Getting the Absolute Best Data Science Talent to Solve Marketing Problems

Author: Beverly Wright

In this podcast, Dr. Beverly Wright, Vice President of Data Science & AI at Wavicle Data Solutions, explores the intersection of data science and marketing in a captivating discussion with Bindu Chellappan, Senior Vice President of Marketing Insights at Corpay (FLEETCOR). They discuss the vital skills and abilities top-notch data science talent today must have to address core marketing challenges. Whether you’re a data enthusiast, a marketing professional, or someone curious about the intersection of these fields, tune in to explore effective strategies for harnessing the power of data science in marketing. 


Speaker details:

  • Dr. Beverly Wright, Vice President of Data Science & AI at Wavicle Data Solutions 
  • Bindu Chellappan, Senior Vice President of Marketing Insights at Corpay (FLEETCOR) 


Watch the full podcast here or keep scrolling to read a transcript of the discussion between Beverly and Bindu:



Beverly: Hello, I’m Dr. Beverly Wright, and welcome to TAG Data Talk. With us today, we have Bindu Chellappan, Senior Vice President of Marketing Insights at Corpay formerly known as FLEETCOR. And we’re talking about getting the absolute best data science talent to solve marketing problems.  


Welcome, Bindu. 


Bindu: Thank you, Beverly. 


Beverly: Glad to have you on here. Let’s start off with a little background. Tell us, why are you so cool? 


Bindu: Well, not sure if I’m cool, but data definitely is cool. A little bit about myself: I have worked for 20 years, and I’ve had the privilege of working in various roles within marketing, multiple industries, and three different countries. 


Beverly: Oh, wow. Are you trilingual?  


Bindu: Yeah, actually, I am.  


Beverly: Wow, that’s amazing. Good for you. So, you’re very malleable, too, just from one culture to the next. I can’t even imagine. I don’t even think I can move out of the south, I’d keep saying y’all. That’s great. 


So, you’re at FLEETCOR now, and you are in marketing insights, so you probably focus on data quite a bit. But it’s also in “marketing,” which is very interesting because that’s considered more creative and squishy. Whereas the data side is almost the exact opposite. So, it must be an interesting role.  


We’re talking about talent and getting the absolute best. Why do we need data science for successful marketing? Because it used to be back in the day, that marketing was just your instincts and your gut. But now you’re saying, “no, we need hardcore data science.” Tell us about that. 


Bindu: Absolutely. Marketing is not just creative anymore. In the last 15 to 20 years, marketing has evolved to a lot of data and a lot of technology. If you look at it, the whole marketing space has exploded with technology, marketing technology, and data. You need data sciences because of the sheer volume of data that we get through digital footprints, sales calls, and what can be recorded; it’s huge. And then, of course, when you’re talking about different technology, you have different data in different technology, and you’ve got to bring all these disparate data sources together. So, the volume and the number of technologies that you have require data science talent. 


Beverly: Absolutely. And this is not just about marketing for online purposes; this is about marketing for any purpose. There’s tons of data. So, what are the types of jobs that we might see that data scientists do? 


Bindu: Again, the role of data scientists and what people call a data scientist is very different. So, in the marketing world, anything to do with data, probably people will be putting out a rec for a data scientist, and it can mean a lot of different things. Larger companies have very defined roles. They have very defined roles for data scientists, and they might be doing a narrow part of analytics. But in smaller companies and companies like Fleetcor, where we have 10,000 plus people, it’s more about a wider scope that a data scientist role would do.  


So we are talking about not just coding and statistical modeling, but we’re also talking about data visualization and data manipulation. You need to be able to ETL the data. You’re talking about visualization, building models, reporting, and data storytelling; that’s also an important part of analytics in marketing. 


Beverly: Yeah, and you listed several different things because there are so many different opportunities to improve marketing, generate revenue with marketing, and make it more efficient. So, with the roles that you mentioned, I’m trying to think about all the different types of skills; there’s probably a core set that’s there, and then some augmentation you add, depending on the role. Tell us about the core set of skills that you’ve really got to have. 


Bindu: Absolutely, the core skills are your programming skills. Today, in marketing, when you say data analytics, you need to know Python and R; that’s a given. That’s table stakes. Then, you should be able to manipulate data and have some data engineering skills, and SQL is really important. And then, let’s not forget Excel. 


Beverly: Yeah, really, thank you for saying that. Very interesting.  


Bindu: So, we still, when we get analysts onboarded, we do test them on Excel because people my age and above are used to Excel. Of course, you have to deal with big datasets. But all the smaller data sets, all the manipulation and final reporting, etc., are all done in Excel, and that Excel goes into PowerPoint. So that’s an important skill. 


Beverly: So, a big deal. That’s so funny because it’s almost like we’ve come to such a point that the newer talent is forgetting some of the stuff that was core to us. I was playing pickleball with somebody, and he was getting his undergrad. And he said, “wow, I just discovered Excel, and I’m doing this, I’m doing that.” I thought, well, that’s funny, I thought that was just what kids do. But now they’re learning Python in third grade, and they don’t know Excel. So, it’s still a big part of the function and thank you for actually saying that because a lot of people have just forgotten about it.  

So those are some hardcore skills like Python, R, and SQL; you’ve got to know ETL and how to manage data and manipulate it. What about the non-technical stuff? What else is there? There are certainly some other things. 


Bindu: Absolutely. And I would clump everything else into four different buckets. So, the first one I would call a domain. By domain, I mean the different functions, such as marketing, finance, etc. Let me talk about marketing. Anybody who is aspiring to be in marketing needs to know a little bit about marketing, even a data scientist. You need to know how data is collected at various touchpoints, and what are the different customer touchpoints. When you have a data set, what is that data set talk about it? Unless you have that context of how marketing is done by a company, you really would not understand the data that well. 


Beverly: So, how do they get that? 


Bindu: Two ways. First, there are enough courses available, for example, Google Analytics; those are free courses available that you should be able to pick up something on before applying for a job. And the second one is once you are in the company, understand what that data set looks like. Ask a lot of questions. 


Beverly: Would it be okay to shadow somebody like, “Hey, if I’m going to look at this data about campaigns, I want to sit with somebody that runs the marketing campaigns.” 


Bindu:  Absolutely. And there’s so much material available about different marketing technology. How does it aggregate the data? What does it talk about? There’s so much material available over the internet that you can really ramp up. 


Beverly: Yeah, absolutely. To give you one quick example, I had a group working on opioid addiction data. I’m not going to say I made them, but I strongly encouraged them to go with me to the client, which was the Davis Direction Foundation. They went and talked to people who have this addiction to understand what it was, how it impacts them in their daily lives, and how they live. They said the change in how they viewed the data was tremendous. They just saw it a different way and understood it. So, I hear what you’re saying about the domain that makes sense. 


Bindu: And in that domain itself, the other one I want to discuss is learning about the technology. So that you may not be able to do before you get into the job. But when we look for talent, we try to understand at least if the talent has some experience with, say, a CRM or a marketing automation platform because it makes it a little easy.  


Beverly: Yeah. Even if it’s not the one that you have, it’s just that they have the ability to grasp the concepts around it. 


Bindu: Exactly. When you’re talking about object versus a lead versus something, they are able to grasp that. So that’s about domain. The second bucket is business. How does the company get its revenue? What are the pockets of ROI, efficiencies, etc.? 


Beverly: Data scientists have a hard time with that. So, I really understand the zooming out. Because right now, they’re thinking about “how I build this dashboard, how do I make this model, or how do I come up with this data analysis?” And they’re not probably thinking about, “what that’s going to do? And how it’s going to impact the business. And what does that mean for revenue?” They’re not zooming out.  


Bindu: Exactly. When a fresh analyst comes in, the work is given to him. He or she is just doing bits and pieces of the work that has been assigned by either the functional leader or his manager. But if you’re going to grow in your career, you need to understand the business context. 


Beverely: Right. They may not understand why. 


Bindu: Exactly. Marketing analysts need to be able to look at the data, build some trends, build models around it, and understand what he can give to the business that would produce either efficiency or revenue. So, that’s the gold.  


Beverely: So, domain experience and understanding the business sounds like the big picture. And then you had two more? 


Bindu: Communication. It’s not about language. Communication is as simple as if you can document what you’re doing. Can you document it so that the next person can understand what you’ve done? It’s a stage-gate approach. After everything you’ve done, can you circle back with the person who has assigned you the work? Ask if you’re on the right track. 


Beverly: Don’t wait until the very end. 


Bindu: These are the very basics of communication, so that’s important. Now it’s more, especially in organizations like ours, where you don’t have a big bench of data scientists, and you don’t report up to a data science ladder. There are a couple of data scientists/analysts who report to a business leader. You are the one with the data; you should be able to tell a story with the data. I know everybody’s throwing data storytelling, and that’s super important. 


Beverly: Yeah, especially in marketing. If you do all this, and you can’t tell the story, it’s dead in the water. 


Bindu: Exactly. And the fourth one. That’s the most critical one in my mind. You can be good with data, but that doesn’t make you a good analytics person. To be a good analytics person, you need to have two things: critical thinking and problem-solving abilities. I have seen a lot of non-data people who are great with that. Who are able to do problem-solving. These are two skills that I feel are missing when we try to get new analysts. 


Beverly: Why do you think this is happening? Because I’ve seen my scientists who have had to go through the scientific method – like chemists, people who majored in physics, or things like that – who know the scientific method, and they’re trying to problem solve? Do you think it has something to do with what they’re taught in school? Why is there this missing piece? 


Bindu: Probably, and maybe it requires a little bit of training, too. You need to take people through an approach of problem-solving. It is not difficult; it is not something that you’re born with. All of us have to go through a learning process, and it’s totally a coachable, teachable skill. It’s about curiosity and asking people to ask questions. They need to be curious; they need to ask why something is happening the way it is happening. And I don’t know who, but somebody has talked about the “7 whys.” That’s a beautiful approach. If you see a number, ask why that is so, and then ask the follow-up questions about that. That’s how you get to understand the true value of that. 


Beverly: So, these are really great attributes. I’m picturing this like a superhero person that has all these pieces. How is it different in marketing? Let’s say I’m talking to a finance person about financial analysis or someone in logistics, and they’re interested in supply chain data scientists. How does marketing offer a unique value prop to the business that requires something different? Is it really a different context? 


Bindu: I don’t think so. Other than the fact that marketing is cooler. But I think these are skill sets that every function requires. 


Beverly: Right. Except that marketing is cool, but also the impact that it can have. If you’re in product analytics, it’s about one product you’ve been working on. But if you’re in marketing, it can be to a broader, not just your customers, but your entire markets and prospects. These are good core types of skills and good advice for really anybody in data science and they’re trying to improve the way they are.  


This brings us to my final question: What final piece of advice would you give if you’re trying to talk to somebody out there? They’re more junior in their career, saying, “Look, all I want is to be a dynamite marketing data scientist.” What final piece of advice would you give them? 


Bindu: A lot. First is, you’ve got to be good at your technical skills. So that’s a given. Second is read, read a lot, not just about technology, but read about what’s happening around you; how is that affecting marketing as a function? And if you already have a job, you know what the context is. For example, right now, all the marketers should be talking about what’s going to happen with the cookies. So, you need to read up; you need to know what is happening around you, where your industry is, and where your function is. That’s critical. Third is build. Build your problem-solving abilities. Talk to people within the company and outside of the company. Network, learn, and one more thing, ask questions. Don’t be afraid to ask questions. 


Beverly: I heard you say that several times. I’m thinking, like, people really don’t ask questions that much anymore. Okay, good. That’s a great piece of advice. I love that. 


Thank you so much to Bindu Chellappan, Senior Vice President of Marketing Insights at FLEETCOR, for being on TAG Data Talk.  


Bindu: Thank you so much, Beverly. 


As highlighted by Bindu and Beverly, the powerful alliance between data science and marketing is rooted in enhancing technical skills, continuous learning, and curiosity-driven problem-solving. This enlightening conversion emphasizes the need to stay updated on industry shifts and the importance of networking and asking questions for a deeper understanding of marketing dynamics. Explore the full catalogue of TAG Data Talk conversations here: TAG Data Talk with Dr. Beverly Wright – TAG Online.