Lomit is the Vice President of Growth at IMVU, which is the world's largest avatar-based social network. Prior to that, he had successful stints managing growth at early stage startups Roku, TrustedID and Texture, all of which either went public or were acquired. Lomit joins us in a fascinating conversation about startups and AI, and we even take a look at how the basic tenets of AI can be applied so that we can enhance our own lives.
Lomit is the Vice President of Growth at IMVU, which is the world's largest avatar-based social network. Prior to that, he had successful stints managing growth at early stage startups Roku, TrustedID and Texture, all of which either went public or were acquired. Lomit joins us in a fascinating conversation about startups and AI, and we even take a look at how the basic tenets of AI can be applied so that we can enhance our own lives.
Description
Lomit is the Vice President of Growth at IMVU, which is the world's largest avatar-based social network. Prior to that, he had successful stints managing growth at early stage startups like Roku, TrustedID and Texture, all of which either went public or were acquired. Lomit joins us in a fascinating conversation about startups and AI, and we even take a look at how the basic tenets of AI can be applied so that we can enhance our own lives.
Transcript
Erol Senel:
Hello everyone and welcome to Success Shorts. I'm Erol Senel. Today we're joined by Lomit Patel who is the Vice President of Growth at IMVU, which is the world's largest avatar-based social network. But before that, he had successful stints managing growth at early stage startups like Roku, TrustedID and Texture, all of which either went public or were acquired by some of the biggest companies that you're all familiar with. Lomit is also the author of Lean AI, which is a book that's focused on how AI can help amplify the quick impact of startups. Lomit and I have a pretty fascinating conversation about startups and AI, and we even take a look at how the basic tenets of AI can be applied so that we can enhance our own lives. So I hope you enjoy our time with Lomit Patel. Let's go.
Erol Senel:
Lomit, it's great to meet you.
Lomit Patel:
Great to meet you too, Erol. Excited to be here.
Erol Senel:
Likewise. I had a chance to look at your background and with everything that you've been involved with and what you're doing now. And I'm just so excited because you have a heck of a track record in this really unique unstable world of startups. I think that's really relatable right now because we're in a bit of an unstable world right now. So to kick things off, I'm curious, what have you learned about yourself during this COVID period, and how do you think we could personally be using some of this startup mentality to ride this really volatile wave a little bit more effectively?
Lomit Patel:
That's a really good question. Having worked in startups for over 20 years, one thing that I've come to realize is to always expect the unexpected. Every day is usually never the same because there's so many different challenges that can come against a startup. I would say the trait that has really helped me personally do well at startups and that I'm able to apply now, especially during COVID-19, is always just expect the unexpected and keep an open mind. Instead of trying to be rigid and close-minded, try to figure out what's going on and then try to course-correct based on what's happening day-to-day, which is very similar to what startups have to do, because there's always different things that could end up turning into issues and problems and challenges that you don't really anticipate, and the best way to do that instead of trying to react, it's good to always take a step back and take a look at a situation and then try to figure out what's the best course of action to take in that given situation.
Erol Senel:
It's almost like having a stoic mindset where you're able to stay balanced and assess situations and try to figure out what can be controlled, what are the things that you just have to roll with and just making sure that you're constantly in that state. And I think that whole idea of keeping an open mind and expecting the unexpected complements very well with that. I think that's definitely something that we can all start to think about how we do that a little bit more.
Erol Senel:
So when you think about your experience working in this really successful startup space that you've been able to navigate for some years now, what would you say are the important nuances to understand that really differentiates startups versus traditional companies that are a little bit further down the road?
Lomit Patel:
One of the big things that differentiates the way startups approach the way they run businesses really comes down to trying to be focused on who their customers are trying to be disciplined about making sure that they're just allocating whatever time and resources that they have into solving that problem for that customer, and data plays a key role to really be able to provide insights into whether the product or service that they have is really meeting the goals that they're looking to hit based on the types of customers that they're trying to acquire to ensure that product is really providing enough value so that it has a potential to become a bigger business.
Lomit Patel:
And then the other thing to keep a big eye on is how much money they have in the bank, because most startups generally have to be really constraint with how much money they spend on hiring people and operations of the business and on the marketing and all the other expenses. Trying to be as disciplined as possible to ensure that they're spending the money as cost-effectively as possible is really important versus a company that's more established can generally get away with maybe spending money that might not be as well intentioned, but they still have money in the bank, so they can get away with spending that money and still have more to go back to. Well, with startups, for the most part, it's you got to be really, really mindful of how you are spending that.
Erol Senel:
From what I understand, it's almost like approaching things from an upside down funnel where you're initially addressing the singular thing that you're good at and trying to do it in the best way possible to make the foundation for everything that can then spread off of that. As you were speaking about that, I was thinking of Amazon where Amazon started as a book reseller, if I remember correctly. I think that was back when I was in college or something. And then from there, because they did so well with that and they built their infrastructure around that, they were able to expand out slowly but surely and then figure out how they can scale so that they could participate in other areas. Am I getting that right?
Lomit Patel:
Yes.
Erol Senel:
You already shared the most important trait to you throughout your career is to really go at things with an open mind. You've really had a great track record with Roku and TrustedID. And now you're at IMVU, which we were talking about that before we jumped on the call today, and it's like, "Wow, what a really unique world that's being created there, especially given the whole COVID thing." So I'm curious, how have you been taking that approach that you cultivated early on? How has that helped you to help them flourish during these earlier years?
Lomit Patel:
Curiosity is really important, especially in startups. The challenge with growing any business is not just having a great product or service, but it's to try and find the right customers that will find enough value in that product or service that you have and to be predictable around how you can continue to build a growth engine to acquire those customers as well as retaining those customers and being able to monetize those customers so that you can build a predictable revenue stream around supporting the potential future growth of that business.
Lomit Patel:
And so the open-mindedness there really comes from taking a systematic approach to build around increasing your velocity of learning. Most startups, if you look at the ones that have become really successful, even the ones that I've been part of, as you had mentioned, some like Roku and TrustedID, but even if you look back at companies like Amazon and Netflix and a lot of these other companies, all of them fundamentally are all about taking the approach of test, learn and iterate.
Lomit Patel:
So it's all about taking this hypothesis approach where they keep an open mind about wanting to try and test as many things as possible to really figure out what's going to work and what's not going to work not only to make the product better, but also to get better at the types of customers that they should be acquiring. What are the right types of marketing strategies they should be using to acquire those customers? What are the flows that they should be doing within the product that continues to build predictable habits that will get customers to not only spend time, but also their money?
Lomit Patel:
A lot of that really comes down to just running as many different experiments across the entire user journey from where your current customers [inaudible 00:07:39] within the product, on how you're retaining those customers, how you're monetizing those customers based on whether you want to monetize them through making purchases or buying subscriptions or through advertising revenue or a combination. But all of it really comes down to just trying to run as many experiments and to quickly figure out what works and what doesn't. And the only way to really do that is to try and increase the velocity of how many experiments you can run at any given time.
Lomit Patel:
So all of these companies, on average, are probably running tens of thousands of different iterations around wearables that they're testing. And that's what really enables them to get better, faster and smarter at trying to figure out how to drive this high velocity, predictable growth in these startups. And at my current company, for anybody who doesn't know, IMVU is the world's largest avatar-based Social networking app. One of the things that's really helped us drive huge growth in the last four years, one was really just moving from desktop into mobile because we have a lot of millennials and Gen Zs that love our product, so it's easier to target those users because they're all on mobile devices.
Lomit Patel:
But the other thing that we've been able to do is that we get a lot of data around customers. By leveraging AI and automation, it's enabled us to start running a lot of experiments. So previously we'd probably run maybe a couple of hundred experiments, but now we're running tens of thousands of experiments every month across all of these different areas of the user journey. And that's really enabled us to really learn quickly on what works and what doesn't work and continue to keep finding those small wins that have compounded over time that have really turned us into a huge business.
Erol Senel:
That's really interesting. So, I mean, without AI, you could never run that sheer volume of the different tests that you're doing. It would be, I think, literally impossible without machine learning.
Lomit Patel:
Yeah. That's one of the big benefits of companies to really leverage AI, but processing data and then layer in automation to take in actions under predictions that the data is telling you, that's the secret sauce, because then you're actually building an algorithm that is personalized to help your business grow. And companies like Amazon have mastered this because anybody who uses Amazon knows that Amazon does a really great job on giving you recommendations on products and personalizing that experience and giving you options around cross-sell and upsell and just how they target you and continue to provide value by adding more and more services into their prime subscription. Because ultimately they know the more offerings that they put in there, the more that someone's going to stick around, and that continues to increase the overall lifetime value of a customer.
Lomit Patel:
And we've taken the same approach here at IMVU. We obviously have a lot of data, and people don't realize there's so much data that you can get around users nowadays just because most of the time people interact with companies. If they're all leaving digital footprints on what they're doing, that's the way companies are getting all of this data. But for the most part, without AI and automation, they're not really able to use that data to the full capacity to truly personalize the experiences and to build these unique algorithms to really be able to get smarter around targeting the right customers with the right message at the right time and giving users the right experience based on what they're doing so that it continues to build more and more relevancy. And ultimately, it gets users integrated into loving the product, trusting the brand and becoming a big fan or an evangelist for the product that naturally leads to more word-of-mouth and referrals.
Erol Senel:
You led onto something there as far as the customers becoming more integrated into it. And I'm sure, like many of us, you've seen the social dilemma and there is a certain part of this that is a little bit scary, especially if we don't necessarily know ourselves. So I'm curious, what would be some things that we need to be aware of, be it how we deal with Amazon, how we deal with traditional social media? Is that a thing? But now we have this avatar-based social media. What are some things that we should be aware of? Because sometimes the computers know us better than we know ourselves.
Lomit Patel:
What I would say that everybody should be aware of is that ultimately all of these companies are gathering data on you based on any interaction that you're doing, whether it's through purchases or through time that you spend on the site or in an app, but there's good and bad sides to how companies use that data. I would say for the most part, people that use services like Amazon love Amazon primarily because they're using that data to really be able to provide great user experiences. Because ultimately if you're looking for something and it takes you a lot longer to find it, that's a frustrating experience.
Lomit Patel:
So what Amazon has tried to do, I'm just using them as an example, is try to predict out your intent on what you might be looking for based on what you've done previously or what you might be browsing and try to get you closer to an answer that you might be looking for. Most people love that because it saves them time, and that's the same analogy. If you take companies like IMVU or other social networks or gaming apps, the idea is to really try to provide a great user experience to delight the customer, not to use the data to sell it. Having said that, there are companies that do that. You should be mindful and try to find out how companies are using your data because now they have to have more transparency on sharing with you. There's a lot of regulations that have been put into place to at least allow customers to have that kind of transparency.
Lomit Patel:
But the other thing to keep in mind is that there's nothing for free in this world. What I mean is most people that use Google as a search engine it's free, but it's not really free because Google still has billions of costs to support all of that infrastructure behind the scenes to make sure that their search engine is providing you the right things that you're searching for. There's obviously costs involved to all of these different gadgets and apps and products that people love. These companies have to monetize you in some way, shape or form. And the ones that offer it for free generally monetize you by selling advertising, but they don't sell advertise by selling your data to these companies. What they try to do is look at what these advertisers and types of customers that they're trying to target, and then they try to provide a better prediction.
Lomit Patel:
But if you go back to the days when you were a student, and you mentioned when Amazon got started, do you remember all those annoying ads that you used to get? Because they weren't really relevant. There was a lot of spray and pray. So that's the thing that we've moved away from because companies are now able to use data to better predict that with AI. Who's the audience that's going to be most receptive to that product or service? I feel that it's a good use of data because it helps the consumer and it helps the company to be able to provide a product or service for free because it's able to compensate that through advertising.
Erol Senel:
That makes sense. I think part of the responsibility does have to lay with us as consumers that we have to understand ourselves. One of the things they almost presented as a pejorative, they're trying to say they could put the thing in front of you and magically they get you to buy it. However, I think, as consumers, we need to be aware that that is how they are using our information so that although, yes, they're putting something in front of us that is very highly linked to us because they know us very well, it's on us to decide whether or not we actually need to go forward with what is being presented in any form of technology, so be it something that you would purchase through Amazon or something that you would look at through Instagram, you name it.
Erol Senel:
So I think it is good to be aware of the fact that they are doing it. I mean, these are massive companies. They're not non-profits, and they're offering real value. So they're doing it in a far more transparent way at this point, and it's on us to decide is this something we want to let in at this moment? I think that's something that gets lost in the shuffle that we have to take responsibility, and we can't just always blame the use of data and the evil AI that's being run in the background that's trying to figure us out. Part of it does lay with us.
Lomit Patel:
And I completely agree with you. I think the key message there is that all of us are active participants in this. It's not that anything is happening against our will to a certain extent. If you want to find out how these companies are using your data, they have areas where you can go and you can actually see a history on how your data is being collected and how it's been used. But for the most part, it will ultimately come down to they're using your data to try and better predict out giving you a better user experience as well as if they monetize it for advertising, then it's to try and get you more relevant ads versus trying to give you an experience where you're seeing things that are just completely irrelevant.
Lomit Patel:
And then the other thing to keep in mind is there's always a trade-off. I'm just using advertising as an example again, but if you don't want ads, for the most part what's going to happen is these companies will have to figure out some way to monetize you, so then it's going to come down to are you willing to pay for the product through a subscription? Netflix charges a subscription rate while, I'm using Hulu as an example, has a lower subscription because they subsidize that for advertising. So ultimately, there's a trade-off.
Lomit Patel:
And then the other thing that people don't realize is the reason why these products continue to get better over time is because they keep taking all of this user data and it enables them to enhance the product experience and to continue to add new features and functionality to make it better. That's what tech companies are doing at the end of the day. They want to continue to provide a better product and a better experience to differentiate themselves from a competitor. If you take away some of the means of how they can fund that innovation, then the product's going to continue to become more stale and more dated and the experience is going to become worse for the user.
Erol Senel:
Interesting. Okay. So I appreciate you sharing all this, and I know we've gone in a few different directions, but not everyone out there who's listening is really going to have the chance to work at a startup, or I don't think they're really going to find themselves focused on building brands and trying to figure out the connection between that and the AI that's driving things forward. But I still think that we all live very dynamic lives, and we are able to be malleable to the right changes and the right inputs. So I'm curious, what are some of the takeaways from your own work in this dynamic space that you think can be applied to everyday life so that we can start to think a little bit more like a startup or with a little bit of an AI mentality going forward?
Lomit Patel:
Really good question. The key thing to remember is artificial intelligence was created by human intelligence because artificial intelligence ultimately is how to get a machine to think and act like a human. When you look at the components that go into building artificial intelligence, simplistically there's a model of inputs of what data can go into the machine. And then on the other side it's a series of outputs where you put in goals and objectives on what you're trying to get out of that data, and what sits in the middle is through either using machine learning, whether it's structured or unstructured learning, is to look at that data and try to come up with predictions on how to get that data to match up to different goals that you're looking to get out.
Lomit Patel:
And the same thing goes with us as humans because ultimately it's always important to have goals in terms of where you want to go in your life, and what machines are basically doing they're reverse engineering what are the goals you're trying to get to, and what are the behaviors and actions that need to take place to get to those? And the same thing happens with humans too. For example, if you want to save, let's say, a million dollars in the next 10 years, ideally what you want to do is try to study other people that have actually achieved that and look at what were the things that they ended up doing over the past 10 years that really led them to really get into that goal and try to replicate a similar journey and take the same tasks and processes in your life. And you have a really high degree of confidence that if you do something similar to what most of those people ended up doing, then you will get pretty close to getting to that $1 million. And that's the way machines think, ultimately, because they're trying to put things together to try and predict out how to get to that output.
Erol Senel:
Yeah. So it's really just taking that mentality, which is fascinating to deconstruct it or reverse engineer it in that way. Taking that same approach in our own lives. And I think people set out to do this. That's why we read books and we read biographies about successful people is to see what they're doing and then see how can we reverse engineer that to put in some of that into our day-to-day to try to enhance what we're doing on our own journey. And then AI is just able to do that faster and thousands of times at the same time, but we're all capable of doing that one stream in our own lives. And I think that that's something to encourage us to be aware of that it's going on in the background, but also how can we do the same type of thing that very efficient machine learning is doing in its own right to enhance ourselves.
Erol Senel:
So Lomit, this has been really fascinating and I appreciate you taking the time to go through this. And I feel like I learned a lot during this, so thank you very much.
Lomit Patel:
Thanks for being here. Thanks for having me, Erol.
Erol Senel:
Oh, most definitely. And that's all we have for this episode of Success Shorts. Hopefully, you found today's topic useful, and remember, have fun, stay curious and keep it short.