Executive Summary
In the third episode of Next Best Action, Lauren Kessler draws a hard comparison between the two industries she has worked in. Before banking, she managed product and marketing for Hilton and Marriott. In her telling, hospitality reads guest data and turns it straight into revenue decisions. Banking collects the same kind of data and mostly uses it to check whether service standards were met. Her line for the gap: hospitality is playing chess; financial services is still scratching tic-tac-toe.
Kessler has spent eight years running analytics inside two credit unions, most recently BankFund, after four years at PenFed managing digital banking strategy. Her argument is that most institutions do not have a data supply problem. They have more than enough of it. What they lack is the talent to translate it, the governance to trust it, and the operational habit of turning a finished dashboard into an actual email, offer, or conversation with a member. Data strategy fails long before the reporting stage. It fails at the point where a new platform gets implemented without a data person in the room, where two departments define the same metric two different ways, or where a beautiful chart never gets built into anything a frontline employee can use.
Listen, watch, or subscribe on your preferred platform:
YouTube | Spotify | Apple Podcasts
We are also available on all other major podcast platforms. Simply search for “Next Best Action.”
Key Takeaways
- Data Supply Was Never the Problem: Every institution Kessler has worked with had plenty of data on hand. The shortage is in translation. Data mining is not a skill most organizations carry in-house, which means the real decision is whether to build that talent or find a vendor who can move between the technical detail and the bigger picture.
- Silos Start the Day a New Platform Goes Live: A new digital banking platform, CRM, or lending system is where data silos take root, because that is when the pressure to prove ROI is highest and a data person often gets pulled in only near the end. Kessler’s fix is to keep a data person in the room for the entire implementation, not just the final review.
- Skip the Word “Ownership”: Kessler avoids the word entirely. Instead, she pushes for a single source of truth with broad access across the organization, while accepting that individual departments, mortgage, cards, deposits, will always understand their own data better than anyone else. Those teams still need to bless the numbers before they go to leadership.
- Governance Means Regular, Boring Meetings: BankFund runs a biweekly sync where every data group across the organization compares notes and reviews each other’s work, including shared code so two teams pulling the same metric get the same number. It is not a dramatic fix. It is a standing meeting that keeps definitions consistent.
- A Dashboard Is Not an Outcome: No report or graph generates revenue on its own. Kessler argues the real skill sits with analysts who think like businesspeople first and technologists second, and with storytellers who can take a finished chart and operationalize it into an email, a website placement, or a digital banking prompt.
- Perfect Accuracy Is a Trap: When her team gets stuck debating whether a number is precisely right, Kessler asks whether the business decision would change if the figure were completely wrong. If the answer is no, the team moves on. No institution can fully isolate its true growth rate from broader market noise, and waiting for that level of certainty is a cultural discomfort with change dressed up as rigor.
- Community Institutions Move Faster: Kessler has never worked at a large bank, but her read from peers who have is that scale buys budget, not speed. Smaller institutions can pivot faster, which she considers a real advantage as AI introduces more disruption into financial services.
- Find the Overlap, Not the Compromise: Her closing challenge to listeners: member needs and institutional goals are not opposites. The job of good data work is finding where the two overlap, rather than treating growth and service as a tradeoff.
Full Episode Transcript
Alex Jiménez: Welcome to Next Best Action. My guest today is Lauren Kessler. Lauren has over eight years of experience in banking. She’s currently director of marketing, analytics, and sales at BankFund Credit Union, where she has been for the last four years. Before that, she was at PenFed Credit Union, where she managed digital banking strategy and analytics. Prior to her time in banking, Lauren was in marketing and product management with Hilton, Marriott, and a few other organizations. Welcome, Lauren, and thank you for joining me.
Lauren Kessler: Thank you for having me, Alex. I’m so excited to talk to you.
Alex Jiménez: Same here. I wanted to talk to you about data strategy. It’s something that’s very close to our hearts here at Finalytics. One of the things we talk to banks and credit unions about is that we have tons of data in the industry, but we’re really not very good at using it. Most institutions are sitting on a ton of data and don’t necessarily know what to do with it. You’ve been working in analytics for the last eight years in some shape or fashion, at two specific credit unions. When you look at why data strategy fails, what’s the first place you look?
Lauren Kessler: Data strategy, there’s definitely no supply or demand issue with data. There’s always a high demand for it, and there’s always, for the most part, a very large supply. I think the gap is really being able to translate the data. Data mining is not a skill set that a lot of organizations have in-house, and you really need that to analyze and identify opportunities, or answer the questions you have. So I think having either really strong in-house talent, or more likely being able to outsource and find a vendor you really trust to go through the data, mine it, uncover it, and bring it all together, it’s a process, it’s a marathon. It’s good to find a vendor or really strong talent who can get into the nitty gritty but also zoom out and see the entire forest.
Alex Jiménez: Something you said clicked in my head. I remember working for a bank where we produced great dashboards, a lot of really actionable data, and we presented it to the executives, and they pushed back and said, “Where’s my report?” They had all this data, but they didn’t know what to do with data that was forward-looking, because they were used to the old-time management of data.
Lauren Kessler: Backward looking.
Alex Jiménez: So sometimes it’s a culture problem, not necessarily a data problem.
Lauren Kessler: Absolutely.
Alex Jiménez: Data silos get blamed for almost everything in this space. Some of the data sits with your core system, might sit with a vendor, with your digital platform, with your lending system. But the silos don’t build themselves. They’re a result of decision structures and incentives made by someone at some point. Where do silos actually come from inside a financial institution, and what makes them so hard to dismantle?
Lauren Kessler: I think ground zero for data silos is the introduction of a new platform, whether it’s a new digital banking platform, a new CRM, a new application platform. That’s where the rubber meets the road with data silos. When you’re implementing a new platform, you’re introducing a huge amount of data, and you just spent all this money on it, so you want to prove the ROI. There becomes a lot of interest in seeing into it. I find a lot of organizations include a data person, but only toward the end. I think it’s very important to include a data person throughout the implementation. Most businesses are walking a tightrope between wanting to move fast and wanting lots of stakeholder input across the board. Too many cooks in the kitchen slows things down, but having a data person in there, getting their input throughout the entire process, really helps prevent that data silo when you roll out a new platform.
Alex Jiménez: I can feel some small bank or credit union folks saying, “Yeah, but we don’t have a data person.” So how do we do that?
Lauren Kessler: That’s why you need a good vendor.
Alex Jiménez: Exactly. Even if that person is a hired gun brought in to represent the data view, that’s absolutely needed. I think sometimes smaller organizations are really reticent to do that. They think, “I’ll just grab this person from IT, they should know that.” And it’s not the same skill set.
Lauren Kessler: No, definitely not.
Alex Jiménez: One of the most common failure points is that nobody actually owns the data. Or in some places, everybody thinks they own the data. When I ran operations, I thought I owned a lot of the data, and the reality is I didn’t. It sat in our core system and I didn’t have access to it. How do you establish clear data ownership inside an institution without it becoming a political battle between different areas?
Lauren Kessler: I tend to stay away from the word ownership. I do think having a data repository where there’s a single source of truth and very broad access is a good start. I hate to say the term, but democratizing data does allow for everybody and nobody to own it at the same time. We all have access to it, some of us understand it better than others. There are inevitably certain teams that own the data in a real sense. Mortgages has its application platform, credit cards uses Valera and Visa, and those teams understand that data in a way marketing and I don’t. Anytime my team is doing application funnel analysis, that’s always done hand in hand with the lending department, because the status codes are different from a credit card application to a mortgage application. When data is truly owned by a specific department, they need to be the ones blessing it before it goes to leadership.
Alex Jiménez: I had a situation once where two different groups thought they owned the data, they both reported to the board, and the data never matched. It was because they had different definitions, in the lending world. One was the portfolio management function, the other was the operations function, looking at the same data with different definitions. The reports were not exact, and the board was always confused about which number was right. They were both right, they just weren’t defined the same way.
Lauren Kessler: Exactly.
Alex Jiménez: So data governance is the answer to managing that kind of conflict. I agree that data should be available to as many people as possible, but with the caveat that the people who understand it need to explain it, rather than everyone coming up with their own definitions. What does governance look like in operational terms, and how does your team approach it?
Lauren Kessler: I don’t know any organization that’s really nailing this. It’s a pretty hard thing to get your arms around. I was once at an organization that consolidated all the data peppered throughout the organization into a customer experience team. The idea was to separate church and state, so the analysts judging the performance of a brand or channel weren’t embedded within that team, reporting to the same boss responsible for the work being judged. It’s tough to have honest performance feedback sessions when you’re under the same leadership as the channel you’re evaluating. By the time I left there, there was a real struggle with access to data and speed in getting it back. When it comes to governance, a great practice is a biweekly meeting where all the different data groups across the organization sync and discuss what we’re working on, and review each other’s work. Sharing code helps too, because different teams use different tools, but within business analytics or lending, we’re mostly using the same stuff, so whoever pulls the number, you get the same number every time.
Alex Jiménez: There are real benefits for community organizations in having smaller teams that are closer to each other. Having worked at Bank of America, the divide between where I sat and where another business unit sat, it might as well have been another company.
Lauren Kessler: Exactly.
Alex Jiménez: So sometimes those of us in community banking think, “Well, the big boys can do this.” But the big boys have challenges that are significantly different, and in some cases much more complicated. Which segues to my next question. You did product management at both Hilton and Marriott before moving into banking. Those industries are in some ways ahead of banking, or at least we think they are, when it comes to loyalty and behavioral analytics. What did you bring from that world into banking, and what did you find you really couldn’t, or didn’t have, when you moved to credit unions?
Lauren Kessler: I was very surprised to see how hospitality is truly leagues ahead of financial services when it comes to behavioral analytics. Hospitality does an incredible job blending the physical experience of being on property with the mobile app. It’s incredibly integrated, whereas the branch experience at a bank and the mobile app experience may as well be developed on two different planets. Hospitality is playing chess, financial services is scratching tic-tac-toe.
Alex Jiménez: We’re not even doing checkers.
Lauren Kessler: Not even that. But something both industries do, OSAT, NPS, ease-of-use scores, that’s tracked across pretty much any industry I’ve touched. In hospitality, though, that stuff is currency. They understand that a high net promoter score leads to revenue and future business. In financial services, that kind of data is used more to maintain service standards. An exceptional outlier becomes a spot bonus for a team, and anything really low becomes “let’s review it and see what happened.” I don’t think there’s a correlation between overall satisfaction scores and revenue the way there is in hospitality.
Alex Jiménez: Two years ago I went to a credit union conference at Disneyland, and one of the keynote speakers was from Disney.
Lauren Kessler: The best.
Alex Jiménez: The way he spoke about guests versus members at a credit union was amazing, and a lot of the credit union executives there loved it. But trying to make the connection, how they could mirror what Disney does at a credit union, they couldn’t make that jump. I talked to several of them, and they basically said, “This is great, I want to do that at the credit union, I have no clue how to do that at the credit union.”
Lauren Kessler: Yes.
Alex Jiménez: Through the years I’ve seen a lot of that discussion, whether it’s Four Seasons, Marriott, or Hilton, they have some really great experiences that we in financial services think about, but we just don’t know how to close that gap. Your role sits at the intersection of analytics, marketing, and sales, which is really where data strategy pays off. What does it actually take to get from analytical insight to a sales or marketing action, and where does that handoff most commonly break down?
Lauren Kessler: There’s no report or graph that’s going to tell you how to generate revenue. It really boils down to talent. Having data analysts who come with a business perspective, rather than an engineering or IT perspective, is important. And particularly in the age of AI, what really differentiates is having a storyteller with a sales and marketing perspective. A lot of data analysts fall into analysis paralysis, and coming in with that business-minded head lets you cut through that and see where the opportunities lie. Another point where it falls off is operationalizing data insights. How do you get from the data you pulled out of your warehouse, the pretty graph you made and shared with leadership, into actual marketing collateral, across email, the website, digital banking? Operationalizing data, and having a kind of data repository, is absolutely crucial to finding the opportunities where the rubber actually meets the road.
Alex Jiménez: I love that, the human touch still needed in the age of AI. Just today I read an article about several large marketing agencies trying to replace not just the creatives, which in many cases they already have, but the analytics people too.
Lauren Kessler: Oh my God.
Alex Jiménez: I think that’s dangerous. At some point they’ll have to turn around and rehire people, just like my friends at Klarna had to do when they shut off their contact center and turned it all into AI, then a year later had to rehire everybody. But I love the idea that storytelling is something AI can’t do well. It’s very rote, and it’s easy to tell when something is AI.
Lauren Kessler: Yeah, exactly.
Alex Jiménez: You’ve seen institutions that have cracked the use of data, particularly in hospitality. What changed? Was it a person, the industry in general, the technology? Why is it different from what we see in financial services?
Lauren Kessler: Moving data from being just a report or dashboard into actually driving decisions comes down to talent and culture. In many organizations, I’ve seen everyone from top leadership down to data analysts get hung up on accuracy, falling down a rabbit hole of needing tons of information and getting lost in the data. I like to play a game with my data analysts: if we get really caught up on accuracy, I ask whether it’s actually going to make a difference at the balance sheet. Hypothetically, if this number is 100 out of 100 versus zero out of 100, how does that change our business decisions? If it’s not going to make a difference either way, let’s move on. There’s so much data, so many rabbit holes, that you need to do a gut check from time to time and zoom out. There’s a cultural aspect to that too, how comfortable you are with risk and with change. When you’re not 100% comfortable with accuracy, you want to isolate every variable to get the pure, true growth rate, and you get stuck. But there’s no bank in the world that has a 100% pure understanding of its growth rate, stripped of COVID, interest rate spikes, and the broader economy. When we can’t get past that, that’s an organizational, cultural discomfort with change. We’re never going to know 100%. We just have to use our best judgment and be willing to pivot.
Alex Jiménez: After eight years in banking, particularly in credit unions, have you seen anything change from a data standpoint? Have we gotten better, or are we fighting the same fight you saw when you first arrived?
Lauren Kessler: I think we’ve done really well. I’ve never worked for a big bank, but from what I hear from friends who do, community institutions are never going to compete from a budget standpoint, but we’re able to move a lot faster and pivot quicker. In the age of AI, that ability to pivot and adapt matters a lot, because AI means more disruptions coming, and faster. I think that’s a huge benefit of being smaller. AI is overwhelming, particularly in a highly regulated industry like financial services. There are real concerns about whether we’re using it ethically, whether there are implicit biases in it. AI with data is endless opportunity, but how comfortable we are with the risk is complicated. In the past eight years, we went from playing tic-tac-toe to doing some really advanced stuff, stitching together data from external partners like Experian and finessing our internal data, so we’re getting a much better understanding of who our members are and what their needs are.
Alex Jiménez: Lightning round. Don’t overthink it, just answer and we’ll move to the next one. What’s one book every banking leader should read? You can also pass.
Lauren Kessler: I feel like I’ve read so many books about Judaism and novels lately that I can’t think of one off the top of my head that would be appropriate.
Alex Jiménez: That’s fair. Coffee or tea?
Lauren Kessler: Coffee.
Alex Jiménez: What’s the most unusual thing you’ve done to improve team culture?
Lauren Kessler: I’ve done personality tests where I could tell communication was a challenge, we did Myers-Briggs, and it really helped in understanding how different people communicated. When I’ve seen a lot of complacency, introducing a little competition, sharing out sales numbers, that can stir the pot in a good way.
Alex Jiménez: What’s one thing banking leaders need to stop doing right now?
Lauren Kessler: It’s hard for me to stop freaking out about the economy, because I don’t have to face the board of directors and answer for it. It’s so far outside our control. It’s a great comfort to know I’m not alone, every single FI is dealing with the fluctuations and the inflation. We’re not alone.
Alex Jiménez: What’s a challenge you’re currently excited about tackling?
Lauren Kessler: I have a new team, a new sales team. I got promoted earlier this year, and I’m excited to work with them. I’ve always been back of house, and this is the first time I’m front of house, member- or customer-facing. I think that’s a really exciting change. I look at what makes community banking and credit unions really unique, that white-glove, neighborly feeling, and now having a team that’s member-facing, I’m looking hard at what metrics we’re judging them on. Traditionally, customer-facing teams get judged on how long the interaction is, how many people you’ve helped within a day or an hour. I’d like to strip away some of those and find other ways. Rushing through a call as quickly as possible walks away from a great opportunity. If somebody on my team wants to spend an hour and a half explaining the differences between ARMs and fixed mortgages, please, I’d love for you to spend your time doing that.
Alex Jiménez: I love that. Last question. What’s the one step forward you’d challenge every banking leader listening to take after this episode?
Lauren Kessler: It goes back to my roots of being very focused on customer experience. There are customer and member needs and wants and things they’re trying to achieve, and then there’s what any bank or FI is trying to do, whether it’s selling mortgages, cards, whatever. There’s a sweet spot in the middle where both sides of the equation can reach their goals. That’s my goal in data, finding where that Venn diagram overlaps.
Alex Jiménez: That’s great. Well, thank you, Lauren, for joining us. I reserve the right to invite you again sometime in the future.
Lauren Kessler: It’s so much fun talking to you, Alex. It always is.
Alex Jiménez: Thank you so much.