It's Payback Time (again)

In a previous post on the value of modeling and how to think about payback, I shared some basic tools and frameworks to approach unit economics and sustainability. 

Now I want to share a follow up that goes into a bit more detail about the tactical decisions that flow from there and start understanding what goes into basic ~~cohort modeling~~.

This is going to build on the content/concepts I covered previously. If you haven’t read Role Modeling or don’t feel like you’re comfortable with these concepts, I’d suggest pausing and checking that out first. Here’s a refresher:

my goal is usually not to determine what will happen, but rather to understand what would need to be true for something to happen. To my mind, the point of modeling is to ask and answer questions rigorously, and to be explicit about your assumptions. Putting things into numbers and breaking processes into discrete steps forces you to be specific in your thinking and with the story you’re telling, even if the numbers and steps are themselves unspecific.

Because startups are money-losing growth machines by design, lots of traditional financial modeling just doesn’t apply. Too often that means overcompensating and looking at top-line performance absent any more rigorous analysis of what I think of as “sustainability.” Is the growth healthy? People throw around all kinds of terms to asses the health and sustainability of startups. I think it’s mostly bullshit and doesn’t capture or describe anything meaningful.

I’ve found myself increasingly creating models (which again are thinking frameworks rather than predictive tools) to blend together all the various top-line figures into a more-startup oriented version of indicative health. I like to think about things in terms of payback in particular.

Once you’ve begun to understand the basic economics of a business, you’ll need to start thinking about more tactical (but no less important) questions using the same general framework. I want to focus on one in particular. What’s the potential impact of an upfront payment versus a pay as you go model? This is obviously crucial to any business with designs on subscription or repeat revenue.

Once again, I’ll use Harry’s and an example and, once again, all these numbers are totally made up and very very wrong

Let start with some simple assumptions and say that a full year of Harry’s blades and shaving cream costs you $48 spread across four, quarterly shipments. Let’s also use the same $35 CPA and 70% gross margin we used in the previous payback analysis. The crucial output here is “periods to payback” because it answers what needs to be true. The 7 lifetime orders per customer is then a reasonable assumption that shows out where things net out. Here’s what that got us last time around:

Back to the matter at hand. If you charge people upfront, you’ll probably have fewer customers (asking for more money today is a barrier to purchase). On the other hand, your customers probably won’t churn as much because they’ve already committed to paying (even if you give them a cancellation option or risk free guarantee). Plus, maybe you can charge higher rates for pay as you go. After all, “pay for the year and get 10% off” really just means “pay as you go and I’ll charge you an extra 10%.”

This seems complex enough for now so I’ll put aside the implications on cash flow for the moment. That’s a topic for another time but suffice to say that upfront payment is favorable to you for all the reasons that pay as you go is favorable to your customers.

Reader beware

As I’ve said, the point of this exercise is to answer what needs to be true in order for me to meet my desired outcomes. Everything here is about being rigorous in our thinking, not trying to predict the future. I’m illustrating a general concept, not proving a specific point.

Now let’s use those same assumptions for Harry’s and add in some more info. We’ll assume that Harry’s converts 1.5% of “quality” (non-bounce) visitors to its website into customers. Seems reasonable enough. Some easy back of the envelope math tells us that that means Harry’s is paying $0.53 for each “lead” (person an ad pushes to its site). Finally, we’ll make some simple assumptions around churn/cancellations after each shipment. Here’s what we get:

You might look at this and think the numbers don’t tie. I said it would take 4.17 orders to pay back the CPA, now that only seems like it happens around order 8. What gives?

Unlike the previous Harry’s payback model, this is a time series. That means that churn/retention happens in “real time” as people attrite off with each order rather than all at once at the end. So if you sum up the cohort population percentages through shipment 7 (when net payback starts to get into the black, you’ll get ≈4 orders on average for that cohort. Orders to payback is right in line for the whole population but it takes longer to get there because so many customers churn off far in advance.

(If you couldn’t already tell, this is getting dangerously close to the cohort analysis post I’ve promised.)

Now, putting on our operator hats, we want to know “how do I make this better?” At bare minimum, we’ll want to think through the tradeoffs of an altered model. Everyone seems to offer some kind of “subscribe and save” or “pay now and save” option so there must be something to it. Let’s see what happens.

To be conservative, we’ll say that pay as you go will costs users nothing extra. Churn should go up because customers don’t feel like they’ve already spent the money and conversion should go up because pay as you go is a lower barrier to purchase. We don’t know by how much either will change but we’ll say that both churn and conversion increase by 25% . That gain on conversion decreases CPA because CPL stays the same but now more of those users are actually buying once they hit the site. Otherwise, the inputs are exactly the same. The outcomes, however, vary widely from the first case:

What we see is that even though orders per user over the two year period decreases from 4.37 (paying annually upfront) to 3.77 (pay as you go), net payback more than doubles from 5% to 13% throughout the same timespan.

So obviously this is the right answer, right?

Not necessarily. You have to remember that I’m making some pretty wild assumptions. The devil is in the details and no matter how robust your model and how much data you have, early stage operators need to have conviction behind their choices and a POV that goes beyond 20 minutes of excel. The “right” answer will vary based on factors this type of model couldn’t possibly capture, factors that are intrinsic to your customers and your product and your brand and your cash flow needs and your goals.

But this is at least a good place to start.

For anyone who’s interested, I’ve updated the payback model in Google Sheets to include this exercise. Play around with it, let me know what I got wrong, and tell me what I should be thinking about next.

Don't work in venture capital

Separate the “job to be done” from the the job you think you want

I get a lot of people asking me how to get into venture capital. How did I get my job? What’s my story? Who’s hiring? What are funds looking for? Is Tusk hiring? Most weeks, I speak to one or two people looking to work in VC, usually for analyst/associate roles.

Most or all of them seem well intentioned, smart, and eager to contribute. I want to help them but we’re not hiring so the best I can do is offer whatever insight I have.

I tend to give them all some version of the same advice/feedback and thought it might be good to write this down for anyone else looking to work in venture capital.

The short answer is that, under generic terms, you shouldn’t. It’s not a good goal, won’t make you happy, and isn’t the thing you think you’re applying for. Here’s why.

1) It’s very hard to get a job in VC.

Capital scales very efficiently against labor. The number of people it takes to invest $100 million is not meaningfully different than the number of people it takes to invest $500 million. Consequently, venture capital funds don’t need to hire at the same pace as the startups they invest in and they don’t generally hire on any particular cycle (at least not one that will be familiar to bankers and consultants).

The VC hiring process is opaque and takes a long time. The job openings are rarely publicized and the competition is intense. Hiring is usually fairly ad hoc and opportunistic. There might not be a job opening at all until someone meets the right person and decides to hire them without a process.

Even when a process seems more accessible and has an open listing, VC is, for better and for worse, a connections-driven industry and the recruiting is no exception. As an applicant you need to maximize your surface area: constantly scouring the earth to get meetings and intel and contacts until you manage to be in the right place at the right time to even find someone hiring.

Taken together, it can often take 6+ months of work for even the most credentialed/qualified-seeming people to land a job in VC.

But even if you can wait that long and do land a job…

2) Most of the jobs are not good.

From the outside, people tend to think that being an associate at a venture fund is mostly about running up an expense account, tweeting, and going to parties. You get to play real life Shark Tank and build companies hand in hand with founders! But all of that is fairly incidental to actually doing the job. In many if not most firms, associates and analysts are glorified assistants. Their primary job is volume sourcing to get meetings on partners’ calendars and then doing diligence on partners’ deals (read: papering those investment decisions ex post facto). This is especially true of some of the larger growth-equity style funds that might have hiring processes that look more similar to a bank’s (cyclical, multiple openings at once, etc.).

Because of the aforementioned scaling relationship between capital and labor, venture capitals firms don’t need to add new partners very often. Most junior roles tend to be 2-3 year appointments with limited room for upward mobility. Given that you’re not going to be sticking around, what incentive does anyone have to mentor/invest in you? That’s compounded by the economic structure of venture firms, where carried interest (keeping a portion of the profits from investments) is a finite, rivalrous good. The more carry you have, the less I can have.

But even if you can wait a long time, do land a job, that job gives you room to grow, and you manage to wring some carry out of the partners…

3) You won’t make as much money as you think you will.

I’ve said this before but it bears repeating: almost no one makes money in venture capital, at least not off of performance (getting fat on management fees doesn’t count).  Benchmark returns are really pretty shit. The generic venture fund is worse than public markets but with no liquidity.

Venture capital is famous for following power laws whereby a very small fraction of investments (and by extension a small fraction of funds) produce most of the profits for the whole asset class. So if you’re getting carry in a generic/random fund, the returns just aren’t very good.

If it’s the salary you care about, you can make the same or more in banking or consulting, or in a business role at a later stage/pre-IPO company.

“But, Yoni,” you say, “you work in venture. Is your job terrible?”

I am very lucky.

I have a great job that does not conform to most of what I’ve described above. I came into a very unique situation, mostly through sheer luck. My experience has been so predicated on being in the right place at the right time that it’s really not repeatable/replicable. It’s almost not worth going into. Most of my analyst/associate friends in good situations at other firms also got their jobs through similarly non-replicable paths.

And if/when I leave Tusk, it won’t be to go work at another venture fund.

So don’t fall victim to survivorship bias. It’s only the people who are lucky enough to have figured out/landed in a really good situation that are left around to be asked advice.

So what should you do?

Look, if you’ve made it this far, you must have a pretty high tolerance for my sage wisdom. Why should I tone it down now?

Take a step back and ask yourself why you want to work in VC. Then think seriously about where/how else you might be able to scratch that same itch in another job.

Maybe you want to do strategic thinking and problem solving in biz ops, growth, or strategy at a late stage company. Maybe you should spend time as a really analytical thinker in private equity. Maybe you want broad experience juggling many balls in an ops role or as a chief of staff at an early stage company. Maybe you should go to business school for the network/community you’re trying to build (or maybe you should just start tweeting more - seriously). Maybe you love jet-setting around and doing meetings and making DEALS and sales could be the right place for you.

The list goes on and on. And maybe you’re ok with doing VC for a couple years and don’t want/need to have a shot at a partner-track position. That’s fine too, so long as you can be clear-eyed going into it.

No matter what the answer is, you have to separate out the “job to be done” from the the job you think you want.

IF you are going to work in venture, try to optimize for partner/fund rather than generic “venture capital.” As I’ve said, most jobs and funds are bad. The biggest brand name funds are functionally impossible to get hired at and working there is unlikely to be any better than what I’ve described above. I’d suggest going to a new fund where you stand a better shot at having upward mobility, economic upside (carry), and the ability to drive outcomes.

Now I know that this is somewhat of a controversial stance. Smart people that I respect have said it’s better go for a brand name and use that to pivot elsewhere where you can have that better job eventually; it’s easier to move downstream that upstream. My preference/bias is to bet on myself and new funds are extremely high risk/reward. Knowing that most venture funds, like most startups, won’t succeed/produce great outcomes, I’d at least rather have the potential for upside. So you need to have a clear sense of where you stand on these questions and accept the tradeoffs that come either way.

I don’t say any of this to discourage you or make you feel shitty. I’m saying this because I’ve seen enough people go through really long, painful process before they come to see what I’m saying here and I love you.

And if you take my advice and I’m completely wrong about everything, at least you’ll be more sure of what you already knew. A little bit of introspection and self-knowledge never hurt anyone.

Get poor quick schemes

Bitcoin to $50k

Couple of things before I get into this. First, none of this is investment advice. I’m an idiot and you really, really shouldn’t listen to my opinions. Second, if you hold me accountable to any of this then the joke’s really on you, bro.

Now let me spit some facts.

The Federal Reserve estimates that 31% of all the US dollar bills in circulation today are $100 bills. This is a 20% from 15 years ago. (US Fed)

80% of all US $100 bills are held overseas, according to the Chicago Fed Board, up from 15-30% in 1980. (Chicago Fed)

Looking at the total US dollar bill circulation, that means that $100 bills stored overseas accounts for ≈64% of the total value of printed US currency. This represents a hair (few hundred million dollars) over one trillion dollars in value (that’s $1,000,000,000,000 or one thousand billions of dollars) sitting in $100-denominated bills parked overseas.

Rich Dave Chappelle GIF

Why are people keeping hordes of $100 bills overseas? Lots of reasons. I’d guess mostly to do with crime on one hand and local economic instability on the other. $100 bills are obviously the most efficient way to store, transport, and transact in cash that you want to keep out of the regular financial system. $100 bills take up the least space, are universally accepted, and are well made enough to keep their integrity for years.

But crypto-assets are better than physical bills on each and every count.

So that $1 trillion in $100-denominated bills stored overseas? That’s the baseline price target for Bitcoin’s market cap. The max number of Bitcoins that can be mined is 21 million. So assuming none get lost (1/4-1/2 probably have been), that sets a price target for Bitcoin at ≈$51,000 before accounting for any other use cases beyond displacing $100 bills as a store of value overseas.

I think Bitcoin will be very price volatile but generally trend upwards until reaching something within spitting distance of that price point and then become relatively price stable for the future. And I think it’ll be Bitcoin specifically (rather than say Monero) because it has the biggest headstart and most “brand recognition” among all crypto-assets, not unlike the US dollar relative to other fiat currencies. To put it in perspective, Bitcoin makes up about 65% of the total crypto-asset market cap, depending on the day. 

I’m gonna be buying and HODLing all the way to $51k.

My DMs are open for any hedge funds that want me to come in and run things from now on.

You’re welcome.

Dunking on VCs

Understanding and enjoying the meme

Justin Gage has has an ongoing twitter thread exploring one question he keeps coming back to, month after month and IPO after IPO: Why do people take such particular pleasure in making fun of and/or criticizing venture investors?

Justin is right. VCs do seem to attract a particular kind of disdain. At the very least mocking them (us?) has become a whole category of memes. No, we’re not an endangered species and yes we’re all fine. It’s a small violin that plays for the woes of the venture capitalist.

Here’s what I think is going on.

First off, I think there’s a perception that venture capital is a house-always-wins scenario. Place enough bets and you make money even if most or nearly all founders get screwed, right?  So it makes sense that people who make fun of VCs. But not only is that perception not true, it also isn’t singular to venture capital and therefore isn’t enough to explain what’s going on. [1]

Private equity investors, for example, don’t get the same level or ire and shade. Venture capital is really just a tiny sub-category of that asset class. Hollywood agents and executives operate under similar power dynamics as VCs but don’t inspire the same feelings. What gives?

I think the answer is that venture capital has so few measurable outcomes and the timespans over which they occur is so long that assessing quality is nearly impossible. A firm might have a great collection of logos on their website but you don’t know how much they paid, when they got in, how involved they are, or even if those companies will ultimately be successful. Just think of all those investors who made their names as big time Blue Apron backers.

No, in venture capital you can’t really build a brand or reputation on performance. You can’t advertise competence via returns. It just takes too long. VCs measure performance in 8-10 year fund lifecycles, not quarterly returns or annual revenue. Instead, VCs have to cultivate brands for themselves and their firms through omnipresence: they and their takes have to be everywhere always. 

That brand is how VCs get access to deals and founders. Moreover, they have to seem like allies, friends, and advisors to founders, not mere financiers.[2] This leads to a kind of new-sincerity particular to this weird little asset class. It’s all precious agreement, and mutual backslapping, and virtue signaling, and affected humility. Everyone is humbled and grateful for their own magnificent, towering achievements.

The incentives are there for VCs to talk and write and podcast and tweet and generally have an opinion on everything all the time. Cutting through the noise means having not just opinions but takes. Preferably steaming hot ones.

Venture capitalists need to look smart because it’s impossible to prove they’re smart. Perception, not returns, is reality.

The result is an insider-y industry dominated by middle aged white guys named Hunter and Travis and Jeff (and all the various spellings thereof) pontificating and self aggrandizing shamelessly, constantly, and at ever-increasing decibels (he said via his blog that no one asked him to write). [3]

Making fun of VCs has become a meme unto itself and has now begun transcending into the kind meta-meme or hyper-take the internet so often produces. Now the cool thing for VCs to do is to make fun of other VCs to show you’re in the joke (the VC associate wrote in his blog post about making fun of VCs).

The best we can hope for is to die young and never tweet.


[1] Almost no one makes money in venture, at least not off of performance (getting fat on management fees doesn’t count).  Benchmark returns are really pretty shit. Benchmark’s (note the apostrophe) returns on the other hand…

[2] Note my use of “they.” I’m one of the good ones and all my takes are great.

[3] Sorry to any readers named Hunter or Travis or Geoff. You’re reading this which already means you’re great. Love you.

Role Modeling

Beware, what follows is a post on startup financial modeling/ analysis. Bear in mind, I have no formal finance education. Proceed at your own risk. You asked for this.

When a startup gives you numbers, how do you assess what’s real, what’s good, what’s bad, and what’s possible? How should you think about not just how the business looks like today, but what could it look like in the future? It takes critical thinking, good judgement, and some pattern matching/experience. But to put that thinking to work, you have to first be able to structure your thoughts and see the picture clearly.

There’s tremendous value in model making as an analytical rather than predictive tool. 

When I’m modeling, my goal is usually not to determine what will happen, but rather to understand what would need to be true for something to happen. To my mind, the point of modeling is to ask and answer questions rigorously, and to be explicit about your assumptions. Putting things into numbers and breaking processes into discrete steps forces you to be specific in your thinking and with the story you’re telling, even if the numbers and steps are themselves unspecific.

Because startups are money-losing growth machines by design, lots of traditional financial modeling just doesn’t apply. Too often that means overcompensating and looking at top-line performance absent any more rigorous analysis of what I think of as “sustainability.” Is the growth healthy? People throw around all kinds of terms to asses the health and sustainability of startups. I think it’s mostly bullshit and doesn’t capture or describe anything meaningful.

I’ve found myself increasingly creating models (which again are thinking frameworks rather than predictive tools) to blend together all the various top-line figures into a more-startup oriented version of indicative health. I like to think about things in terms of payback in particular.

To understand why, I’ll use Harry’s as an example.

(Note: all the numbers here are waaaaay off and if this were a real example, there’d obviously be more detail. I’m illustrating a general concept, not proving a specific point.)

Harry’s baseline razor subscription costs $9 per shipment. Let’s assume that the average customer receives 6 shipments before cancelling and that it costs Harry’s $40 to acquire each customer. So each customer represents $54 in revenue and $40 in acquisition costs. Great news! For every $1 you spend on marketing, you get back $1.35 in revenue, a healthy LTV/CAC ratio (lifetime value/customer acquisition costs). You can check the glossary at the bottom for some definitions of terms:

But wait, you might ask, making, storing, and shipping all razors has to cost something too, right? Right. So let’s say all those various supply chain and logistics costs, eat 40% of the revenue (in reality for Harry’s it’s probably closer to 20%). That would mean that Harry’s only keeps 60% of the revenue from each sale, representing $32.40 over the life of each customer. Not as good as it could be but not too shabby…

This is where the math would break down. If each customer is only really worth $32.40 to Harry’s and Harry’s is spending $40 to acquire them, that’s pretty obviously unsustainable. And this is even assuming you know for sure what the customer lifetimes are. Really by the time you do know that, you’re probably not dealing with an early stage startup anymore. So the question becomes “what would have to change for this business to work?”

Here is the general framework I like to use to see all the levers in one place and begin thinking about that question:

To get that lifetime payback (think or this as CAC-burdened lifetime contribution margin or revenue minus acquisition costs minus the costs of the razors, shipping, etc.) into the black, Harry’s could:

  1. Increase order values, either by raising prices or introducing add-on products

  2. Improve contribution margins by lowering some supply chain cost (this is where the line items that make up CoGS matters a lot)

  3. Extend customer lifetimes/improve retention

  4. Lower CAC through better paid marketing or more organic growth

Obviously some of those are easier or more feasible than others. Some may tend to “naturally” (usually that really means a lot of hard work) improve over time and with scale while others may get harder or even worse over time. Where are the reasonable upper limits? What could this reasonably look like? What’s normal? What do we care about? Is this concerning?

There’s no “if X > Y then I must invest” shortcuts or hard and fast rules.

This is where it stops being “financial modeling” and becomes a more creative/intellectual/interesting exercise. But to get here and to be able to answer any of those questions with any semblance of legitimacy and analytical rigor, you have to first ground yourself in a framework. Otherwise it’s just hand-waving. That’s the ultimate point of the exercise.

What this framework also suggests is that to survive, a startup has to be great on one of those levers and to really thrive, it has to be great on two or three. So when I look at a startup, I want to understand where they are today and where they can get on each. Does the prospect of a super sticky product make me relatively indifferent for CAC? Can the margins be strong enough to support low AOVs? So on and so forth.

With the appropriate tweaks, this general framework works just as well for consumer startups well outside of subscription CPG. I’ve found it helpful for understanding startups with depreciating assets, lease obligations, software/virtual subscriptions, and one-off/irregular (non-subscription) purchases, etc. And it can work in reverse as well, not just on a per-user basis. Here’s the same type of unit economic model for Rent The Runway:

We’re not talking about top-line growth, and certainly not cash flow or EBITDA. Even once all the numbers in this type of model look good, startups still have to pay their employees, and rent, and lawyers, and consultants, and health benefits, etc. I’m not concerned about profitability as an early stage investor. I care about a path to profitability. Anyone can produce great looking growth by burning enough money. Getting to sustainability is another matter altogether.

To make sense of that and understand it in a meaningful way, you first need to do the work of modeling out both the facts as you them and your judgement as you perceive. Then you can start bending and breaking and hopefully get somewhere.


I’ve put the above models into this Google Sheet. Hopefully I can get around to adding more types of payback models. In the meantime, I would love to hear people’s alternative takes on this.

Am I dumb, wrong, or just pointing out something obvious?


Quick glossary:

  • AOV: Average order value. How much a customer spends on average each time they buy.

  • LTV: lifetime value (sometimes CLTV for customer lifetime value). The total amount of money a customer spends over all their purchases: AOV x number of purchases.

  • CAC/CPA: customer acquisition cost or cost per acquisition. How much it costs (in advertising spend, referral codes, etc.) to get a new customer. These numbers can mean slightly different things in practice but I’m using them interchangeably.

  • Payback period: the amount of time or number of purchases until you’ve paid back your CPA.

  • CoGS: cost of goods sold. The cost not just of buying/manufacturing some item but also all the associated variable costs of selling it like storing, packaging, and shipping it.

  • Contribution margin: the amount of revenue after accounting for CoGS.

  • Depreciation: how much does the value of something (like a car) decrease over time/with use.

  • Lifetime payback: this is the metric I’m making up, which works either on a lifetime or transactional basis. It measures (lifetime) revenue minus CoGS minus acquisition costs.

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