Editor’s Note: This transcript was created using AI transcription and formatting tools. While we’ve reviewed it for accuracy, some errors may remain. If anything seems unclear, we recommend referring back to the episode above.
Episode Trailer
How do you advocate for experimentation to be a broader growth enabler? There will always be people who would be blocking it. If the leader is actually looking for growth and you are able to sell the idea that this is actually a validation engine, this will actually drive your growth towards the future, then only people will be convinced. Warren Buffett also says compounding is the eighth wonder of the world. Similarly, as soon as you start experimenting, you have to get that incremental benefit. You can’t really just stay on that linear growth curve. VWO has connectors to emails and also to Slack, which has enabled us to send the reports also to the senior teams. With this, we were able to reduce the cycle time or the decision cycle time by around 30 to 40%. If a person did two to three experiments and most of them failed, you have to be able to congratulate him on his learning that he has gained, not only on the wins that he was supposed to get right.
Guest Introduction
Niti Sharma: Hey everyone, my name is Niti and I welcome you back to another episode of the VWO Podcast where we share stories of growth driven through experimentation, technology, and innovation. Our guest today is Arshdeep Singh. Arshdeep is an accomplished technology leader with nearly two decades of experience scaling platforms, leading high performing engineering teams, and driving AI led transformation across industries.
Kava offers wellness products and is India’s first modern holistic Ayurvedic brand. And in his previous role as senior director of technology at Kava led multiple functions including engineering, data, IT, QA, and infrastructure. He has steered the company through rapid growth platform scaling and adoption of AI workflows.
During his tenure, Arshdeep has also leveraged VWO to turn data into actionable insights, aligning product, experience, technology, and customer understanding into a unified growth engine. Hey Arshdeep, welcome to the VWO Podcast.
Arshdeep Singh: Hey, thanks for calling me out.
Niti Sharma: How are you doing today?
Arshdeep Singh: Pretty good.
Current Work and Interests
Niti Sharma: Awesome. So tell me actually, is there something exciting that you’re working on? Either at the professional front or at the personal front?
Arshdeep Singh: That I did not expect. Right now, I am actually, not really working at anything. I am on a break for a few weeks and then probably I will figure out what to do. But I love working at problems which need scale and probably, figure out how that can be done via, you know, I love to experiment a lot, so I have been doing, I would like to build an experimentation platform, which is no code. Something like VWO has already beaten the market already, so I am not sure how I can be that, or how I can do better than that. But yeah, that’s what I am actually looking at here.
Niti Sharma: Okay. Awesome. And has there been a point in your journey in CRO or experimentation that’s made you think like, yes, this is exactly why I love doing this? Has there been a moment like that?
Arshdeep Singh: So I love data. I have been a long proponent of data driven decisions. There can be conviction. The conviction has to be there, but the data is one point where the facts are facts, right? I mean, no one can deny if orange is better or yellow is better. So that can only be proven by data. So, inherently if we need to decide on objectivity, it can only be driven by data. It cannot be a subjective decision most of the time. So hence, I am more keen on experimental culture, which we are driving and we were driving at.
Scaling and Compounding Loops
Niti Sharma: Okay. Sounds fantastic. And you’ve helped scale brands from early stage to multimillion dollar revenue. So, do you feel like experimentation has played an important role there in building systems and processes? How has it helped to enable this kind of scale, especially?
Arshdeep Singh: Absolutely experimentation has helped us identify the compounding loops. I hope I’m being as descriptive as I can be, let me know if I need to go in deep into it. But experimentation has helped us identify compounding loops very early where we did them with the data and then operationalize them into our system that new team members can also plug into very easily. This allowed us to scale very fast and the marginal effort was always declining, so any new member would have to spend a lot lesser to find out the best combination of colors or UI or maybe even the product. Yeah. So that has helped us a lot. It has been from pricing models to the checkout to the UX. We have always used structured experimentation to discover these scalable levers rather than rely on instinct. I hope I’m making sense. Yeah.
Niti Sharma: Yes, you are. You are. For the sake of our listeners, could you just explain what compounding loops are and maybe give an example if you can.
Arshdeep Singh: Sure. It is just like, you know, finance if you have, if you’re getting interest, the magic word that Warren Buffet also says is compounding is the eighth wonder of the world, or ninth wonder of the world now. That is the ninth wonder of the world, which is compounding the interest. So similarly, as soon as you start experimenting, you try to figure out, you have to get that incremental benefit. You can’t really just stay on that linear growth curve and that compounding can only be received if you have some compounding effect. And that compounding effect is what I am talking about, which we got through the experimentation.
Building Experimentation Culture
Niti Sharma: Right. Right. Okay. Also, you know, scaling experimentation has to be done across teams, right? Engineering, data, product. So do you feel like there are some cultural traits that are essential so that experimentation remains collaborative and does not get siloed?
Arshdeep Singh: Of course. So if you just tell everyone to start experimenting, they may or may not want, then what will happen is they will start experimenting on their own KPIs and their own OKRs. What that means is that someone will actually experiment for better LTV and someone will experiment for a better ROI. But there should be guardrails. There have to be guardrails set, and hence the culture is necessary. So in my experience, what we have done is, we have given shared objectives, shared metrics and vocabulary across the product, engineering and data, and of course, CX, which have helped us set the guardrails. For example, in CX we have set the guardrail as the NPS or the customer satisfaction and the escalations as the guardrails. So everything that you do within the experimentation should not beat these guardrails, and hence, these experimentation will not be in the silos. Similarly, LTV and ROI have been the guardrails for the growth teams. Yeah.
AI and Productivity
Niti Sharma: Right. Okay. Just moving to AI a little bit, I believe you’ve also enabled company-wide AI adoption, right? So developers operate at 10x and QAs also operate at 15x of their productivity. So, how do you think AI, for sure, you know, it’s reshaping speed, but do you also think it’s a struggle for it to also reshape quality and sophistication at the same time?
Arshdeep Singh: It depends on how you use AI. For me, AI is actually one of the finest tools. You know, like a surgeon you use a scalpel, right? Let me know if I need to give any other analog. But if you have a surgeon, if you have worked with a surgeon or even talked to a surgeon, surgeon has always got their scalpels and they use scalpels for very precise cuts. Right? And that is how AI should be used. It cannot be used everywhere and anywhere, right? You have to figure out where you really need to use it. Like my devs are actually right now using it in developing generating experiments for variants in minutes, which actually used to take them a lot of time, like around a week or so. And these AI models can also predict winning variants before even you fully roll out the variants itself. These can be product variants, these can be UI variants, these can be experimentation variants as much as we can. The quality of hypothesis generation has improved a lot because AI already identifies the microsegmentation and behavioral clusters, which very often most of the humans miss, or people miss. Yeah. So it is not just accelerating experiments, but it is elevating them from incremental AB test to strategic foresight tools. That’s what I would like to perceive AI is doing right now.
AI Chatbots and Reliability
Niti Sharma: Sounds fantastic. And also I think you’ve kind of pioneered automation and used AI chatbots in customer experiences. So, can you tell us like how your team has used experimentation to safeguard reliability and trust before you release features like AI chatbots and the like, do you think there is some extra caution that needs to be taken before we introduce these kind of AI powered features?
Arshdeep Singh: Yes. By definition, AI cannot give factual answers or deterministic answers. It learns by the history of human beings. Whatever the humans have fed it or trained it, that’s where the AI learns. And hence it can actually be incorrect or it can give, you cannot have a deterministic answer from it. I hope I’m making sense. What happens is, if you ask it anything like one plus one is equal to two, that is okay. But as soon as you go up the number of digits, probably 11 digits or 12 digits, the AI will probably give you a different answer each time you ask the same question. And hence you apply the same concept to customer service or to maybe healthcare, which Kava was dealing with. Kava is dealing with. Then it becomes very absolutely mandatory, absolutely necessary that we do not expose these chatbots to the customers before putting some guardrails in and before putting the humans in the loop. So what we had done was we had actually created AI bots for internal use first, and then got them vetted from our doctors, and then only we released them to the end user.
Mobile-First Experimentation
Niti Sharma: Right. Okay. Yeah, that makes a lot of sense. And also, you know, with so many digital first brands now, do you think like experimentation has to be approached slightly differently on mobile compared to web or compared to other channels? Do you think like specific stuff has to be done for the mobile?
Arshdeep Singh: I feel mobile first is now everyone is on mobile only. More, I was actually reading that 50 to 60% of the work is now shifted from desktop to mobile. So people have actually stopped using desktop or the laptops. So much so that they are actually emailing from their mobile phones itself and they don’t really use their laptops now. So mobile is actually a behavior, not just transactional. And that’s what I keep telling my team also. So in my opinion, we should experiment around the habit loops, maybe push triggers, session depth, gamified retentions. These are all the triggers or all the habit loops that we have developed around the mobile. Then there can be micro interactions, which matter a lot like the gesture, the load time, the biometric login, the conversion disproportionality, and so on and so forth. The mobile is actually, you know, it is at the cusp or it is at the bottom line. A personal real estate and a professional real estate. So it cannot be just one thing right now, like for example, you had a laptop, and laptop could be only used for your business maybe, or your professional life. But now, you have a mobile, this mobile is at the intersection of that personal plus professional real estate. So the success in this can only come from hyper personalization and not just generic optimization. So you have to have to be mobile first. Yeah.
Prioritization Framework
Niti Sharma: Absolutely. And is there a framework that you’re following?
Arshdeep Singh: Mm-hmm.
Niti Sharma: That or that you generally follow to prioritize experiments when you have to balance different things like impact, feasibility, alignment with long-term goals. Also keeping in mind that resources are not unlimited.
Arshdeep Singh: Yeah, so we call it the ICE and S, ICE hyphen S alignment. What we have used is impact, confidence, effort and strategical alignment. So ICE, like the cold ice, it comes with impact starts with impact. Impact means as soon as you touch an ice, there is an impact. There is a very high impact of that. You have touched a cold thing, but it is not really cold. But what we are talking about there is impact, meaning revenue retention or the LTV effect, which makes the I part of it. The C, the confidence is the data backed or the intuition driven. It could be data backed, intuition driven. So you have to have conviction. You have to understand that being humans, we cannot really just get rid of our subjectivity. We cannot just get rid of our biases, right? There will always be bias, but that has to be backed by data. That’s what I’m talking about. Then the effort which has to be spent in bringing this experimentation or implementing this experiment is the engineering and the ops cost. That’s what I also am looking at. From strategical alignment, I am saying that does it actually make sense in the future? Will it actually be this useful in the future or not? Will it actually be long lasting? So I understand that orange color is better than blue maybe, but it’ll not be very long lasting. There will always be fatigue, which means that as soon as you keep seeing something, people get bored of it. And then utility of that particular thing diminishes very quickly. And hence the orange color will probably not last that long, but probably we bring in some kind of UX change or some kind of product change that will probably last a longer time. That’s what I am talking about, yeah.
Niti Sharma: Sure. You must have seen this framework also evolving with time, right? Like, have you seen it go through some kind of change and especially, I found the part really interesting where you said we can’t completely discard human intuition and like the wisdom we carry. So do you do something mindfully to kind of enhance it?
Arshdeep Singh: So yes and no. What happens is that if you bring it out, if you bring it out to the people, they will straight away deny it. Right. Do you understand? So if I tell someone that this is your intuition, that what you’re talking about, and they will absolutely want to deny it because they all want to be data driven and they all want to be, you know, factually correct. It is not very politically correct these days to be biased or intuition driven. But at the back of our heads, it is the intuition. It is our experience, which is driving our intuition. And hence, there is some merit to it. That merit can always be backed up with data. So if I can give you an example, yeah. So, sorry for taking this example, but let’s say if I asked you which electronic mixer is the best in Bangalore right now. Right. So you would probably know a few names and you will take one of them. And I would probably also have the same, but so I will take Sujata probably, because I have seen Sujata a lot in the juice shops and stuff, right. And they keep on going and going and going, but if you go to Chroma or someone else, they will probably tell you that Bajaj is the best. Right? And this is the, so I already have a hypothesis that Sujata is actually better than Bajaj. Now I have to back it up. This is where the intuition comes in. This is where my experience comes in. So I love this analog. Yeah, I love this concept of being intuition driven, but data backed. Data will be backed if I need it. But for me right now, Sujata works. So yeah. That’s where the confidence comes in. Yeah.
Collaborative Experimentation
Niti Sharma: Very cool. That’s great. And also like, I think we touched upon it a little bit earlier also, that when you’re running experiments across different teams and product data growth, these metrics clash and what looks like a win for one team can create risks for another team. And you already spoke how you create like guardrail metrics and KPIs so that they can reinforce and can work with each other. But is there anything else besides setting up, you know, that cultural foundation and also having these guardrail metrics in place? Do you think there can be some other framework or anything that specifically that you did at Kava that you would like to share? Just so that you know, it’s more collaborative experience for everyone.
Arshdeep Singh: Mm-hmm. It is more, so most of the things I’ve already said, but one thing that always comes to my mind is the North Star metric. If the company has one North Star metric, it’ll always drive all the guardrails, all the other metrics, all the primary goals towards it. So if you have one, something like, let’s say, in Kava we actually optimize for checkout conversion a lot and everything was driven towards it. We did not have any nonsense or upsells earlier on the checkout page just because we wanted to drive the checkout conversion to the highest. And that puts the guardrails. Everything. So we were, we had actually given the teams the free hand that they could use any experiment, they could do any experimentation, they could do any pixel conversions, but they could not really touch the checkout page at all. And if they had to touch the checkout page, the conversion could not drop. And they had to get special approvals for doing a checkout change or a checkout experiment. This all drove all of the units, all of the company’s LOBs into one cohesive unit. And they had this guardrail, they had this North Star metric that this one cannot be changed. This has to stay as it is. And that really drove everyone into that picture.
Experimentation as Strategic Tool
Niti Sharma: Yeah, awesome. Like everything else gets to contribute to it. And I mean, you’re allowed to make mistakes elsewhere, but this is something that you cannot touch. Yeah. I also read that you worked with the leadership there at Kava on a 2.5 billion ARR strategy. And on the other hand, we still see that a lot of folks still view experimentation as simply a conversion tactic. Right? I think it’s just very recently that people have started saying that let’s look at experimentation more broadly beyond conversions. So how do you advocate for this? For experimentation to be a broader growth enabler and something that merits attention at the leadership level.
Arshdeep Singh: Mm-hmm. So in the long term, what happens is, as I already told you, there is always a fatigue with the user of seeing the same product again and again and again and again. It does help in a brand recall. For example, you have probably, you would not want a change in a Parle, right? A Parle will always, you want a Parle-G in the same packing. Parle has actually three to four different varieties. But we will probably always remember the yellow wrapper Parle. Same as the thing with Maggi. Maggi is one of the most iconic brands and they have tried to experiment a lot. You see they have chicken Maggi, they have masala Maggi, they have soupy Maggi, they have Chinese Maggi and so and so forth. But only one Maggi is what is lasting. That is their strategic decision that they have done, but they have also experimented right, and that’s where they have actually found out that this is the one that lasts. Parle also found out that this is what actually lasts. So in both of these decisions, they have actually figured out if there was someone else who’s gonna introduce a new flavor and win over Maggi or Parle-G, why not it be us? Cannibalism is actually better in products rather than in humans. But that’s what all these businesses did, right? And hence, in my opinion, the experimentation should actually be used as a capital allocation decision tool. Where should the capital be allocated next? You have to find out if you have one, one of the products, which is doing awesome, which is actually industry defining, like Jiva was industry defining. Then we came up with Jiva Gold. And then we have two to three more products in line, Diapra, another one which is in line. These are all risk de-risking mechanisms. This experimentation, because of these different products we have been able to put out because if someone else is gonna give me a product or give my customers a product, which is better than Jiva, why not it be me? I can give you a better Jiva in the form of a Jiva Gold. Right? And that’s where all of this, Kava’s new growth engine is coming into picture now. So as a strategy validation engine for the new business lines is what the experimentation should be looked at. It is just not just the conversion uplift anymore.
Niti Sharma: Yeah. And would you say that this is exactly how you would talk about it with the leadership also, like I think that’s a roadblock that a lot of people face, right? That they’re not able to convince their leadership for investment into experimentation and CRO and investing even in a platform for it. Any tips for them?
Arshdeep Singh: See, there will always be people who would be blocking it and there will be people who would be blocking it just for blocking it. But if a person is actually, or if the leader is actually looking for growth and you put it as a growth tool instead of just a conversion tool, then only you would be able to make your case with them. If you are actually telling them that this tool is gonna help you in building probably a better UX that will probably not last. You have to have an impact, as we already talked about, the ICE-S factor. If you are able to sell that, and believe me, everyone is a seller. They may or may not agree to it. Everyone is a seller. So if you are able to sell this idea to them that this is actually a decision tool, this is actually a validation engine, this will actually drive your growth towards future, then only people will be convinced. So yeah, you have to convince them this is a growth tool, not really just a tactical, you know, brownie points tool.
VWO Impact
Niti Sharma: Yeah. Wow. Yeah, that makes a lot of sense. Yes. And from your experience of seeing VWO in action, being part of a team that used VWO, how does a platform like that help operationalize growth? And do you see any impact on team efficiency or customer experience through a platform like VWO?
Arshdeep Singh: So VWO helped a lot. In fact, VWO was the first platform that we used for experimentation and also the heat map. Yeah. So that was where we started with VWO. But as soon as we saw the VWO’s experimentation capability, we shifted to it. The VWO actually provides us a unified experimentation backlog across the team. So this brings everyone on the same platform. So the marketing, the ops, and the engineering teams as well and when needed, all of them are on the same platform in VWO and hence they can drive experimentations on their own. And the results are also visible to everyone. VWO has got connectors to emails and also to Slack, which has enabled us sending the reports also to the senior team, the leadership. This has automated all the test deployments and analytics, so no engineer was hurt in this whole setup. And that’s what it is. So the democratization of the experimentation was actually done via the VWO. With this, we were able to reduce the cycle time or the decision cycle time by around 30 to 40%. And because of this, we have been able to get our NPS scores also way above in the eight or nine scores now. Mm-hmm.
Niti Sharma: Thanks for sharing that. It feels great to hear these numbers. Awesome.
Building Experimentation Culture
Niti Sharma: And one more question, Arshdeep. So you’ve worked in multiple growth environments, right? Hyper growth. Why do you think some companies succeed in building a great experimentation culture? Are actually able to leverage experimentation to the optimum while others just struggle and they can’t get past, like, you know, those few isolated tests that they carry out, maybe couple in a quarter or a couple a year. Do you, would you have any suggestions for these and have you been able to identify any roadblocks that are faced by such teams?
Arshdeep Singh: Mm-hmm. It is mostly actually, in my opinion, it is mostly actually culture. So if you celebrate the learnings, not just the wins, so you have to figure out, if a person did two to three experiments and most of them failed, you have to be able to congratulate him on the learnings that he has gained, not only on the wins that he was supposed to get right. So if you start celebrating these learnings, then you allocate your budgets for testing, and not just for scaling, but also having the shared metrics, cross-functional pods, and probably same OKRs, same guardrails, you know, what we talked about, the North Star metric in the previous sections. If you have done that, then that is where you are creating the culture of a company which does a lot of experimentation and it is gonna be treated as a growth engine. The companies that actually fail most of these things are because they treat them as a marketing activity or they delegate it to one team. That becomes a siloed experiment. It does not really help the company in the strategy course. It might help technically to that team to meet their KPIs, and they will probably get a lot of brownie points, but overall, they will not really go up the pyramid. And hence, these experimentations do not really work in those companies. They actually are treated as a task, but it should actually be used as a growth engine or a governance model. So, yeah.
Niti Sharma: Right. Okay. So you do believe that it has to be more decentralized? It cannot be like very centralized to a single team, even if that team consists of folks from different teams.
Arshdeep Singh: I totally agree with that. They should be decentralized. Diversity is what drives human evolution, right? And this should also drive the experimentation’s evolution itself. But yeah, there have to be guardrails. We all live in civil societies. We all have our own guardrails. The experimentations should also have their own guardrails.
Personal Insights
Niti Sharma: Great. Wow. Some really cool insights there. So fantastic advice to those who are struggling with this. So, but I also wanted to ask you like, what is the best piece of advice you have received, you have ever received?
Arshdeep Singh: All right. I have not really thought about that now, but best advice. So during the COVID period, there was a doctor who used to say that, these were very bad times, the COVID times, right? And this doctor was already on his deathbed. But this doctor still tried to help his patients a lot, and he did help them a lot. And all that he did was, I tell that, the show must move on. This is from a Raju Hirani picture movie. I hope you have seen it. You’re much younger than me, so I’m not sure if you have, so that is one of the best advices that I got from that guy. And my father, my late father also used to say a similar thing, but in a different context. He used to remember Dev Anand’s song, which used to say something like, I used to celebrate my defeats more than my rises. So that’s what actually drives me a lot. Yeah.
Niti Sharma: Wow. Wow. Do you recollect the song by any chance?
Arshdeep Singh: Um, let me think. [sings in Hindi]. So that’s that was one of the best advices that I got. Yeah.
Niti Sharma: Yes, yes.
Arshdeep Singh: Yeah.
Niti Sharma: Yeah. Yeah. Wow. Yes. Awesome song and absolutely awesome advice.
Rapid Fire Round
Niti Sharma: And it brings us to the final segment after which I’ll let you go shortly, but I just have couple of rapid fire questions for you.
Arshdeep Singh: Sure.
Niti Sharma: Yes. Okay. Question one. So if you were starting a career in CRO today all over again, is there one thing that you would do differently?
Arshdeep Singh: Probably I would’ve started experimentation a lot earlier on from the day one or day two. Yeah.
Niti Sharma: One thing that your non-industry friends still don’t understand about your job.
Arshdeep Singh: I’m not sure. It is not all air conditioning that I’m sitting in. There’s a lot of hot water that I’m already in.
Niti Sharma: Yes. And is there one person that you would suggest every CRO professional must follow as in on social media or books they should follow?
Arshdeep Singh: I unfortunately forget the names of the people, but I do follow a lot. Um. I would say follow where your heart goes. Um, mm-hmm.
Niti Sharma: Okay. But any general books that you absolutely love to read. Book or books that you would like to recommend even unrelated to CRO?
Arshdeep Singh: So it is mostly psychological books that I would recommend people to read because it is, and especially they should read about human psychology before putting in their own thoughts or before assuming that anything would work for the people. They have to understand the human or the customer psychology. Yeah. So there is also Design of Things, a book which I am very passionate about. It actually brings out a lot on the daily observations. If you bring them into your daily routine life, you will learn a lot and you will be able to, if you embed them in your digital team, then it’ll work.
Niti Sharma: Yes. Thanks for sharing. Do you have a go-to travel destination, one that you love to visit all the time?
Arshdeep Singh: I don’t really travel a lot because of the traffic. I hate it, but I love to travel to a lot. Um, I was actually introduced to Tao by one NGO, my previous organizations. And they had this, you know, Insect 27 or 27 probably, and they had this place where all the birds would be there in the evening and they would all be chopping around and it would feel like heaven. So, yeah. And that’s the place I would like to be.
Niti Sharma: Wow. Yeah. Sounds beautiful. One thing that you think AI is going to take over in the next three years?
Arshdeep Singh: One thing that AI is gonna take over, you know, a lot of references will probably be not needed anymore, like books probably. A lot of books I won’t probably need to do a lot of research. So yeah, research analyst job will probably become very redundant in future. They would have to probably shift or learn a new skill.
Niti Sharma: Right. Yeah. AI primarily because takes care of all the research and analysis, right?
Arshdeep Singh: So.
Niti Sharma: If not into technology, technology related innovation and growth, anything else, your passion maybe.
Arshdeep Singh: I would probably be a researcher or a scholar at psychology or a philosophy, English medium school teacher. Maybe if I was paid highly. So maybe.
Niti Sharma: Wow. Yeah. Wow. That’s me also then I would’ve loved to be a school teacher, especially teaching English to students, children, actually. Yeah, I would’ve loved to do that. One CRO metric that you wish people would just stop obsessing over now.
Arshdeep Singh: Hmm. ROI, they should be obsessing over LTV rather than ROI. And even if LTV and ROI are one thing, they should actually be obsessing over brand recall over a longer period of time. That is what actually all that matters.
Niti Sharma: Hmm. Great. Is there some dream or some goal that you’ve set for yourself that you would wanna accomplish in the next three years?
Arshdeep Singh: In the next three years. Hmm. I am a married guy. I have people to feed. So not really, financially I can give you the goals, but not on the line. I would love to actually write a book someday, but I’m not sure if people will actually read the books or not anymore. So yeah, I maybe I can do an audible. Yeah, that would be better.
Closing
Niti Sharma: Awesome. Great. Thank you so much, Arshdeep. I wish you were able to do that. I wish you were able to achieve your dream and I see your book on Audible someday. I would love to read it. Thank you so much, Arshdeep. This has been a great conversation and lots of light bulbs for me. Thank you for taking us behind the scenes and sharing your journey. Any last words at all for our listeners? Anything you’d like to share?
Arshdeep Singh: In my opinion, the last one would be the culture is actually a company’s growth engine. And if you have the right culture and there’s no right culture, I cannot really tell you what is right culture, but you have to figure it out for your own company, but make experimentation a part of that culture, and that is what will actually drive your company to astounding growth that you probably have not even thought about. Yeah.
Niti Sharma: Yeah. Yeah, absolutely. Yes. We can’t emphasize enough on building the right kind of culture. Absolutely. Thank you once again, and thank you so much to all our listeners for tuning in. Please make sure to subscribe to the VWO podcast for more of such conversations. And until next time, keep building, keep testing and keep learning. Thanks for tuning in.
Arshdeep Singh: Thanks for inviting. Have a good day. Take care.
