Kirill: This is episode four with business strategy expert, Brendan Hogan.
(background music plays)
Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. And each week, we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today and now let’s make the complex simple.
(background music plays)
Welcome everybody to this fourth episode of the SuperDataScience podcast. Super excited that you’re on board, and today we’ve got a very interesting guest, a good friend of mine, Brendan Hogan. So Brendan was somebody who I worked with when I moved from consulting into the industry. And as you may know, I moved into a pension fund in Australia called superannuation funds. I moved into a superannuation fund called Sunsuper, and there I met Brendan. And I moved into a role which was called Insight Specialist, which later developed into a Data Scientist role. Brendan was in the same role, but he moved on later into a role in the same organisation, into a strategy role.
And we had a very interesting conversation in this podcast, and why is that? Well, because Brendan is a unique person in the sense that he doesn’t actually have the data science expertise, so he doesn’t do R coding, he doesn’t do Python programming. But at the same time, he is constantly in the data science world. So a lot of you will find this podcast interesting, especially if you are in a role where you don’t have that core data science expertise, or you’re not looking to develop it. And even if you do, even if you’re in a role where you’re working a lot with data science projects, and you’re doing Python and R and SQL and stuff like that, you will still pick up a lot from this conversation.
And specifically, here are a couple of things that we talk about. So you will notice that Brendan has had a career going through many different organisations. So he’s been in consulting, in the industry, now he’s moved to the UK and he’s working at a different company there. But every time, some how he manages to create himself a role that leverages his strengths.
So we’re going to be talking about leveraging your strengths and building a career path for yourself. And it’s a very unique skill to have, and it’s a powerful skill to have as well, because imagine that you have these strengths, which you are good at, and instead of adapting to the role in the organisation, and learning new skills and maybe working on your weaknesses, instead of doing that, you just create a role for yourself. You some how find these roles, you some how go about creating these roles so that you can leverage your strengths. And that way you win, because you get to improve your strengths further, and the organisation wins because they get the most benefit that you can offer them. So Brendan’s a great example of that, and I asked him vigorously about how he’s managed to do that.
Also in this episode of the SDS podcast, you’ll learn about how to work in the data science field not actually having the data science expertise and how Brendan has managed to leverage other people’s strengths. So oftentimes, in the data science field, because it’s so broad, you might end up working on a project or working with somebody who has other strengths and other data science experience that you don’t have. So from this podcast, you will learn how Brendan goes about leveraging other people’s expertise to derive the best benefits. So he actually manages different areas of the business and brings people’s expertise together to create the best outcome, create the best synergy.
You’ll also learn about customer satisfaction metrics which Brendan uses, such as NPS and churn, and we’ll discuss those in great detail. Also, you will learn a lot, a great deal about data and strategy. Brendan and I worked on a big project back at the superannuation fund where we were creating a vision for a strategy for the next five years of the funds in terms of data. In other senses as well, but predominantly it was focused on data, and you will learn how Brendan is using data and insights to dictate what the strategy will be and decide and bring it to the stakeholders of the company.
He will also describe his personal three pillars of research, which I found very useful. You will learn how to structure your research in a way that aligns with those three pillars. And also we’ll talk a lot about quantitative research versus qualitative research, which in data science we can get very carried with just the quantitative research and forget about the qualitative, but here in this podcast we will highlight some of the benefits of qualitative research and how you can best conduct it in order to get the maximum benefits out of the combined two types of research.
So without any further ado, I bring to you Brendan Hogan.
(background music plays)
Hey guys, welcome to the SuperDataScience podcast, and today I have Brendan Hogan. Brendan, welcome onto the show, how you going?
Brendan: Really well thanks, Kirill. How are you?
Kirill: Good, thank you. So Brendan, we met a long time ago. We met, what was it, two and a half years ago from now? Back in 2014, right?
Brendan: Yup.
Kirill: And you were working at Sunsuper, a company where I came in as a data scientist, and it was good times. At first, to be honest, when we first started, I didn’t quite understand, it took me some time to get to know everybody, I didn’t quite understand your style and what exactly your role was, but with time, I really got to appreciate it. Can you tell us a bit more how you got into Sunsuper and what you were performing there for your role?
Brendan: Yeah, so I suppose my entry into the data space isn’t very romantic, to be honest. I was in a position when I went into university, I wasn’t really sure what I wanted to do, I did a very general business degree, and I was hoping out of that that something would sort of rear its head and I’d find something that I was passionate about, and I was fortunate enough to do a quantitative analysis class and a research class, and I went hey, I’ve always had a bit of an affinity with numbers, this is quite interesting, there’s an opportunity here to actually make a difference. So I went into a consultancy in Australia, a research consultancy, did my time there like a lot of people did, and Sunsuper, the pension fund, was actually a client at the time, and then I jumped ship over there.
And I came in there as essentially an Insights Specialist, or an Insights Analyst, and my remit was quite broad. I mean, I was doing research projects, I was a subject matter expert on things like segmentation models, etc. And then I suppose the next progression for the business was this data science thing, and it was very clear that within the confines of the business, we didn’t really have that skill set. To be honest, we probably didn’t know what that skill set looked like, and that’s sort of, I suppose, when you were brought in, Kirill, around that time.
Kirill: Yeah, yeah, I remember having that interview. It was very interesting to come into the organisation and it was like the Insights Specialist position at the time, and I’ll never forget how you were always very against changing the titles. Because I was pushing for the title to be changed to Data Scientist from Insights Specialist, because I knew that that was the role I was hired to do. But you were always against it. Tell me a bit more, tell our listeners, why are you so against having the data science title on your business card?
Brendan: Well, there’s probably two main reasons. The first reason is very simple. I personally don’t have a programming background. So any sort of programming or database manipulation skill set that I have, and it’s very high level, I must admit, has essentially been self taught. So that was probably one of the reasons I was very hesitant for someone like myself to have that title.
The second thing was, there was a lot of hype — 2014, it was only two years ago — and there was a lot of hype around data science and Big Data and you had CEOs and CMOs the world over talking about Big Data, and I sort of felt like it was a lot of pressure to put on someone as well, to come in and say ok, you’re a data scientist, on you go. It was almost like, you know, these executives thought that analysts and data scientists just press the button and magical things appear. And I think we all know that that’s not the case. A lot of work goes into it, and a lot of preparation, etc. And I also felt that that business specifically didn’t really have the infrastructure in place to actually allow a data scientist to prosper and to actually make a difference at that point.
Kirill: Fair enough, fair enough. Definitely understand your position there. I found it very admirable when we were working together that I was pushing for the data scientist role, I wanted to be called a data scientist, especially from Deloitte, where that was already kind of like a given. And at the same time, you always were more conservative. You were like, you know what? Until I know the skills, until I have the expertise to be proud to call myself a data scientist, I’m not going to vouch for that. So that was a very admirable thing. And I guess it’s a great aspiration to have, to work on yourself and educate yourself so much that at some point in your career, you’re like yeah, ok, now I’m ready for the title. So that was really a really cool thing that I found out.
And remember, speaking of the hype around the industry, remember that huge project that we did about forecasting the future of the industry in terms of data science?
Brendan: I do, yup.
Kirill: That was pretty intense, right? And that was one of the first times I ever, inside the industry, like not as consultant, presented to a board of stakeholders, or actually Board of Directors. What did you think of that presentation? So we presented to the Board of Executives, and then you were on the Board of Directors presentation, but I think I missed that one. How did you go there?
Brendan: Yeah, I mean, to me, that’s sort of why I get out of bed in the morning, you know, to do those sorts of presentations, because I think unless the work you’re doing has meaning, and by meaning I mean influence on that business, I don’t really see the point, to put it bluntly. So I think that opportunity and you know, we were sort of gazing into a crystal ball a little bit, trying to build a strategy for a pension fund five years in the future, and obviously data had a big part to play in that. But I think that opportunity to, I suppose, obviously present at that C-suite level, but actually to remove yourself one further and go to a Board, that’s very exciting. And for a lot of the Board members, you know, it was probably the first time that they’d heard the term “Big Data”, or you know, “data strategy”, or “data science”. So I think those sort of opportunities, analysts, data scientists, if they can get them, regardless of whether they’re comfortable doing those sorts of presentations, they should really take it with two hands and really get out there and have a crack.
Kirill: Yeah. Totally. And I like how you say that running these presentations is like getting out of bed for you in the morning. For me, it’s still like a lot of preparation, I still — every time, I’m still a bit nervous. But yeah, I’ve seen you present, and you present really well, very confidently, and that’s one of the things I think our listeners can learn from you.
But the other reason why I’m so excited to have you on this podcast is, personally I think you have very strong leadership skills. Like, extremely powerful and encouraging leadership skills because that team that we were building that strategy for 2020, at first, I was like oh yeah, I wouldn’t mind leading this team and seeing how it goes. But then, you know, when I saw how you stepped up to the role, I thought no, it’s actually better if Brendan leads this team, and I can actually learn a lot of things from the way you interact with the people. Because obviously you had way more experience in that space. So can you tell us a little bit more about how you went about developing these leadership skills, was it your background, was it something else in your life that prompted you to work on this side of your career?
Brendan: Yeah, there’s probably a couple of things, and I suppose going to university or college, or whatever you want to call it, I always had aspirations to be an athlete. And a lot of I suppose the people skills, and you can’t stress in any sort of business or any sort of position you’re in the importance of people skills, that’s something that always came quite naturally to me. And as you said earlier, a lot of the more technical skills probably weren’t instilled in me at that early age. And I suppose I was never really one to shy away from asking for help or asking for direction, and I really enjoy the idea of working with people.
I mean the second thing is, I’m really keen to understand how things work. So if I’m in a business, if I’m in, you know, take a pension fund for example, you worked in a pension fund. And I’m in this data team that sits within Group Marketing, which sits within the wider business, I really want to understand why it is I’m doing what I’m doing. So I think that’s really important as well, to sort of get out there working with stakeholders, and actually understanding what’s the business trying to achieve. Because unless you do that, you’re not going to have that influence. And to have that influence, you obviously need followers. And I don’t mean followers as in line reports, I mean people around the business that actually look to you to take a lead and follow what you want to do and what you want to achieve.
Kirill: Yeah. I think it’s a prerequisite to be in any type of meaningful role in a business, that you know what the business is trying to achieve. So you’re not just executing commands that you’re given, or orders and directions, but you’re actually involved in the strategy, or at least you understand the strategy of why you’re doing what you’re doing.
Brendan: Absolutely.
Kirill: And tell us a bit more about your background. Now we know you a bit better, or like when I see we, I’m talking from our listeners’ perspective. And obviously you’ve achieved some great things and we’ll move on to what exciting role you’re performing right now and how you moved from Australia to the UK. But before we go on to that, can you tell us a bit more about your background? What did you study, and how did you go about getting into the professional career?
Brendan: Yeah, as I sort of mentioned, going through schooling, I was very interested in one thing, and that was becoming an athlete. That sort of didn’t work out for me, and I was fortunate with the support of my family that they said you know, go away to university, go away to college. You don’t really know what you want to do, but maybe you’ll find out what you want to do there.
As soon as I left high school, I went and did a very general business degree. As I mentioned, that’s where I — I always had an affinity with math, and I performed well in maths class in high school. And without being outstanding, I must add. And that’s when I got into this quantitative analysis and research space. I finished that degree like a lot of people do, I went overseas and found myself, you could say. Came back, and then started this role very quickly with a research consulting house called Roy Morgan, who essentially look after all the research projects for the large banks in Australia.
So from very early on, I was working with some of the Big 4 banks in Australia. I was working with large asset managers, insurance companies, on things like KPI reporting, segmentations. And I very quickly went from a junior consultant to a senior consultant in the space of two years. I did that for about two or three years, and I said ok, well I’ve done this for three years, I’m someone that likes to be continually challenged, I like to be doing stuff, doing cool things, rather than just doing BAU work all the time.
Sunsuper were a client at the time, and they had an opportunity, so I jumped ship over there, worked in the Insights team. At this point, I said ok, well, I’ve got a business degree, that’s going to probably only get me so far, and at that time I actually went back to university part time and did a Masters degree in Applied Finance. Given I had spent the majority of my career to that point either consulting in the finance space and then also jumping into a pension fund, I thought ok, this is probably going to be, I suppose, my transition into the future, into this finance space. So if I have aspirations to progress into senior leadership positions, as I do, then I probably need to add an extra string to my bow.
So I did the Masters degree. The Masters degree was great for a lot of reasons. It obviously gives you that more holistic view of the financial services industry. But you’re also doing things like financial modelling, and these types of things. So you’re actually building that sort of analytical skill set at the same time. And as I said, my background’s not in programming, I’ve got quite a sound statistical background without being excellent. So it was a good opportunity for me as well to start to build that skill set whilst earning that degree and whilst working as well.
Kirill: Kind of like I’m slowly building up this picture, because even some of these parts of your story I didn’t know back when we worked together. So you’re not a “hard core”, if I may say, “hard core” analyst / data scientist into SQL, into Tableau, into R and Python, all those skills. You’re using data science. You’re on the side, but you’re using data science and analytics, and you’re leveraging them to enhance your role in whether it’s research, whether it’s finance, whether it’s speaking with stakeholders, finding out more about the business, business strategy, because I remember you started that strategy department at Sunsuper. You were the first person in that department after you moved out of the Insights role.
So can you tell us a bit more about that? How do you see yourself and data now, and moving forward, how are you planning on continuing (or not) leveraging data for your daily role?
Brendan: Yeah, I mean, it’s funny you say that, because there was probably a point when I was at Sunsuper in the Insights role, and I had a bit of a mentor at the time that worked in a consultancy, and I had a discussion with him and said, do you think I should go back and actually build up these very technical skill sets? Do you think I should go away and obviously enhance my SQL capability? Do you think I should do a Python course? Do you think I should do all this? And he sort of said to me well, you probably have two decisions. You can probably go back and do that stuff, but you’ll come back out and you’ll probably be just an analyst again. Or you can actually continue on the path you’re on and be more I suppose like an intermediary between these hard core data departments and the rest of the business.
So that was sort of the tact I took, and around that time was when, as you mentioned, I went and established the strategy function at Sunsuper. And essentially that allowed me to do that. Because now I was actually, I suppose, touching every single part of the business on a day to day basis. And I had that ground understanding of what we were capable of as a fund in the data space, and I could point people in the right direction and work with people like yourselves in the data science to ensure that you know, the work you guys were doing was having an impact at an operational level.
Kirill: Yeah, that’s definitely very powerful. To be able to leverage data skills even without having the hard technical aspect of it. Very interesting about your mentor, I didn’t hear that story before as well. Can you tell us a bit more, because I believe that mentors are very important, extremely important. I personally have three mentors around the world. Well, mostly predominantly in Australia and the people I’ve worked with before, even random encounters, and I try to catch up with them, I would say, twice a year? It used to be more often before, about maybe like once a month, but now at least twice a year I try to catch up, or even if I can’t make that, I try to give them a call and have a phone catch up.
How do you find the mentorship that you were part of, or if you’re still part of it, how do you find it’s changed your career and personal life as well?
Brendan: To be honest, I don’t really like to use the word “mentor” too much, but at the same time, essentially, that’s what it was. It was someone in the industry who had a very similar background to me, had come into the industry in a very similar way, sort of fallen into it if you will, and it was someone that obviously I could go to and sort of ask these questions, you know, point me in the direction of any other key contacts in the industry, get me involved in industry events. And we still, by e-mail now, I’m obviously in the UK now, but we still keep in contact by e-mail, because we’re very keen to see what each other’s up to.
So I think that’s very important, to have someone that may not be actually within your business or within your team, external, and they can give you that, I suppose, helicopter view of a situation, rather than just going to your manager, or someone else within your team. So I think that’s very important.
And I think it’s also important, you know, as you gain experience, to actually bring junior people through and share your experiences with junior people and ensure that they’re starting a career in this space on the right foot as well.
Kirill: Yeah, definitely. And how would you recommend for somebody who’s starting out into their professional career, for them to find a mentor? How would they go about not just randomly contacting famous people around the world, but how would you go about actually finding a useful mentor that’s going to be there for them?
Brendan: That’s a really good question. I mean some universities now obviously run robust mentor programmes. My experience was nothing like that. And I must admit as well, I wasn’t actively seeking a mentor. I was just lucky enough to find someone that I could have an honest conversation, an objective conversation, with. It just transpired from there. But I think that’s the key. Not being afraid to talk to people about your thoughts and your views on certain topics. Just let it flow naturally. As you said, I don’t think getting on LinkedIn and e-mailing anyone with an MBA, or anyone with a data science background is probably the way to go, but I was quite fortunate. It was quite a natural thing.
Kirill: Most of the time it happens by accident. Just it’s important not to miss out on that opportunity and recognise it when it comes your way. Can you tell us a little bit please about your move to the UK? I remember we were sitting down one day and I said I’m at that stage that I want to go out on my own, I’m going to leave Sunsuper in the next month, and I think you were one of the first people in the company that I told that to, and then I was very impressed to hear that you return said, I’m probably going to leave this company in the next 8 months. And you held your word, and since I think the end of last year, you’ve been in the UK. So what’s been going on there? What have you been up to?
Brendan: I do remember that conversation, and I’m glad to see that we’ve both ended up holding up our ends of the bargain there. But living abroad was something that appealed to me from a younger age. I’ve done it a little bit later on, a lot of people do it in their early 20s. The reason for that was I wanted to — when I made that move, and I’ve moved to London, which we know is one of the largest cities in the world, was to actually go into a role where I’d be challenged. To go into a large business where I’d really challenge myself to have some influence. So I packed up and moved, as you said, at the end of last year.
Now funnily enough, I had several leads. I didn’t have a role, but I had several leads in the asset management pension space. So the safe bet would have been to take one of those roles and continue on my merry way in pensions. But I felt ok, if I could get out of pensions, that might be a good thing as well, because I might turn around in 20 years and I’ll still be in pensions.
So I essentially popped off the plane and I had a few interviews with some pension funds and some asset managers, and then one day I saw the role that I’m doing now. So I’m working for a company called WorldPay, which is a credit card payments company. They’re the largest credit card acquirer in Europe. And I head up the customer research and insights department there. I’ve obviously got a background in research and insight, so from that point of view, it’s nothing too far removed from what I’ve done previously.
But there’s certainly some challenges that I haven’t experienced before, like working across different geographies, across multiple sites, that type of thing. And the stakeholder management piece has really been my biggest challenge over the first six months. Which is interesting because the thing that really appealed to me about the role was I was building something from the ground up. So there was no customer research programme that had been built across the business. So there was an opportunity there to actually build something from scratch, which really appealed to me. And to be honest, that’s what got me across the line to join WorldPay.
So I’ve been doing that for the best part of six months now. I’m happy to say that we’ve got a really robust customer feedback research programme built in. I’m fortunate enough that we have our own data science team there, so I’m still on a day to day basis working with data scientists, which I’m happy about. The six months has gone really fast. But it’s been really challenging and really enjoyable so far.
Kirill: Yeah, let alone the six months, I think the past year and a half has gone by so quickly. When we spoke just on e-mail a couple of days ago, I was thinking whoa, it feels like we worked together a few months ago. But it’s been so long! Crazy how time flies!
Tell us a bit more about research and insights. When you say “customer research and insights”, I think back to my experience in that space, and I think of segmentation, I think of how to cluster or rank your customers, and prioritise the offers that you send out. Is that similar to what you do now?
Brendan: That’s a part of it. It’s probably broader than that as well. So like one of the things that the business is really keen on is having this NPS score, Net Promoter Score, if people aren’t familiar. Essentially it’s a customer measure, measures customer loyalty. So that’s part of it, managing that NPS programme. Now, that’s a part of it, we’ve also got, as you said, things like segmentations, campaigns. That’s probably handled more by sort of our data science and insights team. So we actually have both that sit next to each other.
So it’s more sort of that customer data and leveraging that customer data to influence business change. And that’s something that we’ve already seen in the first six months of the programme. We’ve had some really good wins in the operations space. Which is great, because previously, the operations teams would just do their thing, and weren’t looking at those continuous improvement and process improvement opportunities probably as well as they could have.
Kirill: It’s interesting to listen how you describe your role, because I remember what you did when we worked together at Sunsuper, and from that, I know what you do. But listening now, and imagining that I didn’t work with you, I hadn’t worked with you before, it’s like you have your hands or fingers in every pie. You’re working with the data scientists, you’re doing research, you’re working with the strategy team. So lots of things you’re doing.
Just so that our listeners can pinpoint a bit better what exactly is that you do, can you give us an example, if you’re able to disclose this, of a recent project that you’ve completed like in a bit of detail so that we can paint a picture in our minds of what Brendan Hogan does on a daily basis, or what one of the recent projects has been for you.
Brendan: The BAU type of stuff that I do, which is not too exciting, KPI dashboard reporting across different customer touch points. So nothing too exciting there. Obviously understanding business performance at different touch points, whether that’s boarding, customer service interaction, closures is obviously a big focus, so doing churn reporting, etc. Understanding obviously what churn’s looking like, but also why people are churning, and what initiatives we can do there to improve our churn rates.
And then the second part’s the more strategic projects, so without giving too much away, I’m working on a large one at the moment, which sounds very, very simple, but a lot of work’s gone into it. So one of the things that I’ve presented to the Executive a couple of months ago was around this notion of, we’ve got customers, we’re a B2B business, so our customers are business owners with card terminals, or e-commerce solutions for example. When they contact us to get a problem fixed, we’re indicating that it was taking them multiple contacts to get that done.
So very, very, very simple stuff, but that’s something that the Executive sort of pulled in now, and gone ok, we need to fix that. Because that has a huge impact on our NPS score, it has a huge impact on our churn rate, it has a huge impact on customer satisfaction and complaints.
So that’s a very, very simple thing. Now, it sounds very simple, that was one number. But a lot of work went into that by understanding the relationship between those customers that have indicated that, that multiple call versus single call. There’s the stakeholder piece, so actually going away and actually saying look, I’ve done this piece of analysis and it says if we improve this number by x amount, we’re going to improve these numbers, and then telling me obviously no, that’s not true. That’s wrong, you’re wrong, we’ve never seen that before. So it’s a long-winded process, but that’s a very simple one. And we’ve actually seen a large programme of work kick off in the last couple of months on that.
And that’s a big win, because that’s never been looked at before. So that’s probably an example of one of the more strategic programmes, and I think one of the things that I’m very keen to do is, once I’ve given that insight over, or once I’ve passed over that report, or whatever, for me, my job’s not done yet. It’d be very easy to say ok, here it is, go do something with it. I’m very keen, and I’m very passionate about being now involved within that project and ensuring that at each stage of that project, there’s still keeping an eye on what they’ve set out to do.
So that’s the other thing for me. Like, don’t lose visibility of a project just because you’ve handed over your piece of insight as well.
Kirill: Ok. Love it. That’s a great example. I was just about to ask you for an example of using data to influence strategy, and I think that’s it. That you use data and your research and analytics to find this metric that nobody has ever looked before. The multiple goals for a B2B business versus a single goal to solve a problem, and taking that to the executives and telling them that improving this will actually improve a lot in their organisation, have a lot of return. That’s a very good example of using data to influence business strategy. I think a lot of our listeners, especially in the managerial type of space, or in the space that’s similar to yours, which is not 100% technical, will find that very, very useful.
And you mentioned just now two words, NPS and churn. Could you please explain them a little bit for the benefit of our listeners who don’t operate with those terms on a daily basis.
Brendan: Yeah, absolutely. So NPS, Net Promoter Score, is essentially a measure of customer loyalty. So it’s spawned out of Bain & Co in the mid 90s. So it’s actually a B2C measure. And essentially, a customer is asked at any touch point how likely they would be to recommend a business, and they’re given an 11-point scale, 10 being most likely, 0 being least likely. And the score is a simple calculation of your promoters, which is your scores are 9 and 10, minus your detractors, which is your scores of 0 to 6. And that gives you your NPS score. So essentially your NPS score sits between -1 and +1.
And what you’ll find is a lot of businesses, banks, retailers, will use this as their key customer measure. So you’ll often see it’ll go on there, the operational score card for the business. So that’s NPS.
And I suppose I’m the owner of NPS within the WorldPay business. So I’m obviously very keen to see that NPS number go up. But I think what’s important to recognise is that I can’t actually make that number go up. The business needs to make that number go up. So that’s why I suppose that notion of influence is so critical here. Because unless the insight that I’m providing, or the recommendations that I’m providing are actually followed through, then you won’t see that uplift in that particular number.
Churn on the other hand, churn can be measured in a multitude of ways across businesses. Very simply put, it’s essentially the rate of customers that are leaving you. So if I had a hundred customers at the start of the year, I had 95 at the end, I could say that my churn rate’s 5%. So very simple.
But in a business like ours, we’re obviously, you know, there’s a large, large push put on acquisition. But often, and I’ve seen this in the pension fund space as well, there’s less influence put on churn. And what we know is, it’s actually far more cost effective to retain a customer than to acquire one. So I’m very keen, and my team owns that churn piece, which is really trying to understand why people leave us, what’s the relationship between churn and things like tenure, things like re-contracting, and then putting a plan in place to actually reduce that number.
Kirill: Wonderful. Thank you so much for that, because I remember the first time I encountered the word churn at Deloitte, everybody was throwing it around, and people like you were operating with it, and you kind of like even shy away from asking the question, what does that mean? Because you think you’re expected to know that. So it’s good to have a good explanation. And NPS, the first time I encountered it was at Sunsuper. I think you might have even been the one who explained it, or somebody, but it’s a very simple metric, and yet quite powerful. What would you say is a good NPS score? Like a solid, strong NPS score that a B2C company like in the finance space should aim for?
Brendan: For financial services, a positive NPS score is a start. Because what you see generally, for pension funds for example, from memory, they sit between -50 and -20. But they’re obviously — those scores are a byproduct of things like returns as well. So if returns aren’t great, then NPS won’t be great. But what you’ll see, some of the real leaders, your Apples of the world, your Googles, they’ll all be over +50, which is a very, very good score. The issue with NPS is, it’s very hard to communicate back to customers in things like annual reports, because if they see +10 on a page, they say well ok, well so your score’s to 10, is it? You just scored 10 out of 100, when really that +10 could actually be a really good score.
So one of the things that I try to educate people about is it’s less about the score and it’s actually more about what you actually do to actually move the score, because often people think ok, well I need a positive score. But if my score was -50, and it’s now -10, that could actually be a really good story. So it’s more about what you actually do to move that score. And what you’ll see is some businesses were benchmarking across the industry, so banking’s a prime example. And then some industries, like ours, don’t really have that benchmarking. So we’re really benchmarking against themselves and the past and present. That’s quite interesting trying to explain that to people.
Kirill: And I’m actually quite glad that we went into this space in our conversation, because a lot of the time data scientists focus on the other data, on the coding, driving the insights, and so on. But you actually forget what it’s all about, what the purpose of it all is. And whether it’s a B2B or a B2C company, at the end of the day, you have customers. At the end of the day, you have clients. At the end of the day, that is the relationship you need to cultivate, and you always need to keep that in the back of your mind, that whatever you’re doing, it has an impact, like an end game impact on your customers. And it’s good to be aware of what that is. And metrics such as NPS or the churn rate are good measures of that. So it’s good to be aware of them at the same time. Would you agree with that?
Brendan: Yeah, absolutely. And I think it goes back to the point about even the most junior analyst having some visibility about what the strategy of the business is. You know, in startups, for example, that might be very clear to people. But if you go into a large multi-national company, you probably don’t actually get any visibility about the strategy unless you go looking for it. So building that understanding very early on is going to benefit you, because you could get stuck into a piece of analysis that you think is really, really cool, I’m going to find out some really cool stuff, but unless it ties back to the business strategy, then it’s not going to get actioned, is it?
Kirill: Yeah, for sure. I was interested in how you mentioned that sometimes you bring an insight, and you discover it to your stakeholders, and you try to communicate it to them, and their first reaction oftentimes is no, that’s not true, we’ve tried that, it doesn’t work, or we’ve considered it and we’ve discarded it before. I’ve come across that as well. Very often when you’re working as a data scientist, communicating findings can be hard. Not just because it’s hard to communicate technical complexities and put them into simple terms, but also because of the initial push back you get from stakeholders who find these findings unintuitive or contradictory to what they had discovered before.
So can you tell us a bit more about how you go about changing people’s minds and what do you do to convince your stakeholders that the insights that you’re presenting, they’re worth looking into further and maybe even actioning on?
Brendan: Yeah, it’s funny you say that. I’ve had instances in the past, not in my current role, but in previous roles, where you would present something to someone who has absolutely zero data background, etc. and you will say this is it. And it will get to the point where they actually ask you to send them the data sets so they can check for themselves. So it’s not an uncommon thing. I think as an analyst, and as a data scientist more generally, you have to be very patient, and you have to be prepared for that push back. And before you even go on to actually share your insight, or to share your findings with people, you need to actually take a moment to actually consider what kind of backlash, or what kind of defensiveness you may experience. Because it’s going to happen. It’s only natural.
Sometimes, as you said, it could contradict the previous report, or a previous number that someone’s seen. But a lot of the time, I find it’s just someone thinks that you may be attacking their area of the business, and they may be getting a little bit defensive. So this is where that idea of cultivating those relationships with stakeholders is very important. Be prepared to actually sit down with the stakeholder and take them through what you’re presenting. Because it’s only natural that if you’ve pulled an absolute nugget out of the analysis you’ve done, and there isn’t someone in the room that’s skeptical, then is it really a nugget? That’s my view. So be prepared for that.
Kirill: Definitely. And I like your advice on patience and preparedness. Haven’t heard that one before, but definitely if you think about it, and be patient to get people to come to terms with the insights you’re presenting and be prepared for the push back that you might get so you don’t get a shock or a fright. Definitely a good one. Alright, that’s really cool.
And like in both your roles, out of the ones that I know about and probably even — no, especially the one in consulting back in Gartner, can you tell us please, you said research has been a big part of your role always. What’s the most important thing that you would say is about research?
Brendan: Just in this role, I’ve learned a little bit about what I think is now important. The most important thing about research is the simplicity of it, and understanding why you’re doing what you’re doing. To give you an example, I mentioned that what I’m doing now, no one had really done before. But that’s not to say that no one had ever done research before. And I think that it’s a good thing and a bad thing, but it’s so easy to conduct research now as well.
So anyone can send out a survey, for example. And they’ll get the survey results back, and they’ll say, you know, 80% of people said this, and then someone who works in the space will look at it and go well actually, the people you surveyed were wrong, to start with. You haven’t really taken that into account. You’ve surveyed the wrong people, they’re not representative of the entire universe of people, for example. And the questions you’ve asked don’t really make any sense either, so from that point of view, my three pillars of doing research, I’m talking about quantitative research at this point, is make sure whoever you’re researching, so wherever the audience is is representative of what you’re trying to look at.
If you’re going to ask someone a question, make sure that there’s an action for that question. So don’t go asking people a whole heap of questions about something that there’s absolutely no way the business is going to action. Because all you get then is people, customers in this instance, that say well, you asked me all these questions about this, but you’re never going to do that. And the third thing is, be prepared to actually act on the research. And what I mean by act on the research in this regard is, if you’ve got a heap of customers who are good enough to fill out a survey, whatever medium it may be, and they’ve brought up some dissatisfaction or something, be prepared to actually go that extra mile to ensure that that person’s issues are resolved.
And that’s something that a lot of businesses don’t do now. They do the research and they churn out the reports, but then they have these customers that are telling them they’re not happy, and they won’t do anything about it. So probably the third thing. But I would consider myself more of a quantitative researcher, but I have done the qualitative research as well. And to be honest, I was very skeptical about it at the start. I was like ok, well this is just going to be people talking, and then I’m going to go away, and I’m going to write some things on post-it notes or something, and then we’re going to hope for the best. But I think qualitative research as well has a part to play in understanding things at a bit deeper level.
Kirill: I’m really enjoying this part of our conversation, this is amazing. Just to reiterate, and then I’ve got another follow-up question on that. Just to reiterate, because I’m taking notes here to see what is going to go in the highlights of this conversation. This is definitely going down there. Three pillars of research. So number one is make sure your research is representative. Two, make sure that there’s an action for your question. So don’t research stuff that you’re never going to act upon. And three, actually be prepared to act on the research. So you take the research in, and you actually act on whether it’s to satisfy those specific people that were involved in the research, or to change something about the business. So that’s the three pillars of quantitative research.
And in terms of qualitative research, it’s very interesting that you mention that, because I did that for the first time at Sunsuper. Not including interviews I was doing back at Deloitte because I worked in the forensics department for a few jobs there, and we had to actually interview people in terms of fraud investigations, but in terms of qualitative research, the only one I participated in in Sunsuper was when I remember after hours, we stayed back with of our colleagues and all these members of the funds came in, I think it was like 10 people. And we would ask them, like they would get rewarded for coming in after hours at 6 pm, but we would actually ask them questions and get their take on certain possible scenarios of changes in the fund and how they would react. And can you tell us a bit more about that? Obviously, qualitative research you can’t do it on a huge sample. You can only do it with like 5, 10, maybe 20 people at a time. You just don’t have the capacity to do it with more. But how do you integrate that with data into your decisions to change strategy?
Brendan: Yeah, it’s an interesting one, because I’ve had a debate recently with someone about when you actually do the qualitative research. Do you do the qualitative research before you do the quantitative research? Or do you do the quantitative first, and then just do the qualitative research afterwards to dig a bit deeper on your findings from the quant piece? Depending on who you ask, you get different views.
I suppose the thing about qual you mentioned, the universe is much smaller. So if you’re speaking to 10 people and then you go away and say ok, well my research has said that we should do this, or 80% of people say we should do this, often you’ll get the backlash from whoever it may be, the key stakeholders saying well that’s only based on 10 people. So then you’re like ok, well do I need to do the quant?
But I mean, there are some very clear benefits of qualitative research. Obviously, what you’re now looking at is data based on enough participants, their own categories of meaning. So it’s actually what they’re telling you. Whereas surveys can be quite leading, or quant research can be quite leading. It’s based on an actual data point that’s been pre-prescribed for them. If you’re dealing with some really complex type phenomena, then often qualitative research is great. If you’re doing things like obviously user testing, if you actually want to test something, like whether it be an application with someone, or a piece of collateral, it could be as simple as marketing collateral. I mean, that’s great, but my view is they’ve always worked better when they’re being paired together. And that’s really important, because, you know, they both have their strengths and weaknesses, you know. And I’m thinking now that Kirill, when you talk about the universe and sample size, because everyone the world over, every CMO, every CEO, every C-suite gets caught up on sample size. The first thing they look for on an insight is sample size.
Kirill: Yeah, totally. It’s very easy also for the analysts and data scientists to get too involved in the quantitative research and completely disregard the qualitative research. Even I myself was like that, and to some degree, I still find myself thinking that way, that I disregard qualitative research because of its appearing insignificance, that it feels so small. But at the same time, like you correctly mention, you can get insights from qual research that you can never get from quantitative research. And it can even be qualitative research within your organisation, right? There are people performing the processes that you’re analysing using data that know those processes like the back of their hand. And when you go talk to them, I found this to be true so many times, you go talk to them, and they give you insights.
Like the most classic example I remember was when we didn’t know why people would answer, when people had 12 funds to consolidate, they wouldn’t. But then when they had 13, they would. And the only reason for that was, if you remember, it was because the amount of funds that fits on one page was exactly 3. So 12 was four pages of paper, and 13 was already five pages of paper. And people would pick up five pages of paper, and they would be more shocked at their financial situation than when they picked up two pages of paper. And there was no way data could tell you that. That was only through qualitative research or qualitative research within the organisation that could give you that hint, that tip. So there’s definitely stuff you cannot find in data.
Brendan: Totally agree. And I think you’ve just opened up another can of worms there when you refer to the research that you can actually conduct within your own organisation. And I think anyone would be silly not to take that opportunity, because in a lot of organisations, pension funds, we’re exactly the same at WorldPay, you know. There’s people that have been doing these processes on a daily basis for years. And sometimes it’s just as simple as going to sit down with them, saying can you take me through this. And you’ll be absolutely surprised with what you can uncover, and how you can actually then take that back and improve the modelling or the analysis that you’re doing.
Kirill: Yeah, definitely. It’s very interesting how we delved deep into this research side of things. I think it’s going to be very powerful information that people are going to get from this.
And standing on the side of data science, or on the verge of data science strategy, and all these other elements, especially research, where you are now, and where your role has predominantly been, by the way it’s very impressive how you’ve managed to create, to carve yourself a career path through different numerous companies, where every single time, you leverage those same strengths. So you know how some people bounce around between different roles, or different companies, and they always end up in a different new role, and they experience new things on one hand, but on the other hand, they don’t cultivate certain qualities that they are very good at.
And personally, I love the saying that the ex-CEO of Deloitte, Giam Swiegers, kept repeating. If you want to be successful, focus on your strengths, ignore your weaknesses. And I think that’s exactly what you’re doing. And that’s what you took out from that conversation with your mentor when he said you have two choices right now, whether go back and learn those data skills, or leverage the skills you already have. And so you’re going through these different companies, or you’ve been going through these companies, and every single time, you leverage your strengths in research, in people skills, in strategy, in data, leadership. So you’re leveraging those skills every single time, so I think even from that, a lot of our listeners can take an aspiration from that to see how it is possible, even having such unique skills, that you’d think it’s pretty hard to carve yourself a career path with those skills. But no, you’ve been able to do that. And my question to you is, from that position where you stand, which is not completely immersed in data science, a bit on the side, what do you think the future of the industry is? Where do you think data science is going? Where do you think the world with data is going in the next 5-10 years?
Brendan: I can probably only really talk from the experience I’ve had both at Sunsuper and now at WorldPay, but I think it’s one of those things, you know, we did talk to this earlier, it was a buzz term a couple of years ago, it’s still a buzz term, in my opinion, like there’s still a lot of, I suppose, data science positions and people potentially doing data science positions when you look at it, they may be senior analysts for example, but they’ve got that opportunity to build that more holistic skill set. The other thing I’d say is, there’s a lot of data scientists out there still that may be frustrated or not leveraging their skill set for whatever reason. Now this may be barriers internally, and I think at big businesses, say Sunsuper, WorldPay, these places, you come in and you’ve got this skill set, you’ve got these great ideas, but then actually putting that into practice is quite difficult because you’ve got teams like Security, you’ve got teams like Business Intelligence that are the guardians of data sets, etc. So you can’t even do what you want to do with the data.
If I was going into the industry now, one of the things that I would be very keen to do if I was interviewing for a data science role is, I’d want to get a very clear picture of the current capability of that business, what I would actually be doing day to day, assessing whether that actually aligns to my skill sets now, but also my aspirations as a data scientist as well. I’ve seen data scientists come in and they’ve got one set of skill sets, say they might be very proficient in Python for example, and they come in and they’ve been told ok, no worries, that’s great, we need someone with that skill set, and then they come in and they can’t even get near the tools they need to do Python, for example. I’ve seen that as well.
So I think it’s still quite immature as an industry. But there’s a lot of opportunity, and I think as businesses become more data focused, and more driven by data, there’s going to be more and more opportunities. But I still think that it’s still quite immature as an industry, and it’s only going to grow. And that might be completely different in places like startups, but that’s just been my experience working in large corporates.
Kirill: Yeah, and I was about to say, it’s good that you brought that up because I think that is probably different at startups, where you can get your hands on any tools that you want, and pretty much as long as you work 12 hours a day, they’ll let you do anything! But as you correctly mentioned, in large organisations, these massive beasts that have all this momentum, all these archaic processes and systems and like you say, security. Not to say that security is bad, but sometimes it’s over-constraining. When you are about to enter in a position like that, or you’re interviewing for one like that, it’s not only about you impressing them as a data scientist or analyst or an aspiring data scientist. It’s not just about you impressing them. It’s also about them impressing you and telling you that they’ve created the role where you will be able to grow in the way and manner that you want to grow.
So yeah, some solid advice there, thanks for that. What are your career aspirations going forward? What are you growing towards right now?
Brendan: I haven’t had much time to think, but to be very honest, and I know we’ve had this conversation, Kirill, I’m very interested in the use of data in sport. It’s something that in my spare time I’ve spent a lot of time reading about. I think one of the earliest memories I have of having this affinity or this relationship with number was watching the cricket at home and looking at all the statistics, not too dissimilar to what people would see with baseball, and then trying to understand the relationship between those statistics and why a guy with a higher average may have a worse strike rate, and understanding that.
So for me, and to be honest, I obviously wanted to live in another country, but one of the things that drew me to the UK specifically was this idea of maybe one day working in the sports industry. So that’s always on the back of my mind. I’ve got a lot of work to do at WorldPay as well, so that’s ticking along quite nicely.
But I think you can see from my career at the moment, I don’t stay in the same role for more than a couple of years. That’s not because I don’t like that security, whatever. I just like to be doing things all the time. So if I do a role, and I think ok, well I’m going to be doing the same thing in this time in 12 months, this time in 18 months, that’s when I start to look at different opportunities. That may be at the same business, but it might be outside as well.
So I think my plan at the moment is to see out this year, tick all the boxes I want to tick at WorldPay, and I’m starting to do that, starting to get that momentum, which is great, and then next year see ok, there might be something at WorldPay that really appeals to me, or it might be something beyond that. Or it might be a job with a football club, which would be pretty cool as well. So I don’t look too far ahead, but at the same time, you know, I obviously have some firm aspirations as a leader in this space. So I think it’s silly not to always rear your head every now and again and see what’s out there.
Kirill: Solid advice. Love it. And all I can say here is I totally agree. As soon as I stop feeling challenged, I move on, and I’ve done that many times without even looking back. Some people say that leaving a company is like breaking up with your girlfriend. But in reality, it’s been probably one of the easiest things for me to do ever, because if I’m not challenged, if I’m not growing, I’m not staying. I’m not even thinking twice. And all I can wish is for WorldPay to continue challenging you and giving you as many interesting opportunities as they possibly can, because I have no doubt that with the amount of soccer going on there in England, that any football club would love to have you and they’ll pick you up very quickly, so it’s up to WorldPay to keep you challenged and motivated. And I’m sure you will return the favour with some amazing work that you’re doing there.
And to finish off today’s podcast, can you tell us please, how can our listeners contact you if they would like to follow your career and learn more about what you do?
Brendan: I must admit, I’m not the most avid user of social, so you won’t find me on any Facebook or Twitter feeds, but simply look me on LinkedIn, send me a note, a request. If I can see you work in a similar industry, and you’re not a stalker, I’ll accept you. And conversely, if you want to know any more, flick me an email to brendan.v.hogan@gmail.com. I’m always happy to have discussions about people’s experiences. I’m always happy to learn new things, because I know there’s so much I can learn from the great people that work in this sector.
Kirill: I’m sure a lot of our listeners will take you up on that offer. And one final question I have today is, what is the one favourite book that you have that could help our listeners become better data scientists?
Brendan: Well, I’ll list a couple, only because there was a book I read very early on for me personally, as someone wanting to know more about this Big Data space, and that’s one that a lot of people probably have seen, but it’s got some good case studies in there, some examples of data science gone bad as well, which I think is quite entertaining, because for me, that sort of stressed the importance of that human overlay as well, rather than just letting computers decide how we do things. So that one’s by Kenneth Cukier and Viktor Mayer-Schonberger, and that’s called “Big Data: A Revolution That Will Transform How We Live, Work, and Think.” It’s about a couple years old now, but if you haven’t read it, I recommend. It’s an easy read.
And the other one is actually one that everyone will be familiar with, and one that I read almost ten years ago now, and that’s “Moneyball” by Michael Lewis. And if you don’t want to read the book, you can see the movie, but it’s a really cool story about Billy Beane and the Oakland Athletics, and sports obviously, and how they use data to actually break the record for all time winning streak in baseball. Which is quite interesting for a team that didn’t have a lot of money or the best players. So you can have a look at that one, it’s a good one, or you can watch the movie if you prefer to look at Brad Pitt.
Kirill: Wonderful. Thank you for that. I’m actually surprised that none of our guests so far have recommended “Moneyball”. Personally, I haven’t seen the movie or read the book, but probably after today’s conversation, I’ll put on my to-do list. And so we’ve got “Moneyball”, and the other one was “Big Data: A Revolution That Will Transform How You Work, Live, and Think.” So there we go.
Thanks a lot for coming on the show. Really appreciate catching up again, and I’m sure you’re doing some fantastic work there. We should definitely stay in touch and see how you’re going in a year, which will probably fly by as quick.
Brendan: Yep, absolutely. Sounds great.
Kirill: Alright. Thanks, Brendan. Take care.
Brendan: Thanks Kirill.
Kirill: So there you have it, I hope you enjoyed this podcast. We went into some very detailed conversations around strategy, around research, around building a career path for yourself. What do you think is the most valuable takeaway for you from this podcast? For me personally, I think the building a career path for yourself was probably the most valuable thing that, whatever you do in life, you can always find a way to leverage your strengths. You shouldn’t settle for working on your weaknesses and developing new skills that you might not want to develop. You should find ways to leverage your strengths.
Not to say that the other parts of the podcast weren’t great, I think there were some great insights there as well. We learned about the NPS score, we learned about churn, data and strategy, pillars of research, quantitative versus qualitative.
So this podcast, even though it’s not entirely technical, no R, no Python, but at the same time, it was very, very much packed with value. So hope you enjoyed today’s podcast, and you can get the show notes at notes at www.www.superdatascience.com/4, just the number 4. And also, make sure to invite your friends to our podcast. Make sure to invite your colleagues. Go ahead and send them the link, share this around, and let’s spread the word. Maybe you’ll know somebody who can benefit from the insights shared in this episode. Don’t forget to leave a comment at the bottom on www.www.superdatascience.com/4. We’d love to hear what you thought of today’s episode, and I look forward to seeing you on the next episode. Until then, happy analyzing.
