Love at First Insight: How Data Teams Shape Customer Experience for Smart Growth, with Bumble’s Lina Mikolajczyk

Transcript

This has been generated by AI and optimized by a human.

Show ID [00:00:04]:
The power of data is undeniable and unharnessed, it's nothing but chaos.

The amount of data was crazy.

Can I trust it?

You will waste money.

Held together with duct tape.

Doomed to failure.

Jess Carter [00:00:16]:
This season, we're solving problems in real time to reveal the art of the possible, making data your ally, using it to lead with confidence and clarity, helping communities and people thrive. This is Data-Driven Leadership, a show by Resultant.

Welcome back to Data-Driven Leadership. I'm your host Jess Carter. Today we're going to be speaking with Lina Mikolajczyk, director of reporting and analytics, or what's now been rebranded as advanced data and analytics at Bumble. Yep, Bumble, a dating app created back in December of 2014 by the once VP of marketing at Tinder, another dating app. 2014, when Facebook was still the most popular social media app and “Happy” was the biggest song of the year, the “Red Wedding” episode from Game of Thrones, anyone? That aired in the same year and the iPhone 6 was unveiled and there was a glimpse at what you'd be wearing on your wrist today if you're an Apple Watch fan. Now, quick spoiler alert.

Jess Carter [00:01:12]:
I actually met my husband in December of 2013 on Tinder and he was the only person I ever met on the app. I was matched with him the night I downloaded it. So I have really strong opinions about these apps and I am a believer. Match.com, of course, everyone's heard of that, was started in 1995 and was largely seen by the next generation as a Gen X dating app. Millennials wanted something unique, and the concept of swiping made it more of a game. Bumble's unique differentiator was simple. Women get to make the first move. Users would set up a profile, select parameters, your phone number, location, name, birthday, gender, what brings you to Bumble.

Jess Carter [00:01:51]:
And a final step is to add some photos. And suddenly, they would see before them a suggested match, a photo, a snippet, and they would get to swipe right to like someone or left to reject them. A woman would need to initiate a conversation with a man within the first 24 hours of matching, or it would disappear. In a same-sex situation, either person can initiate. In 2016, they released a BFF feature as an option to find platonic friends as well. They rolled out paid subscriptions later in 2016 for three features: Beeline, which allows you to see a list of all users who have liked your profile; Rematch, which keeps expired matches in a user's queue for 24 additional hours; and Busybee, which allows users unlimited 24 hours extensions for matchers. There are more but these were the first. Bumble has also adopted technologies to combat challenges to their platform, an AI tool to detect inappropriate images and an AI tool to help identify fake profiles.

Jess Carter [00:02:47]:
Most interestingly, last month, Bumble announced it was developing a tool to help its users with flirting. When we talked to Lina today, one of the things that I find the most interesting in her role is the way that she has shown up in the last year and turned reporting and analytics from a reactive, lagging indicator for the business to a department that's infused into how they think about their business. From CX, from product advancements. No matter what part of the business they're working in, they have somebody from analytics that seems to be by their side helping ensure that they're making the most of those investments. Lina also has this amazing background. She's worked at a bunch of really cool places, and what you'll get from this conversation is an enormous amount about leadership in data and analytics. But also it's like a how-to guide on what to do if you take a new role or get promoted into a director or VP role in data and analytics, and how to really show up the right way in the first, I would call it year, but she did a bunch of this in the first 90 days. So I really think you will enjoy this episode.

Jess Carter [00:03:58]:
I was able to ask a few questions of my own about how the apps work, and she was kind enough to let me riff with her. So I hope you enjoy it.

Welcome back to Data-Driven Leadership. Today we are thrilled to have Lina Mikolajczyk, the director of reporting and analytics at Bumble. Let's get into it. Lina, welcome.

Lina Mikolajczyk [00:04:16]:
Thank you. Thanks so much for having me.

Jess Carter [00:04:18]:
Yeah, we are really excited about this episode. I think that you're. I think the idea of Bumble and some of its competitors and where they came from, it feels very recent. And I also think that in my head, it requires a high degree of data and analytics in order to run a business like this. I am extremely excited to talk to you. Thanks for being here.

Lina Mikolajczyk [00:04:40]:
I hope I can give you what you need.

Jess Carter [00:04:42]:
Yeah, the bar is high.

Lina Mikolajczyk [00:04:43]:
Okay.

Jess Carter [00:04:46]:
So. Okay, first, before we get started on all my, really, my peppering questions, and I will confess that I met my husband on one of these apps, so I'm a believer. Okay, you're talking to a believer.

Lina Mikolajczyk [00:04:59]:
That's what we want to hear.

Jess Carter [00:05:01]:
Tell me about when did you join Bumble? Where were you before that? I'm just curious about your background.

Lina Mikolajczyk [00:05:06]:
Yeah. So I've actually not been at Bumble for very long. I've been there for just over a year. Before Bumble, I was at Dojo, which is a fintech in London that specializes in card machines. And before that, I kind of dotted around some scale ups and startups. But the longest part of my tenure was at Hilton. It was also, like, probably the most fun I ever had because at that point, Hilton was, like, really building out its e-commerce platform. And so I was kind of there for the ride.

Jess Carter [00:05:33]:
Oh, cool.

Lina Mikolajczyk [00:05:34]:
Yeah. So Bumble has been just over a year. And interestingly, one of the reasons why I took the Bumble job was because it was sort of outside of the, like, awesomeness of the data and making an impact for people like you. Right?

Jess Carter [00:05:48]:
Yeah.

Lina Mikolajczyk [00:05:49]:
Met their partner on the platform. But, like, for me, it was really an opportunity to take a team that was really well established and elevated even further because previously, all the roles I had taken had all been focused on really scaling a capability, whereas this one was like, how do we take what we already have and make it even better?

Jess Carter [00:06:07]:
Yeah, that's amazing. Okay, so one of my questions I was going to ask you is, for someone, I think you have a cool job, and I think I'm not the only person who thinks you have a cool job. So one of the things I did want to ask you, like, early, is if you were talking to someone who's graduating college this year and they want your gig in however many years, like, how do you advise somebody on, like, what's, what do you think is the most important part of your journey that brought you to where you are today?

Lina Mikolajczyk [00:06:31]:
I think that starting early on, your technical capabilities will never hurt you. So make sure you do lean into, whether it's SQL or Python, etcetera, have at least a foundation in one of those languages. You don't have to be a super expert, but just know your way around. But I actually think it's the commercial acumen and exposure to multiple different businesses, platforms, and tools that helps shape someone's career. One of the pivotal points in my career was when I worked at agency, and in that agency, I was exposed to, oh, my gosh, like, you know, every. Every client used a completely different tool for their analytics platform and for their experimentation platform. And so as a result, I had to dip in and out very quickly. And that made me so much more hireable later because I could say, yeah, I do have exposure to Amplitude, or I do have exposure to Tableau or whatever, you know? And I think a lot of people bypass that and they focus on.

Lina Mikolajczyk [00:07:29]:
Right, I'm just gonna go into a corporate setting and I'm going to just rise the ranks and, but actually, I think especially as you're starting out, knowing to enter a company and stay until you're like, you know what? I think I've either hit my peak, I can't go any further, I'm not learning. And knowing when that moment arise or arises and what to do with it. Like, okay, cool, I'm seeing a gap in my skills. I'm going to go now and find something that assesses that gap or that fills that gap. So, for example, a lot of analysts will go and start on marketing or like, operations, and they will continue down that path and get really specialized. And that's a fine route if that's what you want to do. But actually being more well rounded and being exposed to more domains makes you just more hireable later.

Lina Mikolajczyk [00:08:14]:
And then when you do make the decision between going down management or going down kind of a specialized, high individual contributor route, you can kind of make that decision in a more educated fashion.

Jess Carter [00:08:25]:
Absolutely. So what I'm hearing you say, by and large, is exposure. Expose yourself to as many things as you can, as much as you can, as early as you can, and it gives you more broad access. I mean, when you're looking for, if you're a BI developer, you're not only constrained to looking for jobs where their tech stack, they're working with Tableau, if you know, BI, power BI and you have more options. And so I really, I love that also. I mean, I work at a consulting firm, so that makes sense to me. I'm a better consultant on each project because I've done the one before it. You know what I mean? That concept of, you know, you're more versatile. One, I already just enjoy hearing the way you reflect on things.

Jess Carter [00:09:04]:
I really enjoy your insight. One of the things that we have to sort of couch this conversation in, because I think people who don't live in data worlds are going to be confused by this. You're running the reporting and analytics at Bumble. In a company like Bumble, I'm anticipating that when we ask about your job, there's like, the business side, there's the algorithm and all of those things, and then there's the reporting and analytics behind all of that. Is that right? Help me understand that.

Lina Mikolajczyk [00:09:32]:
So, okay, so a couple of things. When I joined Bumble, I decided that actually we want to move away from being known as a, like a service-reactive organization. So I shed our name very quickly, and we've rebranded, which took a lot of marketing skills that I, frankly, did not have. We rebranded to advanced data analytics.

Jess Carter [00:09:55]:
I love it.

Lina Mikolajczyk [00:09:56]:
Yeah. Also known as ADA, which is a programming language, and also a historical figure in the field, which I would encourage you to look into. We're all for acronyms at Bumble, and one of the reasons why I did that was because what we were doing was we were doing reporting, and we were doing a lot of, like, you know, where's this dashboard, run this experiment, what are the results? But the capabilities that we had in house were insane. Like, we were able to run models and analyses that were far more insightful, far more impactful than what we were doing. So this. This was one of the reasons for the change. And when I. When I say what my role really is, it's the diagnostic arm of the business.

Lina Mikolajczyk [00:10:37]:
So, if we see a metric declining or even increasing, we want to know what has driven that. So does that mean we dive into the levers that drive our revenue. Does that mean that we look into the retention of our customers and what the customer behavior is and how it's changed over time? That's our primary role. Our secondary role is to figure out ways to drive value through the data that we have. So, what are the opportunities that we have on the behavior of our customers that could help enhance their experience? And that could be anywhere between our app and the actual experience you have in our app, the matching that you have, but also, like, how you interact with the customer service and how safe you feel on the platform, how you engage with our marketing. So it's kind of like we have our tentacles everywhere. It's quite a team, but our primary objective is to drive that insight, drive decision-making that's informed, but also bring value to the company in ways that we may have not thought about before.

Jess Carter [00:11:40]:
Okay, I love the rebrand, and I get why you had to kind of, let's help people think about this differently, because I think even in some of the work I've done, Indiana, I helped kind of, with the COVID project. I watched all these different data projects where the client thought the highest and best use of us was, hey, can you go tell us, we have this question. Go run the numbers and tell us if the answer is yes or no? And I was like, hey, guys, with a model, if we can think differently, we can get way faster, way smarter, and we can start to indicate with data where you, where should we even be asking questions? And it sounds like that's the pivot that you kind of helped the department go through. Has that been, are you done? Like we've done it, everybody's there. We get it.

Lina Mikolajczyk [00:12:27]:
I wish, I wish it were that simple. One of the things with us has been, we have been on a, on a journey with our data landscape at large. So how we look at data and how we look at the entire flow of data that we've been working on for the past year, which is how do we ensure that from the point when you click on yes on a profile, that particular event passes through all of our pipelines, all of our transformations and results in a report or model or whatever it is. So we have been working on that and we've been really working on enhancing our self service for stakeholders. The idea was if you're a marketer, if you're a revenue manager, you can go into our platform and answer your what questions. So any binary questions or any questions that explain what has happened should be catered for by the reporting solutions that we have in place. Anything that's deeper, where it requires more advanced modeling or deeper dive or whatever it is, the why or even how, that is handled by our team. One of my big learnings as a leader has been, you can change the tools and you can change the way your team operates.

Lina Mikolajczyk [00:13:42]:
You can scale them up. But the biggest part of this change, it's a cultural change. And so having stakeholders on board to be able to self-serve and incentivizing them to do so is big. You know, I learned that I wasn't a very good marketer when we rebranded. I also learned that sometimes changing the culture isn't a one-person job. So the one, I think, piece of advice I have, when you have this large change management project on your hands, get yourself a program manager. It has been instrumental to my success. Somebody who can really help you with the communication, the socialization, keeping you structured and organized as you go through it.

Lina Mikolajczyk [00:14:21]:
And also, like, she hounds me for timelines. You haven't talked to this person. What are you doing?

I could see your calendar. So. Really keeps you in check. So, yeah, so I think people, yeah, forget about that. And it has been the factor that has allowed us to move as quickly as we've been able to move.

Jess Carter [00:14:41]:
That's amazing. Okay, so maybe one question to just dig deeper on this. And I do understand if, like, we can ignore the parts of this that might be ugly. Like, I don't want to put you on the spot while we're recording, but, like, you showed up a year ago. You had to come to this conclusion. Like, you did some things during your own version of onboarding to be like, hey, we aren't sort of seeded at the right in the right way to generate the most value for our business. Was it your secret sauce for how you onboarded yourself? Were there aha moments? Like, how did you get there? And how. How much time did that take?

Lina Mikolajczyk [00:15:16]:
All right, I'm gonna. I'm gonna spitball at you with some resources. Okay.

Jess Carter [00:15:20]:
Yes.

Lina Mikolajczyk [00:15:20]:
There's a couple books that I think are really important when you're joining as a data leader to your company. Number one is a book called the Chief Data Officers Playbook, and it is by Caruthers. I don't remember all the names, but it's a great read. And it describes a first-generation as second-generation CDO. Even if you're not at that spot, it has amazing insights into, like, how do you assess the maturity of the organization that you're walking into? So, using that template, the very first thing I did was I talked to every single person on my team, every single stakeholder that we had, consolidated all of this. And then I ran a survey across the organization, and this is something I learned in previous places I've worked in, where you go super vulnerable as a leader, and you say, do you know what? I want the business’s insight about how they view my team and what they think good looks like. So not only is this a vulnerability exercise, it's also. It can come off as quite controversial, especially if you're new to the company.

Lina Mikolajczyk [00:16:23]:
And I was like, you know what? No, because I just want to make it right, and I want as much input and data to be able to make this informed decision, especially because, you know, I might need to ask for more money or think about budgets and think about transformation. So I want to give the board and the execs the comfort that they need, that, you know, what, she has the right idea in mind, and this is their evidence. So this survey can take a form of whatever you need. But what I would advise is, like, ask very honest questions, like, is your team impactful? Do you find, at that point, we were called RNA, do you find the RNA team impactful? Do you find the work that we provide helps you in your day job? How often are you blocked by the fact that you don't get support? So I took all of this together and compiled it, and I took my leadership team out of the office into a safe space and I was like, all right, we're going to go through these results. And I think this is a really important part because often when you come into a leadership role and there's a pre-established leadership team, they're so entrenched in it, they're so used to the way things have been working that they kind of don't think about what could be better and how they could shape it because they're just trying to fight fires or provide for their stakeholders. So getting them on board in a way that doesn't put their defenses up is really important.

Lina Mikolajczyk [00:17:42]:
Rallying them around, look at what people have said. Do you guys agree? You know, getting their buy-in, that helps. Then, whatever vision you decide on to move forward and to put together a plan. And then, this next stage is really important. The book I can recommend is Scale at Speed by Felix Velarde, who is a mentor of mine. And the book is intended for agencies, actually, but it's super applicable for teams that want to be more effective. And the way he approaches it is he goes, right, do a SWOT, even though it sounds basic, but actually just do a SWOT and, like, get people to say what is the strength that we have as a leadership team? What is a weakness? And do the same exercise for the wider team. And then from there pivot into understanding, what does good actually look like? Because that exercise in itself will show you the maturity of the leadership team that you're dealing with.

Lina Mikolajczyk [00:18:31]:
And if they can't, you know, spell out what it is, then you need to do the work to be able to come together to, you know what, by the end of 2025, this is what we want it to look like. So now you've got the input from the survey. You've got the input from the stakeholders. You have this analysis, this workshop that you have around. Now you put it together and then this is key: You have to go and socialize what you've done with the wider business because that helps keep you accountable. My aim is to do this, and I might be a little bit ambitious, but within the first 90 days. So, yeah, don't sleep, don't eat, just do this.

Jess Carter [00:19:10]:
Yeah, who needs to do anything like sleeping or eating? What I love about part of that, I love the whole thing. And I, I think a lot of times leaders forget that showing your work helps other leaders. So I really appreciate you walking us through, literally, tactically, this is how I behaved and what I thought. But my favorite part, again, continues to be Lina's insights. You have this skill set. I don't know if you've heard this in your life before, of sort of being able to step back and see the bigger picture and catch insightful things that, you know, like, not everyone sees.

Jess Carter [00:19:39]:
But you mentioned this, you know, it's like, it's not just about the tactical back and forth exchange of surveys and interviews. It's helping you then at a higher level, kind of assess the assessors. Where are our gaps based on what you heard and maybe what you didn't hear? And how does that help you understand what do they need in order to become even better at what we're doing as a business? Does that make sense, what I'm saying?

Lina Mikolajczyk [00:20:02]:
Yeah. What just occurred to me is, you know, even as you rise in seniority in data, don't stop being an analyst. It's just the data you work with starts to change. So it's just an evolution of what you would do if you were just starting out but applied to a team.

Jess Carter [00:20:19]:
And that is so well said, and I've never heard it said like that, but to your point, it's. Yeah, it's like, once an analyst, always. So now we're playing with real data, like data that's in a system, but there's still data that's not in a system that's critical to you. And so you're trying to figure out where and how do I understand if that's changing and at what pace, and yeah, I think that's amazing. Okay, so you did all of that literally in 90 days, is that right?

Lina Mikolajczyk [00:20:42]:
Yeah. And I will add one thing, though. This just fixes the first part of the data maturity curve. So I'm not sure how familiar you are with the data maturity curve, but it starts with, like, just having data to making it accessible, democratized. Then you start using it in terms of a product, then you start monetizing it. Right. So that's kind of the curve. This exercise usually helps fix the fundamentals, which, is there data, does the data help and is accessible to people? The problem, and the thing that I think I'm still trying to crack with speed is how do you move from that to the value component? Because the value component has this cultural context to it, which is quite unfamiliar to many settings.

Lina Mikolajczyk [00:21:29]:
And what I'm finding is a lot of businesses put data in a little bit of a box. Like what we talked about. You know, you guys give us the reports, and you guys give us service, but then how do you break through and actually show how much value you can bring as a team? What has worked for me before is, if you want me to give you an example, actually, at my previous company, we had a couple of processes that we did every year, and it was a pretty formulaic process, like, you know, line by line, this is what we do. Do this and you will get the same results. But it was like, you know, either a regulatory requirement or, or a cadenced thing. And so when I came in, I was like, well, if we do this a little bit more smartly, like, will this not yield different results? And what I found was, in order to get buy-in, to do this kind of 10% increase in cleverness approach, you do the approach that everyone's familiar with, right. But then you also present this alternative approach with that 10% in it.

Lina Mikolajczyk [00:22:24]:
And, man, all of a sudden, that buys you space, it buys you breathing room, and then that is how you start working at multiple areas to try and get people to be like, can you guys try that, like, different method that you tried with the other team? And then analysts are asked to do not just create a report, like, do you know what, build a model. Like, go and figure out how to make this better. And ultimately, like, analysts want to solve those kinds of problems, not a dashboard visualization problem.

Jess Carter [00:22:53]:
Yeah, every day, right. They're waking up every day with, with a desire to add value in a purposeful way. And to your point, cleaning up the front end of a visualization is not exactly it, necessarily. If they get to work on the business, they're going to be 100% more engaged, too. That's. Man, I love it. You just, in 90 seconds or less answered the question, how do I get people to stop asking me for a transactional report and actually come to me as a trusted partner in the business about how do we solve these problems with data? Cool. Well done.

Jess Carter [00:23:25]:
So, okay. Because that was what I was going to ask, too. So, man, we're on a different trajectory. I just didn't know where this conversation would go. And I'm loving it, because here's where my other curiosity is, though, and I feel like you're talking about the last year. In most worlds, this would have been the last half-decade. When you got to this point. You know, one of the questions I have is, how are you thinking about your capacity as a team and your ability to say yes to however many hypotheses or questions, like, how do you make sure you're setting your team up for success? Do you know what I'm saying?

Lina Mikolajczyk [00:23:58]:
Oh, this is juicy. Okay, right. Let's talk about this. So I think, I think a lot of data teams struggle, teams in general, but data teams specifically in prioritization. But I'm not just saying, right, like, we're going to do this, this and this, and this stakeholder is screaming the loudest, so we're just going to appease them. There isn't a method to the madness that allows you to measure the impact that you've made through yelp, through the priorities that you have set out. This is something that is really widely talked about in the data landscape. So it's basically saying, right, so I have all these requests that have come in. How do I know which one I work on that will drive the biggest value and how do I know which one to tackle? Because it will ruin our brand or it will pay off or, you know what I mean?

Well, this is where going back to that program manager or product manager, whatever you want to call this person, this person needs to really help you with these kinds of processes, which is if you have a request, first of all, even if the analysts kill you, like, go and make them track it, especially if it's something that will take longer than, you know, two-three hours, just track it because it will then help. And you have to sell this into the, to the analyst.

Lina Mikolajczyk [00:25:08]:
Right. Like they have to understand why this is valuable and why they have to do this. And it has to be embedded in your processes. But within each request, have them put this request in and then track it according to what the cost of this request is. Meaning what is, for example, if you're using, I don't know, a data center and it's running up your Snowflake cost, or if it's a salary, like how many hours you've spent on it. So we can calculate that, have a cost, right. And then make sure that the analyst is able to attribute some sort of decision or impact or bottom-line metric to whatever you're working on. And then you have an ROI figure for your different tickets.

Lina Mikolajczyk [00:25:49]:
And then that starts to become interesting. One of the best methods I've seen is if you build a dashboard, execs are asking for a dashboard. Build it, build it, build it. And then you start surfacing things like, huh, this dashboard costs, you know, $5,000 to run every single time. Oh, the execs don't ask for that dashboard anymore.

Jess Carter [00:26:06]:
Uh huh.

Lina Mikolajczyk [00:26:08]:
And then, yeah, I, so what you kind of like, and I know that that can seem like you're almost being like an internal agency to your stakeholders. But I think it's really important for us to be able to tie back what we've worked on to the actual value that we've brought to the business. And then you can start talking about actual ROI of the data program and ROI of your data. Right. But that's kind of the end goal. That's where you want to get to. The in-between is the difficult part, because you want to get into rhythm and you want to get visibility to the stakeholders, and you want to have stakeholders battle out priorities between them so that you're not the one that's making decisions on their behalf and then taking the consequences. So if you've got a marketing stakeholder and, I don't know, a revenue stakeholder asking for very important, high-urgency things, let them speak to each other and go, which one is actually more important before it comes to create that setting?

Jess Carter [00:27:00]:
I just think it's very, very important because I do think in the observations I've had in the industry in the last five or ten years is, you know, we were working on systems as consultants, and then we suddenly started working on data. It's like we chased the same maturity model of, now there's data products that we're maintaining, but then we still have businesses who, it's not their problem or it's not their fault, but they don't understand that in some respects, how do we mature the data organization? How do we help it become more valuable, and how do we also just attribute cost and investment to it appropriately? And so I just. It's been interesting to me that every company I've observed that has a data team, the way they think about those things is, it's like we're all just on this journey and it feels a little bit in the dark. Like, it feels kind of like we're stumbling around in a closet that without the light on, where it's like, hey, where's my coat? And there's different people at different maturity levels. But no matter what I think, to your point, on that data maturity, like, we're all on the same road, actually. Well, everybody's on it.

Lina Mikolajczyk [00:27:59]:
Yeah, and I'm going to say something that's quite controversial, but the end-all be-all for me in driving true value from a data team is they need to be on the hook for a line in the P&L. They need to be able to go, I have a data product that I have created, that I have enriched, that I have ensured engineers create the right things. It's well-tracked. There are data contracts, there's documentation, and it is my job to make sure it is an asset that is used wherever. So, whether it's for segmentation, for CRM, for any kind of marketing, and then it becomes, this data product is directly related to revenue or a cost optimization. And then your data team becomes jointly accountable, and all the people involved in creating this piece of data jointly accountable for this, you know, OKR, or this KPI, or whatever it is, then it's a different ballgame. And there are companies who have done this, right, the Amazons and the Twitters, etcetera. But many companies are quite far away from that.

Lina Mikolajczyk [00:29:02]:
So it's like, how do we move up incrementally towards that space?

Jess Carter [00:29:06]:
Well, okay, so I need to ask. I seriously think I could talk to you for three and a half hours, and I have so many questions. Let me ask you just a couple other things, and then I want to see if there's anything that you want to double down on that we haven't talked about yet.

One of the questions I have for you, you started to talk about some of these hypotheses, and in the way that your team has sort of infiltrated all aspects of the business, which I think is rightly so. Do you have an example that you could tell us about that's like, hey, on the user experience, here's data we've leveraged to make an adjustment to it? Maybe the data team is helping improve. You guys have these other features now of, like, how to meet a friend. Like, there's, you know, I'm assuming that some of that was born out of data insights, but do you have any examples?

Lina Mikolajczyk [00:29:44]:
Yeah, I mean, I can talk to the different teams that we have and how they work as examples. So, for example, like, on our product analytics team, they are completely in tandem with the product managers, so they are jointly responsible for all experimentation. No feature goes live without an analyst's help. It's actually really robust. We have a whole process. We have a platform where, you know, we run all of our experimentation, but all of the analysis is also really interesting because what it does is it looks at the user experience, but it also attempts to go right.

Lina Mikolajczyk [00:30:16]:
So if we extrapolate this user experience to the wider audience, what is that going to translate to in terms of business KPIs, so that we can then feed that back into any adjustments for how we think we're progressing across all of our different KPIs? That's just one example. But in general, we find opportunities work best for us, and we are able to partner with teams the most if we are brought on as early as possible for a project. So if we say we are, you know, we're honing in on a segment of users, and we really need deep insights about the segment and how it performs against marketing or in the app. If we're there during the planning, we're able to assess the feasibility of, like, can we actually create these segments? Can we operationalize them? This is actually something that we've been doing most recently. And what we found is like, yeah, we do have to create some dashboards to allow our stakeholders to kind of self monitor what's going on in these segments, but ultimately actually taking these more enriched segments that we've created and applying them to various marketing tactics or promotional taxes, whatever, has been instrumental in being able to kind of enhance the customer experience, but also just target better. You know, just like, you know, you don't want to see an ad that, like, doesn't make sense to you. So those are the kinds of projects that, like, we really see the most value from my team is when we are there from the beginning in the ideation phases.

Jess Carter [00:31:37]:
That makes sense if the investments better prioritized. When you've got somebody who's analytically thinking about, is it the right thing and what is right? Okay, my other question is a little bit out there, so it's okay if you're like, meh, pass, but you are insightful. In your crystal ball, Lina, do you have an opinion? So, yes, we're talking about how to infiltrate. How do we leverage data in every aspect of a business? Does that include strategy for Bumble? I've always had this question since I met my husband on one of the apps, and I guess it was nine years ago, ten years ago, which is like, at some point, data science is getting so smart that there's a plausible concept that, like, could somebody click a button one day? And, ta da, there they are. There's your future partner.

Jess Carter [00:32:24]:
Are those real conversations, or am I making this up? And it's like, no, that's not really. Like, that's not a thing. Like, when you think about the strategy of Bumble, are you guys involved? And do you have an answer to my, my geeky question?

Lina Mikolajczyk [00:32:41]:
We are very data driven as a company. Data is in all of our planning, it is in all of our strategy work. It is in our, you know, multi-year vision. So, that's for sure. Well, I'm struggling to answer is, like, going to be that instantaneous? Because what I'm thinking about is the macroeconomic factors and you as a human being, how mutable and changeable both of those things are, no matter how good your algorithm is and how well trained your LLM is, is not going to be able to account for in real time. I mean, we're getting there, right? Let's not lie.

Lina Mikolajczyk [00:33:30]:
So the tech is getting there, but. But the other thing is, is people don't want to get on the platform and meet the first person, and there are some who do, who do, but a lot of people don't.

Jess Carter [00:33:51]:
Right. That's not the only experience people are looking for.

Lina Mikolajczyk [00:33:54]:
That's right, exactly. And I think the difficulty in it all is figuring out which kind of person you are, and that might also change given on you.

Jess Carter [00:34:02]:
Yeah. Right. In the future, maybe here, this is free consulting. You could just have a little radio button of, I'm looking for my life partner, or I'm not. And it's like, okay, daily.

Lina Mikolajczyk [00:34:11]:
What if some days you're like, yeah, I want a life partner.

Jess Carter [00:34:13]:
This, let's be honest, it does probably change daily. You have one bad date and you're like, never mind, that's amazing. Well, I am so impressed. I'm so impressed with you, and I really appreciate both. I mean, it just seems like you have walked this incredible balance beam of leadership insights and experience and solving the hardest problems you can find to get your hands on in your career that led you here, and this really incredible data journey. So I'm just. Color me impressed, Lina, with where you're at, the leadership skills you have, and your willingness and generosity to share those insights. Before we go, is there anything else that you really, really want to get to talk about that we haven't discussed?

Lina Mikolajczyk [00:34:54]:
As you know, I think we've covered everything. I've had a lot of laughs.

Jess Carter [00:34:58]:
Well, I appreciate that. I want to get one more question out there that I'm curious about. That's selfish. Here's my other question. This is, well, and maybe it's. I'm going to tell you what some of my thoughts are, and you're going to tell me what's real. One of the things I was curious about in your data modeling and the work you guys do is in my community a decade ago, I noticed that it was like whole clusters of people would kind of be on one app for a minute.

Jess Carter [00:35:23]:
Oh, we had some bad dates. It wasn't great. And then they'd all kind of move over to this other app, but then they'd eventually come back. It was like this weird ebb and flow of users leaving and coming back and leaving and coming back. Is some of that things that you guys track to when it comes to users? Are they deleting the app and then are they signing back up, or do you have, are you tracking long-term user attrition? Like, how do you guys measure? I guess what I'm really getting at, too, is, like, success. Like, to your point, not everybody wants to meet someone. And then for your business model, then are they not on the app anymore? You know what I mean?

Lina Mikolajczyk [00:35:56]:
Yeah, that's a complicated question. I mean, what's interesting is that ebb and flow is just network effects. It's actually not just present in dating. It's something that we see. When I was at Hilton, it was the same thing. People go direct or go book through an online travel agent, and it just depends on what the incentives of the users are. Right. So for us, ultimately, we care about the customer experience.

Lina Mikolajczyk [00:36:18]:
Right. And we want customers to have the best experience in the app and, yes, of course, find the love of their life and then never turn the app again. Yes. But life isn't that easy. Right. So there is a certain level of expectation for us where, like, you know, people might be coming back and forth, and all we can do is ensure that, like, you know, the people that we provide them with and the app experience that they have is what they want and what they need for the time that they're in.

Jess Carter [00:36:44]:
That makes so much sense. That's amazing. Awesome. Hey, thank you for letting me give you some curveballs here. I've just been so excited to hear and learn from your, your amazing, amazing mind.

Lina Mikolajczyk [00:36:55]:
Thank you. Thank you so much for having me.

Jess Carter [00:36:57]:
Yeah. And, hey, if people want to follow you in life, is the best way for them to reach you LinkedIn, is that right?

Lina Mikolajczyk [00:37:03]:
Yeah.

Jess Carter [00:37:03]:
Guys, thank you for listening. I'm your host Jess Carter. If you're interested in learning more from Lina, you can connect with her on LinkedIn. Like we mentioned, the link will be in the show notes. And don't forget to follow Data-Driven Leadership wherever you get your podcasts rate and review, letting us know how these data topics are transforming your business. And you. We can't wait for you to join us on the next episode.