can't have their back. And so that bi-directional communication up and down and ensuring that the executive in charge is clearly bought in and supporting the programs and the initiatives, I think takes away that inherent feeling that you're getting sacked from behind, right? There should be no surprises either way. And that requires executive support, executive communication, and constant updates both sides. And that requires collaboration.
Awesome. Let's pivot this conversation a little bit. You have talked about in this day of technology maintaining human connection. Connection with your customers and quite frankly, the people within your own organization. How do you view the role of technology in today's marketing world and trying to maintain that human element as you're going about doing your daily work?
And so again, kind of, going back to the importance of building a system. The processes, tools, data, and people. Like people are such an important piece of that. And when we think about being, in B2B especially, right? Sometimes we forget how critical. It is. This is an emotional decision. People are making a million dollar decision and their job is on the line, their reputation is on the line, and we as B2B marketers often forget how critical that emotional and human connection is across our customers.
Erik Martinez (00:26):
Whether it's B2B or B2C, I think that's the same story, right? At the end of the day, we want to create meaningful connection. What does your team do to ensure that you're maintaining that human connection, even as you're leveraging some of these more powerful technologies.
So we started with the manifesto and that's really critical. And then because our team is distributed, we've got a team that sits in Denver, but we also have a lot of folks that sit across North America. And so we built a culture committee that is a monthly connection point for all of our teams. We also have an internal Gchat Slack channel, right? That is really dedicated to connecting the team. We have a weekly question that we ask the team that is personal or professional, depending on the week. Somebody is assigned to be that chief Vibes Officer, every week to come up with a question that kind of spurs the connection.
And then every year we rotate out the two people that lead our culture committee and they're in charge of planning those monthly activities. It can be anything - like we've done, you know, MTV cribs, show off your workspace. We've done learning and panel discussions with marketing leaders that we've brought in. We've done just a variety, really, of kind of get-to-know-you, team bonding, and learning and development. And they know to look forward to those cultural events every month. People carve the time out, they make the space for it, and really show up to help each other. And I've really seen, that marketing manifesto in particular really manifest itself in terms of the actions and the behaviors, and the team cheers each other on, right? One person's individual win is a team win. And you really see that across the board.
Let's expand out to the customer now. How do you maintain that connection? You know, let's just talk about advertising for a moment. Right. We're out, prospecting for new customers to come into our sphere of influence. And there's a lot of research that says, we can put the best promotion and that may be a trigger for somebody. But more times than not, the trigger really is more emotional than it is rational.
Kimberly Storin (2) (00:30):
So really think about it
And we also know that it takes 15 touches to get anyone to make a decision. We know that people want to do 80% of the research before they talk to a seller and so that makes us think about how do we create the moments that matter? How do we be there in the moments that matter? How are we, first on the mind of a decision maker when it matters most, and so it really comes down to are you effectively leveraging all of the channels at your disposal? To be the voice of, and the voice to the customer means that you have to know your customer and know them inside and out, and understand the psychographics and understand where they get information and be in the relevant places where they do get information. If there is a buying committee. Are you influencing the buying committee at the right time?
Where the right media mix is for that buyer, and every buyer has a different strategy. So you can have a framework that you can generally apply, but there's never a one-size-fits-all, because every buyer group has a different pain point, has a different psychographic and demographic view and spends their time in different places with different influencers and your product can have a variety of product market fit. You know, either great product market fit or some challenges. Or not differentiated. Or differentiated.
So I think sometimes it's easy to kind of get caught up on all the things like we cannot cut through the noise anymore. It's just impossible, right? We can't capture attention anymore. Earned media doesn't have the same value that it used to, paid media absolutely does not have the same value that it used to. So how do you find the right place at the right time and the right mix that influences your people that you're trying to sell to?
I would say it's all the things that we talked about at the beginning, in terms of using controlled experimentation to find the tools that work best for your team. If content is a place that you can see the uptick pretty quickly, like picking those things and leveraging it as a superpower, but not thinking about it at a replacement for human intervention.
Erik Martinez (00:35):
Yeah you're really talking about how do we accelerate the process and not completely overhaul the process, right? You probably have a good process in place, but you can leverage the technologies to enhance it. As we move to close what last piece of advice would you like to leave with the listening audience?
Awesome. Kim, thank you so much for your time today. If somebody wants to reach out to you, what is the best way?
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