Understanding Your True LTV and Company Value | Prof. Daniel McCarthy

31:15Daniel McCarthy:Quick one before we get back into the episode. Every business spends money to make money. Ads, software, flights, travel, the hundred unavoidable little things to keep the lights on. All that spending every month and none of it comes back to you. It just goes out the door. Incard is a financial platform built for digital businesses and it flips that on its head. They're giving 2 % cash back for your first year on ads, SaaS, travel and everyday business spend. And it's all uncapped. The same money you were spending anyway, except now a slice of it runs back in your account. You also get multi-currency accounts, corporate cards and Shopify, Meta, Stripe and Xero connected in one place.

31:55Daniel McCarthy:A true source of truth. But honestly, you're spending regardless, so you may as well earn on it. Sign up for free. Use the code SWU2K26. Link's in the description. Now back to the episode. I wanted to just quickly touch on, I guess, subscription specifically, subscription businesses and churn and your approach to measurement of churn because I know that's something that you also see as often being flawed in people's approaches, I feel like everybody we come across is trying to launch a supplement subscription business in consumer at the moment. So it's definitely a question of when will subscription fatigue really hit?

32:39I feel like they're about to roll out a law in the UK around making cancellation far easier and more mandatory. So we're interested to see how that plays out into the US as well. But how are you approaching measuring true churn, whether it's subscription or not? then where do you see people getting that wrong as well? Yeah, I mean, that's kind of alluded to the biggest distinction, which is subscription versus non-subscription. Non-subscription customers still churn, but you can't see it. They don't tear up a contract or something like that. And so it's actually ironically at Theta, so subscription, it can be more like shooting fish in a barrel.

33:23It's just easier to do. You can specify a richer model for churn. But, you know, we'll often find that we add more value for non-subscription firms because it's just trickier, you know, because you need to kind of deal with this non-observability. You will say that you kind of need to sort out how often customers repeat and whether they're churning from the same purchase data. Whereas for a subscription firm, you can sort out like retention just off of the retention data. And then you can sort out the, you know, how much they tend to buy, how well they monetize while they're alive from, you know, what people do while they're still with you.

34:07And so it just becomes a much more separable, easy to model process. So usually, you know, if you're within one of these firms, there's a whole bunch of best practices. Subscription firms got to model by cohort. You got to separate out, you know, the seasonal cohorts versus, you know, the cohorts that are happening when you're not in a heavy promotional period. you really want to do the modeling again at the individual level and there it really matters because you'll have a lot of customers that drop out early and then you'll have some that stay for a very long time and so if you assume that they all kind of share the same retention rate you're going to horribly underestimate the value of the cohort so so that's incredibly important to do But, you know, I would say to their credit, if you're at a reasonably large subscription firm, typically they'll have at least a decent, half decent model for customer retention.

35:11You get to the smaller firms and they often just don't. So, you know, you really want to make sure you're kind of doing that right. Now, non-subscription, it's like the wild, wild west. You know, we'll work with like the biggest companies in the world. And it's amazing. Some of them, it's like they have nothing. So, yeah, so there it really becomes helpful to have basically what we would call a latent attrition model. It's just a model that allows for repeat purchase. And at some point the customer goes kaput, but you have to infer it from the data. You know, it's just not something that's observable.

35:48And thankfully, there are some time tested models to do that. they do better than you know than models that don't allow for that but at the same time you know we found again over the course of the engagements that we've done that there are a handful of enhancements that you really need to have to be able to to do the modeling well in that setting so yes we've got this model that we call clv ultra and and that's what we think to be like the very best model for both kind of repeat purchase and retention modeling, uh, in, in a, in both a non-subscription setting and in hybrid settings. Cause the, I'd say that's the other setting that often has been getting more and more popular these days is it's not quite full subscription, but it's not full non-subscription either.

36:37You know, they kind of, it's some mix of the two. Um, and this model tends to be really good for those kinds of mixed subscription, non-subscription type settings. What's some of the complexity? Just speak to it at a higher level. What brings so much complexity to modeling that for non-subscription businesses? Why is it such a gap that you see? Is it just difficult to generate the data or general lack of understanding of how to build and run that model? What would you say are the common issues or bottlenecks? uh yeah the first issue is you can't observe churn and so churn's happening but uh but we need to infer it and so that's kind of level one um level two is there are a lot of these kind of systematic dynamics that just we tend to see over and over and over again there's a kind of dynamics as a function of the customer life cycle so oftentimes what we find is that there's like a this honeymoon phase, you acquire a customer and over the first, it could be between the first month or the first, you know, three months that they tend to, um, to purchase more frequently than you would think.

37:57And then they kind of settle into this baseline. And, um, you know, we just find that time and time again, before we actually had a model that didn't allow for that. And, uh, then we started doing work for one of the largest gaming companies, and it just did not work. And you can kind of see, because I'd say the one thing that you always want to do is be empirical about your data. And you may have your beliefs about how customers behave, but the way you can test it is just hold out the last six months of your data, train your model on everything else, predict the last six months, and then see how good your predictions were along a handful of different dimensions.

38:36And that will tell you, is my model good or not? And we just found there were systematic issues when we did not allow for a honeymoon period. So, yeah, so you want the data to tell you the length of that period. You want basically you want to account for all those those dynamics that happen as a function of the life cycle. The third thing is calendar time effects. And there's really kind of two of them. There's the ones that are seasonal. People buy more Christmas time. they buy less during some other time of the year, spring season. You want the data to tell you that. And that will kind of hit all of the cohorts within those certain calendar periods and they will recur year after year.

39:21But then the other calendar time effect is what we call the non-seasonal calendar time effect. And that's the stuff that kind of hits the one time, but does not tend to recur year after year and so probably biggest example of that covid hopefully hit one time hopefully it'll come back yeah but boy it hit all those cohorts you know so yeah so you really you need to account for all that and you know there's kind of that that baseline point that i made before that all your customers are really different so you want to allow for those differences to exist too so it's kind of like there's just a lot of stuff there that you have very complex yeah you got to deal with all of it uh or your predictions will be bad makes sense that honeymoon phase is definitely see that it always blows my mind how many people like will repeat on month zero from like when looking at brand's data especially somewhere they're up into like the double digits percentage wise for for repeat rate in the first month of after acquisition even if they're selling loads of products where they just can't physically need more.

40:31So you can see that kind of abnormal behavior in some of that data. Yeah, exactly. Yeah, and to your point, so let's say that in the first month people buy a lot more. Then what ends up happening if you're doing a cohort model and you don't allow for a honeymoon is you see you got these customers that were born two months ago and their first month was amazing. And you're like, wow, these cohorts are just getting better and better over time. And it's like, actually, no, they're not getting better. They're staying the same. They're just in the honeymoon phase and they're going to, you know, they're going to kind of settle down to baseline just like all the other cohorts did too.

41:06So, yeah, that's just kind of model misspecification. Yeah, it makes sense. We've talked through a lot of financial metrics. A lot of this language may be new for some of the listeners, may not for those that are more senior stakeholders or in those rooms more frequently. I know you speak to that disconnect between like marketing and finance language. If we're looking at, if we're giving, for those listening today, like what do you believe that if they're in a marketing role and they're wanting to improve their like rigor of reporting and their ability to drive tactics off the back of some of these metrics?

41:52What do you think they should be looking at from a dashboard perspective every week? What are some of those metrics they should be reporting into maybe the next board or leadership meeting to really tell that growth story through the lens of customer metrics rather than aggregate top-level metrics? Yeah, I think it's a very helpful exercise to ask, what would the CFO care about? And would the CFO agree with this? And so the discount rate thing, use the weighted average cost of capital. That's what the CFO does. But I can't count how many times I'll just see undiscounted figures. And immediately you lose credibility with the CFO because they're like, well, my investors are demanding a certain rate of return of my firm and you're not accounting for that, you know, in your equation.

42:49So, yes, that'd be one. But, yeah, I think that the perceptual metrics, CFOs, they struggle with perceptual metrics like net promoter score or the open rate on the emails. Or, you know, we had this much engagement with our last social media post. It's like, okay, it's nice, but like, where's the money? You know, I want to see the money. And if it's not trans, if you can't translate it credibly to revenue, then it's like the CFO can't get all that excited about it. So I think it's, when it's done right, everyone feels a little bit uncomfortable. that the marketer needs to kind of like elevate up to kind of cohort monetization and cohort, you know, cohort costs and cohort revenue.

43:41And, you know, the CFO, they're not going to be quite used to that, but at least we're saying this is how much we spent on customer acquisition. This is how our cohorts are monetizing. This is how much revenue we got off these cohorts. And that suddenly it's something that, you know, they think in that, in those terms and, you know, net promoter score, it could be very helpful if you can then establish like a data driven relationship between NPS and monetization. And then it's like, okay, now I got it. You know, you've got the low NPS segment, the high NPS segment, and they monetize like this.

44:19I can get behind that. And oftentimes, you know, if you look to, you know, I've spent a ton of time. If you go to my LinkedIn, I'm always talking about kind of company disclosure. And we'll have all these examples of public companies that are reporting on this stuff. And the CFO will have in the investor day presentation, you know, cohort curves. and I love it every time I see it. But the fact that they're disclosing that and like communicating that to external stakeholders is such a good sign. I mean, it's like an invitation to the marketing department to be a part of the conversation because they're really the ones who can own that and kind of like manage that better than most of the other people in the C-suite could.

45:03So stick to that, you know? That's like, that's where you're gonna get their attention. and you know the other thing i would say about the getting the cfo on board why that can be valuable again they're the ones that control the purse strings you know so if you can kind of get them excited and seeing that you're a demand generator and not a cost center you're going to be more likely to get budget you know i think you can make a more credible case to the cfo that you know some investment that you want to make is actually going to to pay off over the long term I really like that approach as well, like taking some of the, I really like the idea of that approach.

45:41And the NPS was super interesting. Maybe quite easy to connect together as well with connecting NPS to customer profile and therefore LTV versus some of the more like front-end metrics. So probably one that some of the listeners can maybe take away. One follow-up question to that is. obviously it's somewhat easy to articulate how like I guess more direct response like meta some of these lower funnel channels I guess if you want to use that term translate into customer metrics compared to maybe some of those like bigger swing campaigns those things that are harder to predict the value of. Say we're testing, say marketing teams wanting to go to explore out of home, they've never done it before.

46:38Is there a way that you'd recommend people handling some of those conversations or thinking about or lens to think about that through? For my own benefit as well, it's just, it would be interesting to hear your thoughts on that. Yeah, it becomes tougher. I have a whole lecture on customer acquisition costs where we go into this in my class. And And it's really, you know, I'd say it's tougher because the trackability is often not as good. Yeah. And the potential effect might happen over a longer span of time. So if you're right at the bottom of the funnel, you either click on the ad and buy or you don't.

47:17You know, like oftentimes that's just a much quicker cycle. And the tracking can be really good because, you know, you can track the clicks, you know, so the attribution is better. But if you have out of home, you got people going by the billboard, you know, they look up and they see it. And, oh, yeah, that's an interesting, I might, I might check them out some point down the road. But, you know, the company won't know, like you, Ali, saw that billboard. You know, they just won't be able to attribute exposure in the same way. And if it's more upper funnel, which, you know, like a digital brand building campaign would also be, then the effect might take longer.

47:57And that kind of degrades your ability to do attribution. So those make it trickier. I'm not sure there's like a silver bullet answer to it, but I'd say, you know, one thing that can be potentially better than nothing is some sort of model that explains variation in purchasing or conversions as a function of your spending on out of home. you know so something more like a marketing mix model yeah like a good one uh that's properly calibrated and saturated you know so that way you can say i've got these other things i'm doing i've got this thing too i can allow for you know carryover effects that might spend today might affect you know this outcome measure of interest x number of months from now and let me let the day to tell me the strength of that relationship.

48:51You know, that oftentimes might be the best that one can do. Makes sense. I guess that's the, that's the conversation to have, I guess is like the rigor and bringing, bringing it to the CFO with that level of like intention and from both an execution and, but also like a best approach to measurement. It's probably going to get their sign off rather than a, let me chuck 50 grand at this and see what happens at least then you'll know yeah it's like after it's done you know did did it pay off or not and that can help also then it could be a learning experience you know that if you're not betting the farm you kind of run a few tests but you have a very clear measurement framework for success versus not you can kind of pre-declare it to the CFO.

49:41And then based on how that goes, then that can help inform whether you scale it more or pull it back. But at least then it's all laid out in advance in principle. So I think they would appreciate that. I just wanted to flip into, we were chatting just before we started actually about Codex very briefly and your use and enjoyment and value that you're getting out of that as a tool and I just wanted to spend the last 10 minutes before a quick couple of closing questions on AI. Can't really escape AI on a podcast nowadays. It has to be in there somewhere I feel. I'd be interested to hear your thoughts on how investors

50:26and general maybe public markets as well is looking at AI's impact on valuation, impact on um company structure as well if you if you're exposed to that um and just yeah your general thoughts on how how that should be viewed within today's like landscape i mean there's a lot of different ways one can slice that but certainly a couple of them could be you know are you a part of the i'll call it like the data or action layer or not and um you know just to kind of give one example of that i don't know if you're you know if you're like a competitive athlete but um i'm kind of amateur but decent yeah i'm definitely not i'd love to say yes but um but yeah definitely not yes i'm like you know decent with running um and i've used strava and um strava one of the big things that they've launched recently was this uh mcp connection to clod it's all yeah chat gpt they have like an indirect connection through the apple health app so that way you can see the workouts you can't get all the detailed granular data but at least you can see you know you can see some level of detail um and now i find that i'm constantly saying so how was my workout you You know, it will tell me like, well, the evolution of your heart rate, you know, from the first half to the second half.

52:01And here's like the seven year trend, you know, the story arc of your running career. You know, it tends to take you like 40 weeks to get to peak. You know, I'm like, wow, there's no way I could have ever known that stuff before. But suddenly it's like enriched all that data in this really powerful way. And I'm like, there's no way I'm going to turn from Strava, you know. So locked in. yeah so it's basically part of the data layer and um honestly i think of theta as part of the data layer too that it's giving you all these good metrics and suddenly it's like ai has allowed us to to understand what it all means for us and what we can do with it in a way that didn't used to be possible before you know that just makes it all that data it's not like i didn't have the data.

52:48I could do an export into a CSV file and I could start trying to like run it through an statistical software. I'm like, you know, I have a day job and I've got a baby and, you know, I just have a lot of other demands on my time. I can't do that. You know? So now I'm just like talking at my phone and I can get all that. And so, so I think there is like a generalizable lesson there that if you're either able to take advantage of it, people look to you as, I want access to this data and now I can, you know, glean a lot more insight from it. That's a winning position to be in or, you know, for action as well.

53:25Yes. I use superhuman. I know they've got this MCP connection as well. So you can kind of very easily, you know, interact, you know, through interact with the LLM through superhuman. You know, that makes it that much more useful to continue using superhuman. Um, so yeah, I think that that, that, that's really helpful. I think there's like a whole separate other set of companies where it's just like a crappy version of Anthropic, you know, or, or open AI. It just, you go in there and you can kind of summarize my email or help me draft this, but it's using some crappy model. Yeah. It's just not good.

54:10And that's just, you know what i call it those the wrapper companies um i don't see that as being uh nearly as as useful so um so i think figuring out where on that spectrum are you you know i think that that can be a useful exercise um i say the other big thing is um when prospects are deciding who they want to buy from. I think that whole process is changing a lot right now. Most of my major purchases, I do a lot of me search. Like, you know, I feel like maybe it's a terrible way to go about it because I'm like really different from the typical consumer. But most of my major purchases, I'm doing the research through ChatGPT.

54:59And, you know, whether it's my coffee machine, the heavy bag, treadmill, anything that's above a certain dollar amount and i'm always doing that research and what you might find is that the answers that surface within the llms can be potentially quite different from the answers that you would have surfaced had you done at google search so yeah most brands they don't even know you know they don't even know where they are on the on the share of voice within llms And so step number one, figure out where you are. Hopefully there's something you can do about it, but step number one, at least know where you are.

55:40Yeah, the inputs and what drives rankings is very interesting. You can use things like Triple Whale, I have a good tool for measurement of LLM visibility for those watching who maybe want to do that for a consumer brand quite quickly. Yeah, reviews, sites, and Reddit, Quora, things like that are having a much greater impact than obviously on Google where they aren't factored in for rankings. Do you feel like what you've explained is super interesting because you and I also think it's like yourself and maybe that like higher income, more educated consumer is making more considered purchases through AI.

56:23um i'm not sure that the average customer is doing is on that journey currently um will that happen it's hard to say but i think we're definitely seeing that like um fragmentation of of like paths to purchase across more and more channels like when i'm looking when you're like zooming out you've got that as an example you've got things like app loving that have become more meaningful. You've got obviously meta Instagram, TikTok. There's just, I feel like there's just more and more routes to attention than there was maybe like three to four years ago for consumer brands. She's interesting. Yeah.

57:01That is kind of like the open question. You know, two years from now, what is like the typical Joe consumer doing? Yeah. You know, um, say am I the canary in the coal mine or, or not? I think adoption is going to go up a lot. it's just going to get smarter and smarter. So if I had to make a bet, I bet that a lot more people will be doing it that way. But yeah, that's kind of an open question. I agree for sure. That segues nicely onto my final question. I wanted to ask if you were to zoom out and look at say the consumer brands that you've worked on over the last 12 to 18 months, and this isn't in the notes I sent over, so sorry for putting you on the spot slightly.

57:43Mentioned discount rate as something that I've seen as like an aggregate thing that's just increasing and maybe decaying value over time. Is there any like real macro trends when you look at consumer metrics across consumer brands that you think businesses need to be cognizant of or aware of? Or is that just not something you can, that may not be something you can pass out? I'm just interested to hear your thoughts. Yeah, the macro trends. Well, certainly cross-cohort dynamics are big and you always want to be mindful of them. And that's kind of the whole question. Are the cohorts we're acquiring today, are they the same, better or worse than the cohorts that we acquired a year ago?

58:31And, you know, they can get better for some businesses. They can get worse for other businesses. So you want to make sure you know for your specific business. in terms of the macro trends you know i think um you know certainly there's the ai factor you know that uh that can be a source of cross-cohort variation um and i think the gravitational pull that it exerts is going to be a function of where where you stand relative to your competitors within your category and uh and then you know as you were saying a moment ago the degree to which we see adoption of AI as being a way to do market research.

59:13You know, so more adoption, it's going to pull more. Less adoption, it'll still be a factor, but it won't pull quite as hard. So I definitely see that as being, you know, one factor. You know, for direct-to-consumer businesses, I do feel everyone always loves to talk about the hot thing, the jour. And, you know, if you're like a shoe brand, you're a shoe brand, you know, like people are still going to be buying shoes. They might change the way they do the research. You know, are there kind of other macro factors that might influence the success or failure of a brand like that? But I would again say that potentially the big B brands have more to lose through AI share of voice.

1:00:02Because oftentimes we found that people who are asking in LLM and they say, hey, I've got this issue with my ankle. I've got weird arch support. What are the best shoe brands for me? It's going to surface brands that might not have otherwise shown up as much. so um so incumbent brands could stand to gain a little bit more um yeah you know trend towards privacy yeah i think that that's something that we had seen with apple's att um you know that's for yeah less targeted marketing um yeah so certainly not in europe for years americans don't like privacy the macro trend uh that's still playing out it's still playing out right now um so yes to the extent that continues to manifest you know i think that um less targeted marketing you know it could be harder for them to have precision so that's all else equal going to be you know something of a negative for for d2c brands uh so yeah that would just be something to expect um beyond that you know i guess it's the whole thing of the supply chain and uh the fragility of it and yeah i think that that's kind of an open question but um you know in general the closer you are to your you know to where your products are being manufactured you know i think that that lowers the risk that there's going to be some major disruption that suddenly leaves you you know high and dry with your supply chain um but you know i'm not sure i would call that a macro trend you know it's just like a potential black swan type of risk you know that uh seems more possible now than it did you know five years ago definitely i feel like we're in um that expecting volatility just as i can that is normal rather than it being like a freak event like we are in an era of just like macro shock after macro shock on a short time scale it's not how you plan for that but yeah well yeah thank you thank you very much for i found that super super interesting i i definitely learned a lot i'm sure um many of the viewers did who have stuck around to the end i really appreciate you you're walking through through that uh today i'd love just to end with a view of like where can people find yourself where can people learn more about the work you've done.

1:02:35And then if there's any resources or things you want me to link specifically in the description that people can go to to learn a bit more, we'll definitely get those added as well. Yeah, my big social media platform is LinkedIn. So, you know, so if you search for Daniel McCarthy UMD, you'll find me there. I'm regularly posting about kind of all the sort of stuff that we talked about. So if this is interesting, I think definitely let's connect. and in terms of other resources yeah i'll follow up on that but uh i would say the whole idea of kind of linking customer behavior to corporate valuation you know we've got this harvard business review article that's a really nice introduction to that so you know certainly i'll be posting that too but uh yeah theta's theta's my firm uh theta clv.com uh check us out and uh i think if there's anything else i think um you know you know certainly i teach this class on customer lifetime valuation and uh more and more i'm doing executive education at university of maryland as well so yes if you find this stuff to be interesting um you know certainly you know professor dan you know can uh i have a spike in uho uk cohort next year yeah we're very uk heavy so people might be popping across the pond to let me know perfect well yeah i'll be sure to drop all the links in the description again thank you very much for your time really appreciate it um yeah thanks everyone for stuck around like and subscribe if you're here till the end and we'll catch you on the next episode