Tech Transformed: How Mejuri Cut Through the Noise with Noibu

[00:00:00] Rohit Nathany: The part where I've still seen folks struggle is, uh, what I call silent failure, so the revenue leakage. And these add up over time, and that's where I really love, like, what Kailin and you guys are doing because that's really help us cut through the noise, find stuff that's actually hurting customer experience.

[00:00:17] Christina Stathopoulos: Welcome to Tech Transformed, the podcast that explores how technology is reshaping the enterprise landscapes. I am joined by Kailin, president and cofounder of Noibu, an ecommerce analytics and monitoring platform that unifies site monitoring with experience analytics to surface both revenue risk and conversion opportunities in one console. And we're also joined by Rohit, CPTO at Mejuri. It's a leading direct to consumer jewelry brand, and he's going to share how they've leveraged Noibu's platform to drive measurable results. So, Kailin and Rohit, welcome to the show. Very excited to have you both on. Before we dive in, could you each introduce yourselves to our audience? Tell us a little bit about what you do. We can start it off with Kailin.

[00:01:05] Kailin Noivo: Thanks so much for having us on the show, Christina. My name is Kailin, cofounder, president of Noibu. Um, like you mentioned, Noibu is an ecommerce analytics and monitoring platform. We'll get into what that means just in a little bit, but, uh, yeah, super excited to be on the show.

[00:01:19] Christina Stathopoulos: Fantastic. Well, great to have you on. Rohit, what about yourself?

[00:01:23] Rohit Nathany: Hey, guys. Thanks for having me here. I'm Rohit. I support multiple functions at Mejuri of marketing, product management, and engineering. Uh, as Christina mentioned, we are a global direct to consumer jewelry company with 59 worldwide stores. Really excited to be here and talk about ecommerce and ecommerce monitoring.

[00:01:41] Christina Stathopoulos: Fantastic. Well, we're we're lucky to have you both. They're both here to talk about from two different perspectives, I think. So to kick things off, I want to start by looking at the day to day reality ecommerce leaders are operating in today. It's far more complex now than it was even just a few years ago. Things like, you know, more tools, rising costs, even the fact that consumers have much higher expectations, it's all contributing to this. So from your perspective, what are the biggest challenges when it comes to driving revenue and conversion growth? What do you think about this, Rohit?

[00:02:16] Rohit Nathany: Yeah. Uh, good question, Christina. I would say from from the brand perspective, from the retailer's perspective, we start with the macros. What happened last year with the tariffs, that was one of the biggest headwinds that the industry is still still struggling with, and we all had to figure out how to adjust to this new reality. On top of that, the second macro factor is what's happening with AI is really rapidly changing the consumer behavior. And I got that's one thing that every single brand leader, someone in in my shoes, is constantly trying to stay on top of. But having said that, like, what you mentioned, a list of ongoing challenges. And within the given context, I would assume that personalization and ability to connect the dots, those are the two challenges that does not seem to go away. So when you think about personalization, it comes down to one to one. How do you crack that one to one personalization? What do you know about your customers so you can offer them the best best features, best product selection, and best discovery experience. And then when it comes to connecting the dots, it's all about online click to conversion, and no one seems to have really cracked it. We have taken some strides over the end. We have seen improvements along the way. There's so much more to do here.

[00:03:31] Christina Stathopoulos: Yeah. So there's just a lot going on. I think for all of us, there's so much going on, but you've you've highlighted a few of these important things that ecommerce leaders are having to are having to confront. Right? Kailin, is there anything else that you would add on, something that you're seeing? Maybe probably even you would confirm exactly what Rohit said as well.

[00:03:50] Kailin Noivo: Yeah. I think it's it's it's kind of a a bit of a perfect storm. People definitely built out large infrastructure during the COVID era, largely on the back of horizontal platforms that kind of bring you 80% there, but you have to bridge the last 20% yourself. And I think what we are kinda seeing with, like, the specialization of commerce engines in, like, the Shopifys and and and those types of businesses is that really kind of owning your niche and doubling down compounds over time. Um, so just to really add on to what Rohit mentioned, we're seeing a lot of cost pressure. People are not willing to have multiple tools that kinda do the same thing because then you're paying for multiple data you're paying for data capture and storage for kind of the same data in, like, three to four different places.

[00:04:39] Christina Stathopoulos: Okay. So the perfect storm pretty much is how you've summarized it all together. And we'll go we'll go back. We'll we'll explore a couple of these points more today. I want to I wanna explore how these challenges surface in a real concrete example. So So I'm gonna throw it back over to to Rohit. Rohit, thinking back to when Mejuri first started using Noibu for front end error monitoring, what problems were you trying to solve for exactly? And and how did those initial challenges influence your broader approach to conversion optimization across across teams?

[00:05:11] Rohit Nathany: Yeah. So so the context here is that we had just completed our replatforming to Shopify, and it's similar to rebuilding plane while flying it. We really had to keep the business running. Uh, we we were really hoping that, like, once we had this new tech stack, most of all our problems will go away. But some of these error monitoring problems, uh, were still unresolved. And what essentially was going on was that this the core problem was the signal versus noise. Right? Like, we would get this fire hose of all the alerts, but no no real data behind the the the customer context or the commercial impact or how many users are impacted. And this is such a common problem that that that engineering or tech teams faces, and that's where Noibu comes into the picture. And we did a did a two week POC. I still remember that. And it immediately identified one spinning card issue for us, and it was going on for several days or maybe couple of weeks since the migration. And we were just unaware of that revenue leakage. As soon as we saw that, we were able to investigate it really quickly and were able to resolve it. But you can imagine how many of those revenue leakage is happening for any brand that's out there.

[00:06:21] Christina Stathopoulos: Fantastic. Well, I think we we so we're seeing about learning about how these real world issues we're learning about how how what happens as they're happening. The next question then is, how can brands stay ahead of these of these issues, like what you mentioned Rohit? So throwing it back over to Kailin. And we already talked a little bit about this, but AI. So AI is becoming increasingly central in ecommerce as it is across most most industries today. How can we make sure that it's genuinely adding value and we're not just following some hype trends? And also how can brands leverage it? How do they leverage it to do more than just detect errors?

[00:06:59] Kailin Noivo: Yeah. So like Rohit mentioned, and AI is a big part of why we've really so the cost pressures and and customers asking us for features that were adjacent that we didn't have two, three years ago is part of the reason we started going down this journey. In commerce, you don't care about 10,000 bugs. You care about your top eight. Right? So, like, getting from 10,000 to eight was a really hard problem that took us years to solve, and we actually solved the final mile of it by implementing AI. And it wasn't until that, like, we had it working, but, like, it really wasn't, like, quote, unquote perfect as defined by, like, right nine out of 10 times until we implemented AI. And that was probably in, like, 2022. So at that point, we're like, hey. We have to broaden out. And what does that mean? Well, you know, now that you've stabilized the website for bugs, you wanna make sure that it's fast. Right? So what do you do to make it faster? Well, you need to know your core web vitals. You need to understand, as you're making releases, if it gets faster or slower, and you need to do automatic release updates with AI where, basically, you don't have to pay someone to go look after every code release, what KPIs got better and worst. You can just automate that. So we went from, like, errors to errors and performance. And then we said, okay. Well, once your website has no bugs and it's fast, what do you wanna do? Everyone wants to do AB tests, and they wanna basically innovate. So that's why we launched page analysis to help you understand your customer journeys. Where's your marketing spend going? How are they converting, what's the behavior on the website, so that more behavioral layer. And that's really where we helped consolidate a few vendors for Rohit's team as well is bringing all of that into a single platform. But to answer your initial question, like, where does all of this play in with AI? Well, how I think about AI is that if you are just everyone's doing the same thing today. I use WHOOP, and WHOOP's a great product, but everyone's taking the data they have and connecting it to a foundational model and enabling you to, like, interact with the data you have is basically everyone's AI strategy right now. And that's our first AI product, but how I'm really, really thinking about AI is how can we help automate micro workflows? And what I mean by that is a good example of a micro workflow is when someone does a release in most organizations, and Rohit, let me ask you. When you guys do a code release, what do you do after?

[00:09:30] Rohit Nathany: We go back and take a look at we we, of course, run all the testing that we have to we go back, look at the metric, and, ideally, it's an AB test, so we can go and compare compare performance.

[00:09:40] Kailin Noivo: Exactly. So you look at your performance. Did we introduce any new bugs? Like, there's, like, a very common checklist. Right? So

[00:09:46] Rohit Nathany: Yes.

[00:09:46] Kailin Noivo: With our new release monitoring agent, the agent just does those checks. It'll tell you in one hour, six hours, twelve hours, two days, did your website get slower? Did you introduce bugs? Is there a large change in conversion rate? So we believe that the value is in amalgamating hundreds or thousands of those micro workflow automations.

[00:10:12] Christina Stathopoulos: Okay. That makes sense. So the first step you said when when it comes to the AI part is that the people are plugging into the foundation models, like you said, and then they're using it to chat and ask questions about the data and so on. But one thing I'm thinking about here is how how manageable is that overall setup for teams on the ground? That's the next topic, actually, that I wanted to discuss, which is around tool consolidation. So when brands consolidate tools, you've already mentioned quite a few already in our conversation today. What should they be consolidating around? I mean, is it data types? Is it teams? Is it outcomes? What does a good consolidation look like in practice? So you don't lose visibility while you're trying to simplify everything. Rohit, I don't know if you you all have experience with this. What are your what are your thoughts on the tool consolidation?

[00:10:58] Rohit Nathany: I'll say, ideally, it has to be around the outcome, but you have to be really careful with how you are defining that outcome. Right? And if you if you're one of those organization which has a very clearly defined KPI tree, it's easier to get there. You can have a point solution for a specific node on that KPI tree, which is what you really need, or you can keep going up on the KPI tree as you consolidate. So what I mean by that, like, if you take Noibu for an exact as an example, we you think about errors as an engineering metric. It can be part of the KPI tree, but I think of this in terms of sales per visit. That's our core digital file. So when I think from the sales per visit perspective, monitoring is just an element of it. I think about the funnel analytics as well. I think about the the session replays, which is typically used by our product managers or more by product designers. So all of that, these tools can now be consolidated against a single outcome, which is sales per visit. So the starting ideally, you want to go through the outcomes, but you need to have a strong KPI tree where you know how this all KPIs ladder up. But you can also do it along data types and themes as well. Like, if you call workflows, that's where teams come into the picture, let's say, Linear. So Linear is a good project management tool. I have rolled it out across marketing, product management, and engineering. Typically, it's used by engineering. But so it's a workflow solution, so it applies to applies to other use cases as well. Similarly, like, you can go on and on and look at, like, other examples.

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[00:12:36] Christina Stathopoulos: Of course, simplifying tools can make a really big difference, but I think the real test is how all of this holds up in in moments of uncertainty. Right? And we've already mentioned it with the tariffs that that started last year. There's so many different things that we that we can't predict right now. So throwing it back to you, Rohit, what operating habits help you protect revenue and still keep momentum, especially in these types of uncertainty, um, on conversion improvements, uh, as we've been discussing, without slowing, you know, delivery or without creating any any war rooms?

[00:13:08] Rohit Nathany: I would love to say there's a solution out there, but for me, personally, it comes down to culture. It comes down to the mechanisms that you have established in your company. So one big bucket, I would say, is continuous improvement. Do you have proactive monitoring? Do you have a really robust culture around experimentation, the AB testing? Do you have mechanisms to review those outcomes and create feedback loops so you can adjust your road map in a in an agile fashion? Right? This this sound theoretical, and execution is not easy. It requires a certain form of discipline, and that's where the culture and mental model comes in where you allow for calculated risk, where you create allow the freedom to learn from mistakes. Processes like postmortem and all those things comes into the picture as well. So in my mind, it's a lot about process and culture. And the second element is I I keep bringing up connect the dots. I think it's really important, like, especially in the period of uncertainty. Right? For us at Mejuri, we we sometimes forget how important it is to understand the new marketing campaigns which can happen inside out. To give you an example, I remember, like, we were scratching our head about the conversion and drop off, and we saw that it's all coming from Meta. It led to, like, this big deep dive. But what they what they did not take into account, that, like, we had completely changed how we were doing campaigns on Meta. We went from conversion driving to more click based, which worked really perfectly in our context, but it had a negative impact on one of the KPIs. But overall, it worked for me too. Right? So I think that connecting the dots is often missed in companies. So when you focus on those culture of processes and mechanisms, right, like, you you get better over time.

[00:14:46] Christina Stathopoulos: Okay. So connecting the dots, the culture piece. Culture, of course, I think is is incredibly important. Yeah. So, Kailin, I wanna I wanna throw it back over to you, and I wanna talk about one of my favorite things, which is data, the data itself. So many teams, they have lots of data, but they're struggling to act on it. Do you have some sort of framework for turning data into decisions? Like, how do you connect all this data from customer experience, all the signals that you're receiving into business impact so teams can prioritize?

[00:15:15] Kailin Noivo: Good question. A common theme we hear is data rich, insight poor. Rohit would know this. The amount of data capture from tools is really, really high, and a lot of the data captures overlap with each other, which actually, ironically, like, slows down the website, creates conflict of, like, third party code. So you really actually don't want, like, 30 different things pulling data from your website. And then the worst part is these providers are actually charging you to store it because that's one of the the things that that's one of the things that they basically use to to come up with your pricing. So how we look at this is if you wanna turn data into insight and you're a horizontal company, meaning you sell retailers, banks, this, in the absence of retail being 90% of your business, you're optimizing for a different variable, which is like portability of data ins like, basically, you're you're bringing somebody build your own adventure versus bringing them an opinionated product that they can, like, fine tune the last 5% in easy way. So, like, I think we realized that, like, pretty early on where when we were just an error monitoring company, people were like, but I have New Relic. And we're like, sounds good, but, like, how many bugs have you solved in the last month? They're like, well, not that many. And we're like, why? We're like, well, there's 20,000. We don't know which ones actually we should work on. And, like, okay. Well, you have two options, either buy a product or you hire a person to study the errors and try and understand which ones are hurting conversion, and I think that initial insight could be applied across multiple different categories of tools. So, I I mean, I'm just gonna be super candid. Like, I don't think you can solve this problem if you're serving multiple masters, and the only people that are gonna be able to solve this at a data and an intelligence layer is doing it vertically. And unless there's some super quantum leap in AGI, even with our, like, AI even with our AI products, it doesn't have the context to just, like, solve that problem for you. And there's also another thing which is kinda separate but adjacent to this. Rohit would know this. Transforming first off, collecting, storing, and transforming that data so that it's actually usable is really, really challenging. Multiple different formats, different things, and you have to solve that problem before you can get to the end state of automating some of those micro work workflows or having like a to be able to interact with your agent in a way that you get very valuable insights. So to answer your question directly, I think not only the reason why Noibu is well positioned, but I genuinely don't think this problem is solvable horizontally, and I think that's how most people are approaching it. So, yeah, that's that's that's my that's my bid on it. Obviously, I'm a little biased, but yeah.

[00:18:15] Rohit Nathany: Yeah. I'll just jump in with one comment over here. Like, I'm completely with what you said. And the way I try to bring this up in my team meetings is, like, know your business, know your customer, know your goal. Like and they're all related. And when you do that, you know what you're seeking. Right? Like, you know the questions you wanna ask, the answers you're seeking, and that's when data becomes easier to action on.

[00:18:38] Christina Stathopoulos: And I and I do wanna quote back to sum up everything that we've just said. I really like one of the first comments that Kailin made, which was that we're very data rich, but we're insight poor. Data is incredibly valuable, but you actually you gotta you gotta work with it. So back to you, Rohit. New new technologies, new features are moving very fast in this space. How do you all at Mejuri innovate quickly, but also without introducing unnecessary risks to the customer experience, especially around around releases, for examples. For example, what guardrails, what signals, what shared metrics help you move fast with confidence?

[00:19:14] Rohit Nathany: I I would say, like, it it again goes back to the culture. I think you need to have strong culture. You need to have strong set of operating rhythm and process. It all starts there. It's it's the culture of risk taking. It's the culture of, uh, looking back and understanding what went wrong and really truly getting into the deep of, like, what what you're thinking, why those decisions were made, and that's how you take corrective actions. And assume that, like, those hygiene factors are in place, then you're looking at the basic table stakes like whether it's Noibu or any other analytics tool, error monitoring to make sure after the release, you have a way to monitor monitor progress and and make sure everything went right. We ideally like Way because everything to reduce the risk and also to be able to quantify what we what's working, what's not. But I think most of the tech teams or leaders that I talk with, it's these are table stakes at this point. The part where I've still seen folks struggle is, uh, what I call silent failure, so the revenue leakage. And these add up over time, and that's where I really love, like, what, Kailin, you guys are doing because that's really help us cut through the noise, find stuff that's actually hurting customer experience or creating some friction, and find those opportunities and go ahead and fix them and prioritize and fix them. And it can it can save hundreds of thousands of dollars or millions of dollars.

[00:20:35] Christina Stathopoulos: Yeah. And can I ask, do you have any, like, tips, tricks when it comes to the establishing the culture, the mindset that you've been that you've mentioned? Like, anything that any behaviors, anything you've done at Mejuri to be able to prepare within the company?

[00:20:51] Rohit Nathany: Yeah. So, like, I'll I'll I'll give a simple example. When I joined, we we were not experimenting. We were not doing AB test. And I had a very simple goal. I want my team to run one a b test per sprint. No matter how they do it, just a very simple I was like, I'm not gonna put any constraint. I'm not gonna care about being data driven and all that. Just run one a b test. It sounds simple. In order to run an a b test every sprint, every two weeks, or every month, it takes a lot to get to that point. And once you get that discipline in, it it organically helps you clean up a lot of mess. It helps you figure out, like, do you have the right team? Do you have the right processes? Do you have the right data? Do you have right the tools? So for me, personally, like, sticking to the basics really helps. And then, of course, like, you have to be clear about your expectation. You have to be there to support your team because when you think about experimentation, things gonna go sideways. You're gonna make mistakes as you have just mentioned. Right? Like, so being there to support your team and also create mechanisms to not make the mistake again. I always say that, like, don't repeat the mistake. And it goes back to the learning. So it's like, go back, look at where the company is, where your teams are, and and go go focus on the basics.

[00:22:04] Christina Stathopoulos: Yeah. So learn from your mistakes. Make a culture where people are not afraid to fail because you're gonna learn from it and and move on from that. Kailin, do you have anything else to add to that around, like, the culture, the mindset? Because it sounds like you all at Noibu as well. You're you're innovating fast. You've got to have the the right culture to be able to do that.

[00:22:24] Kailin Noivo: Yeah. It's so interesting. I've seen, not in our organization, but with with customers and prospects, When you start to have silos, and and Rohit knows this, I'm a huge advocate of the CTPO, so the consolidation of technology product and marketing in in retail. I think that is, like, the smartest consolidation, and I'll explain to you why. If you zoom if you look back, you have three distinct sometimes four distinct departments that roll up to two strong function heads. You have product and technology, and then you have, like, ecom and marketing. And when you look at this, they each have their own tools. They'll have an APM performance tool. They'll have, like, a heat mapping, UI, UX, digital experience tool, and then they'll sometimes have, like, an analytics tool that kinda somewhat sits in the middle, and that's kinda like the Switzerland. It's like the neutral ground, but then you have your own tools. And often, I've seen in very poor cultures, obviously, fortunately, not not with, uh, Rohit and Mejuri, but those tools start to get weaponized. People fine tune them. They don't give full access to different teams, and I've seen that create a lot of challenges. Right? And even in certain some buyer more old school customers. Right? The concept of putting revenue signs beside technical issues, woah. I don't want this team to have access. I don't want this person to have access, or you create a lot of conflict internally if you're not rowing in the same direction. So from a cultural standpoint, I'm a huge fan of the everyone who rolls up to the same leader. That's the first thing that I would say, where marketing's using a tool, engineering spends 90% of their time questioning the validity of the data, and then it becomes about proving the validity of the data, and that's where all the effort's going versus, like, solving the problem. And even if the data's not accurate, it's directionally accurate. So from a cultural standpoint, I think it starts at the top and unifying the teams. And then as much as the tool could support culture, I think that is gonna drive the most amount of efficiency because sometimes you're looking at data that and you don't like what it's telling you, and you have two options. You either work on the solution or you try and disprove that there's a problem in the first place. And I find when there's a unified leader and a unified tool, it's a lot easier to actually roll in the same direction on that topic.

[00:24:42] Christina Stathopoulos: Okay. That makes a lot of sense. Unified leader, unified platform. Right? With unified tool. Okay. So I wanna shift gears a bit. There's this one one question that I wanted to come back to. Kailin, you've described Noibu as ecommerce analytics and monitoring platform. So when you say platform, what does that mean in plain terms? Like, what workflows become possible that aren't possible with point tools, the centralized view that you were just discussing right now as well.

[00:25:12] Kailin Noivo: Yeah. No. It makes sense. And I I wanna preface this where you need to really, really you almost need to build a series of point solutions that are interopively connected. And what I mean by that is, like, you don't wanna build, like, a platform that kinda does everything but does nothing. And how we approach this is we went really deep for a good amount of time on certain things, and then you expand it out. But to answer your question, what can you do? Well, first off, connecting these systems and managing in like, and managing in integrations, especially as SaaS products are shipping code every week, disaster. You don't wanna pay someone to do that. Right? From there, for me, a platform, it needs to be out of the box, but have configurability, kinda like very similar to what Rohit's team did with Shopify. Right? Using Shopify out of the box, but you're using a custom app on the front end through Hydrogen where you can control the customer experience. So for me, a platform, it needs to be good enough to serve the teams that it says it's gonna serve. It needs to be opinionated enough where where you don't need to hire big teams to manage it, and there's very little overhead, and people need to actually work out of it in unison. And for us, it means that we need everybody from customer service to marketing to product to engineering collaborating out of the tool and trusting their part of the tool that they care most about, and that part of the tool needs to be powerful enough for them to be able to do their job.

[00:26:42] Christina Stathopoulos: Good. Well, I we're coming towards the end of the of the episode, and I do have one last question for both of you, a very important one. So to wrap things up, this ecommerce leaders, they have a lot on their plate right now. It's clear. They're trying to keep up with this rapidly evolving space, lots of different moving pieces. We've only gotten a taste of it through our chat today. So if you could give ecommerce leaders three practical strategies for 2026. So one for visibility, one for speed of action, and one for team alignment. What would they be? So, Kailin, I don't know if you want to start start this one off. Visibility,

[00:27:23] Kailin Noivo: I actually maybe as a contrary opinion. I don't know if people have a visibility issue. And what I mean by that is, like, the data like, if you have a data access problem, that's very kind of a t zero problem. What I'm hearing in the market is actually the opposite. People have too much data. So I would say, definitely, you wanna solve visibility problem. You shouldn't have tools in the org that other people don't have access to unless there's sensitive information that they shouldn't see. Everyone across these teams should have access to all the different tools regardless if they use them daily. So I would say from a visibility standpoint, you obviously have to solve that first. If you're either missing data or missing access to data, that, for me, is like a t zero problem. From their speed, this is, by the way, the biggest organizational drain that I see. It's what I just kinda went on a micro rant about. You have different tools that are pointing you in slightly different directions. It's like going on a on a boating journey and, like, you're one degree off. Not a big deal at the beginning, but, like, that's the difference between ending up in Panama or Cuba if you're leaving from Miami. Like, that's a big, big, big difference. And I think teams are dragged down in speed because what ends up happening is you end up spending weeks litigating the source of truth. And then by the time you do that, you kinda forget why you embarked on this conversation anyways. And, like, you stop working on the problem or it becomes less of a priority. And then culturally, uh, I think, ideally, you unify everyone under a single team. I think in the absence of that, it is pretty challenging. But with that said, if you have a good trust between the teams and an incentive alignment, I have seen that work as well. Because, obviously, in some organizations at a certain scale, you can't unify that. But, yeah, uh, it's not too different from from what I just mentioned, but that's that's my take.

[00:29:09] Christina Stathopoulos: Okay. Cool. Well, what about you, Rohit? What would you say for so it was one for visibility, one for speed of action, and then one for team alignment slash culture.

[00:29:20] Rohit Nathany: I I agree with Kailin on the visibility piece. It's it's not about getting more data. It's about figuring out how to efficiently and effectively action on the data. So, like, I'll I'll actually I'll actually talk about it in my world. Like and, personally, it has never been easy try to, like, even coach people to use different tools. So even simple thing as session replays, it's not easy to watch the session replays. So it's not about, like, uh, whether you are able to do track errors or or check, like, your funnel performance or session replays. Like, how you connect the dots is the key. Wherever seen slowness in execution is down to slowness in decision making. Uh, I think that's that's a really important piece where it's all about the leadership, what culture they have created, culture around risk taking, safety net around risk taking, mechanisms, process, tools, support, like, they're all coming together to really help teams move faster. Um, and then that concept of one way door, two way door, where a bigger decision, like, you take more time. The way I like to talk about it is a one way door, try to break it out into multiple two way door decisions and still be able to figure out a way to move faster.

[00:30:31] Christina Stathopoulos: Perfect. Well, thanks to you both. Thanks to you both, Kailin, Rohit. Thank you for joining us. Thank you for sharing all of your thoughts, your experiences. We've covered a lot of ground today, I think. So thank you both again.

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