The Asia InsurTech Podcast spoke with Gourab Mukherjee, the CEO at Aktivo Labs about how InsurTechs and HealthTechs can help consumers live a healthier life and avoid lifestyle and chronic desease.
Find the transcript of our conversation here:
Michael Waitze 0:00
Okay, the recorders on Hi, this is Michael Waitze, and welcome back to the Asia InsurTech Podcast. This is the only podcast in Asia focused on insurance that gives entrepreneurs thought leaders and investors a platform to discuss how technology is reshaping the insurance industry in Asia. Today, I’m joined by Gourab Mukherjee, apparently I’m good at pronouncing that name, a co founder, and the CEO of Aktivo Labs. Gourab it’s nice to have you on the show. How are you doing today?
Gourab Mukherjee 0:32
I’m good, man. Thank you for calling me. How are you?
Michael Waitze 0:34
I am super, let’s just jump right into this. What do you think is the biggest trend in InsurTech in Southeast Asia?
Gourab Mukherjee 0:44
Right, I think it’s just not to Southeast Asia is something that I’m seeing pretty much globally. My exposure is, I think, primarily would be Southeast Asia for now. But one thing that definitely stands out, at least with the insurance industry, and I would say for both life, and general insurance is the need and the push to get to build more contextual ecosystems. And when I say contextual ecosystems, I mean that the whole idea is to move from an insurance to a push product to a pull product and how data driven services can enable some of that, you know, just to understand, be more closer to the customer, be more customer centric, and understand more about the different context, as the customer goes through his or her life, and then be at the right time at the right position to offer the right services or the product. And I think that’s where the focus is. Technology is definitely the enabler. And you see a lot of exciting InsurTechs, I get the chance to meet some of them at you know, this online events and conferences, and I see many of us InsurTechs are partnering up with incumbents to enable some of that. So yeah, exciting times, I would say to be in this industry.
Michael Waitze 2:00
Yeah, I mean, I’ve been told it’s the perfect storm right now. And I don’t disagree with that at all, particularly, we’ve done 100 and something of these interviews and everyone that I talked to just gets more and more excited. Can we, i want to cover some of this? There’s a lot to unpack there. But can you just give our listeners a little bit of your background for context?
Gourab Mukherjee 2:18
Sure. Well, my backgrounds in computer science, and I have been in the digital health space for the better half of the last decade, I would say most part of my professional career have been in digital healthcare, healthcare technologies. Mostly been about trying to commercialize innovation in data and digital health data. And around 2016, that’s where the idea of Aktivo Labs came about, in terms of how we can really use real time data as opposed to static health data, and how that can be used to improve the lives of people to help them understand how they can lead healthier and happier lives. So that’s about how the whole idea of Aktivo Labs came about. Yeah, that’s my backgrounds there. And what I do right now full time, and that’s all I’ve been doing for the last three and a half years is trying to build Aktivo Labs.
Michael Waitze 3:16
So talk to me about real time data, is this the concept that you have of the score? In other words, how do you access that real time data? How do you get it from people? And then what do you do with it when you get it to have some kind of impact?
Gourab Mukherjee 3:29
Right, I think good to take a step back, look at the healthcare industry as a whole. So traditionally, healthcare has always been about treating diseases, right? The management of health risk has always been and still to a certain degree till today has been reactive rather than proactive. Then the concepts of tracking, you know, what is real time data and what is static data, that’s something that I got introduced to very early on in my career, I would say 2014 / 2015. That’s where we saw the proliferation of tracking devices, the fitness tracking devices, wearables, smartwatches, even your smartphones in terms of how much data they capture, you know how much steps you’re taking, your resting heart rate, how much you’re sleeping, this is all real time. Whereas everything that is stored inside the traditional electronic health records and hospital information systems, that would be the static data, it’s all past data. So trying to understand the data that the human body generates. That’s just real time and continuous analysis of that. And that lead to some exciting consumer journeys that would improve their lives. That is how you can utilize the real time health data.
Michael Waitze 4:43
Sorry, does that mean that if I’m wearing a Fitbit and or an Apple watch that the data that’s getting accumulated or gathered by those devices and similar devices, somehow feeds into an active or real time data generation but sort of understanding platform Then you can do an analysis on that data in real time and give me feedback about different ways to behave.
Gourab Mukherjee 5:06
Yes, exactly. So what we do is, we start off at the data capture process itself. We want to make sure that we are building, we want to build a platform that can scale and is meant for deployment at scale in an affordable manner as well. The first piece we looked at is that can we be device and platform agnostic. So that’s data coming in from the Fitbit, or the Apple Watches or the Garmins of the world? Can we capture data from all of these devices, including data from say, even your smartphones as well? The second piece would be to really harmonize that data, data from different sources, how do you classify them, harmonize them, the third part is to tell the user what is really the meaning of all their data. If you look into the current fitness tracking application, they’ll give you so much different data points, you know, from resting heart rate to heart rate variability, deep sleep, REM sleep, stairs climbed, calories burned. After a point of time, the consumer sees that, okay, that’s a lot of data. But what does it mean, for me? I mean, I’m not a doctor or a sports scientist, what should I do, then that’s where the concept of Aktivo Labs came about back, you know, if we can explain to that man on the street, what is the meaning of the data and then give them insights that can slowly nudge them to do leading a healthier lifestyle, I think we have something on our hands. So that’s what the Aktivo Labs does is that it captures your data, how you’re living your day to day, contextualize it to your age, and your gender. And then based on that, it tells you that it’s a good score, bad score, an ugly score, and what little changes or tweaks that you can do in order to have a balanced lifestyle, and they’re about, you know, significantly reducing your chances of getting chronic diseases like type two diabetes or hypertension.
Michael Waitze 7:04
Okay. So let me ask you this, too. In most cases, I will ask companies if they have their own data science team, but it almost seems like your team is built around data science from the get go. Is that a fair characterization?
Gourab Mukherjee 7:18
Yes, absolutely. I mean, there was no ways that we could build a data company without the right skill sets. I think Aktivo Labs happened because we got the right multidisciplinary team is coming together, I would say the primordial soup. So we have doctors, data scientist, actuaries, data engineers, all working together with one primary goal. My co founder, Professor David Lai, David’s a cardiothoracic surgeon, he spent a lot of time doing research in cardiac biomechanics back in Stanford, he really understands disease, the domain experts also understands a very strong appreciation for the data sciences, a very strong data science team itself, data science, I mean, we are 22 of us. And out of the 22 of us 18 of us are engineers and data scientists, two doctors, one nutritionist, and then this one, me, I’m the guy who goes on talks, tells the story for the rest, really doing the work.
Michael Waitze 8:19
Welcome to the world of being a seed CEO, Although to be fair, there’s way much more going on. We know that for a fact. Can you talk to me about this, though? If there’s all this data science going on, is there an ml ops platform in the background that’s actually helping you build the infrastructure to be able to handle all of the data that you’re getting? So you can actually do the science on it?
Gourab Mukherjee 8:39
Yes, it’s quite complex. But we just try to make it as simple as possible. So we have ended up building a very complex data storing procedure. So let’s say that we know we have a warehouse in which data from all these different sources that comes in. So as of today, we have close to 170,000 users on our platform, and we are continuously capturing this data. And it goes into the warehouse. And then from there on, we try to analyze and come up with insights. That’s one part of it. The Aktivo score by itself, it was built and validated on public health data sets. Again, those data sets like seriously large in size, and it will longitudinal health data sets, we knew what the cohort was doing in terms of how they were spending their day to day, we had access to the longitudinal health outcomes, including access to their mortality registry, and post that you know, we build the score, file and then keeping some public health guidelines in mind. And continuously the idea is to keep looking at you know how to improve the score as we go along as we capture more and more data in conjunction with our partners. In the life and health insurance space,
Michael Waitze 10:01
So do you want to define in a little bit more detail on what the score is?
Gourab Mukherjee 10:07
Yes, this score, basically, it captures what it feeds in, it’s when we have 56 different parameters that feeds into that score. But what we basically try to tell the user is that you spend your day in basically being four different physical activities, you’re either exercising, which is anything more than a brisk walk and above, or you’re lightly active, that can be brisk walk and below standing minutes, all that goes into your live active minutes, sedentary minutes, which probably both you and I are doing right now, if you’re not standing up and sleep, right. So this is how you really you know, you spend your day to day. If you’re getting the right balance, are you getting the right balance, contextualize your age and gender and some other data parameters that can be plugged in? Then we say that we would apply to understand this right balance of how you’re spending your day to day, does that get you a higher score and a higher score would mean that you’re lowering your risk of getting a chronic disease, like diabetes, type two diabetes, hypertension, dyslipidemia, and so on and so forth. So it’s basically just telling the user that there are multiple ways in the manner in which you can hit an ideal balance day, if you’re sitting time is a bit too excessive, can you make it up a little more exercise? Can you plug in a bit more sleep and reduce your sedentary time, and just you know, there are multiple combinations in which you can hit it. We do definitely tell the user on the platform or on the application, whoever whichever way the score is manifested to the consumer, is that ideally, an ideal day looks like 30 minutes of medium to vigorous exercise, try and sit less than eight hours a day. And between seven to nine hours of sleep is a public health guidelines. But in order for your personal lifestyle does not allow you or each other to hit each and every one of those goals there are certain ways you can add up to a score of 80, which we tell that is an ideal score 80 and above is an ideal score significantly reduces your chances of getting a lifestyle or chronic disease. And anything below 60. If you’re consistently scoring something below 60, your risk is quite high. That means your lifestyle is not balanced, either you’re extremely, highly sedentary, or it can be that you’re not getting enough sleep, or you’re not getting any exercise at all.
Michael Waitze 12:45
Does food intake matter as well? Is there a way to measure that or that’s not relevant to what you’re trying to track?
Gourab Mukherjee 12:52
So the idea was to make it as low compliant as possible, as low touch as possible. What we have seen in in terms of food intake, yes, in terms of lifestyle risk, nutrition does play a big part. There is some noise in the sense of the science behind what is good food, we know what is bad food. But in terms of saying what is good food, there’s a bit of noise. And but there is a growing consensus among experts, that adequate consumption of fiber in your diet could lead to healthier, better health outcomes. And when we built our second product, which was Glucolife, it was in addition to what we were doing on just Aktivo , just try to keep a healthy population healthy. Glucolife was trying to reduce the risk of people with pre-diabetes and early stage type two diabetes. And not only do we tell them that, hey, maintain a healthy, active will score, but also consume enough fiber. And that we do this through a very simple fiber tracker, that basically ask the user anytime to come back to that application, just tell me how many servings of a certain food group you had yesterday, and based on that we can track it, but it does not flow into the main Aktivo score right now. There is a way in which we interact with the insurance company where all these other different data points plug in a=nd then we can give them an overall risk score as well.
Michael Waitze 14:26
You’re making me want to go out and buy an apple watch right now. I feel like just hanging up and going getting one.
Gourab Mukherjee 14:33
It doesn’t have to be an Apple Watch I think you know what devices out there in the market works. But yeah, I like the Apple watch.
Michael Waitze 14:41
Yeah, absolutely. I’m just seeing that ecosystem. So it’ll be easier for me to do that. Do you want to talk more in general about what makes the Aktivo Labs platform unique?
Gourab Mukherjee 14:52
Right, I think three things: One, like the first thing I mentioned is the device and the platform and App Store agnosticism not really dependent on a particular ecosystem or a particular brand to provide you with that data. The second piece is, I think the data engineering that has gone in the sophistication in the manner in which data is captured from a consumer, how seamless it is. When I say seamless, you just mentioned Apple Watch. So for example, say if Michael’s a user, he wears his Apple Watch, only while going for a swim or his exercises for the rest of the day, he prefers not to wear it, or Michael wears it throughout the day, and does not wear it when sleeping. Right, our system or algorithms are sophisticated enough that hey, we aren’t able to capture sleep. Michael’s sleep from his watch, or his Fitbit device or his Garmin device, we would be able to switch back to data captured from the smartphone and try and estimate his sleep time. And for that as well, we have our own proprietary algorithm that picks up minutes spent in bed from the smartphone, it takes into account 90 different parameters. Parameters, like what time of the day it is, is the phone moving or not screen on and off times, connectivity to charge of connectivity to a WiFi. All these different things really goes into our algorithm and then variable to say, whether it’s sleep or not. So there is one part of our IP is the data engineering that has gone in over the last few years. And the seamlessness of all these different trackers coming together and working together. The second bit is would be the score, I think the most important part. And the reason why our partners prefer our score is the simple nature of it, it’s very easy to explain, you know, it’s it’s between one to 99, the higher the score, the better it is. And they’re only, you know, a few, just a few few tweaks in your life, you can do and consistently do it to keep that score high. So the nature, the device agnosticism, the third part of it is that our partnerships in the insurance and reinsurance space where we are really going in and trying and talk to the product and the actual teams that how they can use this in their product development, and then ultimately in coming up with better underwriting and pricing as well to using that. So this score is not only just about telling the user how they can improve their lifestyle, but there is an opportunity for them to generate actuarial insights out of our platform. And the fourth piece is that we are not just confined to being just a digital health scoring company bought in we have been because of the expertise in house, we have been able to create digital therapeutical journeys for end users of local life is was one example that Okay, fine activity score gives me an idea of the entire room in terms of the different health risks, and you know, for this particular segment of this population, I would be able to advise that, hey, you know, why don’t you use this blue Life Application, this can help with improving health to give you better health outcomes. Similarly, we are now building the aktivo mind product, which we will be launching in q2, this tackles the whole piece of the mind body nutrition, but the holistic view of wellness. And the idea behind active mind again, came earlier last year, when you know, it was quite evident in our data sets that we were mining, that there’s a clear correlation between mental on wellness, and early mortality and early morbidity. So what we did was we went and on boarded some experts in the CBT, or cognitive behavioral therapy space. And what we’re doing is basically we’re digitizing many exercises that disability experts practice with their clients return, we’re digitizing them, they will be self guided meditations on our platform. And this entire platform is also built in, everything’s built in a very modular manner. It’s all the technology is decentralized. And when I say that everything can be available to API’s. So someone’s really trying to build out their own mobile application and you know, bringing in other aspects, we can be that one partner that’s providing them with all their preventative digital health needs.
Michael Waitze 19:19
So you said have 170,000 people on the platform. And you also mentioned that you use a lot of the publicly available health data. Let’s say somebody new comes on the platform, right? Let’s say I go out and buy an Apple Watch and I start feeding data into the Aktivo system. I’m presuming that you take all of this data that you that exists already that you already have, and you anonymize it. And then you make comparisons to my data that’s feeding in real time. And you make presumptions based on all the existing data that you have, about what my I guess my mental health, my physical health and what my the likelihood is that I’m going to get that type two diabetes just based on the information You start gathering on me?
Gourab Mukherjee 20:01
Yeah, absolutely. And that’s, that’s the value proposition. So based on your data, I can estimate your risk. And that understanding comes from analysis of similar profiles on the public health data set. So that’s that’s precisely what we do. What is the risk factor in which risk bucket is the high, medium or low risk? And then the second part would be that now how can you manage that risk? That’s where we come up with our entire aktivo gamification framework, where we can say, okay, Michael, you know, we will march you have get the right framework for you to improve that helpless. So yes, to answer your question, that is the first premise that getting your data, we will be able to plug in which bracket you fall from a high medium and low risk factor. Even from probability right now we can do, we can predict the cross sectional probability of comorbidities like diabetes, just from real time data, you know, we just get data, if you’re plugging in data from your Apple Watch to us, we’ll just be able to say again, that again, you know, this is in which bracket and then again, the entire program takes over to make sure that if you’re in a medium risk, are we moving from the medium risk bracket to the low risk bracket?
Michael Waitze 21:24
So can you talk a little bit more about how you work together with the products that you’ve built with insurance and reinsurance companies? And how they can use that data using actuarial math to then create products for users? And can they do this on a granular level? I presume they connect somehow to an API that you have. But can they do that on a personalization and a granular level as well, using the sort of data and the insights you get from that data?
Gourab Mukherjee 21:51
Yes, I think the two parts of that question. Let me let me say that the first piece, like I mentioned earlier, the first question, what is that trends that we’re seeing, and as they try to build this contextual ecosystems, I think trying to have a conversation with their policy holders about healthy living is pretty much I think, forefront, it’s quite an obvious conversation to have, we definitely want to add value to the one thing that they’re insuring, and that is their life and health. So the first piece comes in terms of how we can enable our partners The life and health insurance companies to have that conversation. So that this comes in terms of how easy it is to provide that service to that consumer, whether it is on our app, as is whether it is a white label solution, a co branded solution, or simply our platform can be integrated through an SDK and APIs into any native application that any of this in combination from insurance companies are building. So the first piece is really about the policyholders picking it up, right, it only works if there’s uptake. And that’s where the focus was, how do we remove as many friction points as possible for the consumer to pick it up in the first place. So that’s one thing that we do that engagement problem that insurance companies have been trying to solve that the second part is really about understanding the risk on their books. So they roll it out to that population, a certain segment of the population, they will be able to understand what is the underlying risk on their books. second piece is also when you have solutions like this, it naturally attracts a healthier book towards you that if you are giving out services around health and well being, you know, sometimes the early adopters are is really like preaching to the choir. And then you add incentives to that there’s much more a better way to retain as well, the healthiest book in the market. The third piece that comes around, and that’s really where the fun happens is that understanding this risk, what can you do to incentivize users further? I don’t think the insurance companies or the reinsurance companies will throw away their existing models of underwriting and then their actuarial tables anytime soon. But using something like a bacterial risk engine or the data that’s coming out of aktivo, can it help them to you know, create further segments into their insured population? So for example, if Michael and Gourab are of the same age, you know, same smoking and then genetic profile, but Michael has a score of 80 and Gourab has a score of 50. consistently, can Michael be a preferred standard light? Can that be rewarded? And that’s where I think We really are right now, I think where the industry could go in the future is just using this data. Can we also go into the space of pre sale risk as well. So this particular population, that’s where we know we are working with banks and telcos. And because they have that outreach to the consumer population. So I’m just giving one example is that if I’m working with a bank x, the bank x has 3 million users on their platform on using their digital banking application, if 5% of them have an aktivo will score of 80 and above, can we offer them a higher cover at the same premium, so that way that the folks get rewarded for getting the data and leading a healthier lifestyle, the bank gets to offer a product, which is not available in the open market, the insurance and reinsurance company gets to act private, how useful. It can be further extended to all those who are moved from, say, a score of 60 to 80, or moving from 60 to 70. Can you get further brackets? Again, anyways, all you’re doing is incentivizing folks to lead healthier lifestyle. And that’s one way of proactive risk management.
Michael Waitze 26:23
And do you have other sort of successful case studies that you can share? About users, insurers, whatever that have benefited from using this?
Gourab Mukherjee 26:34
Yes. So I think one thing that definitely comes to mind is we started as still early days for us, but we went live only last year, on our platform, we probably we have a year’s worth of longitudinal data to talk about it, one of the projects that we did was with one of our partners, the marsh and universal benefits, where he rolled out this aktivo platform for three and a half thousand of their employees in the region. And all this was done just to regularly to evaluate the platform and then fine understand that how they can use it and provide this as a potential value added service to their corporate clientele. The data that came out of it was quite exciting in the sense that none of we did not provide any kind of financial information in or any kind of extrinsic motivation, no financial rewards, nothing is just in there you have the app, the bare minimum app, it gives you a score every day, that just you know, sends you a notification saying that Hey, Michael, only to go to sleep by 1130. We noticed on a weekday, you sleep by 1130, you end up getting more than seven hours of sleep, and that improves your actual score all the basic stuff. So after 12 months, we still have I mean, without providing any kind of incentives, we still have 40% monthly active users Well, at what we both we saw was the most exciting part was over a period of 12 months, the median Aktivo score increased by 5.6%. So that was like, I mean, I think it’s just as thought and then based on the data, now we are doing some more deep dive in terms of understanding a look at all the folks who move from, say, 62 ad and then what did they do? So when somebody comes to the application right now, we can personalize it and say that Michael, that is removed from 60 to 70. In the first three weeks, they increase their sleep time by 40 minutes, right. So those are the things that you know, we can provide little changes such that you know, we drive you to an healthier lifestyle. One more example that I can share is that we did a pilot with the fastest growing health insurance company in India, we would be going commercially live in April, and we would be able to announce that partnership very soon. But I’ll tell you a bit about the pilot, so we integrate it into their policyholder application and roll it out to 20,000. Of those policyholders. What we saw was, I think 20%. Let me see the numbers I have right. I have it with me. Yes. So what we saw was a 20% increase in something called active days. And the definition of an active days is days in whichyou have 30 minutes of exercise or take more than 10,000 steps. So it’s just by providing all these users an aktivo score, suddenly, there was no dependency of this users or all the users in India requiring a Fitbit or an apple watch or a Garmin watch. We were able to take the data from the smartphones. And then suddenly within the policyholder population, they were able to gather much more of this real time data and then overall this or that, hey, we now understand a bit more about our policyholder population is just 20,000 is a small segment, but within that we have seen that you know all the guys want to have that Aktivo score versus this 20,000, we do not have that aktivo score, there’s a 20% increase in terms of activities that this data suggests. Yeah, so these are some some exciting stuff, case studies that have come out. I think the more a big focus of our team is to just really spend a lot of time looking at the data and what our consumers are doing on the platform, and then see what is working, come up with hypothesis, test them out and see how we can get to better outcomes.
Michael Waitze 30:32
With all this data that you’re gathering, and with actuaries and with doctors, and I think you’ve even said a nutritionist, did you say that? Did I miss? Did I get that? Right?
Gourab Mukherjee 30:42
Yeah, we have.
Michael Waitze 30:44
It almost sounds like you’re building an infrastructure, potentially build your own products and do your own underwriting, you know, becoming a full stack insurer, is there the possibility that that’s the case, then you can use the data yourselves directly?
Gourab Mukherjee 31:01
Well, yes, it is quite an ambitious project, or what you just described, but potentially, yes, I think right now, the focus is to provide this infrastructure to existing incumbent insurers, and then, you know, help them better. But I think what insurance was many years back, I think they had the monopoly of compliance, that is slowly and slowly reducing, there is no reason why, you know, Aktivo Labs down the road cannot become an insurance company or put in a particular market itself, it would be a lot of work. But whether or not if we go down that road is that that remains to be seen. But right now, the focus is really on, we are a tech company, that’s what we do really, really well. We are a data, we are an amazing platform to capture data and analyze it and come up with meaningful insights. We bring together, we help the incumbents to bring together both their their policyholders and their product team in terms of inside the whole ecosystem, when the insights are generated to the usage of the platform. So that’s what the focus is. But yeah, to your question, there’s I don’t see any friction in terms of there’s no shortstop or to go down that road. If I think enough effort, capital and everything is put through,
Michael Waitze 32:18
And how do you guys make money? Is this a SaaS platform that you sell? or?
Gourab Mukherjee 32:21
Yeah, it is, it is very straightforward SaaS, the multiple SaaS models depending and we’re flexible, in the manner in which it adds, or it immediately adds up to the business value of our partner, subscription based per user per year, we also get an API call kind of a situation. Right now, we’re just rolling out to the partner in the Philippines, where they have a limited user pool, and they would prefer to have an annual enterprise license for eat as much as you can, and signed up for like, you know, a good two years with that. So we are flexible in the manner in which we want to do, we like to keep our upside on some value. So if we are delivering value in terms of improving or getting some kind of engagement numbers, then you know, we would want to we’d like to participate on that on that upside. Same as if we improve using the platform if it improves the health outcomes or aktivo score pulled from a certain baseline, we would like to be participate on that upside as well.
Michael Waitze 33:32
And have you been bootstrapping up until now? Or have you been funded externally as well?
Gourab Mukherjee 33:36
No, we’ve received a raised close to $4 million to date. And that, like I said, it’s been three and a half years, little close to three and a half years to get to where we are. If I start off from scratch, it will probably take me about three and a half years, three years, again, to get to where I am, and maybe not $4 million, maybe $3 million, but it’ll take me that much.
Michael Waitze 34:03
Got it. And the last thing I’ll ask you is what kind of advice would you give to the insurance companies that you deal with about how to deal with sort of smaller companies and the disruption that’s taking place in the insurance industry right now?
Gourab Mukherjee 34:16
I think it’s more about really identifying some business use case I think it’s just not about just the innovation team picking up and doing a project it’s just really what we have seen where the magic really happens with some of our partners where it was the business team lead and innovation team supported and athat’s that’s where I think they’re, I understand there’s a big businesses they’re focused on you know, quarter on quarter results, but when certain business team picks up that you know, some of these if rolled out quickly, which platforms like aktivo labs definitely helps them in very short turnarounds to go lives can just test it out in terms of rollouts of these, how does it lead to exact business outcomes? Obviously, I mean, it’s not just only a long term play, but you can start off in terms of generating business outcomes and in very short time, and start now because I think there are some certain players who have taken the initiative to get into the space, the longer they have the data, they would be in a much better position to come up with innovation in product design, and also on customer engagement and retention.
Michael Waitze 35:42
Okay, that’s awesome. Look, I wanna I really want to thank you for coming in and doing this today. Gourab Mukherjee, a co founder and the CEO of Aktivo Labs today was really awesome. Thank you.
Gourab Mukherjee 35:53
Thank you so much, Michael. Really appreciate it.
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