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Curinos (F)insights: How An Innovative Credit Union Is Approaching Customer Segmentation

David Eldred, chief experience officer at Solarity Credit Union in Washington state, details his institution’s approach to segmentation and the key lessons they’ve learned along the way.


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Hello and welcome to the Curinos (F)insights Podcast. Today, my guest is David Eldred, chief experience officer at Solarity Credit Union. Welcome to the Curinos (F)insights Podcast.

Hi, Rutger. Thanks for having me, glad to be here.

David, can you tell us a bit about Solarity Credit Union and also explain your role of chief experience officer, what that entails?

Sure. Solarity is, I guess, what I’d call a mid-sized credit union. We’re about $850 million in a traditional balance sheet, about $1.3 billion if you count our servicing portfolio, about 55,000 members, six branches. Eastern Washington is our home market, but our charter is the entire state. So that’s the overview for Solarity. As far as chief experience officer, that’s the role that brought me here. When I first joined, I had oversight on, of course, a CX team, but also business intelligence, our marketing, project management facilities. And since then, let’s see, I’ve been here three and a half years, so I’ve evolved to let go of business intelligence in favor of branch operations and digital experience. So those are the business units that roll up under chief experience officer.

Now, during the height of the pandemic, financial institutions, both banks and credit unions, had to close branches and turn to more remote and mostly digital channels. This exposed many customers to engaging with their bank for the first time through remote and digital channels. Now, many of those customers actually went back to in-person channels. Did you see any of that switching back at Solarity? And if so, to what degree?

Well, I think it’s fair to say the trend within branch visits has certainly been going down over the course of the last decade or more. But the pandemic was certainly an acceleration of digital adoption. And I went back and did some research, and we were pleasantly surprised, quite frankly, at how well and how sticky the digital adoption has stayed with those members who, as you said, tried it for the first time, as well as those members who had always been using it all along. To paint a picture, prior to the pandemic, Solarity was doing about $5 million per year in mobile deposits. And then in 2020, of course, the lockdowns happened and a lot of adoption within mobile occurred, and we went from $5 million in mobile deposits to $26 million. And then we saw in 2022, we did $126 million in mobile deposits. So that was another fivefold increase. And in 2023, year to date, we’re on track for about the same. So no, we haven’t seen a lack of use in digital. What we have seen is that members who want to come into branches have certainly come back in full force, and the role of the branch has once again come to be the place where people are trained to go when they want to switch financial institutions. And so they’re coming into our lobbies as if they always had. Digital adoption and this whole change was something that we weren’t passive about even before the pandemic. Solarity had made quite a few investments in that. And so the pandemic, really, was seen as an opportunity for us to take advantage of the lockdowns and also to extend digitization to how we deal with business in our branches. And we made the decision to eliminate tellers from our lobbies and replace them with Smart ATMs, not the kind with a live teller on the screen, but really self-service kiosks. So digitization of banking is now also in our branch channels too.

So you actually merged the two. That’s very, very interesting. Now, so you were responsible for this branch transformation and this digitization. Tell us a little bit about the strategy behind that transformation and how you went about planning for it.

Well, I’m not going to claim credit for it. Our CEO was the one who saw the opportunity, quite frankly, as the pandemic became much more pronounced and lockdowns became a thing. It was an opportunity, she felt, that was great for us to kind of remodel or transform the branches into this better way to bank. And so once that decision was made, our entire executive team, it was a whole enterprise effort, extend the investments that we’d already made in digital and self-service systems to also cover branch experiences. You asked about the strategy behind why we did the branch transformation, and really, that was to kind of take the everyday banking busy work away from frontline staff and free them up for more consultative conversations, opening accounts, applying for loan, solving problems. And Smart ATMs would be the channel through which what a teller used to do, it could now be done electronically, digitally self-service through the Smart ATM

When applying segmentation of your members and how they would respond to this change in branches, and tellers going away, and Smart ATMs, what was your original thesis and how did you go about testing it?

Our challenge was to understand and predict the impact of this transformation on members and to quantify it with data. I think the year before the pandemic, we looked at over 10 million different transactions done by our members. And as a result of that analysis, we ended up with six distinct segments that are all based on the data from individual transactions. And if I were to describe those segments in kind of a high level, on one end of that spectrum was a persona that we identified as Branch Betty, and these are members who do a 100% of their transactions through tellers or our call center. And on the opposite end of that spectrum are our Digital Daves, who do 95% or more of their transactions through digital and self-service channels. Every member was tagged with their corresponding persona. Then we used their persona tagging to modify how we communicated to them about the branch transformation project. And so you can imagine that for a very digitally engaged member like a Digital Dave, we would talk to them about how the Smart ATMs represented a fast, efficient way to do their everyday banking with no waiting lines. And for Branch Betty who’s very human-centric, teller-centric kind of approach to traditional banking, we talked about how the same employees who they knew and love, that used to help them as tellers, were still there to help them learn how to use the Smart ATMs certainly, but also then to open accounts, apply for loans or solve problems. Or for Branch Betty, in particular, just to say, “Hello, how you doing?”

Now, what did the data, from a program of rolling that out, tell you about your thesis? And how did you have to adjust your thinking as this actually became real? Were there any surprises about how your members responded to the change?

So when we first reopened the lobbies, we had about 20% of our members who had been tagged as a Branch Betty, very branch-centric, maybe they call the call center, but needing a human to help them with their banking. And those Branch Bettys came in when we reopened, they looked around and they said, “Where are the tellers? I don’t like this. Bring back the tellers.” And, of course, we didn’t. We just welcomed them in, we showed them how to use the Smart ATMs. And were very supportive of how we believe this was a better way to bank. Sure enough, what we found was within three to four months, generally, those same types of members, Branch Bettys, came back and said, “When I first saw this, I really didn’t like it, but now I love it.” And they cited things like, “There’s no waiting in lines, I can do my transaction very quickly and easily, and your people are here when I need them. And now, I love it.” That evolution of how members, who were resistant to what was a pretty disruptive change for them, came to pass as being something that they ultimately embraced. And now when we look at those, that segmentation of our members, we went from 20% of our members as Branch Betty down to only 8% of them today. At the opposite end of the spectrum, it was also a learning where we had to scratch our heads a little bit. And we had some of our Digital Daves, very embracing of technology and doing 95% or more of their transactions through digital and self-service channels, who would come into our lobby 5% of the time, so to speak, saw the smart ATMs, and some of them would express displeasure. They didn’t like it, just like a Branch Betty, and they wanted the tellers back. That’s not what a Digital Dave should be saying, at least according to what we thought a Digital Dave would represent. As this persisted, we knew that something about our segmentation model was not robust enough. So the CX team got together again with the BI team, and we CXed the heck out of it and came up with a new model, and that’s what we’re working on today.

Now, as you mentioned that you went further into your segmentation model, you had to adjust it based on the data that you were seeing. What other dimensions did you add to your segmentation definition and have you been able to create a segmentation framework based on this experience?

Yes, it is an ongoing process, but we’ve learned a lot. So for example, our new model is far more robust than the original segmentation. The original segmentation was purpose-built for answering the question of branch transformation, and that was really all about channel utilization, who’s using branches, who’s using our call center, who’s using digital and self-service channels? And so the new model is more robust compared to one or two different dimensions in the first model. Our new model has 98 different data points, and just like before, we’ve tied every single data point to each individual member, and now we plot them on a dot density matrix that we update every week. It was a big lift by our BI team to get that in place, but I now have a Tableau dashboard that I can log into every day if I want to. And we’ve updated all of that data every week to inform how members migrate within their transactions and engagement. To that end, there’s two primary dimensions. The first one, if you want to think about it as the horizontal axis, is the dimension of engagement, which we’ve defined as any transaction that’s initiated by a member. It could be anything. It could be writing a check, it could be swiping with their debit card. So that’s engagement. The vertical axis, if you will, is sustainability, which measures to what degree each member either adds to or detracts from the collective. So with those two dimensions, you end up with kind of a 2×2 matrix ranging on the top right to kind of highly engaged and sustainable members, to the bottom left, which is low engagement and not sustainable. And over the top of those two primary dimensions, then we’ve added in the other 98 data points, things like products and services, the net promoter score, age, income, owner versus renter. There’s just a ton of both first-party and third-party data that we append through demographic data that we purchase, things like Claritas’ cycle segmentation model. So we’ve got our own segmentation model and we’re overlaying it with third-party segmentation models as well.

Now after adding the additional segmentation dimension, what additional insights did that give you and what did you learn from that? And how did that impact your approach of defining your ideal member?

One of the first insights was, I was expecting a dot density map that would cover all four quadrants almost equally, right? Highly engaged, less engaged, highly sustainable, less sustainable, and that’s not what we found at all. What we found was the dot density matrix really kind of follows an upwardly sloped curve with clusters of members, I’d call it, within the standard deviations of that curve. And as a result of that, what we were able to home in on is what we were working toward, and that is the belief that our target persona, or if you want to call that the ideal member, is contained within a fairly dense area of that dot density curve. I call it the center of the galaxy because it’s kind of like the supernova right in the middle of it with all the stars around it. And those all represent individual members. So we can plot the engagement and sustainability of those. And our target persona is what we’re trying to very tightly define and deeply understand. The goal is, if we can define our ideal member, this target persona, we can make all of our business decisions with that ideal member at the center of that decision. We believe that there’s a lot of Digital Daves within that target persona, that ideal member for Solarity, at least, but there’s a lot of data in order to get there. And we believe, right now, we are engaged in this project to employ machine learning in order to parse that data set. It’s just far too complex for a human to parse and find commonalities, particularly within that center of the galaxy-density cluster. And it’ll be pretty exciting to see what insights kind of come from that analysis.

When you’re trying to prove or disprove a hypothesis, you can leverage different data sources. It sounds like you’re already leveraging a lot of your internal transaction level data. How have you been able to leverage external data sources? What insights did you get from, for example, mystery shopping that you were not able to glean from other internal data sources?

So mystery shopping is a great example. So yes, mystery shopping is a very important element for us, not only to understand our members’ experiences, but also quantifying how our employees are delivering on what we call our Measures Of Member Obsession. We call them the MOMOs for short. In fact, we use Curinos for our mystery shopping program, and we focus the shops on a very qualitative approach. We want to know how our financial guides, relationship guides, and personal advisors make our members feel because we believe that emotion is a driver of action and loyalty. And so mystery shopping is a great example of how we try to tap into that with third-party observers and tell us how we’re doing.

Can you share how that member obsession came into play in this transformation program that you were talking about earlier?

Organizations that are member-obsessed place their target persona at the center of their universe, and make all the business decisions with the target persona at the beginning, not as an afterthought. And so if you think about, “Well, what is member obsession?” It’s more than just providing great service. It’s about challenging assumptions. And in a member-obsessed organization, policy and procedure should always bend within regulations, of course, but always bend to do what’s right by the target persona. And so that’s what member obsession is. So for our branch transformation initiative where our target persona was this very digitally enabled member, the persona of Digital Dave, substituting Smart ATMs for tellers is the right thing to do. And that decision was made because of what Digital Dave tells us, not only in surveys and interviews, but through their behavior, and what they do, and how they transact. So Digital Dave was at the center of that. And other members like a Branch Betty will either adapt or they won’t, and that’s okay. If you’re a member-obsessed organization, you understand that you can’t be all things to all people. So know your target persona, make decisions based on what they need, want, and desire from their financial institution. We believe that’s the way that we’ll win.

That’s great. That’s very powerful. Thank you, David, for joining us today on the podcast and for sharing all these wonderful insights.

All right. Thank you for having me. It was great and fun to talk about it.

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