Today, Betsy is speaking with Mary Purk, Managing Director of the AI and Analytics Research Center at Wharton. Join us as Mary discusses leveraging AI and analytics to enhance the customer journey, overcoming resistance to new technologies, and bringing more diversity into revenue operations roles.
Guest Bio: As the Executive Director of Wharton AI & Analytics for Business, Mary Purk leads the academic research center that focuses on the development and application of cutting-edge analytics methods. Through AIAB’s experiential programs, Ms. Purk connects students, academics, and professionals across multiple industries to solve complex, real-world business challenges using machine learning, AI, and big data.
Ms. Purk has held other principal roles at Nielsen and IRI developing and implementing enterprise-wide analytic platforms focused on customers and brand equity. Her expertise includes forecast models, consumer segmentations, marketing mix models, product assortments, pricing, and digital strategies.
Other work experience includes leading the Retail Research Center at the University of Chicago Booth School of Business and consulting roles with Accenture and AT Kearney.
Ms. Purk is a proud MBA alumnus of the University of Chicago Booth and the University of Illinois. Ms. Purk has designed and instructed executive education courses, presented at numerous conferences, and published papers in the Journal of Marketing and Journal of Retailing.
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Announcer: Welcome to the RevTech Revolution Podcast. Today, Betsy is speaking with Mary Purk, managing director of the AI and Analytics Research Center at Wharton. Join us as Mary discusses leveraging AI and analytics to enhance the customer journey, overcoming resistance to new technologies and bringing more diversity into new operations roles. Let’s get started on the RevTech revolution.
Betsy Peters: Well, welcome back to the RevTech revolution. And today I’m just thrilled to have the Executive Director of AI at Wharton and corporate engagement for all of analytics at Wharton. Mary Purk is the Director of this group. So excited to have you here on the Rev Tech Revolution and welcome.
Mary Purk: Thank you. It’s my pleasure to be here and to talk to you, Betsy, especially about what we do in AI and how it’s going to impact Rev Ops.
Betsy Peters: So tell us a little bit about your role at Wharton’s AI and Business Analytics Research Center.
Mary Purk: How did you get here?
Betsy Peters: Who does the center serve? And how would you like our audience of Rev Ops leaders to think about the work you do and how it might help them?
Mary Purk: Sure. Well, I always like to start with the bigger entity before I start with. So the center that I am leading is AI at Wharton. It’s relatively new, although AI has been research at Wharton for a number of years. But we’ve really now consolidated it into a use center. And it serves not only the faculty and academics and students at Morton and Penn, but industry wise, it is there to serve the industry leaders as well as professionals, to expand their knowledge and application around AI in analytics, and how humans, basically customers, consumers, interact with AI and the future of that technology and how it’s going to change our society. How it’s going to change our society, but change how we do daily tasks. So that’s what the center is focused on. And the center is focused on not only how to use it today, but obviously how to use it in the future.
So they are really looking AI is changing every day, so it’s looking much as fast as it can into the future. And then I think, you know, how did I get to this great intersection of analytics and AI and business? And fortuitously enough, when I started out after graduating from the University of Illinois, anyone who’s out there IOL ini. I joined a company that’s now called Accenture, learned all about way back when, Cocoa programming. And some of those skills that I learned and my very first job, I’m still applying today. And that is where you are always asking the right question. And then solving was creating an algorithm or hard coding for an algorithm to solve a problem using data. Data was very important back then. It would make or break your particular module, and it’s still doing that today. And through that then I just progressed into working at University of Chicago, have an MBA from there booth and worked on our retail center, where we implemented pricey and merchandising experiments at a grocery store and collected granular data by store by week.
And we implemented a B testing, which was unheard of to be able to do at that time, collected that data for over three years. And that data today is still being used for researchers to understand consumer behavior. And then I’ve also gone into industry, worked with companies called Nielsen and IRI around retail data and studying the different customer journey around that. And then Wharton came calling and said, okay, and so now I am at Wharton.
Betsy Peters: Terrific. What a background. You were just talking a little bit about the work that you’ve done on customer journeys. And of course, the audience here, that’s their focus, right, is how do we take the tools and the processes and the people to support that customer journey and the people who are working on it directly. Tell me a little bit about some of the most important things that people who work on those customer journeys should consider when thinking about using data to identify growth opportunities.
Mary Purk: Yes. So I think fortunately, or unfortunately, anyone who’s been really close to the customer or consumer, I mean, there’s two different ways people can look at it. Let’s just think about the consumer. Customer, they are fickle and they are hard to understand, and they produce a lot of data points, especially now that we have the mobile devices. Fortunately, technology has caught up to the location of where people are and identifying where that data comes from, despite the fact that cookies, you can no longer track cookies on the Internet, but what it affords us is the geolocation of the mobile device to know where that consumer is experiencing the journey with the company. So, as I say, all these different things, a very common theme is the data. And people continue to invest in the data and house the data. And the customer journey is still all about data, good data, but it’s also understanding what you’re trying to solve for the consumer.
Is there a certain pain point you’re trying to solve for, or is it more variety you’re trying to provide for the consumer? I think you always have to decide what it is you are trying to improve upon in that customer journey. And do you have the data to improve to answer those questions? And it is complicated. Complicated simplification is always a good idea, but also being able to repeat what you’re doing again is also incredibly valuable. As you move forward to solve a problem, you’re not solving the problem for a day or a week or a month. Try to solve a problem or understand a journey over the period of possibly think several years, maybe three years might be the most you really need to solve for at this point, because data and technology is changing so much. I wouldn’t really solve for much beyond know.
Way back in the day, with loyalty, there would be baby loyalty programs or sears had a program I worked on where were trying to do it was called we talked about customer lifetime value, which is still super important. But to me, why don’t you solve for the lifetime value? If you can capture it for three years, then that tells you and your organization can capture it for a lifetime. So don’t tell your people to capture it for a lifetime. How about like three years of that? And if they can do that, you have a golden ticket there and you should pay them. Well.
Betsy Peters: Your point about repeatability is really interesting, too, because you start to see some flash in the data that you get excited about, but then being able to replicate it and then scale it is also super important. So, interested in understanding some of the fundamental truths that you’ve learned about data and analytics that most people miss?
Mary Purk: I would say you need to make sure you spend the time understanding the question you’re trying to solve for. You hear that in every podcast. You hear it on everything. But it is a fundamental truth. You know, if you need to understand what you’re trying to solve for. And I have this innate nature which drives people nuts sometimes. But even though I’m not an engineer, I want to know, why do you need that information to solve that problem? Why do we have to care about having a canvolier underneath a shelf so the shelf doesn’t fall down? Someone can say, well, it’s physics if you don’t understand. And again, I think it’s important to say, why are you trying to solve this question for this problem? Why is it relevant to the consumer? Why is it relevant to our revenue? Is it because we’re increasing our revenue?
Is it because we’re decreasing costs? I think it’s good to explain to the team, this is why we’re trying to solve the problem. So it’s not only about the question, but it’s about the rationale and explaining to the team why you’re trying to solve that. And in doing that, you empower them to possibly bring their own creativity to that solution. So that’s the first thing, is the question and informing people around that and put a variety of people around that question, because then you can get the best answer. The next is, where are you getting the data? How old is the data? Where is the data stored? Is it synthetic data or is it real data? How costly is it? It’s everything around that data and the accessibility of that data and how you use it. So I think it’s important as a third piece, is understanding the ethics of that data, the governance of that data.
Do you have that in place? And then finally, you talked about it yourself, Betsy, and that was around scalability. Are we solving something that’s in one part of the country or it’s because it’s a phenomena right now, is it COVID related or is something that’s going to stand the test of time? So understanding the scalability and the longevity of potentially the impact that this solution might have. So I think I reviewed what the question is and bringing in a wide variety of people to think about that question, knowing where you’re going to be, getting that data, making sure you’re in compliance, ethically, you’re doing all the right things with that. And then scalability in terms of its importance and then finally making sure you have your It team, someone from the It team very much involved and that they want to support this solution that you’re going after.
Betsy Peters: So really good rules of thumb there. And we’ve had other guests and certainly some of the people that we work with on a regular basis feel that pain point about the stakeholders not being able to articulate what they’re really after. They have a gut instinct or a directionality about it. But in order to get that really crisp problem statement, what are some of the things that you’ve done? Or how do you advise people to get stakeholders who are very experienced but somehow don’t think in terms of data accessibility, et cetera, or even good question formulation?
Mary Purk: I think there’s a couple of different tools in the toolkit, as you would say. And I’ll start with a story to help with one of them. We are working with a quasi government entity that had a malware many a couple of years ago and so they are very resistant to any technological advancement because they feel technology is going to come and create it’s. Just bigger risk. So why are we going to do that? We’re going to do the same old because it’s going to be less risk. So there’s some new leadership there that’s trying to change the cultural norm. So I think it’s important for the stakeholders to understand is it the question or is it the cultural change? Which means it’s a larger issue and the whole senior leadership team needs to be on board. And there has to be multiple messages sent out to the organization that we are now here.
And if we aren’t going to continue to advance our processes using new technology, we are not going to be in existence a couple of years. And there is risk, there is inherent risk, but there are two things we are going there, we are moving forward. So this is the message that senior leadership can provide. We need you to say we are moving forward and tell them where you’re moving forward to give them a vision and then give them a stake in it and say we need you to be part of this. You have all the domain knowledge, we need you to express the knowledge and we really can’t have put up roadblocks to this knowledge of where we’re going. Also, if we don’t change, we won’t be competitive in the future. So it’s a conversation that the team who’s doing the interviewing, if they sense that there’s constant roadblocks and changes, I think they need to get senior leadership more involved, but also educating people about technology.
And possibly that means giving them resources to attend online classes, giving them examples of possibly other companies in the industry that have used the new techniques or the new industry and where they are now so that they can be hopeful that they can envision their role in using technology, but that their role is not in eliminate it, but it is in fact enhanced. A lot of times it’s resistance to change because they’re worried about their own position. So I think people just have to be super sensitive as to why would people not be wanting to adapt to the technology or provide knowledge that would help the adoption and why there’s a lot of resistance. And I’m not saying this is easy. Words aren’t cheap. Look at here. This is costing us nothing to do other than our time that we’re willing to give. But to really affect change, you need to bring people along.
And sometimes that takes a lot of patience on the leaders to listen and understand what those motivations are and establish trust. So those would be some words of advice. It’s not easy, but you need team leaders that are willing to go the distance. I always say in some of these projects, you need to clearly identify taskmasters and project managers that don’t ruffle feathers, but they love moving a project along. And those are the people who you say, you guys guide this along and then that way some of the leaders can step back and reevaluate how they’re going to reintroduce the new ideas or different maybe new ways that they can help those employees that are a little bit more resistant to change.
Betsy Peters: Yeah, and it’s a really good recipe in general for change behavior change and change management.
Mary Purk: Right.
Betsy Peters: And were just talking about it in the lens of going from maybe more of an instinctual and experienced based approach to revenue to a data driven approach to revenue.
Mary Purk: Right.
Betsy Peters: And then layer on top of it AI and suggestions of the next best step to take with your customer or the things that are starting to come online in terms of time saving, efficiencies, those kind of things. Is there anything different about AI when it comes to the same type of advice and behavior change that you’re experiencing works?
Mary Purk: Again, I think, one, you need to know who is resistant to change, you need to identify them and need to get HR on top of it right away and understand what is going to happen, like within the company. Is that one of the things you’re going to go to the street with and say, we’re going to save 700 jobs. And if there are but we’re creating the other 400 jobs. That I think is just very you need to paint the picture of the future. And if you can’t paint that picture of what that future is in your company, people aren’t going to believe you. They’re not going to believe you. So why would they contribute to the solution? So the OD cool communication and knowing where you’re going to be and yes, there’s change, but there’s going to be all these openings of these other jobs also.
The fact of the matter is people then you can retain people and say you are going to learn new skills that are going to be valuable in the industry. Like you said, Rev Ops is a brand new phenomenon around cost savings and better customer journey experiences and more enjoyable work. AI is going to be incredibly impactful in this area. So not only are you learning about a new area called Rev Ops that people don’t even know about, it’s a combination of marketing and finance and supply chain and operation. You’re going to be like a unicorn. Not only could be the unicorn, but AI is going to be part of it. So you’re going to learn AI, which is super popular too. And it’s much more beyond the chat GBT that everyone is using. Like they’re using word and PowerPoint. You’re going to understand how to apply that AI.
An example of that AI would be in a chat bot. You’re going to learn how to develop the correct chat bot for our customers. That’s going to save you time so that you get to be, you’re the concierge of our top clients because they’re the ones that the chat bot can’t interpret things for. So we are going to put you in charge of our eight plus customers and you’re going to help them so they have a better customer journey because we want our top customers to have that concierge journey where some of our other customers, where the AI can interpret everything, they’re going to be handled by other people. You used to be handling those know Tom or Susan, but now you’re going to be the know concierge service person that helps with that customer journey. So that’s how I think companies have to think about it and that’s how AI is going to be fine.
It is going to change jobs and roles and descriptions. But especially in customer journeys, there’s not only a chat bot, but there’s also predicting what someone wants to do. Give them different recommendations in there when they’re coming onto your site or they’re coming into the store, someone might invite them in for a special customer awareness event. I could see this very much being applied both online and in person. I mean, maybe someone doesn’t want to come to a store. So someone’s like, I’m going to set up a zoom call with you to explain all these new products that we have and there’ll be more engagement with a customer because of AI because of the time saved.
Betsy Peters: Sounds like you’ve been doing some convincing of your own here because you’re very persuasive about it. Tell me what excites you the most about the growth of AI when it comes to applying analytics and new ways of making customer journeys in this kind of increasingly omnichannel.
Mary Purk: Know. I think that the people who are going to be using it the most and the quickest as always are the marketing and retail and CPG companies because they’ve been using granular data forever. And that’s what AI is all about, granular data. So they’re very comfortable with it. They’re very comfortable with complexities, they’re very comfortable with marketing mix modeling. I mean AI now before as an example for both companies and for advertising agencies, you would have to hire consultants every year to build your marketing mix modeling or tweak it with some economists and statisticians. Now AI is going to be able to do that for you and you can do it in house and it’s.
Betsy Peters: The analysts that can do it.
Mary Purk: Like you don’t even have to hire out specialized A that is crazy good. So I think that’s a big impact right away that’s going to be happening. But to me the biggest area that is going to have the most impact is in healthcare. And healthcare is not only in what you’ve already seen. For example, everyone’s heard about where a radiologist AI has been able to has so many different screens now that they can say these all or say here’s some that really have to be looked by a radiologist. So now a radiologist value is they can see and predict potentially more patients that need to be seen. So they’re impacting saving lives. So that’s one of the basic things. But think about it. Where AI is now going to be able to impact when they’re talking to their doctors. And their doctors are now going to be much more effective with what they’re able to predict or see or take in all the different history of different things for an individual so that they can be more proactive with their health care.
And also I think consumers, this is where the consumer aspect comes in, they are going to be much more satisfied with their HR outcome. So I think all those things are that’s where I see AI saving money. It’ll cost a lot of money, but it’ll save money and people will have better outcomes and better experiences. And when people have that, then we’re going to have a much more productive society. And it doesn’t have to be productive, I’m saying productive in terms of our overall well being and our happiness. Productivity. When I’m saying that is not about more hours spent at work. I predict AI, we’re going to spend less hours at work, we’re going to be healthier and so we’re not going to be taxing our healthcare system as much due to AI.
Betsy Peters: So does the center work primarily with healthcare in the healthcare industry?
Mary Purk: No. So very much how we’re mostly in the business aspect of it in algorithms, because business is always kind of ahead of adapting to technology. Remember, they’re very familiar with technology and healthcare is familiar with technology in terms of the machines and stuff. But the digital transformation just kind of started to occur with digital records. I mean, we’re talking about digital records like we’ve had digital records in the business for quite some time. So they’re playing catch up. But that’s going to accelerate much more quickly and there’s going to be companies healthcare continues to grow, so that industry is going to continue to grow. So I see businesses maybe gravitating more into the healthcare space now. It’s not delivering healthcare per se, but just the management of healthcare, the experience of healthcare. I see more intersections with that. We have a healthcare lab, but it’s just starting.
It’s very small and it’s starting to grow. With our center, we look at human and AI intersection. So one way we look at it is, and I probably haven’t emphasized that enough, but it’s how do humans adapt to AI? How do they accept AI in their life? And this is really important in terms of consumer behavior because businesses need to understand how humans are going to be adapting to the use of the how do you want to be treated by a chat bot? Some consumers are like, I don’t want to talk to a chat bot, but some consumers might not realize that a chat bot is even talking to them. So that’s really important. So it’s not just the efficiency and our revenues. I know that’s what Revas is about, but there’s a lot of revenue associated with understanding the adoption of some of the new services you want to bring on that customer journey.
So you can’t ignore that part either. And that’s what we study is the cost of accepted AI applications or rejection of AI applications, or the implementation of AI applications that weren’t very effective because they didn’t study the consumer behavior aspect.
Betsy Peters: Yeah, that’s fascinating, Mary. I mean, our group really usually is on the line for things like CRM adoption, sales acceleration adoption. So they are often talking to, again, really skilled relationship builder type sales folks who may not want to put all sorts of data in the CRM or may not want to take the recommendation of a chat bot that’s telling them, based on the data, this is the next thing you should do with this client. So I think it’s very relevant for this audience and that kind of human factor studying, I think is so important when any new user interface comes online, which of course, that’s what chat GPT is. It’s a user interface into the large language models. So is there anything in terms of employee experience that you guys work on that might be interesting to share with this audience.
Mary Purk: So there’s two things. One, we have an entity called Wharton People Analytics that actually looks at what’s impacting the employee satisfaction, employee productivity. There’s a lot of analytics around that. But then how are they adopting the AI, especially around hybrid work? So we do work on things like that. And as were talking about this, one thing I wanted that you asked me about, what are some of the truths that people should follow or good rules of the road when they’re looking at AI? And one thing I forgot to mention that I learned way back when I was doing some research at the University of Chicago at Boost was you need to do a B testing. And so that I think is really important in Rob Ops as well, and specifically around employees. When you were talking about the CRM system that maybe AI is now recommending a specific recommendation for a customer that someone should follow, but they’re very resistant to that.
If the company is seeing that employees are resistant to the changes that are coming through, this is when you should employ an A B test. I mean, you should really begin to understand it. And it’s not to say that the employee is wrong in not doing that many times. There’s a lot of good intuition that employee has, and maybe it’s something you haven’t captured. So A B testing is good, and I think it’s always good to do that, to possibly discover and solve that problem. So I wanted to throw that in there as well.
Betsy Peters: Yeah, that’s a great point. Is there any case study that you can share that wouldn’t trip any wires in terms of confidentiality? That was a really good and maybe counterintuitive AB split test.
Mary Purk: I’m trying to think, because recently we haven’t done as much ay testing, but I will refer back to one that I know of where we implemented pricing, a pricing study where everyone thought that everyday low pricing was a big winner. It should be a big winner because that was what Walmart was winning. And so a lot of these high low stores were just trying to figure out to crack that net. They just couldn’t figure out how to crack that nut. So we had one store test, these high low, and that one stores. Stores kept an everyday low pricing. They basically imitated Walmart. And then they continued their very good philosophy of high low pricing that they knew how to do very well. And as it turned out, the high low pricing was actually a way better for that particular company to do because their customers had been programmed to understand.
There was this big promotion. There was an excitement about a promotion. They liked shopping that and then unless you could maintain for a long runway everyday oil pricing, and it wasn’t just at the store shelf. Walmart brought it through every single piece of their business, of course. Which reminds me now back to where we are here with Rev. Rev Ops is across every part of the company, so you probably tell anyone this, that Rev Ops is not going to work unless it touches every part of the company. So in terms of a case study, I would say you have to make sure that if you’re going to implement it, you have to implement it across the whole company. And secondly, if there is an intuition of why something is working, it is worth really trying to understand it. And not every dress fits every person.
So you need to understand why. Yeah.
Betsy Peters: Tell me about something that keeps you up at night about the growth of AI. Are there any mistakes or pitfalls you hope that leaders in Rev Ops will avoid as we become increasingly reliant on it?
Mary Purk: Well, one thing that keeps me up at night is some caution. There are cautionary tales out there around AI, and that is real. What I hope doesn’t happen is that I hope everyone continues to lean into AI and use AI to improve our experiences. But if you see something not going right, everyone should speak up around that. They should speak up. And employers, especially leadership, should explain to everyone that we’re all responsible. This is a very new technology and it could really do a lot of good, but it could also do a lot of harm. And everyone is responsible for noting that this could happen. And I think it’s important that they tell their managers, listen to the employees around them. So I see a lot of good in AI, and I really hope that people lean in to learn about it and support it.
Because if we don’t really support it well in business, then it really can’t affect solving a lot of really hard problems we have in society. So I feel that being where we are, we’re in a very privileged spot and we should recognize that and we should continue to be responsible for it. So that’s kind of what keeps me up at night. What should I be influencing or telling people? And for me, I don’t like it when I hear people like, oh, I heard so many companies were shutting down. They weren’t letting anyone touch AI, and it was like, you can’t touch it. It was like in a vault. And I understood why they were doing that, because they were worried that some of their secrets were going to be going out. Catjpt was also brand new. It was going to go out into the wild.
Betsy Peters: Right.
Mary Purk: And then weak secrets were going to be out there. But they’re now figuring out how to put their own walled gardens around that. And we need to have people lean in and learn about it so that we can all better knowledgeable, better leaders around AI in the application.
Betsy Peters: Yeah, interesting. I definitely agree with what you’re. Saying about everybody seeing something and saying something, right? That’s the only way to make sure we’re norming it for brand experience, for customer journey, for the overall health of the business. But when it comes to rev Ops, a lot of times they’re not in charge of the algorithms that are running in the background, all of that. So does it come down to data observability? Like the data you’re seeing just doesn’t match patterns in your head? Or what’s the practical advice for how to do that? See something, say something.
Mary Purk: One, first of all, like I said, the leadership needs to say that’s acceptable. Let’s just say that’s a given. And then I think that sometimes it’s intuition. This is really the honest thing. We’ve never seen this phenomenon. Why is this occurring? Why do we have this segment of customers buying so much in this particular marketplace? Like, what do we think is going on? And it comes down to really basic statistics. AI is super complicated and there’s lots of algorithms to your point, but something looks like what we would call statistics, an outlier in the tail. That the big bell curve. Then maybe look at that a little bit more deeply and ask the questions. And the data scientists, all the data scientists I know, they’re really happy to explain how the algorithm works. And then you can ask another question. Where is this data coming from?
Is this coming from our main server or is it synthetic data? Just ask more questions about the data. Data usually problem is within the data.
Betsy Peters: For sure.
Mary Purk: For sure. And you know what? Data is not complicated. So the algorithm, there could be a problem in the algorithm, but typically if it’s in the data, anyone can find it in the data with help, obviously, from your data.
Betsy Peters: And persistence.
Mary Purk: Yes, persistence. Yes, persistence. Angela Duffworth wrote a book called Grit. Grit is a really there’s a lot of persistence in grit. So it does take grit, but it also takes passion. It takes passion, it takes grit, takes integrity. All these things, curiosity, all these need to make sure that they’re strong in that. Their culture is strong in that. Yeah.
Betsy Peters: And this is a good segue to my next question. So what advice do you give young talent who’s seeking positions in revenue, technology or analytics or AI like this?
Mary Purk: The best advice I give is, well, I don’t know if it’s good advice. I’ll have to ask them in five years if it was good advice or not. One of them is ask really good questions. And by asking a good question, it means two things. One, you’re a good listener. Two, you’re inquisitive or curious. And three, you’re willing to gain knowledge. And knowledge is not only gained by reading, but also by attending conferences. And conferences can be on the web. I’m not saying you have to spend a lot of money, but there’s so many things available now to gain the knowledge. So gain the knowledge, have the curiosity and be willing to listen. With that, you can go a long way. Grit is one of those things in persistence, of course, but being intellectually intellect, people say, be intellectually curious. I mean, what’s intelligence, right?
It’s a wide variety of things. We’re all intelligent in different ways. So being curious, I think, is a very good trait. It’s probably the best one I could give to people.
Betsy Peters: And for folks who are in hiring positions, how do we bring diverse skills and experiences into the fold? So how do we get more women, minorities, people outside of revenue functions in this fastest growing job function in the United States? According to.
Mary Purk: For, I think one hopefully they’re going to campuses and holding talks about know, I don’t know if you really had to go to high school, but I would definitely be going to undergraduate classes. I would hit the undergraduates. By the time people are master’s students, MBAs, they already deciding where they want to go. But I would hit undergraduates and I wouldn’t just go to the business school. I would go to across all the different departments economics, psychology, all these different things and explain why you need people in Rev Ops. Like why would you need a psychologist or someone who’s interested in psychology? Well, Rev Ops, we want to understand consumer behavior and how it impacts all these different parts of business or in engineering, telling them more about the business aspect of it. So that would be one thing is going in and sponsoring, I would say, lectures around it.
Another way is to work with universities such as Wharton, where we have an experiential learning program where companies can bring Rev Ops problems. And then we put together a highly curated group of students to solve that particular problem. I think that’s a really good way to seek to get talent, and especially if the pipeline is really narrow for that. And then finally, I’m not as familiar with this topic as much as I should be, so I’m not sure if there’s coursera classes around Rev Ops. And can someone get a Rev Ops certificate? Do they get more money? How does a Rev Ops role compare to a consulting role or a financial analyst role or a data scientist role? So I think helping students navigate it and not making them navigate it, but if it’s a growing field, it’s going to grow from, whatever, one X to ten x in five years.
Educating people about that as well, but going to the campuses, I think it’s good. And then being involved with some of these organizations that can kind of spread the word.
Betsy Peters: Yeah, great advice. And again, just as somebody who’s working in higher ed and sees all those opportunities, it’s invaluable advice. So thank you for that. Just as a last question, if there was a corporation who’s listening to this, who was interested in getting involved with.
Mary Purk: What you do at Wharton.
Betsy Peters: How do you engage with corporations and how do they get in touch with you?
Mary Purk: Well, the way they would get in touch with me or our AI at Wharton Center. There is an email and we can.
Betsy Peters: Put that in the show.
Mary Purk: Yeah, don’t put that in the show notes. But one way to do that, we have a program where they would contact us and they can even submit it online. Say, we want to solve this particular problem in Rev Ops, or we want to educate people about revs. From there, we’ll either connect them with career services or we’ll connect them with student clubs around Rev Ops. But if they want to work with our center, they have a company they want to solve for and they really want to attract students to that. We have a program called our Analytics Accelerator. We could define what that project is. And we have data scientists that work with the company to define what that is, clean the data. And then we have a competition where we have data scientists from the engineering school and analysts from the business school. They develop a team.
They work on the project over terms of a semester and then provide that solution back. And it’s a real solution. So this is not like this is just for classroom time. It’s a real solution that can be implemented. Another way they can do it is if it’s a very technical solution that needs academic focus. We can set up what we call a research opportunity and then we define what that is. We do a webinar and we have 3500 academics around the country, actually around the globe, and they all listen in and then they apply to work on that particular project. So there’s a lot of resources right there. And I’ll just tell you the value of both of those. Whether it’s students or academics, they’re both very well qualified. Individuals work on it. They get things done. So I don’t know about you, but I still have like a pile of photos that I’m supposed to have put in photo albums for the last ten years.
Like if I hired someone who has no emotional connection to it’d be done in an hour. Good point. Instead I have this cultural emotional connection. Can’t touch it. So sometimes when on your to do list for a certain period of time, you need to get it done, this is a way to do that. It’s very refreshing. And the other thing it is it involves people from your company that get fresh perspective on how to solve a problem or new approaches. And it really invigorates, especially if you want it to be a catalyst for change. So yes, Wharton does this other schools do it. You could do it in your community, but I would recommend that go and talk to an outside entity that has a lot of and has a really good reputation that you can involve with your team. It’s very inexpensive. It’s much less expensive than consulting with a top consulting company.
Mary Purk: Nothing’s wrong with that.
Betsy Peters: But I’m just saying, it’s no, I hear you. The creativity that comes from people from diverse backgrounds getting together to solve problems is different than a consulting culture, for sure.
Mary Purk: Yeah. And the other thing is, I don’t know, but I don’t know if Rev Ops has done this, but connected with some of these other data organizations, these cheap data officer conferences and such. We know we talked about how you connect with talent, but I think that’s another good thing to do is figuring out how to connect with some of these other established organizations where the CMO is there, or the CDO or CIO, those are really prominent people that probably impact cheap revenue operations. And so I would say possibly getting on some of those stages and aligning with CEOs and CIOs and CAOS, chief analytic officers, et cetera, would be a good thing as well. Terrific.
Betsy Peters: Mary, it has been so much fun talking to you. Thank you so much for all the time today. And any last thoughts or anything that you haven’t shared about why you love your job?
Mary Purk: I can’t believe it, but I’ve been involved with data. It was just by luck that I was attracted to working with data and solving interesting questions around consumers. And I just am very grateful for the opportunity to do that. And so those of you who are in positions that can influence younger folks, younger individuals to be part of this Rev Ops analytics AI, please encourage them to be part of that, because you’re helping a trajectory for their career. So I’d like to share that. And it’s very exciting time, and we’re just so lucky that technology finally caught up to be able to manage all this data, so now we can solve the problems in real time.
Betsy Peters: Wonderful.
Mary Purk: So thank you very much. It’s been a great pleasure talking to you, Betsy and Laurie, a little bit more about Revot, so I appreciate that.
Betsy Peters: Thanks again, Mary.
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