On this episode Amit Somani in coversation with Arjun Sethi and Jake Ellowitz , partners at Tribe Capital

A Quantative Approach to Product Market Fit

Listen to the episode to learn about:

1:51 Arjun’s journey and formation of Tribe Capital

8:30 Arjun on quantifying product market fit

10:01 Jake’s thoughts on quantifying product market fit

11:01 Understanding growth levers

14:59 Cohorts and Simpson Paradox

16:39 Arjun on cohorts

21:12 Every customer segment tells a different story

24:36 Growth Accounting, CMGR and Quick Ratio

30:03: How do you know that you are delivering value?
 

Read the full transcript below

Amit Somani 1:22

Welcome to the Prime Venture Partners podcast. Today I have with me, two special guests, Arjun Sethi and Jake Ellowitz from Tribe Capital. We’ve had the pleasure of working with Arjun and Jake over many years first at Social Capital and now at Tribe. Welcome to the show guys.

Jake Ellowitz 1:37

Thanks for having us Amit.

Amit Somani 1:39

Alright, so we’re doing this for the first time with two guests. So I think you’ll get our voices in a few minutes here, but let’s get started with Arjun. Arjun, can you talk a little bit about both your journey and the purpose of Tribe and what you guys do there?

Arjun Sethi 1:51

Sure. My journey is pretty typical, Founder turned VC but the way in which it happened was maybe starting around 2006, 2007 timeframe. Came back to the valley and started building companies in the social gaming ecosystem in the mobile gaming ecosystem. First company was called Lolapps. What we had built was applications that were on Facebook and then applications eventually on mobile devices like iOS and Android. So it grew very quickly, grew from zero to a couple hundred million monthly active uniques.

I think at our peak there were close to 300 million monthly active uniques across all devices. And I think there was a point at which Facebook had said that there were about a million applications on their ecosystem, and about 999,000 of those applications were with us, Lolapps. So we grew very quickly. And what that really meant in order to stay successful and grow from applications to games to building out your products that had sustainability, was that we had to leverage our data our infrastructure our analytics our teams to build better product at a faster feedback loop and this was a time when people started using the term growth hacking, growth ninjas, all these sort of marketing terms where you would, again leverage data to build better products, but also leverage data to do better marketing.

Many people have called it going from Mad Men to Moneyball, that thought process. And that was the timeframe of when I was building my companies and managing a lot of these folks figuring out how product managers data scientists, data analysts, data engineering, all work together, we were part of the first customers to use AWS and some of the first customers that started building not just our own internal analytic systems, but using third party data and analysis that were at the top of Amazon.

So sort of fast forwarding my time from Lolapps, sold that company to a company called Nexon, which went public in Japan, but based in Korea when IPO around 10 billion, we ran their whole playbook around publishing and in the mobile gaming ecosystem or in the social gaming ecosystem, you are leveraging your data and your marketing it’s really important. The reason was that you would be spending millions and millions of dollars in marketing, if not on a monthly basis, in some cases for certain types of titles you are doing that on a daily basis. And I think, as we were growing up, it was kind of scary when you would spend $9 million a day in marketing.

So this became a DNA within the company. That’s why we were acquired and what we were well known for, at least within the Silicon Valley ecosystem for growth, leveraging data and leveraging your growth capabilities and your product capabilities. So what that really meant was that you’d have to have frameworks to think about how to build your products from scratch, frameworks on how to think about marketing your product from scratch, and understanding what the definition of growth was. And we’ll talk about that little bit more today. But that was sort of the beginning of my career.

And then I replicated a lot of that brand value in my angel investing over the course of last 10 to 15 years, by coming in and understanding what it takes to sort of think about growth and think about how to build out your teams and structure that and that was with a company called MessageMe, sold that to Yahoo. Ran the growth and data science team there. Basically leveraging the exact same blueprint that I created in my past companies. And then fast forward into Social Capital and Tribe.

A lot of the work that not just myself but the rest of my team, Jake here included Jonathan, who used to run the data science team at Facebook, all of us came from a collection of leveraging data to build better products, make better decisions, build out abstract layers of thought into something that could be useful towards either investment decision or managing a portfolio. So a lot of it was taking these pieces that we had used to build and operate human capital and products into investment and financial capital and how do we deploy that and if I fast forward the journey to what we do at Tribe today, a lot of what we talked about is the value exchange of us being a part of your cap table and having the Uber or the Slack or the Airbnb and Facebook data science team at the helm with you.

Amit Somani 5:58

Wonderful Arjun, very helpful. Jake, you wanna talk a little bit about what specifically you do at Tribe before we get into some of the data magic that we’re going to talk about on this podcast.

Jake Ellowitz 6:08

Yeah, totally. So at Tribe, I’m kind of split in a few ways. There’s the one side understanding businesses working with our portfolio and these types of things often aided through frameworks using data analysis, digging real deep into raw metrics and things like that. And on the other half is building out our capability, coming up with some more quantitative framework, some of the stuff we use in house and also building out a lot of our infrastructure. How do we make ourselves smarter over time? I think ultimately, the question I’m trying to answer is how do we leverage the ability to compound everything we’ve seen into every next thing we’d be and what does that look like in venture.

So I think those are the high levels where my mind is, and obviously leaning very heavily on my partners at Tribe having a great experience, a lot of insight in terms of how ventures around the world works. And we’re trying to kind of build that into a super venture brain. And so that’s one of the big things I’m excited about.

Arjun Sethi 7:11

Jake is a little modest here, but he’s essentially our resident CTO. I mean what’s important to note is the way we structure Tribe is more akin to a company. And we think about building systems and software and delivering capital, and aligning with our founders and our management team. And so what that means is that we build products, we run them the same way startup companies do with a scrum team stand ups and thinking about what type of artefacts and products that we can deliver beyond just capital to the investments and the companies that we’re representing.

Amit Somani 7:50

Great guys. So let’s dive right into it. Product market fit. It’s a term that’s been around for many years over a decade perhaps and it hasn’t really been quantified. People will often tell you, you’ll know it when you have it. Or Ben Horowitz had said in his book that when the phone’s ringing off the hook, then you know, you’ve got product market fit. But if you go to mature companies or later stage companies, even the public markets, there is the gap statement. So can you guys talk a little bit about what it is to quantify product market fit? And in particular, the eight ball framework or any of the other frameworks that you have, and how should one go about quantifying it, both from an investing perspective and a company building perspective?

Arjun Sethi 8:30

Yeah, absolutely. And I’ll let Jake dig into the pieces. But I think what’s really important to note for any company is, when you look at the qualitative examples of the investors that are out there in the ecosystem, saying, when you have x, you have product market fit. The issue and the challenge we give to that is what that really represents is gross demand that there is some sort of demand that exists in the ecosystem, your customer sets that want your product. Now that could be in the form of a APIs, that could be in the form of a product that you deliver that’s akin to a service, and a lot of what we wanted to be able to do, given that we had come from this background of building our products and quantifying product market fit, the reason you wanted to do a basic foundation of understanding what it means to quantify product market fit was to understand what the growth and demand signals could look like in the future.

And what do you think about the use of proceeds? How do you build a company for growth? and growth can happen in many different ways, but in order to understand how growth can happen in many different ways, and how do you augment that, you have to understand what your underlying foundation looks like and what that means. And so we have these family of concepts that we’ll go through in a second. And when we put all of those family concepts together, we call that the magic eight ball. That’s an artefact that we deliver back to the founders and the entrepreneurs and the management teams, or LPs, or other investors around the table board members who were able to hold an artefact that is a ground truth around the past and now the present.

Jake Ellowitz 10:01

Yeah, and just to add a little to that, fundamentally what we’re trying to understand is what are the levers of a business? If they were to change pricing, if they were to change channels or change marketing tactics, if they were to heavily subsidize something, or pump a bunch of capital into something, and how can that all change? So I think there’s one piece like, what are the levers of a business? How can we understand those? And then there’s the other stage of how can you understand it early? Because you’re saying there are lots of examples of after product market fit, you can really see it, but how do you see the signals before it’s screaming and obvious, I think is one of the tricky, nuanced parts.

Amit Somani 10:38

Jake, let’s drill down into it, there are three components that I could figure out from your website. One is just looking at growth accounting, and the other one is really understanding cohorts. I have lots of questions for you on that. And then distribution of product market fit. Can you elaborate a little bit about those obviously, we won’t have time to cover all the metrics, but you can give our listeners some sense of what these various things are. And some numbers around those.

Jake Ellowitz 11:01

So these are just different ways of understanding the way that the business is evolving and growing. Growth accounting, so I’ll just talk about them one at a time, then maybe we can get into more details. But growth accounting first seeks to just understand the top line is their growth, what does the growth look like? What does the acceleration look like, combined with decomposing the nature of the growth into different types of categories such as new versus churned versus resurrected?

And what that does, it gives you kind of one flavour of what is contributing to the growth. Just a quick example is one way you can have growth is lots of new users and lots of churn users. Another way is not a lot of new users, and very little churn and cut in revenue expansion maybe. So it’s not like one is better than the other. You might be the former and a B2C company. And now you’re asking different questions. There’s a latter which is a more typical B2B SaaS company. That’s just because you want one flavour of how the growth works. So cohorts I think are no secret.

Many people look into cohorts. This is not just us. But I’ll talk about how some of the ways we think about it, to us cohorts is probably one of the most important things to look at, in general, so many businesses, it’s not like it is the one to look at. But what it does tell you is the evolution of a customer, and how the evolution of the customer looks as the business evolves. For example, if you add tonnes of users, are they remaining engaged? If so, maybe the market is deep. One thing is, is there seasonality? And is that something that you should be very mindful of because you’re looking at the top or a bottom there. And then there’s all sorts of other things you might imagine that might show up there like COVID, that will show up on a cohort analysis. But then there’s also other things that you might be curious about in terms of leading indicators of acceleration. I think this is really one of the places where you might start to see it, where in particular cohorts are strengthening as there’s more value extraction or cohorts are strengthening as there’s more scale.

Those are really some of the holy grails in particular, like LTV, the total value, maybe you’d imagine over the life of a cohort. If that increases over time, and it’s so called super linear, which is pretty rare, sometimes it’s a good thing and sometimes it’s other things. But those are just some things to look at. We say, Aha, oh, this is a thing I should drill into that, I should understand what this lever is. And the final one is the distribution. There’s a few ways to think about distributions.

This one is mostly for us to look for breadth, what is the variety of users and how do they compare to each other. One case is, things that are concentrated, if things one or two customers account for most of your business. What does that mean? If you’re an enterprise company and you’re early on, what does that mean? If you’re not an enterprise company when you see that, so I think there’s lots of questions, that you can derive from each one of these, you can imagine you would layer these on over a business, look at the business model, we’ll look at kind of the evolution of what they’re doing, the new markets they’re entering, and then start to ask questions, looking at how the engagements unfolding. So that’s just like the high level of how we normally would engage with these types of metrics through this framework.

Amit Somani 14:25

Wonderful. Let me drill down a little bit into it, Jake. And let me pick particularly on the cohorts since I think they are quite misunderstood and for that matter, the distribution as you described it off the cohort itself, often people confuse it and look at it as the aggregate whole or perhaps averages around cohorts and so forth, that really don’t sort of break it up a little bit. And like the example that you just cited, or at least the notion of even in the distribution, think about customer concentration, think about the various segments inside the cohorts, not just everyone who came in the month of April 2020. Can you talk a little bit about that and then we’ll come to the growth bit in a second.

Jake Ellowitz 14:59

Yeah, for sure. Actually a good example of this is Shiprocket, which is a recent investment of ours. And the interesting thing is what you’re alluding to is something sort of a Simpson’s paradox that you might see in our business, without referencing statistics here, the sum is sometimes different than the parts. So one thing that could be meaningful is to break up and say, Okay, well, what are the different types of customers? In Shiprocket’s case, it might be customers shipping just a few shipments a month, and then customers shipping many, and then maybe customers shipping a tonne, like enterprises. And so you would look at how those different customers are engaging and actually each one tells you a pretty different story, in the case of Shiprocket, and actually each one’s evolving differently over time, the smaller user segment, you actually see that they’re actually not a big contributor right now, but they’re growing really rapidly.

And they’re engaging more over time. And so in a way that it was special, as compared to many of the other segments, and then you could start to ask questions about what’s happening there. And you wouldn’t actually see that type of engagement if you didn’t dig in and pry it apart. I think that you’re really right that the cohort analysis is not a cohort now. There’s lots of different cohort analysis that you can do. And they can all tell you different things. And it’s really important at this point. I think understanding the business, understanding the customers, understanding what’s happening in the market, will give you hints in terms of how to pry it apart. If this feedback loop of asking questions and going deeper with these different types of cuts that you can think about. And you can definitely veer off of this framework but but this is a good starting point

Arjun Sethi 16:39

Yeah, to echo Jake’s thoughts and his relation to Simpson’s paradox, but it basically really means, for more layman’s terms is instead of thinking about two topics, I think this was a really famous one here at UC Berkeley, where they just kind of looked at men versus women and were women or men biased against. So they came up with this conclusion, that women were biassed against. But then if you take a look at the segmentation and the cohorts for different groups within men and women and sort of look at them, similar to how we look at a company and say it’s not just revenue, it could be engagement, it could be usage. And then how do you overlook that all together?

And you can start seeing different aspects of what Jake mentioned, as different parts, how they all add up to sum and when, are there different types of directions that the company is going in? And so what he meant with a Shiprocket is that, there are people that send zero to 10 shipments a month, there are folks that send 10 to 100. And then there are folks that send 100,000 above and just when you start segmenting them, you can see that there’s different behaviour sets and understanding the businesses and what levers may or may not be pushed. And so when you kind of take that you take a step back and say okay, great, you’re starting to understand usage, engagement, revenue.

How’d they all behave together versus separately, then you can start thinking about what are the types of products that you build either those being feature sets, or being an overall proper superset of the product. And that’s a lot of what comes from just leveraging data to build a better product. And that’s where it comes from.

Amit Somani 18:17

Wonderful. I think Jake, you also mentioned earlier in the podcast, this notion of your packaging pricing, Arjun just said, your features and your product strategy, everything could ensue from that. So in practical terms, have you guys met and interacted with lots of entrepreneurs, you’ve done a lot of 8 balls in both Social Capital and Tribe. Have you seen folks kind of think about this consciously or entrepreneurs tend to be much more intuitive and they’re like, all growth is good growth. Let’s go. Or maybe perhaps they’re seeing at the higher level thing? Well, if MRR is growing, MAU is growing, engagement is growing. Let’s rock! We’ll figure it out later.

Arjun Sethi 18:53

Yeah, I mean, I think part of what you see from a lot of founders is as long as this is when I go back to growth and just gross demand. Like when people say that I have phones ringing off the hook, that’s gross demand. The problem with that is you don’t know how that demand is coming in, you don’t know which parts are strong, which ones are weak, which ones churning out or not, and that’s why we have all these concepts.

And so at a high level, I think what’s really important is that we probably skip is, when we do this work, the reason we’re able to do this work is we’re not asking a founder for like a deck in a data room and tell us like, what’s the number that’s going up into the right the demand metrics. We’re asking them for raw pile data similar to when you look at a financial ledger, and you get a raw pile of data, you’re starting to build out the family of concepts. Our quantitative approach to product market fit all of these frameworks come from us building a bottom up view of the company ourselves. And that artefact is called the 8 ball. And when you do that, then you’re able to start asking these questions in the first place with the founder. So now we’re able to see how your customers are interacting with the product at this segment or at this stage or at this region, etc. Obviously more of the better.

But we see that for a lot of companies that are early stage, even at the series seed and series A, where you’re seeing a lot of these leading indicators of what type of product that they have, what that really allows you to do is start having a conversation. Some of the founders might not be really attuned to their market, because they come from those markets, they kind of have an idea where they want to go. And so the data substantiates the direction they want to go and the roadmap that they want to build, but a lot of times what happens in the actual this is the case ship rocket. The first thing the investors had said was we need to move up to mid market and enterprise and then, super enterprise.

And then what you’d see from the data is that they were just so strong in light and small to medium shippers and mid market was starting to emerge that the largest concentration of their growth is coming from that segment, and that’s they’re the ones that are using it the most, and it’s counterintuitive for a company that is shipping products from very small to medium merchants to an end consumer And they could have completely changed their roadmap, they are focused on Midmarket and enterprise because that’s what the market was dictating

Amit Somani 21:06

Arjun I have a question for you and perhaps Jake as well, I think you guys said that every customer segment tells a different story. Now, oftentimes, as VCs, we tend to say focus on one thing like don’t do SMB and mid market and enterprise and blah, blah, how do you reconcile that? Even with the Shiprocket example because the lightweight shipper will have a different set of requirements and needs and an organisational cadence and a wavelength that is required to serve them as the large enterprise doing I don’t know, whatever, 10,000 orders a day.

Jake Ellowitz 21:35

Yeah, I would think that you can be focusing and still get different engagement from different customer segments. And when you think about maybe your product development, you might have something a feature, for example, and you roll it out to all your customers, and then you find engagement and who’s engaging and how are they all engaging? So you can actually see focus on the product side and emerging on the engagement side, all sorts of different things. And if you can understand that, then you can continually iterate and make your product better. But maybe there’s just a few different types of use cases. And it’s just worth understanding them.

Arjun Sethi 22:16

What I’d add to that is, we have three examples in our portfolio and past portfolio. So a company called Slack, Carta and Bolt, and all three of them, if you have to think about the lifecycle of product market fit and where they were at the time and Shiprocket falls, this kind of same trajectory, where they’re starting to create something special, they all started with a segment that was highly engaged growing very quickly, and they started deciding if they wanted to move up market or down market or do all of them at the same time.

Now, as you know, you can’t do everything at the same time, especially when you’re a team of five then moving to 10 and then doubling sort of, let’s say, if you’re growing healthily every three to six months. And so there isn’t actually a right or wrong answer here. It’s more about like, how do you prioritise your time and what you need to build in your product. But for those four examples, they all started off small to medium style businesses, in some cases, miniscule businesses to create a bottoms up growth pattern, and that’s how they grew.

And then some went in a direction that was full enterprise like an example like Carta, they went full enterprise and then they picked mid market instead of moving up stack to stack small and mid market and then enterprise. And then they build multiple products for all of them, two to three years later. And then for Bolt, same thing, what they actually did is they built small to medium, then they actually stopped building for small to medium, they moved up mid market, stopped building for mid market and then moved to enterprise, enterprise has been their core. And as they started dominating part of their enterprise market, they’ve slowly started coming back down to self serve small and medium and mid market where they haven’t really built a lot of products i’d say for two years.

And so when you go, example by example, there’s just a lot of nuances of what you need to do in order to grow at a certain pace, and what’s healthy growth versus just throwing dollars at the problem without any yield?

Amit Somani 22:18

I have a question for you on that. But before we do that, we didn’t talk too much about growth, accounting or growth itself. And there are two metrics that caught my attention, that I would love for you to explain to our listeners, one is CMGR. Often people will focus on month on month growth, and I’ll let you describe what CMGR is, and CMGR v6 and so forth. And the other one is this notion of quick ratio, which also, I think, probably somewhat inspired by the financial statements. Can you talk about what those metrics are? And maybe some examples of what they mean?

Jake Ellowitz 24:36

Yeah, and I think one more to add, just in the context of quick ratio is the net churn, and we’ll talk about it when we get there. But CMGR is the compound monthly growth rate. And what that is, is over some time horizon, you might say you’re measuring revenue, let’s say you’re measuring to use per views or whatever you’re measuring. It’s the way you would basically do it three months ago, how many did you get in a month? Take today. How many did you get in a month? And then say, grew at the same rate every month, how fast would it be growing every month? You’d look at different horizons.

For example, CMGR 3 is more as the three month horizon is pretty recent. It tells me what’s happening recently. But maybe there’s noise, maybe there’s seasonality, maybe who knows what is messing it up. So you might look at a longer horizon to you look at CMGR 12. That’s a good one that controls for annual seasonality that will then tell you the average growth rate every month, the last year. The reason why they’re good to look at in terms of the CMGR is because it actually gives you a way of comparing. Those numbers are going to be pretty close to each other versus if you said, how much did I grow. Let’s say I grew 50% in three months, and I grew 240% in 12 months, it’s kind of hard to know exactly what’s happening. And then one more thing is, you can sometimes see things like acceleration when you have sustained high CMGR 3 that’s compared to the CMGR 12. I guess you can even imagine others, but that’s on the compound monthly growth rate.

Amit Somani 26:09

I want to interject for a second here, growth is often a function of marketing dollars and access to capital. So what if I just said, Hey, I’m gonna go pitch Jake and Arjun at Tribe Capital, I’m gonna pump up marketing for the next three, four months. It’s a little bit facetious. So how do you figure that out? Or it could even be a genuine case where you’re running out of capital. And so things are going great. But now, CMGR is falling because cash in bank is not looking all that great.

Jake Ellowitz 26:34

Yeah, 100%, I think very tied to CMGR are the concepts of retention and marketing. And I think this actually comes now, it’s kind of more to the quick ratio. And I don’t actually want to talk about the quick ratio too much because the quick ratio is a very useful SAAS metric. The problem with a quick ratio when you have businesses that have a large month to month variation, it will actually artificially depress the quick ratio.

So the net churn will actually tell you, if you know the growth rate, for example, if say you’re observing 12% growth, 9% is from new users, from growth accounting, then you know that 3% of growth is from net expansion from negative net churn. So that is kind of one indicator of, Okay, well, are these customer’s net churn is low or negative, then if I’m pumping money into acquiring these users, well, maybe it’s a great thing, because over time, they’re gonna pay that.

But even more important is something that we didn’t talk about yet. But it’s the unit economics. So when I put dollars in, what is the lifecycle of that dollar? When do I get back? So it’s this concept of what does it cost to acquire what is the LTV over time, it could be sublinear, linear superlinear and so on you can model it. And then what’s the gross margin, and so those things together will tell you basically the lifecycle of your dollar, when you’re out of these kinds of things. single customer J curve. So these are all things that matter. And especially, we always look at acquisition, and it’s a thing you’re talking about is very important. How does acquisition run. And a great experiment running tonnes of acquisition would be awesome because then we could dig into it and understand what would happen. We pumped a bunch of money in, right?

Amit Somani 28:18

Understood. So are there any kind of good benchmarks that one could use? Because I’m two person startup or 5 person startup, we’ve got some of this data, I got inspired by this podcast. But now how do I know am I doing well or not? Any thoughts on that on any of these metrics, not just on the churn or whatever.

Jake Ellowitz 28:37

So I think you know that’s tricky. I think there isn’t a way of necessarily saying we can see the future we know it’s going well. And I think that even when you see good metrics, you always have to keep that in mind because especially when you’re thinking I’m going to grow 2,3,5 maybe 10x in a year. I’m going to be a totally different business when that happens. And sometimes the whole world might change by then it’s very tricky to say, this is going well or this is not going well. And you’ve always got to be really deep in it and really understand what’s happening. And all of these metrics will just give you proxies. And it’s really the higher level strategy, product execution, and hiring great team and all these other things that are running a business that are going to really matter, I think that you can get, you can kind of get a temperature it’s almost like going to the doctor and getting a tiny, maybe some opinion about maybe your throat sore because you would not drink enough water. But maybe it’s something else and you’ve always got to keep in mind.

Amit Somani 29:44

I think you guys had mentioned earlier and during the course of this show here that you can use this for way more than revenue, on engagement, on other metrics. So where does it stop? And where is it most effective? Clearly it is very good for revenue, clearly it’s probably good for retention and some of that, can you talk about the scope of what you can use this for?

Jake Ellowitz 30:04

Yeah, I think the answer is technically anything but things that matter things that are indicative of product engagement and in value, how do you know that you’re delivering value? So there are cases where there aren’t revenue consumers, maybe a free consumer platform, a social media platform or something, you’d probably measure things like number of days active in a month that you probably measure things like number of posts or number of friends or things like that, what is the value you’re delivering?

That’s really what you want to measure. And I think even better than revenue, and when you have a meaningful engagement metric, if you’re talking about payments business, for example, how much total payment volume are you processing, if you’re going to understand the leading indicators of revenue, which are typically engagement metrics, then you can start to understand if there’s deeper product market fit and staying price, then you can start to see that a product might actually be in the position to price higher, or go into segments that are a little less lucrative than the early adopter. I think that those are really the things you should be looking for. What are the value indicators? And which ones are potentially leading indicators of revenue? And that’s really what you want to measure.

Arjun Sethi 31:21

It’s also leading indicators of churn. And that’s why we think about engagement. Because engagement can be defined in many different ways. If you’re an API business or transactional business, that’s just, it’s a request from the customers. Those are not SAAS businesses. So you have to look at it very differently. Do you care if they come back every month, you may not. You want to make sure that there’s a certain amount of frequency from your total base and how they use it.

And so defining engagement is really important because that’s a part of demand and that is going to be your leading indicator of where you think you may be able to price charge, or understand what your revenue metrics are going to look like. And I think that’s really important, a lot of companies that we’ve seen, they’ll have some amazing revenue metrics, they’ll come back and say, Hey, you know, we’re at like 4 million in revenue, or annual recurring revenue, and then we’ll take a look at it.

And then we’ll use the same family of concepts to understand engagement and no one’s using the product. And so you can basically underwrite risk by understanding and saying, hey, do I think people are gonna use this product and software more or not? And if the answer is, you don’t think they will, then you can, you know, pretty damn well sure that they’re going to churn out and that 4 million will start looking like a depressed number 2-3 years later, depending on the contract. And I think that’s really important. And so where this really comes into play for us is anything that’s software enabled or software engaged, where we can measure and understand what engagement looks like or customers and consumers, folks in the middle that are part of the transaction, and any business that is communicating with software today.

Amit Somani 33:02

Wonderful, guys. So from MadMen to Moneyball, that’s the big takeaway. Lots of insights for our listeners here, we will put the links to the Tribe capital site, the eight ball metrics and any other sources. Thanks again to Jake and Arjun, for being on the show.

Jake Ellowitz 33:18

Thank you very much.

Arjun Sethi 33:19

Thanks for having us, Amit.

 

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