Data Monetization part 1

In the first part of our engaging interview with Kevin Munley, listeners are treated to a captivating exploration of data monetization in marketing. Kevin's wealth of experience and expertise in data analysis, integration, and decision intelligence shine through as he discusses the various types of analytics that organizations can leverage. From historical reporting to real-time insights and the intricate world of predictive analytics, Kevin paints a vivid picture of how data can be transformed into a strategic advantage. He delves into the challenges of data ownership and silos within organizations, emphasizing the critical need for clear data governance. Kevin's insightful conversation highlights the power of experimentation and the potential for marketing teams to become more like scientists in their approach. If you're eager to gain a deeper understanding of data's role in marketing and how it can drive growth and innovation, this podcast episode with Kevin Munley is a treasure trove of knowledge and inspiration.

Here is the transcript of the podcast:

Mauricio
Welcome to Studio Databranding Podcast. I am your host, Mauricio Romero. Today, we will talk about data monetization in the marketing world. The conversation with our guest, Kevin Munley, was phenomenal and vast. So, we will divide it into a two-part series. Kevin is a renowned expert in data analysis and data integration with a passionate focus on decision intelligence.

Mauricio
Kevin isn't just a strategist. He's a true evangelist for customer success, dedicating his career to empowering companies to make smarter, data-driven decisions. Kevin, thank you so much for being here with us today. It's a pleasure to have you here.

Kevin
Mauricio, thank you very much. It's a pleasure to be here with you on Studio Databranding.

Kevin Munley's Journey into Data Analysis

Mauricio
Could you tell us about your journey into data analysis and what sparked your interest in this field?

Kevin
I've always been intrigued by how things work and how people make decisions. You know, my dad was an educator, so I was raised in an educator's home. He was a teacher, a principal, and the superintendent of a school district. And so, education was always very important in our household. Knowledge comes from data.

As I learned over my life, I just thought knowledge was something you had. But no, knowledge is something you gain from understanding the data that underlies life, whether it's how to drive a car, or whether it's how to run a business, or whether it's how to learn at school. There are data elements that you can investigate and things that you can know to help you do better and all of those things.

So, data has always been intriguing to me. And so I followed that and I got a degree in finance. I had an amazing counselor when I first started in college. And he was, "You're a numbers guy, Kevin. You should look at the data, look at the numbers, and you should align that with some systems knowledge."

So, I also have a secondary degree in systems. So, seeing data move into the systematic world and become part of computer systems and how people then interacted with that was, again, just one more hook into my soul of wanting to know how things work.

The Buyer's Journey in Marketing

Mauricio
That's great. And there's a very important or essential fact in data in terms of marketing, how those people make decisions. We call that the buyer's journey. What kind of information do you need to go forward into a purchase decision? I believe that's the future. If we can gather all that data on how we make those decisions that would be great in terms of marketing, right?

Kevin
The buyer journey is a critical path for both sales and marketing. So any organization that's truly interested in meeting their buyers where they are, wants to understand as much as they can about the buyer, not just demographics, but emotional complexities, right? Things that they're going through in life. Decisions are made in a couple of different ways. There are emotional decision makers or gut decision makers, which is where I lean.

And then there are intellectual decision makers. Reasoning comes into play on that side. Those two need different quantities of data around a given situation to make a decision. So I need from my experience with people I've worked with and people, you know, in my family and relationships, I find myself reaching a conclusion and a decision long before many of them.

And I've always wondered, why is that? Why? Why do I not need so much data to make those decisions and end up at the same point? I don't have an answer for that. It's intuitive. It's just how I am wired to see the connections between data and action, right. So, others need far more data points to be able to draw the same conclusions and reach the same decisions.

So you have to understand who that consumer is. So if you're delivering analytics inside your organization, you want to know the recipient of that information that you're delivering. How much data do they need to make a solid decision? You can have all kinds of supporting data behind that, but how much do they as an individual need? So you don't want to overwhelm them with too much data.

You want to give them as much as they need to make a really insightful decision. Others, you need to deliver far more layers and nuanced data so they can reason that data into making a decision. But the buyer journey is even more difficult because you're not personally connected to that buyer in the field, right in the market.

So you have to depend on any of the sort of logistic points that you have any contact with that buyer. Then you have to also include social media and trending. How are people influenced by that kind of data and how do they put that into their reasoning process to make a decision? To buy a product or buy a service?

So, it's data that is king. It's making sure you have the right data. Just because someone lives in Orlando doesn't mean they're going to buy a certain thing, right? So you have to understand all the nuances around the data that they're using to make their decisions. You can't just make general statements about the market if you will.

Kevin Munley's Role at KASH Tech

Mauricio
And Kevin, can you describe your current role at KASH Tech and your primary responsibilities?

Kevin
Sure. I am Director of Customer Success, which is another way of saying business development, sales, marketing. All of those things come together within that role. So my responsibilities are to understand the market where where our clients or prospects in need of our services in the data and analytics space or the application development space, and then understand their business drivers, right. It's not just understanding the data about them, it's understanding what they're trying to accomplish. So, I spend an awful lot of time in relationship development and begin to gain first data about my prospects. I turn that data into knowledge, and then with that knowledge, I open the door to a conversation, which then allows me to gain empathy and understanding because without understanding, I haven't really reached the pinnacle of engagement.

I can have the fact that, you know, there are $10 Million company. Great. That ticks off a single box relative to the kind of market we're going after. But, I don't know what their vision is. I don't know what their goals are. I don't know what their challenges are. I don't know what keeps them up at night.

So, then I can move beyond that data to knowledge and gather that kind of information. But then I have to take that knowledge and I have to internalize it and turn it into an empathy and an understanding of how that is impacting their organization and their objectives. So with that understanding, I can go and position solutions that will help them overcome those challenges and put away those things that keep them up at night.

Defining Data Monetization in Marketing

Mauricio
Kevin, how do you define data monetization in the context of marketing?

Kevin
Well, data monetization, which simple definition for that is turning data into cash, right? Turning it into revenue, turning into money, turning it into profit, whatever term you want to use on the other end of that statement. But, turning data into a value for an organization is monetization. Monetization does generally tend to guide people to the thinking of revenue or cash, but monetization is value, right?

And data is the biggest asset of any company that has existed for more than a year. Data is being generated at levels and and exponentially that we have never considered before, right? The entire Encyclopedia Britannica from my youth is created every 3 hours now. That much data is generated. The problem is to back to my previous point. There really is no knowledge in that raw data, right?

So to monetize, you must take the data and gain knowledge from it. That knowledge again, leads you into understanding. Now, you can go and market. Now you can you understand that buyer's considerations. You understand that buyer's emotions their motives, their concerns. Now you're turning that knowledge into understanding of that buyer and you're meeting them where they are when they need to be met.

So you can you can meet somebody with all the information about your organization, but they're not looking to make a decision about software services. My knowledge was enough to know there are target, but I didn't have the understanding that right now they're not looking for my service. So I have to gain that understanding. And that's the same thing with marketing.

It's, how do you get to understand that market? That target? That audience? That buyer? That consumer? Whoever that is, so that you meet them where they are at, the need that drives them to your product.

The Role of Data in Marketing Strategies

Mauricio
And we use data in marketing for everything as an example. Demand elasticity is really important. When you move your price just a little bit up or a little bit down, what's the proposition? Or probably you have a store --- what's the marketing mix of products you needed during your your stock? 

Kevin
We have a customer, a large CPG, consumer packaged goods customer that has all the information you could possibly want and understanding -- they've achieved understanding relative to their raw materials, relative to their production cycles, relative to the general market. But, they did not have the understanding of the basket of goods that the consumer was buying in any given moment.

So they had no correlation between Product A and their product. So in working with them, we identified a third party source of data that allows us to get that retailer information to be able to see the basket of goods purchased at the time the consumer is in the retail store. Now we can take all of that data and we can analyze it, we can we can separate it and we can look at it to see what are the correlations between any other products on the market and our product, right?

And that's powerful because now I can co-brand I can offer discounts if you buy product A or if you buy our product, I'll give you $10 off on that product B, which you always buy with our product anyway. So, I'm going to incentivize you to buy more of my product and I'm going to give you a discount on that other product that is always in your basket.

That's the understanding that marketing needs to achieve.

Mauricio
Yeah, and I got the opportunity to go to Japan and they take it to the extreme. They change three times per day the whole layout of the store. And of course that decision is made from data how those customers buy which product.

Kevin
Right. Yeah. As a director of I.T for a video distributor earlier in my career, one of the things that was critical to success, to to sales was what we called the planogram, and that's what you're speaking about. They have a planogram for the store. We had a planogram for our display and then we obviously negotiated with positioning within the retail outlets to have our display in the most appropriate place for the products we were selling, which were generally children's entertainment, edutainment kind of videos and content.

And so but our planogram, we at that point were already getting retail information, but just about the products we sold. So at any given store, we knew how many units of any given product we sold. We didn't know what else they bought. This whole basket of goods that you can do today with retail information is light years ahead of what we were dealing with, but what it is doing is looking at their store planogram and they're finding that traffic patterns within the store instantaneously are recordable.

And you can take that data and analyze it very quickly to determine, okay, if I have overstock on this, this location seems to be getting the most activity today. I can move my overstocked goods to that location so I can start to diminish stock and not be left have leftovers that I have to either return to the manufacturer or that goes stale or bad because they're they are shelf life dependent.

So having a really good planogram and understanding the flow of people within your retail store is critical as well.

Breaking Down Data Silos

Mauricio
Kevin, I love one of the concepts you talk about in a very intelligent phrase. Silos are for corn, not data! Yeah, it's really intelligent. I love that analogy. And, and could you explain to our audience what is a data silo and what could be the most common silos in marketing and sales?

Kevin
Yeah. Silos are for corn or any kind of crop product that you want to store specifically for a purpose, right? And it has no relationship to the silo that's next to it. It doesn't matter if in silo one you have corn and in silo two you have wheat. The two of them do not interrelate to each other and them knowing anything about each other is of no value to anybody.

But in business, historically, every department within an organization has held on to their data in a very parochial way. Right? It's mine. I own it. I keep it. To me, I only share what I want to share. And then, you know, it doesn't value the organization to have that data in those silos. It values the department, and the department can do their jobs very efficiently and effectively because they do control that data.

But over the all, the organization is lacking a broader view of what's actually happening, and they're not empowering decision makers with those insights, if you will, into what's happening within the business and in the convenience store. Their insights were instantaneous. They knew -- they could see into what was happening within the organization in that location on a minute by minute basis.

Organizations, companies, businesses need to get to that same level of ability. It is critical that they have those insights, those actionable insights where people can make decisions that will affect what happens at 3 p.m. this afternoon as opposed to 3 p.m. next Tuesday, right? Because it's time is value. Time is of the essence in business today. We are moving at light speeds across business and the longer it takes for that information to become correlated, which is really the key here, it's correlating data between department activities and decisions and showing how that correlation of data impacts the organization as a whole.

So marketing has a campaign and they don't tell sales that the campaign had a 40% response rate. That's incredible, right? 40% response rate on any kind of a campaign! People would kill for that I think! If you get 3% on a on a campaign, you're really happy. Yes. But marketing wants to use that to promote themselves because they're in budget season or maybe they're trying to hire more people and they use that siloed information to promote their personal specific needs within the department.

But sharing that with sales would allow sales to repackage product pretty quickly and put it into the campaign. So they had products that were not part of the campaign, but they have products that are, you know, languishing on store shelves, are not selling. If they know the campaign's doing that well, they could move new products into that campaign and start to move those products out of the warehouse and into the hands of consumers.

But if marketing doesn't share that, again, if that silo exists, then that correlation can't be identified and vice versa. Sales likes to keep all their data to themselves. They like to keep -- what are we selling in this region? What are we selling in this district? What are we selling in this geographic area? It's not intuitive for people to share that kind of information for some reason, and I'm not sure why.

Mauricio
Well, yeah, I believe marketing and sales have a big problem if they do not work together. They were born from the same department. Sales marketing was to support sales and something happened in the way that they are choose different departments and they should start working again together. In fact, one of the best strategies we've done in terms of SEO is go to sales people and ask them, "What are people asking?"

When you have a normal sales conversation, what are the concerns of the consumer? And when we bring that to data and look for all the SEO thing, it's wonderful! They tell us the whole story. So they need marketing should be relying on sales people because they have the pulse of how is the real clients moving forward.

Kevin
I agree 100%. We're we're specifically talking here about marketing and sales, which did have their genesis in a single structure and at some point they split and businesses adopted this bifurcated approach to marketing and sales as opposed to one organization that is cohesive and collaborative in delivering those results. But I would say it goes beyond. I think all data and information within an organization from the top to the bottom should be shared and known and available to all other departments -- everyone within the organization. There should be no data limits on who can consume what data because, you know, have no idea this this idea that we now have this layer of what is hyped and promoted data scientists, every person in your company should be a data scientist. You cannot make viable decisions without data, which becomes knowledge, which then gets morphed into understanding.

You cannot make good decisions without every person in your organization being empowered to be a data scientist. Yes, you need architecture at the top. You need someone who's looking at all of this data. If I can -- sidebar for a minute --- All data has value is true, but not all data has value in every circumstance. This is where, you know, as I said, I'm a foodie, right? I'm a most frustrated chef and I love food. But here in in Florida, let's speak in terms that are relatable. An entire orange has value, right? But if I'm a harvester, if I'm a plantation owner, let's say, and then I have a I have a bunch of orange trees, what's most important to me, if you look at an orange is data.

The most important thing to that person is the seed, because the seed is going to grow the next tree from which I'm going to harvest my next crop of oranges. But if I'm an orange juice manufacturer, I don't want the seed right. The seed is not important to me. I want the pulp. I want to be able to squeeze that pulp down and I want to create my orange juice.

So the rind and the seed don't mean anything to me. And if I am a spice manufacturer, the rind is the most important thing to me. I want the rind so I can dry it and turn it into zest so I can add that flavor to my foods and my life. So an orange in and of itself in total is very valuable, but various components of that are valuable to certain people.

Data is exactly the same. So all the data of an organization is indeed valuable, but to any given area, specific sets of data are going to be what's most critical for them making good, viable decisions for the organization and growing the organization.

Components of a Data Monetization Strategy

Mauricio
I love that analogy because it help us to foresee that we need to change the lens. Depending on which point you want to see the data it's going to be worth or it's not going to be good for anyone. In Databranding, we always talk about strategy, so you can you're showing us a little bit of strategy. What could be the main components of a data monetization strategy?

Kevin
There are many, many, many more than we can really discuss here. But just to start, it's understanding the objective of the organization. Honestly, in my opinion, I find companies are spending a lot of time doing things that don't align with the mission and vision and goals and objectives of an organization. "It's just what we've done. It's how we've done it. It's just how it is. I do this spreadsheet every day." But, who looks at it? "I don't know. I've just done this for 20 years and I hand it over and I put it in that inbox over there." What do you know? What happens to it after that? No, that is irrelevant to that person, but it shouldn't be because they're spending valuable time doing something that may not be valuing the business.

So understanding that corporate objective for why we are here, why do we exist as a business. KASH Tech exists as a business to deliver data and analytics solutions to our customers. I'm not going to go wash their windows, right? It's just not valuable for me. I'm going to care and feed my customers. I'm going to take very good care of them. I'm going to share everything I have with them. But when it passes from a value part of the relationship to ancillary, I'm very careful not to spend too much time there. If it values the relationship, fine. But if it's just lost conversation, there's no reason to have it, right? So beyond understanding the objective, you have to define that target audience.

You have to know who you're going after, and that's your marketing team. They should be they should be very knowledgeable about what the product positioning is and who the potential consumer is for that product. And they should be very clear on being able to articulate what that target audience looks like, where it exists and what they're what they're looking for.

What is it they really, really want? Right? That's critical in the marketing side of things, the data sources. Again, I've got an orange, I've got a seed pulp and a rind. Which of those components do I need to drive the company's vision? What is the critical data, the sources of that data? Do those sources integrate? Can I integrate those sources?

What technologies do I have in place to gain access to those data sources? And then how will I pull them together into a single view, something we call single source of the truth, which is ensuring that everybody is working off the same collection of data in a given area of the business. So many times I've been in, you know, corporate meetings where marketing has one story to tell and they have all the data to back it up and sales has another story to tell and they have all the data to back it up and production has another story to tell and they have all the data to back it up. But they're using data silos. So there's no there's no correlation. And so they're at odds with each other instead of unified on a common vision and understanding the value of that data to the target audience, does it really matter to the audience that the Orange is six inches around or four inches around before you made the juice? Did that data help them make a decision right.

So what data, again, to the consumer or the audience that you're trying to reach. What data is actionable for them to make a purchase? Once you understand that and you start feeding that that data and understand what they want in that data, you're going to grow your market and you're going to grow your your, your demand by your consumers.

Mauricio
Kevin, thank you very much. We will continue our conversation on our next episode.

Kevin
Welcome, Mauricio. Thank you for having me.

Mauricio
To our listeners, thank you so much for joining us today. We hope you enjoy the first part of your interview with Kevin Munley as much as we did. Before we go subscribe to the Studio Data Burning podcast on your favorite podcast platform so you will get all the exciting content we have coming up. If you enjoyed this episode, please leave us a review or share it with a friend.

Your support means a lot, and if you're ready for more, stay tuned for the next episode, where we'll continue our conversation with Kevin Munley and dive deeper into data monetization.

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