Innovation Fuel: Real-World Business Cases
Authentic Stories, Real Impact
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Innovation Fuel: Real-World Business Cases
Data-Driven Storytelling for Marketing Success
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In this episode of Innovation Fuel, we are joined by Cindy Greenglass, a thought leader in data-driven decision-making and storytelling with data. With over 20 years of experience, Cindy shares her insights on transforming raw data into compelling narratives that drive business success. We discuss the importance of data context, the evolution of data accessibility, and how businesses can leverage storytelling to connect with their audiences. Tune in for an enlightening conversation that will inspire you to rethink how you use data in your marketing strategies!
>> Gelareh: Got an idea that keeps you up at night or one that makes people say, hm, that will never work.
>> Dave: Perfect. That's what we talk about here on Innovation Fuel. So let's get that conversation started. Today on Innovation Fuel, we're joined by Cindy Greenglass, a thought leader in data driven decision making and storytelling with data. With over 20 plus years of B2B and B2C experiences, she has launched new products, led go to market strategies and built high performing teams in direct response and performance marketing. She's also a, passionate educator like Gelareh myself here, helping professionals and students understand how data visualization, critical thinking and optichannel marketing drive real business success. Cindy, I can't wait to have a conversation with you. We've been trying to do this for a while. Welcome to the show. How are you doing today?
>> Cindy Greenglass: Oh, Dave, thank you so very much. I'm thrilled to be with you too. You know, people say my goodness, you're such a data geek. But really that's not the case. I'm just a passionate about, you know, telling stories with data and the power that it, you know, has for us. So I'm so glad to have this conversation.
>> Dave: So yeah, Cindy, you built a reputation around turning data into stories. How did you first realize that data could be more than numbers, that it could be a story?
>> Cindy Greenglass: Well, this goes back to my very first experiences with data. I used to, back before I was in the the current commercial world. I worked for the the U.S. foreign Commercial Service, which is a branch of the government that helps businesses find partners in export and import business. And I got to work on some of the largest databases available, globally. And there was so much data that came in and what they did is they put me on a team that said here, go out to companies all around the world and get them to use this and this data. And what I realized is that it was meaningless to have all this bits and bytes and data without any relevance and meaning. And what they really needed was somebody to help them figure out how to use it, tell them what it means. Because without the context, the data was absolutely meaningless. And that was like my first aha. About making the connection between the raw data and the meaningfulness and context of it was a huge disconnect.
>> Gelareh: That is absolutely interesting. So is it the secondary data or you got those data from your clients?
>> Cindy Greenglass: Well, this was actually government data at the time. I've done a lot since, but back in the day this was, you know, showing that I've been in the Business for a while, as Dave said, you know, the US Government was partnering with, and the Canadian government was partnering with a company called Dun and Bradstreet. Dun and Bradstreet, at the time the largest collector of, business to business data in the world. And so they have the global data coming in. And because they had all this raw data from what businesses did, what their business was, what we called sic codes or NAICs codes, all of about the revenues and everything from around the world, they had this huge database of global business. Now, I subsequently went on after understanding how important this was to working for my very first client was Hyatt Hotels. And they wanted to build a business database of people who were guests at all their properties all around the world. Well, the data existed every time you checked into a hotel. You create what's called a guest portfolio or a guest day. But all that data doesn't mean anything unless you make context out of it. And then the real beauty of it is, as we say, the storytelling is how do you create the avatar, how do you create the, the, the Personas of who's a person who stays at a Hyatt versus who's a person who stays at a Holiday Inn? And the raw data will not do that for you. so you know, the beauty of it, my eyes got open big to say, think about the power if you could unlock the raw data and turn it into a story that communications people can use, marketers can use, and businesses can use to be success.
>> Dave: So Cindy, we're talking about data. I just want to clarify things for our listeners. When we talk about data, there's a couple different elements here. We have, Gelareh said first party data. That's first party data that we're collecting internally through our organization. Then we have second and third party data that tends to be more of that big database data. Is that correct?
>> Cindy Greenglass: That's correct. So in the first case, like when you're looking at D and B data, they're collecting it firsthand. It's, they're doing first party data collection. They're then compiling it and aggregating it and turning it into third party data. When you're talking about Hyatt, Hyatt is first party data because it's all their guest day information, all of the information they collect. When you check into a hotel, everything you do will go to the restaurant, order in how much you paid, what your class of room was, et cetera. So that would be first party data. So compiled data, as we call it compilation data, third party data comes through a third party source compiled from others first party data. I collected it with permission from my customers or contacts directly to the company that is collecting that data. Does that make sense Dave? Does that help? Yeah, yeah, absolutely.
>> Gelareh: I just have a question about which year are you start off understanding at the context of data and using your raw data. What was the first time you did it?
>> Cindy Greenglass: Oh goodness. believe it or not this would have been late 80s because you know the US government had tremendous access to global data at a very early stage and I worked on the very very first databases in a computer called a Wang. So that kind of takes you back a bit right? but fast forward to what's now available going beyond a government data. Canada had great data. So I worked with U.S. foreign Commercial Service, then I worked at the U.S. the Canadian consulate and then I worked on U.S. canada trade relations. So I had access to a lot of data and in those days we were using it to do things like nafta, the North American Free Trade Agreement back in the 80s. And so access to data really didn't come into the hands of private companies until well into the 90s. And that's like when you think about when I started to work with like Hyatt we're talking or Sprint which was one of the leading mobile companies at the time that created the mother of all databases of consumer information. So that was well into the 90s when it became available and cost effective for companies to start aggregating data into quote databases or data sets and then software became available to start analyzing data.
>> Gelareh: So how those early access I'm talking on 80s and of course we don't, we didn't have kind of machine learning techniques there time. Yes of course and then Python with the theory axis of coding. Of course we don't have those, we didn't have that one. how it shifts influence the way organization approach data.
>> Cindy Greenglass: so let me see if I understand the question. How did the access to the data shift away A company looks at their business.
>> Gelareh: Yes, you said that there was a huge data before but did companies, private companies have a great, good access to them that could make sense for them or, know, know just a specific.
>> Cindy Greenglass: No they really didn't. You know if you think about it was all mainframe based computing, you know we're talking about IBM 360s or you know huge mainframes that were not capable of doing any of what we do today for sure and certainly there was no such thing as Big data. So what they had, you know, you have to say what happened. If I wanted to look at the evolution of it, data became available to collect. Technology became available to churn through huge amounts of data. Ultimately we had access to technology to create something more than raw data through very, very primitive means like access and some of these early software. And now we've progressed well, well, well beyond it, of which we could have a whole conversation about how the evolution of the technology, what came first, the ability to churn and crunch data, or the ability for us to comprehend and understand it in context. And I would say that it's a little chicken in the egg. You couldn't understand it if you couldn't have access. We gave access to data, we gave access to the, compilation of it. But our ability to understand contextually what the data meant did not keep up with technology for some time. And I would argue today that as communicators, marketers, communicators in the field, in the profession, we are well behind what the technology can do for us. Well, well, well behind. We still do not understand how to contextualize most of the data we have available to us. Technology has taken us way beyond what we use and way beyond what we can, seem to make sense of. And that's our big opportunity. So, you know, if that kind of helps you understand techno, we couldn't do so much of what we do today without having the tools, but they're just tools. It's like a car. If you don't have a driver's license, doesn't matter how fast that car goes. It doesn't even matter whether you have a Lamborghini or you have a, a, fancy Tesla or a fancy car. If you don't know how to drive and you don't have a car, you know, a driver's license, it sits there and goes nowhere. That's what data does. If you don't have a data driving license and a storytelling license for data, it's absolutely fascinating.
>> Gelareh: But those time as well, we didn't have that much data. Privacy and privacy issues, especially for marketing, is a very big challenge.
>> Cindy Greenglass: Right?
>> Gelareh: So, when you say marketers are behind of technology, I totally get it. But how about hapex, the trust of the customers for their data?
>> Cindy Greenglass: well, we certainly have increasing privacy based on where you are in the world. Clearly GDPR is much more restrictive. The US has far more lenient privacy laws. Canada is somewhere in the middle. And so it really depends where in the world you are on what? You know how restrictive that data is. First party data, if you acquire it as a company, you acquire it with the permissions, on privacy, you have access to a tremendous amount of data. You still do. So I think that while we don't have vast legislation that restricts us from appropriate use of data that has been shared by our consumers or customers, there is still a tremendous amount that you can do. And if you think about the information within the four walls, I'll call it virtual walls of your company, you are collecting incredible amounts of information that if you use it, the right way and maintaining, the privacy of the people who give it to you, you still can have great learnings and you still can make great decisions from it. The, issue about the sharing of that data, the, is a whole different conversation. Plus the advent of all of the walled gardens and how that has now decreased our ability to get access to third party data is changing and making it more complex. so there are a lot of issues around privacy, but there's still an enormous amount of data available to each and every one of our companies. And for marketers, there's a massive amount of data available to us that our consumers and our constituents readily give us if we are very, true to how we use it.
>> Dave: So because this leads me down the pathway of artificial intelligence, because that data that's coming in from artificial intelligence and we're almost shifting the process of marketing towards how do we use the, the AI tools to help us in analyzing this data and getting information out of it. But is there almost an opportunity where we're going too far with the data and maybe going too far into. And maybe that's going to weaken the customer connection and the storytelling.
>> Cindy Greenglass: That's a great question, Dave. And you know, this takes me back to. And I think you're right. What happens is we as marketers or communicators, because we're all, what if people, our brains are innovative, creative, we go, oh, what if we could do this? And then what we tend to do is run fast with scissors and break things. And sometimes what we do is go out there with a great idea before it's really fully thought out and we say we'll learn from it and we'll get better. But what that can do sometimes when you're talking about data and when you're talking about people's information and how you use it is you can hurt your credibility. So here's a good example of exactly what you're saying, Dave. Back when they first came up with QR codes. Brilliant. QR codes. We as marketers jumped all over it because it was a massive shift from print to digital, from analog to digital. The ability to have somebody use their mobile device to communicate with us. It was huge. Okay, but what we didn't do was understand what that, like, we didn't think the whole thing through. So what happened? We didn't do responsive design on, any of our websites. We sent people from QR codes to crappy landing pages, and we made a really lousy customer experience. Right? And then everybody stopped using QR codes because they sucked. Okay, fast forward. We figured out what we did wrong. We, the communicators, the technology got better. We figured the tool has to be better at what it does. We understand more about the best use of the technology, and we got smarter about it as communicators. So now QR codes are everywhere, and they're great, and we couldn't live without them.
>> Dave: Right?
>> Cindy Greenglass: So AI tools are going to be the same way. We're going to make mistakes, and we're going to use them in ways that are probably not appropriate. But if we learn from our mistakes of the past and be more careful with it, then we can implement them more successfully than some of these things we've done in the past.
>> Dave: So in that vein, sorry, glory in this one vein, because we're on the same pathway to this element, is how can businesses use data in storytelling to build better define the place in the crowded market?
>> Cindy Greenglass: First of all, let's talk about the people who have to make this happen. Okay? Where does storytelling live? Storytelling doesn't live in the IT department, and storytelling doesn't live in the machine that's churning through the data. That's not their job, and it's not the job of the analyst. Storytelling lies in the hands of the marketers, the communicators, the people who have to put the context around it and use it to make a compelling case. Whether that's a compelling case to a customer, a constituency, to the business itself, to your boss, to the finance people. What we have to do is, is understand the difference between reporting, fundamentally, reporting, analysis and intelligence. Okay? The computers, the technology, the tools, the AI can do the reporting. Reporting is just churning all that data. You gotta slog through a lot of data to get to the next step, which is to analyze. All right? How is AI going to help you and be better at it? You got to ask AI the right prompts. Prompts are the same as the questions we say in business. If you don't ask the right question, you don't get the right answer. And so AI is not going to give you an answer if they don't know what the prompt is. And if the prompt's not the right prompt, it's really not going to give you the right answer. So all it's going to do is turn the data for you. So the people who are going to ask the right questions prompts are going to be helped by the end consume person who consumes the data, who has to tell the story. What is the story we want to tell? What is the question you're asking? What is the problem you're trying to solve? And if you don't start from that place, all you're going to get is hallucinations in your AI.
>> Gelareh: So one more question. It's so we gather data, we create a story for our customers based on the data. So what make customers trust the message if it's come from data?
>> Cindy Greenglass: Well, they're trusting you. They're trusting the person or the company who is relaying the message. And the message has been curated and informed by the information. Not the data, but the information and the knowledge that you've gleaned from it. They're trusting you. Gelareh They're trusting me, they're trusting my company that I am, thoughtful, credible, trustworthy, and I'm communicating with you about something that I care about, whether that's a product or service, whether, whether that's a company doing business with another company on a B2B level. The data informs in the back end and it allows us to understand better the needs and the wants and desires of our customers so that we have relevant communications with them. If we don't have relevant communications with them, they don't trust us and we spam them. Or we are, you know, we're not personalized or we're personalized with the wrong information. and therefore the data isn't what makes us trustworthy, it's our use of it, us that make us trustworthy.
>> Gelareh: So I understand for something, for Hayati, it means, people know the brands, right? But imagine for startups and they want to start communications with early customers about, it means any communications, but it's data relevant. So in what, what we do in
>> Cindy Greenglass: This case, well, we have to start by saying if you're a startup, entrepreneurial, you're a startup. What information, what, what information is important to you? Okay, you can't look at. Let me use my car analogy. If you're going into a new market. If you're a new startup or you're a new business, you can't look in the rearview mirror because you got no history. You really don't have data to work off of. So you're looking through the windshield, you're looking forward thinking as opposed to back in the rearview mirror because you don't have any historical data. If you're starting new, if you're entering a new same with a new product, new product, launch to an existing business, adjacency products, those are all looking through the windshield, not in the rearview mirror. So you're going to have to start to collect data that's relevant and meaningful for you so you can develop a go to market strategy that makes sense for you. So where are you going to start? Well the marketing people aren't going to start. We're going to start with our, probably our five year plan. We're going to have to do our financial homework to say what do we think is available? What do we think we're going to build? And then you're going to have to use available third party analytics. That's where I'd start. You can use trend data that's available to you. You're going to look at your industry benchmarks, your trend data in the markets you're going into, ah, tons of great curated third party data. And then you're going to identify who's your target market, what's your addressable market. These are all things you can do with available third party data, right? You're not collecting any first party data yet. So your target market, your addressable market. Now you can go out and start to ask within those addressable market important, meaningful questions about how they feel about a product like yours. Is there a take rate for it? Is there like all the things you'd want to ask to say does your business have traction? Okay, now that's going to be heavy lifting, first party collab like you're going to have to go out and curate that data yourself with potential audience members if you're a brand new startup, right? So you're going to be collecting your own data. Now if you say okay, but how do they know who I am and how do they trust me? Well I'd say you probably want to use some tools that are or third party companies that are you know, relevant and have reputation in the marketplace that can say I'm capturing this information on behalf of a brand that's new or on behalf of a company that's new. There's lots of ways to do it. We could talk about how you could do it, but, you know, I don't know if that helps.
>> Gelareh: Absolutely, absolutely. That was great. But I just really want to have a one, example from you. If you can share an example. When focusing on the wrong data led a business to send a wrong message or then how they fixed it.
>> Cindy Greenglass: Well, here. Okay, this is a really. Okay, I laugh because this is one that, is easy for everybody to understand and kind of easy for everybody to see why it would happen. Okay. I worked with a very well known and very large, global cruise line company and they were, they built this, this data set, this very large data set of everybody who had cruised with them. All of people had inquired about a cruise. All the people who had, you know, signed up for their, you know, information and downloaded brochures. All the people would, ask questions. And then all the people who cruised with them and the routes and, and where they went and they churned through all this data and they came back and they said, aha. We found the one variable in the data that can predict that somebody is going to go from an inquiry to a cruiser. And we're all on the edge of our seats. What did you find? And they said it's the presence of a phone. And there's. Okay, now some of us started laughing. The most people in the room were dumbfounded. Like the presence of a phone? Yes, the presence of a phone. Okay, what did they do? They churned through the data and they found records that had a phone number field filled in, converted higher than, and, and ended up cruising than people who had no information in the phone number field. So what did they do? They went out and they said, okay, we're only going to talk to people who, who have a phone. Well, everybody's got a phone, for heaven's sakes. So what they did was they interpreted the data wrong. What they didn't understand is if I'm willing to give you my information, which at this point in time is I'm giving you my phone number, my mobile number to contact me, I am farther along in the buying cycle than somebody who is strictly doing their anonymous searching online. And the real aha. Uh-huh. Was anybody who gives you a contact information phone or email is higher up in the conversion process. They need to be contacted right away and they need to have a separate path of communication to convert them to a sale. And when they understood that, they increased their conversion rate. It wasn't that you ignore people who don't have the following. What did you do if people didn't give you a phone? They are not in the same, consideration set yet. So they needed more time to be persuaded. So what they did is they went on an entirely different communication schedule and they started the why cruise. What does cruising do? Here are some wonderful places you can go. Here's the experience. They didn't hard sell you into cabin boats B, that's leaving in three weeks. They started the story about the cruise experience and it completely bifurcated their communications and it made a massive difference. But you can see how data could lead you down. I mean, it's kind of silly, but wow, what a simple thing made all the difference in the world.
>> Dave: Well, I just want to close the circle here because you talked to the beanie about context and now we're talking about, what we call a customer journey map. From awareness through, interest to consideration to conversion to that element and where, in context, where are they in that element? And leveraging the data is going to help you tell the right story in order to get you to the right place where you need to be to build that content. Now what I want to swing into is you've had a lot of experience building teams around this element. So how is you help teams inside companies use data stories so that everyone communicates with the consistent voice that customers need to hear?
>> Cindy Greenglass: Oh, Dave, isn't that the holy grail? you know, data is so siloed and different parts of companies use data for different reasons. And I want to say that I don't think that I have an answer, for that. I can't say that I've, you know, figure that one out. The Holy Grail. Here's what I can say. people need to share information more readily across the silos in different areas and understand what the power of that information can be. But that's really hard because in today's environment, knowledge is the ultimate power. And the person who holds that knowledge will hold onto that in their silo or, individually because it's their power base. So when you can help somebody else be successful by sharing information, that gives them more power in their role or in their situation. You build collaboration. When you build collaboration amongst team members, they're more willing to help you and they're more willing to data share. So when I'm building my story, I have, and this is something that's, you know, I have a group of people you have to identify when you're building your story. And I say you have to socialize your data. Just like we're social, right? You go around the water cooler, the virtual water cooler, to socialize your data and get buy in and who are the people you got to get buy in from? And I say there's really four types of people you have to find to socialize your data. You got to find Doubting Thomas, the person who's going to tear down your data and say it's not right.
>> Gelareh: Okay.
>> Cindy Greenglass: And you've got to run it by and tell the story to them and have them poke all the holes in the world in it. you want them to poke holes in it because you don't want them to deep six you at the moment. You're presenting it in a powerful way. Right? Okay, you need I call her and the advocate, the person who really likes you and the person who is well placed enough and has credibility in the organization to take you forward and say, okay, I'm on board and I've got, I've got credibility and I've got, you know, you've got agency, but I got credibility. I'm going to bring you along. you got to be very careful of the person I call the powerless Penelope. The person who strokes your ego, has no power in the organization, doesn't know what they're talking about, beware of them and stay away from them as the helping you decide on your story, you have to kind of minimize. There's lots of people like that, but they just don't know. They don't know. and then you gotta find, you know, the person I call Chuck, the check writer. Who's the person who's going to endorse this? Like, if you're telling a story with your data in your company, it's because you want something to be done with it, right? Write me a check, approve my budget, let me do a campaign, do something with this story. And you got to find the person who's the ultimate decision maker there. And that's the person you at some point have to get this story in front of.
>> Dave: Transforming marketing from a cost center into a profit center.
>> Cindy Greenglass: There you go.
>> Gelareh: So may I have another question? That means we are in this, the world of data and accessing to data. More people are getting more expert in data analysis and, also private sector government, all of them. Do you think that how this knowledge of data analysis, predictive analysis, can support lobbying? Because you mentioned about NAFTA, NAFTA policy. So how this can impact the lobbying?
>> Cindy Greenglass: For private sector with government so advocacy and lobby. Okay? Two different things, right? Advocacy and lobbying. So lobbying, you want to promote your. Your specific company or industry or vertical markets, to help influence potentially what's happening in local, regional or national government politics. advocacy, on the other hand, we're looking at how do we educate our legislators and our, And from the standpoint of your. Their constituents on what a particular issue means, what's the comp. Why it matters. Now, lawmakers ultimately are influenced or should be influenced by the constituents that elected them. So when you're doing advocacy, being able to share with them what this is different data. Okay, now we're talking about macroeconomic data. What does this, what is the impact of making this decision have on your constituents? Because the constituent. How big is your constituency? If you do X, is this to the benefit of your constituents or not? And why? And that advocacy can help them decide where they focus their attention on what bills or legislation or issues are important. Important, because they're going to say, is it important to the area or my constituents that I support? And if it doesn't hit their radar, then they tend to. I mean, they got too much to have to vote on and think about. Lobbying is, you know, I think of as more like trade associations lobbying, generally done by more communities of groups of companies who have, resources collaborating together. For, for example, the nonprofit association who comes together to try and help, legislators and lawmakers understand that if you make it, if you are going to tax people's donations more, then people will give less. And if they give less, the impact directly on human services and the communities is great. So they're going to go out as the entire organization of nonprofit profits to say, what is the impact of doing this? Do you understand it? Okay, if you're advocating. I'm going to advocate for. Think of all the good that the commun. The homeless shelters do in your constituency. Because you, lawmaker, have a lot of people in your local constituency who are affected by XYZ nonprofit. See the difference? So it's two different types of data sets. One you can have influence on, the other you need to collaborate with larger communities.
>> Dave: Cindy, thinking about now, the future, we're moving into AI. AI marketing. You think AI marketers should have certification to work on primary and secondary data?
>> Cindy Greenglass: Absolutely. I think they need certification just like we do with data science. I mean, it should be part of data science education. I think that just like we learn how to use other tools. Python, you mentioned Galera. Other types of tools that are available to us, I think that the distinction is the technologists who have to actually manipulate the tool is different than the marketer who has to analyze it and contextualize it. And I think there are very few educational programs, even certification programs for marketers for data driven marketing science. I don't need to know how to program in Python, I don't want to know how to program it, but I need to understand enough of it that when I'm looking at the output, I can make context and decision storytelling from what I'm seeing. And I also have to be able to ask the right question to the person who's running the technology for me. So marketers need a different kind of certification than the people that are actually going to, you know, run the AI engines that we're putting on all of our systems.
>> Dave: When it comes to listeners out here, we've got listeners, that are listening in. We have entrepreneurs, students and faculty here.
>> Gelareh: You'Re mostly focusing on your challenge, as if you want, how you think that you have to go to next stage.
>> Cindy Greenglass: The challenge for businesses today, I think that this is universal. I don't know that it's that different from any time I started. You know, my first startup was actually a database, you know, management startup back in the day. And at the time there was no such thing as data and databases. Convincing companies they needed it is never a way to go. Companies need to solve a problem they see in the market or bring a solution that they think is needed to a specific area. Creating a product or service and then going out and trying to find a market for it is a very, very, very heavy lift and generally not very successful for businesses. So start the other way around and find a niche or listen to the marketplace place and find what's missing and find some product, service or category that is in need of something that you can contribute and bring. You can listen with social media and listen with all of the tools that we have available to us and identify where a need could be that you could fill. It can be very niche and niche marketing. I think niche products and services are where we're at today. To be successful solving a big problem like being the next DHL or Federal Express is not what companies should do. So what did I do? I looked for what was missing and what was missing was the companies could not figure out how to use the data they had. And I said, I can tell the greatest story. I can figure this out for you and Dave, I've said it to you before. I can make a, gourmet meal in anybody's kitchen. That's what I'm going to do for you. Put me in your kitchen. I'm going to figure out where all the everything is and I'm going to make the meal and you're going to sit down and you're going to eat it and you're going to go, how did you do that? And then I'm going to teach you how I did that so that you can do it next time. And that was because I saw that companies were deer in the headlights when they looked at all the data. And worse, the people working in the companies didn't have any idea, didn't have the skills to do it. So I said, let me help you. I'll, teach somebody in your company how to connect the dots. That was a need that developed into more needs I identified in other businesses and ultimately grew my company through collaboration and acquisitions into, you know, $100 million business and sold it by identifying other needs that businesses had. Like, I can't handle all my transactions in paper and I want to go digital. My customers don't want to write checks and pay their bills. You know, they want to do things digitally online. But I can't figure out which customers want to be digital and which customers don't. Hey, I can help you solve that. That's a problem I can fix. Let's create a business that can convert analog to digital payment services.
>> Dave: How can our listeners connectivity learn more about you and the things you're doing?
>> Cindy Greenglass: Well, I encourage anybody who's listening to please reach out, through this episode when they hear it. Please link in with me because I am on LinkedIn and I do post a lot as well. So I tend to share a lot and I'd be happy to connect with anybody or connect through YouTube, through Galera and Dave, through your wonderful innovation fuel. You can send them my, LinkedIn. You can send them to my email. It's kind of a long one through my company. It's called Livingston Strategies can connect through my email. Drop me a line. I would love to converse with you. I just want to say for your students and, I'm going to get a bit on my sandbox here. Marketing students, all of you in communications and marketing, please take a map class. Absolutely.
>> Gelareh: Please tell it to my daughter! Yes, absolutely!
>> Cindy Greenglass: End this conversation without getting on my soapbox. Now. Don't be afraid of, the math. You can't do analysis if you don't understand numbers. And when I have students who say they're afraid of an Excel spreadsheet, I want to cry. Please don't be afraid of math. Absolutely.
>> Gelareh: Thank you, Cindy. Thank you, Dave.
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That was another episode of Innovation Fuel. Big Ideas don't grow by Themselves.
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>> Gelareh: Innovation Fuel is produced by JPOD Creations. Find out more about the show at theinnovationfuel.
>> Cindy Greenglass: Cat.