Midlands Credit Management | Jessica James on Maximizing Data Management | Ep 189

Adam (00:01.618)

Hello everybody, Adam Parks here with another episode of Receivables Roundtable. Today I'm here with my new friend and fellow data nerd, Jessica James, a senior performance manager with Midland Credit Management. How you doing today, Jessica?

Jessica James (00:16.813)

How about yourself?

Adam (00:18.642)

I cannot complain. I really enjoyed the opportunity to get to know you at the last RMAI conference in 2024 and start talking about some of the data nerd things that we both enjoy. But for anybody who has not been as lucky as me to get to know you a little bit over the last year, could you tell everyone a little bit about yourself and how you got to the seat that you're in today?

Jessica James (00:41.263)

Yeah, of course. Like several others in the industry, I kind of just stumbled upon it. So I actually went to college for my marketing degree. And at the time I was doing door to door lead generation, which was quite interesting to say the least, but I naturally transitioned into marketing where I did SEO, digital marketing and event marketing. And I never felt super connected to marketing. And I knew I wanted to get into the finance industry. And so I found a job from Midland about three years ago and took the job and

Here I am today and I love it ever since.

Adam (01:13.106)

And so talk to me a little bit about what your role is over at Midland.

Jessica James (01:16.75)

Yeah, of course. So like you said, I'm a senior performance manager. I'm on the consumer data team. And so essentially I help our business get the data they need and make sure that they extract maximum value out of that data. And so really, when you look at my data day, it's managing three main things, which is managing the relationships of the data servicers, the processes of that data, and then helping the business move strategic initiatives through the company. And so really if I was
To look ahead, I want to leverage our data to uncover even deeper insights, but I also want to expand deeper partnerships between companies within the industry just because I find that collaboration is where I find the most success.

Adam (01:58.802)

Well, and probably you find a lot of fun there too. Now, because it's just kind of part of our process, I do want to talk a little bit about Midland as well. Could you tell everybody a little bit about Midland and what it is the organization does?

Jessica James (02:09.806)

Yeah, so we are a debt buyer. So we help banks do what they do best by issuing credit. And we do what we do best, which is focusing on how to best serve and help our consumers pay off their debts so that way they can ultimately reach that economic freedom.

Adam (02:26.098)

Perfect, and I think for our audience, if they're not familiar with Midland Credit Management, they're probably not spending a whole lot of time in this space. So I definitely appreciate you going through the process and coming on and having a chat with me today because I felt like as we started talking about kind of taking datasets and creating operational efficiencies with it, right? It feels like you're kind of that bridge between the datasets and the operations.

Jessica James (02:55.12)

Mm -hmm.

Adam (02:56.114)

What is it about your kind of day -to -day role that brings you the most joy in your job?

Jessica James (03:02.767)

Yeah, I think it's just because I'm the one that usually brings the data recommendations on what data will be predictive for the strategy. So, I mean, I get to work closely with our analytics team and our decision science team, and they're the experts on understanding the data testing design. But I help connect the technical and non -technical audiences, and I enjoy seeing the success out of that. And so bringing that recommendation and then following it through to find that the product did succeed.

Adam (03:09.81)

Yeah.

Jessica James (03:31.983)

or adjusting where needed and just being able to monitor it throughout.

Adam (03:37.074)

Well, you're creating a hypothesis and then kind of translating between the technical and the business needs and kind of going through that experimentation process, reviewing those results and kind of making some decisions or recommendations as to how things could march forward from that point. You know, what is it do you find to be kind of the biggest challenge when you're trying to translate kind of this tech speak into a more general business language?

Jessica James (04:05.327)

I think it's just the biggest challenge would just be that there's so many KPIs that we look for. And so it's making sure that all stakeholders are aligned on what's the measure that we want. That's most important to us. because some measures might be like, this isn't good. We don't want this product, but at the end of the day, the ROI might be the best. And so it's taking all the different factors and then determining like, how are we going to interpret these measures to ultimately represent success?

Adam (04:34.577)

So do you spend time prior to doing a data experiment defining what success of that experiment would look like, or is it more, let me see what my results look like and then I can find the best path?

Jessica James (04:46.447)

I would say it just depends on the type of product. We probably do a little bit of both. I mean, of course we define the variable, the measure, how we plan to interpretate that measure. And then when we look for data, we also, at least for me, I always like to start with finding smart data, just like the same way you find smart goals. I want specific, measurable, attainable, relevant, and time -bound data. Time -bound being just like, do I want historical data or more relevant data?

And it's really hounding down like where to look for that data that would be best for our strategy.

Adam (05:18.834)

Well, it seems like from a data perspective, you know, there's a lot of similar information available across the space, but then it's how do you actually interpret, augment or massage that information or that data set in order to move it towards an end goal. So you've probably had an opportunity to look at just about everything across the space, right?
being part of an organization that would be highly sought after in terms of a client for a data provider, what is it that you find to be kind of that one of the core items that you look at in evaluating data quality from a vendor?

Jessica James (05:44.527)

Mm -hmm.

Jessica James (05:58.127)

Hmm, that's a tough one. There's so many different aspects to it. Yeah, but I mean, of course, first, we have our foundation, like with any good thing, usually you have a solid foundation and our foundation is consumer treatment, compliance, regulatory stuff, and just your overall environmental factors. And then when you look at the data itself is kind of going back to the smart data, like I want to make sure that I could rely that we will get the data we need when we need it and

Adam (06:01.394)

It's different for every data set, right?

Adam (06:25.746)

Hmm.

Jessica James (06:25.999)

I also want to make sure that this data is used in the industry, that other people in the industry are using it and learn from others that are using it as well. So those are just some of the things that we look at.

Adam (06:40.21)

So you get to engage across the entire data community, not just those vendors, but actually working with others across the space that are facing similar challenges, right? In terms of identifying, let's call it core aspects that would drive value in the various strategies that Midland is using to recover receivables.

Jessica James (06:59.439)

Yeah, aside from being like a super data -driven culture and company, I think it is really important to stay in the loop on industry changes and best practices and just really learning from others because that collaboration is key.

Adam (07:13.266)

1000 % I think there's we've all learned so many little things throughout our time in the space and when you start bringing everybody together for those collaborative discussions, I think it's really important for me. One of the driving factors in terms of kind of organizing my mind around data experiments has been going back to 6th grade and using the scientific method right and making sure that I'm walking through it in very much the same way that we did when we dissected frogs in biology, right? You need a high.
hypothesis? Well, first you need to research it, then you need to do your hypothesis, work your way through what are you defining in terms of those variables and experiments and kind of working our way through the process. But are you going through a similar process when you're evaluating or an abbreviated version of that process? It's probably not so minutely formulaic at Midland, but do you guys go through a similar process in terms of kind of establishing those experiments?

Jessica James (08:09.487)

Yeah, definitely. And I don't know if it's exactly the scientific method. I haven't looked at how that is outlined in a long time, but yeah, we start off with the problem and the issue, the problem statement, and then create a strategy around that. How is that going to solve our problem or issue? And then that's when we spell out how the concept is going to be measured. And then we define all of our operational definitions. So the measure, the variable, and the interpretation. So that would be like the step -by -step process, which I'm sure is pretty aligned with the scientific method.

Adam (08:39.602)

It look, it's definitely pretty close. And ultimately, for me, I have to go through those motions of actually writing that document and putting that stuff in place, because it forces me to simplify my thought process. I find that when you bring together a lot of smart data people, we tend to talk at very high levels. And it really does require trying to find opportunities to simplify that conversation as much as possible. Because it makes it not only does it allow us to communicate across departments or
organizational functions, but it also improves our ability to understand something when we're able to discuss it in its most simplistic, let's call it, least common denominator.

Jessica James (09:18.511)

Mm hmm. Yeah. And I think even like not even across like different departments. But one thing I find that Midland that we really strive ourselves in is collaboration through cross managerial collaboration. I don't know if that's the right term, but you have your junior level, senior level managers and so forth. But having that collaboration through the chain of command, even if it will specifically for the strategic initiatives, the ones that have big and

Adam (09:45.458)

I would think from a so from your perspective, like a strategic initiative is kind of laid in front of you and then you're trying to help find those data sets to move that initiative forward or you identifying data sets and then there's strategic initiatives being created from kind of the that I'm gonna call it chaos but from the idea pool right of new new thoughts.

Jessica James (10:03.791)

Hehehe

I mean, usually our strategic initiatives are like we create a strategy and then we find the data and then smaller projects might be like, we see this data. I think it might be useful. Let's work with our decision science team and our analytic team to see how we could use it.

Adam (10:23.826)

That's interesting, you know, from an analytics standpoint. And so how do you kind of cross the bridge between the understanding of the raw data to the understanding of how you're going to use it in the real world? Is it based on kind of what your experiences are? Is it based on how the data providers are ultimately explaining new potential data sets to you? Like, how do you go through the process of exploring a new data set?

Jessica James (10:50.421)

I would say mainly it's about how we, how we operate our processes. Usually our data science team or our analytical team has a very strong idea of how they want to use it, but we definitely, try to get feedback from our data servicers to understand how other clients are using it, to see if there are any opportunities and to ensure that we don't go do something that we're not, not that we're not supposed to do, but maybe that's a waste of time or something like that. Like, a bunch of clients already tried this. It's not helpful for what we think it is.

Adam (11:18.514)

Yeah.

Adam (11:24.018)

You're not going to find a correlation between A and B. You should probably look at C and D because this might be a better use. I think coming from the data side of the world and the projects that I've done on that side of the world, that's my approach has always been to try to understand what's in front of me, right? Like understand what is this raw data? How could it action actionably be used across the space? And then it's that it's that conversion from data to actionable intelligence, right? And
then if I've got some actionable intelligence, how can I share that or experiment with various organizations to better understand or hone in the value proposition, right? Because the data sets could be filtered a number of different ways. They could be, you know, massaged or modified or augmented based on multiple data sets, but then trying to find the correlations within that data set and how it would...
drive value, but then it takes time to go through the experimental process, right? Because you can't just like overnight know what that performance would have looked like.

Jessica James (12:24.661)

Yeah, definitely. And with that, I mean, even after you test, let's say you love it, whatever you're testing, so you put it in production, you have to continuously test to constantly ensure that that valid proposition is still there because things change or maybe you did mistake the data set that you picked. It wasn't the best data set. And so you constantly have to be testing and analyzing to reconfirm that value proposition.

Adam (12:49.554)

And was the population in the experiment an appropriate representation of the whole? And for me, that's always been, I think as I went through statistics classes and other things like sample population was always kind of like that struggle because it's like, what was the previous treatment of, there's no two accounts that are truly equal, right? And so where there's no two accounts that are truly equal, you're always looking at pools of information and how those things come together. I've always found it to be.

Jessica James (13:02.517)

Mm -hmm.

Adam (13:15.474)

interesting as to how you look at that sample population and try and find something that is truly representative of the on the whole. And when you're the larger the volume, the harder it is to establish that sample size.

Jessica James (13:29.621)

Yeah, and definitely coming from a big company like us where we have just so much data. We definitely encounter that often where it's like, is this the good sample size? Are you sure this is a good sample size? And so it just takes a lot of meetings, a lot of collaboration, just going back and forth to really analyze and ask a lot of questions and get perspectives from all over to ask these questions to ensure that it's the best path forward.

Adam (13:55.89)

I mean, if you're not starting with a good sample, it's gonna be awfully hard to get to the end result that you're looking for, right? Because if your sample's not right, then anything that's coming out the backside of that experiment is probably not gonna be as valid as you would like for it to be. But again, the larger the organization, the harder it is to establish a valid sample because you're doing so many different strategies and so many different products simultaneously. And I think that's a challenge for a lot of debt buyers because it's just not a homogeneous product.

Jessica James (14:20.085)

Yeah.

Adam (14:25.17)

Right? Like accounts are not all the same.

Jessica James (14:25.461)

Yeah. Yeah, but we did better and better at it the more you do it. So that's why we just constantly test and analyze. So that way we could concrete that sample size down.

Adam (14:39.346)

Do you ever find yourself in decision paralysis that you've just got so much data that it becomes almost more difficult to solve a problem?

Jessica James (14:47.989)

Yeah, definitely. And that's when we just go to a different analyst or a different data decision scientist who asked them for their perspective.

Adam (14:58.13)

Well, having a whole bunch of different perspectives around the organization has got to be a benefit when you're trying to manage a process like that. Just having different pools of knowledge that you can dip into and kind of combine as you're going through that process with a keen eye on what the end result that you're looking for, right? The business operator.
operationalization, if that's a word, of those data sets that you're ultimately using. So any advice that you would have for anybody in a similar role to you across the industry?

Jessica James (15:22.165)

Hehehe

Jessica James (15:34.005)

Yeah, I think just really work on your relationship management. I think relationship management is a really key point in our industry just because I think in order to make big impact, I think at the end of the day, we all work for an industry that's super important for our economy. And so it's important that we all strive for the same purpose, which is to preserve our economy and protect our consumers. And so to do that is really through relationship management and collaboration and just working with
individuals outside of your organization, but as well as inside of your organization. And luckily at Midland, I mean, it's super easy to go to our leaders, even if they're SVP position, like I feel comfortable with approaching them. I know that's not the same at every company, but I would say don't shy away from it and ask questions and just ask as much questions as possible. I mean, I am young and so that's how I've been able to learn so quick is just by continuously asking more questions.

Adam (16:31.026)

I think that's great advice for anybody at any level of any organization. Ask questions, don't be afraid, write and connect. Jessica, I really thank you for coming on and having a chat with me today. I really appreciate your insights. I've enjoyed the opportunity to get to know you a little bit and to understand how your mind looks at some of these challenging problems that I know all of us across the industry are facing at the same time.

Jessica James (16:35.033)

No.

Jessica James (16:55.769)

Yeah, thank you. I appreciate the opportunity to have a platform to have a voice and just for everything that you do for the industry.

Adam (17:03.026)

Well, thank you so much. And for those of you that are watching, if you have additional questions you'd like to ask Jessica or myself, you can leave those in the comments on LinkedIn and YouTube and we'll be responding to those. Or if you have additional topics you'd like to see us discuss, you can leave those in the comments below as well. And hopefully I can get Jessica to come back at least one more time to help me continue to create great content for a great industry. But until next time, Jessica, thank you so much. I really do appreciate your insights. And thank you, everybody, for watching. We'll see you all again soon. Bye, everybody.

Jessica James (17:26.649)

Thank you.