From Amazon to Apple, the largest, most successful companies are using data to gain more significant consumer insights, produce better products and services, and capture more business. Financial institutions are no exception, with large banks currently having a competitive advantage over market and member data. However, podcast guest  Sam Brownell, CUCollaborate CEO and founder, wants to change this with his work and personal goal of making credit unions the number one financial partner of every American.

Naturally drawn to cooperatives throughout his career, Sam has more than 15 years of experience working with credit unions, establishing CUCollaborate in 2014 to facilitate impactful collaboration within the industry. Through disruptive innovation, the organization provides creative solutions, insights, and know-how to help credit unions adapt and grow today and long term.

AnalyzeCU, CUCollaborate’s latest development, is a data consortium credit unions can use to learn more about their members, compete with big banks, and deliver more value. Sam discusses the platform, features such as customizable reporting, and how credit unions can balance profitability and their mission to capture more market share, ultimately strengthening the credit union movement.

Why Is Data Critical to Credit Unions?

Prominent industry players are capturing and leveraging consumer data, which remains a significant missed opportunity for most credit unions. Enter AnalyzeCU. With nearly 200 credit unions pooling over 35 million pieces of data, the platform equips credit unions with a wide range of anonymized information and reporting for various purposes, such as benchmarking, analytics, and predictive modeling. Sam explains how data, however evident, can also drive strategic action among boards in addition to just a leader’s instincts.

While most data reporting available today focuses on profitability, what makes AnalyzeCU different is that it considers various factors so credit unions can maximize their mission, such as total benefits to members, and margin, such as appropriate capital allocation.

Credit Union Data Use Cases

The benefit of accumulating more data is that AnalyzeCU can be as predictive as possible for scenarios such as opening more successful branches, creating better performance metrics, attracting and retaining younger members, and building best practices. 

However, according to Sam, the sky’s the limit for possible use cases and report generation. While team members generate reports based on their expertise, they hope more credit unions will participate, query, and input values that are most important to them and their members. The more data available, the more AnalyzeCU can refine its features, standardize reports from familiar patterns, and benefit credit unions. 

Stream the show to learn more about AnalyzeCU and how your credit union can benefit, plus:

  • How collaboration over competition among credit unions can ultimately benefit the credit union movement as a whole
  • What Sam’s hopes are for the future ownership of the data consortium
  • A compelling use case that has shed light on a top performer and possible industry best practices to leverage

Hear the episode now. You can also learn more at

Sam Brownell and CUCollaborate are not affiliated with or endorsed by ACT Advisors, LLC. 

Audio Transcription (pulled from the podcast)

Doug English  00:00

Welcome, everyone, to “C.U. on the Show”! I’m thrilled to have Sam Brownell, the visionary founder and CEO of CUCollaborate, joining us today. In this episode, we dive into the exciting world of AnalyzeCU, a cutting-edge data consortium and analytics platform exclusively tailored for credit unions. Sam shares his insights on how AnalyzeCU empowers credit unions to make informed decisions, amplifying their collective impact and fortifying the credit union movement as a whole.  

Well, Sam, I’m delighted to have you with us today. Tell our listeners a little bit about how you got started working with credit unions and what your connection is to the credit union movement.

Sam Brownell  00:45

In college, I was, I would say, sort of half jokingly brainwashed by my advisor into the philosophy and basically the merits of worker cooperatives. And fell really in love with them; I still believe very passionately about worker cooperatives to this day. In my own thesis, the conclusion was entrepreneurs are not incentivized to form worker cooperatives. The way entrepreneurs are rewarded is by ownership over the business and their ideas and for the risks that they take. And in my case, quitting a comfortable job, emptying out your 401(k), and trying to bootstrap something. That there would be very few worker cooperatives basically. So entrepreneurs are not incentivized to create worker cooperatives. Worker cooperatives are better for workers and for the world ultimately. After college, I wanted to do consulting for worker cooperatives and help start more worker cooperatives. But for the very reasons I outlined now, there aren’t a lot of worker cooperatives so there isn’t a lot of opportunity to do consulting for worker cooperatives. Fortunately, there are a lot of credit unions. And also fortunately for me, now the managing partner at Callahan & Associates was an alumnus of the college I went to. And so out of college, my first job was at Callahan as an intern; I worked there for six years. By the time I left, I was in charge of their sales account management and product development team. After I left, I started CUCollaborate with honestly a desire to sort of disrupt, move faster, innovate, and deliver the innovations I thought would help credit unions take real meaningful market share, increasing their number of members and reaching more people. My personal mission, although it might be impossible to basically get every American, is that they think of their main financial relationships with credit unions.

Doug English  02:59

Well, that’s going to be the outcome we’re going to focus on today, to take those innovative ideas you and your company are building and to talk about them. In the preparation for our call, it was pretty interesting to learn the idea you have and have been working with AnalyzeCU. So maybe take us through your thinking behind AnalyzeCU and what it does and what your company does. And then let’s kind of get into the ultimate use of this dataset.

Sam Brownell  03:37

Yeah, so the idea of AnalyzeCU is really a data consortium. So credit unions, as everyone knows, are generally smaller than banks and data is really a competitive advantage. The more data you have, the better you will be at predicting things, the more you can know about your customers, the more targeted your marketing can be. Data is king. There’s a reason Facebook, Google, all these companies are so successful; they have tons of data on consumers, tons of data period. And even Navy is at a competitive disadvantage compared to any sort of the big big banks. And so what we’re doing or have done is build a data consortium where credit unions can pool their data together and make better predictions, better understand their own performance, benchmarking, predictive modeling, impact measurement. There’s some regulatory reporting stuff too but ultimately it will be sort of like an industrywide data consortium that will help equip credit unions to make more informed decisions and provide better services to their members.

Doug English  04:52

So AnalyzeCU is a database credit unions can participate in and how many credit unions are? Is that the right name?

Sam Brownell  04:58

Like a data consortium and analytics platform is sort of the way I would describe it. I think for people who are familiar with technology, data analytics companies out there, it’d be blending Rattan with Callahan peer to peer. Callahan peer to peer is looking at structured government datasets. Rattan is generally pulling in credit unions’ member account loan files, transaction data too. But Rattan’s reports are more static and Callaghan’s more sort of like custom querying. And this is sort of the combination of those things. We include public data too but the sort of key differentiator here would be their own internal member, really product-level data, share and loan-level data that then can be used for benchmarking and predictive analytics.

Doug English  05:47

Is there any other data consortium in the industry that has this scale?

Sam Brownell  05:52

You could argue Rattan, there are people who would like to do it. The big issue historically has been that large credit unions, even if they’re at a competitive disadvantage against banks, know their data is valuable and provides them with a competitive advantage over smaller financial institutions. And there is a concern or fear of sort of a freeloader or rider problem; they are concerned other people will get more value from being analyzed than a large credit union would get from accessing data. And the data consortium is sort of like chicken or the egg type problem. We got around that by getting enough small credit unions that didn’t have that concern to pull together enough data now to make it rational for pretty much every credit union except arguably Navy to participate. So I think we now have 35 million person records. There’s really no argument that if you have less than 5% of the consortium you shouldn’t do it. So for any credit union with 1.8 million members or less, it’s pretty unarguable that it’d be rational for them to participate—I think maybe up to 3 million if I remember correctly. And then as we grow that, obviously, then at a certain point, it will become compelling. Nope, sorry. Navy has almost 13 million.

Doug English  07:16

So how many credit unions are in the data consortium now?

Sam Brownell  07:19

Just under 200. 

Doug English  07:21

Wow. That’s a lot of the industry. If you got to the level where you had all the data or enough data so you had the ability to be as predictive as possible, how would AnalyzeCU help the industry as a whole?

Sam Brownell  7:39

I’m going to start with just sort of benchmarking. Being able to benchmark things like attrition or next serve. Or if you’re looking at things on the call report, you just have total number of members, and you don’t know what percentage of them are actually new or old, you just have how many members is one person as of this day, how many members they have the next day, and you don’t have a sense of churn. So sort of what are ways of having stickier longer term memberships. Who’s having more success with younger audiences? A lot of what was powerful about digging into call report data is identifying top performers and best practices from it. And being able to do that from account-level data will be able to identify either even more precisely successful credit unions and people or successful in things you can’t do with public structured data. So one example of this would be, we have a client, the numbers I’m about to say should not be held out as appropriate benchmarks for most credit unions—they are an outlier, but an amazing outlier. So there’s a credit union called People’s Advantage that serves the Richmond area; it’s a CDFI, low income designated credit union, a majority of their loans, I believe, are made to people with credit scores, I believe, below 650. Their ROA is in the top percentile for their peer group. So they’re highly profitable, they charge really high interest rates compared to their peers. But they’re also lending to low tiers of credit, really low tiers of credit. And so prior to having the sort of database and tools we have now I even would have looked at them like they could be a predatory lender, right? Like they’re making so much money, their interest rates are high. This could be a sort of bad actor. But when you look at the data, we look at every product, they’ve provided their members, and we benchmark the interest rate they provided that member, taking into account the person’s credit score. So the risk-based member benefits. We look at the credit score, the product, the term, the collateral code, and then benchmark the risk-based pricing of their credit union against the market and then back into how much more money their member gets from that product overall, and the whole membership relationship, or on average. That credit union, on average, provides over $600 in annual member benefits, which is far and away the highest risk-based member benefit of any of our clients.I think the next best one is like around $200. Really, if it’s positive at all, I think that’s pretty good. And there’s even arguments that at times, it should be negative if a credit union really needs to build up capital or something. Running a credit union is ultimately about sort of balancing mission and margin. And there are times when margin is warranted. It sort of supersedes mission if it puts the credit union in a position to deliver on its mission even more in the long term, essentially. But this credit union is highly profitable and delivering by far the highest member benefit too. So it’s pretty awesome. And being able to do that, suss that out, figure out how they do it, and then hopefully help other credit unions find a path to do similar things or pursue that same sort of mission and figure out how to maximize mission and margin simultaneously. 

Doug English  10:52

If I understand the use of the data consortium, it could be used to query on all sorts of different objectives depending on what the credit union needs. What I took away from our pre-recording conversation is that the objective is to have as much data as possible as an industry and to be able to make the best decisions to strengthen this industry and to make it the primary institution, or whatever the current language is, for as many members as possible.

Sam Brownell  11:24

Yeah, I would say performance metrics is sort of the straightforward one. Performance metrics, predictive models. So we’re building a branch predictive model, next best product attrition, quite frankly like loan performance. We’ll have very powerful data for all that. But then also just things like market analysis. We could, in theory, get a database of all consumers in the United States. And then if we have a pretty strong database of credit union members, basically like back out credit union members and focus solely on non-credit union members, and really focus on capturing market share overall rather than potentially marketing to an existing member of another credit union and keep competing for that same 8 to 10% of the market credit unions have historically had. Yeah, so we could really target non-credit union members, and then within non-credit union members who are the most likely to get value from credit unions? Who are they eligible to join? Who would offer them the best products? And focus credit unions on acquiring industrywide net-new credit union members rather than just trying to steal members from each other.

Doug English  12:32

That kind of outcome, sort of serving the industry as a whole, sounds like a classic CUSO structure in this industry. And currently, your structure is where you’re the owner of the company. I mean, when you think about how this continues on, is that something that maybe sort of separates itself from the rest of the work you do and becomes a CUSO structure? What’s your vision of how this grows, and its ultimate outcome?

Sam Brownell  13:04

We are growing very fast. So I think we’ve doubled every year for the last four years. Ultimately, I’m balancing my love and affection for credit unions, also with my love and affection for worker cooperatives. So I envision the company ending up as some sort of hybrid, part credit union owned, part employee owned, probably some part also investor owned. Things that are critical, sort of, like infrastructure for the industry, I think, should ultimately be owned by credit unions. I get worried with some of these fintech startups that are becoming sort of critical infrastructure for credit unions that are run and owned by people who are seeking IPOs. And what does that mean for the long-term infrastructure for the industry if they want to go to the people who maximize profits for them? That might not always be credit unions. How safe is it for the credit industry to build sort of critical infrastructure with organizations that have different incentives than credit unions do? So very sympathetic to that. The reason I have avoided being a CUSO up until now is yeah, I do have an entrepreneurial spirit. And I like to move fast and come up with new innovative ideas and want to be able to pursue them and take probably more risk in doing that than a credit union should want to do. I hope all the risks I take are successful but assuming they are then I think once it gets to a sort of like plateau stage or like once it’s up and running and it has taken root, then handing it off to credit unions I think would be the appropriate timing.

Doug English  14:52

Yeah, it makes you wonder if the largest credit unions in the country would have enough interest in the data you already have plus the data remaining, if they would be so interested that they could create some sort of a CUSO structure to own some portion of this dataset. So the industry owns the data, and then the industry uses it to benefit itself. And to be as strong as it can possibly be in the use of capital, right? That’s the bottom line; this data is giving you the information you need to use your capital as effectively as possible. 

Sam Brownell  15:29

Yeah. I would hope that is the end result.

Doug English  15:32

AnalyzeCU has existed for how long?

Sam Brownell  15:35

Not very long, six months.

Doug English  15:37

Six months? So it’s moving very quickly, and there’s 200 credit unions already in there. So at that pace, how long is this going to take?

Sam Brownell  15:48

I’m not sure. We are hoping to work with credit union leagues. We initially planned to work primarily directly with credit union leagues to accumulate data. And so the first thing we made is a sort of community impact report that shows it’s an incredible advocacy tool. It’s good for annual reports, board reports, other stuff too. But the idea was the credit union could come in and see their own credit union impact in any communities they want. They could look at state- level legislative districts, congressional districts, cities, counties, states, globally, whatever it is, and be able to communicate what their credit union has done, what is the total benefit they’ve delivered to people living in that community. How many jobs have they created, sustained contribution to GDP, what percentage of the loans are made to different income strata, credit tiers, race and ethnicity, rural areas, all these different things. And so credit unions, when they basically hike the hill, they’d have their own impact report, and leagues would be able to have aggregated anonymized impact reports, and be able to say this is our impact. And this is all credit unions’ impact. And this is why you should care about us essentially. If things take off with leagues, we could be accumulating a lot of data very, very quickly. Selling one to one, I mean, we’re a bootstrap company, we now have three salespeople, we’re adding roughly 10 new credit unions a month. But that’s fast. But that means we’ll add 120 over the course of the year and there are 5,000 credit unions. If we’re selling one to one, it’ll still take a very long time.

Doug English  17:37

Depends on if you’re getting the big ones.

Sam Brownell  17:40

Well, yeah, so the counterpoint is, if we’re getting big credit unions, which we are now, how long will it take us to cover most like persons in the United States? So we only have just under 200 credit unions, roughly but we have 35 million unique individuals. And there are only, I think, 330 million in the country. So if we’re adding larger credit unions we will reach a critical mass of like person data more quickly. So I don’t know what is more complete.

Doug English  18:16

I think it’s whenever the predictive model peaks out, right? Whenever you’ve got all the data to be able to make the most accurate predictions. And I would think if I understand the nature of what you’re building, that it’s almost be a bit like network marketing, right? Because if your credit union has been in there, and you’re looking to have your data become more and more and more predictive, then you would want more data to be in the system.

Sam Brownell  18:44

Definitely. Yeah, I mean, credit unions benefit from other credit unions participating unquestionably; it’s a network or platform effect where what the product really is, is all the credit unions agreeing to pool their data and let each other query it anonymously. And the data is anonymized and aggregated; being able to query aggregated, anonymized data is still incredibly valuable. And especially when it’s broken down to like geography. So you wouldn’t know I’m just making this up. But let’s say a national credit union has people all over the place; you wouldn’t know that one of the people who is a member in this census tract is a member of a specific credit union. But you could now know 30 out of the 500 people in the census tract are credit members, sort of like credit union market share, actually, and product market share, what is the pricing in the market. Pricing stuff seems really interesting, and product fit and market share. There’s a lot of like market analysis that can be done with it too. Even without predictive modeling. Predictive modeling is definitely where things get really interesting in the long term but even just straight conventional analysis, like benchmarking market analysis, geographic-based market analysis, can be really interesting as well.

Doug English  20:09

What’s the role in the credit union that’s interfacing with your company? I’m sure that varies by size but who’s the role that is coming to you guys?

Sam Brownell  20:19

Most often CEO, I mean, it depends on a credit in size, it’s going to be primarily CEO or someone involved in setting their credit union strategy because we’re talking about one product but we help credit unions, our professional services, their field of membership, helping credit unions attain low income designations, obtain or retain low income designation CDFI certification, mergers, technology, we do a bunch of other stuff. And it’s generally all sort of helping credit unions that their strategy to best fulfill. Yeah, basically maximize mission and margin, balance those things and grow healthily while delivering as much value to their members at the same time. And so yeah, I think that dictates oftentimes who we’re talking to once crediting is a client of ours, then we’re working with all different areas, marketing, business development, chief financial officers, lending officers, oftentimes community development, people who are representing the credit union out in the communities and need to articulate the credit union’s social impact and have measurement data that helps them make their case.

Doug English  21:33

I imagine this way of thinking and understanding what’s possible to predict and getting to understand, how this method of work would be different from what you would get from Rattan or from other industry players? What are your thoughts about how to think through those ideas?

Sam Brownell  21:53

This is actually a thing we thought about a lot internally when we first made the product. We made basically static dashboards. And quite frankly, our plan was to go through leagues and sort of redistribute through leagues and channel partners. Then we found interestingly enough, particularly very large credit unions are really interested in quantifying member benefits, which honestly we did not anticipate. We’re like, oh, this is really important. And we think credit unions should care about this but didn’t know reading was top of mind at readings. And then I’ve been pleasantly surprised that many readings are the number one focus for the year—figuring out sort of like KPIs around fulfilling their mission, key performance indicators to sort of better understand how they’re doing at delivering value to their members. They’re very good at knowing profitability, sort of traditional financial institution performance metrics, but there hasn’t been very detailed work on I would say the performance, good credit union performance, and sort of like credit unions fulfilling their mission. And so that has been great. But what we are now working on is essentially a way where people can just write their own formulas and choose what data they want to look at; it is all anonymized. But you can write whatever formula you want. And you can choose what you can, never structure so any one institution represents more than 5% of the data analyzed to ensure data anonymity. But any credit can pick whatever they want to look at essentially. And that’s because we were like, oh, we can’t anticipate all the ways this could be used. And you know, pricing. Coming from Callahan, I’d always been like, historical performance is what we should care about. And then I realized quickly, like actually being able to tell people last week’s data, it’s also very valuable being able to predict next week’s data and a year from now as data is also really valuable. So I think we started with some static reports and we’re very quickly going to just let people create whatever reports they want. The predictive analytics is if you’re drawing a product roadmap, it’s like the step after benchmarking. We’re doing some of our own, like building our own models for specific things. It’s actually a great idea we’ve not really thought about but it’s like, how could we? I wonder if we could take what we’re doing on benchmarking of writing your own formulas, and then just sort of extrapolate from this, build a predictive model? Could we let our users specify the things they want to predict? And sort of be drivers of those predictions? We probably could. Right now we’re not doing that but in the long run, we might be able to let people sort of choose how to structure their own predictive models.

Doug English  24:45

It seems intimidating and vast and unfamiliar to probably a lot of the leaders of credit unions. And if you’re just trying to start to try to get an idea of okay, if I have situation x, then I should become more educated on this, AnalyzeCU and other other predictive data consortium ideas, what would it be? What would you be looking for? And then what would be your first step to just begin to understand where to go?

Sam Brownell  25:26

That is such a good question. I feel like my answer will be so biased by the things we have expertise and knowledge in. I’m like, we take your data. And we have already made things where when a credit union becomes a client of us, we run a bunch of analysis on the data they give us to quickly identify is CDFI a thing that should be on their roadmap or not? You know, could they or could they not have a low income designation? And actually that’s an issue about their data cleanliness, and all they need to do is take these steps, and actually they can have a low income designation, or do they have a low income designation, then to  revise their workbook, greatly reducing the number of people who count as low income designated, so a bunch of guardians are gonna find out they haven’t already over the next year, that they no longer have a low income designation, that a very high percentage of guardians will have that news broken to them? And like, the next time they have an exam will they be notified? We’re trying to identify things like that, looking at, quite frankly, their risk- based pricing and a whole bunch of things. There’s so many things that can be identified from this data that definitely our own expertise is driving, sort of the opportunities we identify. But again, going back to creating your own sort of customer reports. Part of the reason we’re doing that is to also learn what things people would create reports for and what are the other things because we can’t think through every potential; there’s so many different applications and possibilities for what you do with the data. We’re never going to think of every single one. So we want to equip people with the tools so if they think of something, they can figure it out. And then honestly, we’ll be basically looking over their shoulders and seeing if there are really keys we should incorporate into our own thinking essentially. That’s the way we do a lot of stuff with customers. 

Doug English  27:32

Yeah, I would imagine it comes from your strategic plan. It comes from the longer-term vision your board is putting forth in your strategic plan, and then you figure out what you’re working on and whether or not you have good data to figure out what are the actions you’re going to take. Does that make sense for where you decide to?

Sam Brownell  27:52

Yes. I think one of the things I’ve also found is the things that make it onto credit union strategic plans have a lot of biases already in them, right? Like there are lots of credit unions, our own clients, who had never heard of the CDFI fund. They were a slam dunk to get CDFI certification and grants and just didn’t know about it, so how does exploring CDFI certification and grants get onto a credit strategic plan? Honestly, it’s probably typically going to be done by either vendors or trade groups educating them on it. Most credit unions are incredibly busy in the day to day of running your credit union. And it’s not like they have tons of time; I don’t even know how you would learn about something like that. But like Google, random credit union strategies, I don’t know how new ideas sort of get introduced to credit unions’ strategic plans; it would be very interesting to see just as much as in the data consortium, which should be kind of like an idea consortium to agree so we can see things. If one of our clients had us make a branch predictive model, which I would have thought would be primarily focused on where to place a new branch, or what branches should potentially be relocated, their whole thing was setting goals for branch managers, which I had not thought of as a thing. And I like to think I come up with good ideas but I certainly won’t come up with every good idea. So definitely pooling together all the smart people in the credit industry with the tools for them to get answers to their questions or basically figure out what opportunities exist to improve their credit unions operations, if we’re structured correctly, and we can sort of see what people are doing, we should be able to see patterns and things that will spark ideas and these customer reports may become standard reports that every time a new credit union joins, we look at their attrition rate, and if their attrition rate is very high, try and figure out how to get their attrition or like lower their attrition rate, how to get retain members at a higher rate, or there’s certain credit cards that are incredibly effective at cross-selling and direct borrowers. Figure out what they do. That’s so good. I’m desperate to know what People Advantage does. How are they so good? And I wouldn’t have known they were so good without this data, basically.

Doug English  30:45

Yeah, and it’s stories like that one, I think, that would be meaningful. Maybe it’s too soon to have a slew of stories you could tell us but about credit unions using the data consortium and finding out this unexpected result. And then they took this action resulting in improvements of so and so; I think that those stories really resonate. Do you have any more of those for us?

Sam Brownell  31:14

Yeah, we do. I think the thing we are doing right now, which to me feels incredibly exciting is basically helping with product design. So helping them understand their own products and how things are priced and how they could price things to both balance mission and margin to create value for their members and also still be healthily profitable, getting healthy net income building capital so they can keep providing a great benefit to their other members. I do have really high hopes for basically predicting, quite frankly, like loan performance. So we’ll have a lot of data on loan performance that credit bureaus never get, you know, people who had to skip a payment or restructure loans without actually coming to a credit bureau will be able to see those things. I mean, marketing has its own end sometimes. And this is like the opposite end of the story. And definitely, if I were better at just painting the best sort of the most positive, the present better salesperson, I would not harp on these types of things. But we also have clients who come to us with things that I think the answer is very obvious—a branch that never performs well and you look at it on a map and it is in the middle of a residential area with nothing else around it. And you’re like, site selection is the problem but that type of answer isn’t maybe thorough or robust enough to be persuasive. I think it is actually the right answer, it’s just you put it in the wrong location. And that’s very obvious. Whereas having something that probably is a bit overkill, candidly, where it’s like, here’s the predictive model based on 1,000 other branches and the demographics around them, and the income and competition and commercial locations and the foot traffic or mobility data; this will only ever become successful, have this many members if this was run by the best branch manager in the country. If it’s run by the average branch manager, this is what you’re expected to be. And if we can just show that is more persuasive than just being like I looked at a map and it shouldn’t be there. Which, in a lot of cases, you can just look at a map and be like, it shouldn’t be there. But that isn’t an answer that is sufficient oftentimes to make people make big strategic decisions. And so having a lot of data and a superior methodology to basically say the same thing can help move forward, basically.

Doug English  34:13

Yeah, having data behind the decision instead of just instinct. And instinct is incredibly powerful, especially with the senior leaders in this movement having been around, generally speaking quite a while, they have a lot of knowledge from all those years of history. But adding data science, like what you’re talking about, and what your company provides, is how you get the ability to allocate capital as effectively as possible. And that’s how we protect and grow the credit union movement as effectively as possible.

Sam Brownell  34:49

Yeah. And what is successful deployment of capital, I think, also the analytics we’ve already created do a better job of that than what is out there in terms of quantifying, not just sort of profitability and sort of fine. Well, in the degree it is financial performance but both margin and mission, like what all the other metrics I’ve seen just focus on sort of, ultimately. And this is super oversimplification, but let’s just say profit. And then the analytics we’ve already created help balance profit against benefit. And I do think that is what successful deployment of capital is for credit unions. And yeah, building the data consortium, all these ventures, I mean, benchmarking, predictive analytics will improve credit unions, decision-making can help them better deploy their capital to maximize mission and margin.

Doug English  35:51

So as we wrap up, if I’m a credit union leader and I’ve been listening to this podcast, and I want to learn more, where should I go? What should I do to get to understand if the data consortium you collaborate makes sense for us?

Sam Brownell  36:07

Go to Or you can give me a call directly at 202-897-3459. Yeah, reach out to us; we’d love to show you what we do. And also, more innovative credit unions that want to hear people’s ideas—all of our product creations and enhancements, our whole product roadmap, is really informed by the requests of our customers. And I am sure there are people who have heard this and been like, oh, what industrywide data consortium, I get that idea. And here is the problem that could solve that no one on our team has ever thought of. So please bring your ideas because we’re hearing new ideas that I oftentimes think, how did we not think of that? All the time.

Doug English  37:09

Well, the brilliant idea you have is this data consortium and making it so the credit union industry can access it or even own it, and use it to strengthen and protect this great industry. I think that is the magic you have brought into this equation. So going forward, I know from our pre-call you have many more ideas than we have time to talk about today. But they all are born of the same thing, which is to grow and protect this credit union industry in ways that help it to charge forward. So thank you for thinking like that. Thank you for your innovation, for getting constantly distracted by your new ideas. I think this is exactly what the industry needs. And I thank you very much for your time today.

Sam Brownell  38:03

Thank you. I appreciate it.

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