User/Subscriber Economics: An Alternative View of Uber's Value

In the week since I posted my Uber valuation, I have received many suggestions on what I should have done differently in the valuation, with many of you arguing that I was being a over optimistic in my forecasts of total market, market share and margin improvements and some of you positing that I was too pessimistic. I don't claim to have any certitude about these numbers but the spreadsheet that I used to value Uber is an open one, and you are welcome to convert your suggestions into valuation inputs and make the valuation your own. In just the last few days, though, I have been watching an argument unfold among people that I respect. about whether the reason for my low valuation for Uber is that I am using a DCF model, with the critics making the case that valuing a company based upon its expected cash flows is an old economy framework that will not yield a reasonable estimate of value for new economy companies, driven less by infrastructure investments and returns on those investments, and more by user and subscriber economics.  I have long argued that DCF models are much more flexible than most people give them credit for, and that they can be modified to reflect other frameworks. So, rather than deflect the criticism, I will try to build a user based model to value Uber and contrast with my conventional valuation.

Aggregated versus Disaggregated Valuation

If you are doing an intrinsic valuation, the principle that the value of a business is the present value of the expected cash flows from that business, with the discount rate adjusted for risk, cannot be contested. That is true for any business, manufacturing or service, small or large, old economy or new economy. Since that is what a discounted cash flow valuation is designed to do, I have to believe that what critics find objectionable in my Uber DCF model is not with the model itself but in how I estimated the cash flows for Uber, and adjusted for risk. I followed the aggregated model for discounted cash flow valuation where I estimated the cash flows to Uber as a company, starting with its revenues and working through the consolidated expenses and total reinvestment each year and discounted these cash flows at a cost of capital that I estimated for the entire company. Along the way, I had to make assumptions about a total market that Uber would go after, the market share that I expect the company to get in that market and the operating margins in steady state. 

Disaggregated Valuation

Value is additive and you can value any company on a disaggregated basis, breaking it down into different divisions/businesses, geographical areas or by units:

• Business Units: In a sum of the parts valuation (SOTP), you can break a multi-business company into its individual business units and value each unit separately.  I have a paper where I describe the process of doing a SOTP valuation, using United Technologies, a conglomerate, as my example. If that SOTP valuation is much higher than the value that the market attaches to the company, you may very well find an activist investor targeting the company for a break up. 
• Geographical Groupings: When valuing a multinational, you can break the company's operations down geographically and value each geographical grouping (Asia, Latin America, North America, Europe) separately, not only using different assumptions about growth and risk in region but even different currencies for each region. 
• Unit-based Valuation: More generally, when valuing any company, you can try to value it on a unit-basis, building up to its value by valuing each unit separately and then aggregating across units. Thus, a pharmaceutical company can be valued by taking each of the drugs that are in its portfolio, including those in the pipeline, and valuing that drug based upon its cash flows and risk and then adding up the values across the entire portfolio. A retail business can be valued by valuing individual stores and adding up the store values and a subscription-based company can be valuing by valuing a subscription and multiplying by the number of subscriptions, current and forecasted.

I may be misreading the critics of my Uber valuation but it seems to me that some of them, at least are making the argument it is better to value Uber, by valuing an individual Uber user first, and then scaling the value up to reflect not just the number of users that Uber has today (existing users) but also new users it expects to add in the future. 

Aggregated versus Disaggregated Valuations: Weighing the Trade offs

Valuation on a disaggregated basis allows you to be much more flexible in your assumptions, allowing them to vary across each grouping but there are four reasons why you seldom see them practiced (or at least practiced well) in company valuation.

1. Law of large numbers: As companies get larger and more diverse, there is an argument to be made that you are better off estimating on an aggregated basis rather than a disaggregated one. The reason is statistical. To the extent that your estimation errors on a unit basis are uncorrelated or lightly correlated, your estimates on an aggregated level will be more precise than the unit-based estimates. For example, you will have a much better chance of estimating the aggregate revenues for Pfizer correctly than you do of estimating the revenues of each of its dozens of drugs.
2. Information Vacuums: Information on a disaggregated basis is difficult to get for individual businesses, geographies, products or users, if you are an investor looking at a company from the outside. If you are doing your valuation from inside the company (as an owner or venture capitalist), you may be able to get this information, but as you will see with my Uber user valuation, even insiders will face limits.
3. Missing Value Pieces: When valuing a company on a disaggregated business, it is easy to overlook some items that are consequential for value. In sum of the parts valuation, for instance, analysts are so caught up in estimating the values of individual businesses that they sometimes forget to value "corporate costs", which can be a multi-billion drag on value.  
4. Corporate Structure: There are some items that are easier to deal with at the aggregate level, because that is where they affect the business. Thus, you can model when taxes come due and the effect of losses easier when you are valuing an aggregated business than when you are valuing it on a disaggregated level. Similarly, if you are concerned about legal penalties or corporate governance, these are better addressed at the aggregated level.

It is true that aggregation comes with costs, starting with the blurring of differences across disaggregated units (business, geographies, products, users) as well as the missing of competitive advantages that apply only to some units of the business and not to others. It is also true that using an aggregated valuation can result in a process that is disconnected from how the owners and managers at user-based companies think about their companies and thus cannot help them in managing these companies or valuing them better.

User Based Valuation
Now that we have laid out the pluses and minuses of aggregated versus disaggregated valuation, let us think about how you would construct a disaggregated valuation of a company that derives its value from users or subscribers. In general, the value of such a company can be written as the sum of three components:

Value of user-based company = Value of existing users + Value added by new users - Value drag from corporate expenses

1. Valuing Existing Users
The key step in a user-based valuation is estimating the value of a user and that value is a function of many variables: the cash flows that you are currently generating from a typical user, the length of time you expect that user to use your product or service, your expectations of how much growth you can expect in cash flows from a user over time and the uncertainty that you feel about all of these judgments:

Consider the implications that emerge from this simple framework:

1. The value of a user increases with user stickiness and loyalty (captured in the expected lifetime of a user and the annual renewal rate).
2. The value of a user is directly proportional to the profitability of that user (captured as the difference between the revenues from that user and the cost of servicing that user). 
3. The value of a user is directly proportional to the growth that you can generate in profits over time, by either getting the user to use more of your product or service or coming up with other products or services that you can sell that user. 
4. The value of a user decreases as you become more uncertain about future cash flows from that user, with that uncertainty being a function of the revenue model that you use and the discretionary nature of the product or service. A subscription-based model, where users agree to pay a fixed amount every period, will generally be less risky and more valuable than a transaction-based model or an advertising-based model, that delivers the same cash flows. A product or service that delivers a necessity (transportation) is less risky than one that meets a more discretionary need (travel). 

If you can value a user, you can then estimate the value of an existing user base, by multiplying the value/user by the number of existing users. If you have multiple types of users, with perhaps different revenue models for each, as is the case with LinkedIn's premium and regular members, you can value each user group separately. 

Value Added by New Users

The second segment of value is the value added by new users that you expect to see added in the future. To estimate this value, you can start with the value per user from the last section but you have to net out the cost of acquiring a new user, which can take the form of advertising, introductory discounts and/or infrastructure investments to enter new markets. That net value added by a new user  (value per user minus cost of acquiring a user) then has to be multiplied by the number of new users that you expect to add each period and brought back to the present, adjusting for both the risk in the cash flows and the time value of money.

Again, I will agree that this is simplistic but consider the common sense implications:

1. The value added by a new user increases with the value of a user, estimated in the last section. A strategy of going for fewer and more intense users may create more value than one with more and less engaged users, a warning that pursuing user growth at any cost can be dangerous for value.
2. The value added by a new user decreases as the cost of adding users increases. That cost will be a function of the competitiveness of the business (increasing as competition increases) but also of networking effects. If you have strong networking effects, the cost of adding new users will decrease as you accumulate new users, thus creating a value accelerator for your business.
3. The value added by a new user decreases as you become more uncertain about user growth. That uncertainty will be a function of competition and whether the technology that you have built your product or service on is sustainable.

Corporate Expenses and Value
To get from user value to the value of the business, you have to bring in the rest of the company into your analysis. To the extent that you have expenses that are unrelated to servicing existing users or adding new ones, i.e., corporate expenses, for lack of a better term, you have to compute the value of these expenses over time and reduce your value as a company by this amount:

While at first sight, this item may look like wasteful that should be eliminated, it represents both a danger and an opportunity for young companies. It is a danger to the extent that bloated corporate expenses can drag a company's value down, but it can be an opportunity insofar as it is at the basis of economies of scale. If corporate expenses represent necessary expenses to keep a business going, and they grow at a rate much lower than the growth rate in users and revenues, you will see margins improve quickly as a company scales up.

Valuing Uber: A User based Model

Can Uber be valued using a user-based model? Yes, but it will require assumptions about users that are, at best, tentative and at worst, based upon little information. While I will attempt with the limited information that I have on Uber to do a user-based valuation, I will leave it to someone who has access to more information than I do (a VC invested in Uber or an Uber manager) to tweak the numbers to get better estimates of value.

Deconstructing the Financials

The numbers that we have on Uber's operations are minimalist, reflecting both its standing as a private company and its general secretiveness. In 2016, according to the financials that Uber provided to a Bloomberg reported, Uber reported $20 billion in gross billings, $6.5 billion in net revenues (counting all revenues from UberPool) and a loss of $2.8 billion (not counting the $1 billion loss on the China operations). According to other reports, Uber had about 40 million users at the end of 2016, up from 24 million users at the end of 2015. Finally, other (dated) reports suggest Uber's contribution margins (revenues minus variable costs) in its most profitable cities ranges from 3-11% of gross billings and its contribution margin in San Francisco, its longest standing and most mature market, is 10.1%. Bringing in these noisy and diverse estimates together, here are my estimates of user statistics:

These numbers are stitched together from diverse sources and vary in reliability, but based upon my judgments, I break down Uber's operating expenses in 2016 into three categories: to service existing users (48.17%), to get new users (41.08%) and corporate expenses (10.75%); the last estimate is a shot in the dark, since there is no information available on the value. The annual profit from an existing user, based on 2016 numbers, is about $50.50 (Net Revenues - Expense/user) and the  cost of adding a new user is about $238/75, and both will be key inputs in my valuation.

Valuing Existing Users

To value Uber's existing users, I use the framework developed in the last section, in conjunction with the estimates that I obtained from the limited financial information provided by Uber. I valued existing users, assuming four additional parameters: a lifetime of 15 years for users, an annual renewal likelihood of 95%, a compounded growth rate of 12% in annual revenues from users expanding their user of Uber services and a growth rate of 9.9% a year in annual user servicing expenses (on the assumption that 80% of the servicing cost is variable). Assuming a cost of capital of 10% (in the 75th percentile of US firms), the resulting value per user and the overall value of existing users is shown below:

Download spreadsheet

The value per existing user is about $410 and the overall value of Uber's 40 million existing users is $16,412 million. Not surprisingly, this value is sensitive to user stickiness (as measured by user lifetime) and user growth potential (as measured by the growth rate in annual revenues):

In a market where investors swoon at user numbers, this table makes an obvious point. Not all users are created equal, with more intense, sticky users being worth a great deal more than transient, switching users.

Value Added by New Users

To estimate the value added by new users, I start with the value per user (estimated in the last section to be $410), which I grow at the inflation rate to get expected value per user over time, and use the cost of acquiring a new user from 2016 (about $240/user). Assuming a growth rate of 25% a year for the next five years, 10% between years six and ten and overall economic growth after year ten, I estimate the value added by new users over time. (With those growth rates, I more than quadruple the number of users over the next ten years to 164 million.) In coming up with value, I assume that new user growth is more uncertain than the value created by existing users, and use a 12% cost of capital (at the 90th percentile of US firms) to get today's value.

Download spreadsheet

The value added by new users, based upon my estimates, is $20,191 million. That value is sensitive to the net value created by each new user (value of a new user minus the cost of adding a new user) and the growth rate in the number of users:

This table illustrates the point made earlier about how some companies will be better off trading off higher value added per user for lower user growth, since there are clearly lower growth/ higher value added scenarios that dominate higher growth/lower value added scenarios in terms of value creation.  

Corporate Expenses and overall Value

The final loose end is the corporate expense component, a number that I estimated (arbitrarily) to be $1 billion in 2016. Allowing for the tax savings that these expenses will generate and assuming a 4% compounded growth rate, well below the 15.16% compounded growth rate in total users, I estimate a value for these corporate expenses (using the 10% cost of capital that I used for existing users):

Download spreadsheet

The value drag created by corporate expenses is about $10,369 million. Bringing together all three components, we get a value for Uber's operations of $26.2 billion
Value of Uber's Operating Assets:
= Value of Existing Users+  Value added by New Users - Value drag from corporate expenses
= $16.4 billion + $20.2 billion + $10.4 billion = $26.2 billion
Adding the cash balance ($5 billion) and the holding in Didi Chuxing (estimate value of $6 billion) results in an overall value of equity of $37.2 billion for the company (and its equity, since it has no debt):

Value of Uber Equity = Value of Operating Assets + Cash - Debt = $26.2 + $5.0 + $6.0 = $37.2 billion

This is close to the value that I obtained for Uber on an aggregated basis, but that is a reflection of my understanding of the company's economics.

Pricing versus Valuing Users

As you can see, valuing users requires assumptions about users that can be difficult to make. So, how do venture capitalists and other early stage investors come up with per user or per subscriber numbers? The answer is that they do not. Drawing on an earlier post that I had on how venture capitalists play the pricing game, venture capitalists price users, rather than value them. What does that involve? Very simply put, the price per user at Uber, given its most recent pricing of $69 billion and the estimated 40 million users is $1,725/user ($69,000/40).  To make a judgment on whether that number is a high or a low number, you would compare that price to what you the market is pricing a user at Lyft or Didi Chuxing and if naive, argue that the lower the price per user, the cheaper the company. Using the most recent estimates of pricing and users for the five big ride sharing companies, here is what we get:

Company	Most Recent Pricing (in $ millions)	# Users (in millions)	Price/User
Uber	$69,000	40.00	$1,725.00
Lyft	$7,500	5.00	$1,500.00
Didi Chuxing	$50,000	250.00	$200.00
Ola	$3,000	10.00	$300.00
GrabTaxi	$4,200	3.80	$1,105.26

If you follow the user valuation in the last section, you can see why this pricing comparison can be dangerous. The aggregate pricing that you get for individual companies reflects not only existing users but also new users, and dividing by the existing users will give you much higher numbers for companies that expect to grow their user base more. Even if every company is correctly priced, you should expect to see users at companies with less cash flows per user, lower user growth, less intense and loyal users and more uncertainty about future cash flows to be priced much lower than at companies with intense and sticky users, with more growth potential.

The Bottom Line

If your argument against using discounted cash flow valuation (at least in the aggregated form that it is usually done) is that you have to make a lot of assumptions, I hope that this process of valuing users brings home the reality that you cannot escape having to make those assumptions. In fact,  the assumptions that you need to make to value a company on a disaggregated basis (based on users or subscribers) are often more involved and complex than the ones that you have to make in an aggregated valuation. That said, I do agree that looking at value on a disaggregated basis can not only give you insights about value drivers but also about questions that you would want to ask (and get answered) if you are thinking about investing in or building a young company whose value is coming from its user or subscriber base. 

YouTube Video

Attachments

1. Uber User-based Valuation
2. Uber aggregated DCF

Previous Posts on Uber

1. A Disruptive Cab Ride to Riches (June 2014)
2. Possible, Plausible and Probable: Big Markets and Networking Effects (July 2014)
3. Up, Up and Away: A Crowd Valuation of Uber (December 2014)
4. On the Uber Rollercoaster: Narrative Tweaks, Twists and Turns (October 2015)
5. The Ride Sharing Business: Is a Bar Mitzvah moment coming? (August 2016)
6. Uber's Bad Week: Doomsday Scenario or Business Reset (June 2017)

User and Subscriber Businesses: The Good, the Bad and the Ugly!

In a series of posts over the course of the last year, I argued that you can value users and subscribers at businesses, using first principles in valuation, and have used the approach to value Uber riders, Amazon Prime members and Spotify & Netflix subscribers. With each iteration, I have learned a few things about user value and ways of distinguishing between user bases that can create substantial value from user bases that not only are incapable of creating value but can actively destroy it. I was reminded of these principles this week, first as I wrote about Walmart's $16 billion bid for 77% of Flipkart, a deal at least partially motivated by shopper numbers, then again as I read a news story about MoviePass and the potential demise of its "too good to be true" model, and finally as I tripped over a LimeBike on my walk home. 

User Based Value

My attempt to build a user-based valuation model was triggered by a comment that I got on a valuation that I had done of Uber about a year ago on my blog. In that post, I approached Uber, as I would any other business, and valued it, based upon aggregated revenues, earnings and cash flows, discounted back at a company-wide cost of capital. I was taken to task for applying an old-economy valuation approach to a new-economy company and was told that that the companies of today derive their value from customers, users and subscribers. While my initial response was that you cannot pay dividends with users, I realized that there was a core truth to the critique and that companies are increasingly building their businesses around their members. 

Consequently, I went back to valuation first principles, where the value of any asset is a function of its cashflows, growth and risks, and adapted that approach to valuing a user or subscriber:

To get from the value of existing users to the value of an entire company, I incorporated the value effect of new users, bringing in the cost of acquiring a new user into the value:

I applied closure by consider all corporate costs that are not directly related to users or subscribers in a corporate cost drag, a drag because it reduces the value of the business:

Cumulating the value of existing and new users, and netting out the corporate cost drag yields the value of operating assets, i.e., the same value that you would derive by discounting the free cash flows to the entire business by its overall cost of capital. You would still need to clean up, by adding in cash, netting out debt and dealing with outstanding options, but that process is the same in both models.

I would hasten to add that a user-based value model is not a panacea to any of the valuation challenges that we face with young, user-based companies. In fact, the difficulties with obtaining the raw data needed on user renewal rates and acquisition costs can be so daunting that any potential advantages that you obtain by looking at user-level value can be drowned out by noise. It is also worth emphasizing that its user-focus notwithstanding, this model is grounded in fundamentals, with value coming, as it always does, from cash flows, growth and risk. I am still learning about this model, but I have put down what I have learned over the last year, when valuing Uber, Amazon Prime and Netflix, into a paper that you can download, read and critique.

Good, Bad and Indifferent User-based Models

One of the motivations for my user-focused valuation was based upon casual empiricism. In my view, many venture capitalists and public investors are pricing user-based companies on user count, with only a few seriously trying to distinguish between good, indifferent and bad user-based models. One of the bonuses of using a user-based model is that it provides a framework for differentiating between great and mediocre user-based companies.

Drivers of Value

A standard critique that old-time value investors have of user-based companies is that they all lose money, but that is not true. There are user-based companies that make money, but it is also true that the user-based model is still in its infancy and that many user-based companies are young, and therefore lose money. That said, there are elements of the cost structure that you can look at, to make judgments on which user-based companies are most likely to grow out of their problems and which ones are just going to grow their problems.

a. Cost Structure: Most young, user-based companies lose money but at the risk of sounding unbalanced, there are good ways to lose money and bad ones, from a value perspective. 

1. Servicing Existing Users versus New User Acquisition: From a value perspective, it is far better for a company to be losing money, because it is spending money trying to acquire new users, than it is to be losing money, because it costs so much to service existing users. The latter signals a bad business model, at least for the moment, whereas the former offers a semblance of hope.
2. Fixed versus Variable Costs: For mature companies with established business models, it is better to have a more flexible cost structure (with more variable costs and less fixed costs). With money-losing, high-growth companies, the reverse is true, since it is the fixed cost portion that yields economies of scale, as the company grows.

b. Growth: Repeating a value nostrum, growth is not always value-creating and not all growth is created equal.

1. Existing versus New Users: A user-based model, where you can grow cash flows from existing users is more valuable, other things remaining equal, than a user-based model that is dependent on adding new users for growth. The reason is simple. Since a company already has expended resources to get existing users, any added revenue it derives from them is more likely to flow directly to the bottom line. Adding new users is more expensive, partly because it costs money to acquire them, but also because new users may not be as active or lucrative as existing ones.
2. Cost of New User Acquisition: This is a corollary of the first proposition, since the value of a new user is net of user acquisition costs. Consequently, user-based companies that are more cost-efficient in adding new users will be worth more than user-based companies that spend considerable amounts on promotion on marketing, to the same end.  

This contrast is best illustrated by looking at Netflix and Spotify, both subscriber-based companies, but with very different models for paying for content. Netflix pays for content as a fixed cost, and derives economies of scale, when it adds fresh subscribers, whereas Spotify pays for content, based upon how much subscribers listen to songs, making it a variable and existing user based cost. As a result, Netflix derives much higher value from both existing and new subscribers:

	Netflix	Spotify
Number of Subscribers	117.6	71
Annual Revenue/Subscriber	$         113.16	$         77.63
Subscriber Service Expenses (as %)	18.90%	79.24%
CAGR in subscriber count	223.93%	369.86%
Value per Existing Subscriber	$         508.89	$       108.65
Cost of acquiring New Subscriber	$         111.01	$         27.30
Value per New Subscriber	$         397.88	$         81.35
Value of all Existing Subscribers	$    59,845.86	$    7,714.28
+ Value of all New Subscribers	$  137,276.49	$  20,764.56
- Corporate Cost Drag	$  111,251.70	$  13,139.75
=Value of Operating Assets	$    85,870.65	$  15,339.10

c. Revenue Models: There are three user-based models, the first is the subscription-based model (that Netflix uses), the second is the advertising-based model (that Yelp uses) and the third is a transaction-based model (that Uber uses). There are companies that use hybrid versions, with Amazon Prime (membership fees and incremental sales) and Spotify (Subscription plus Advertising) being good examples. Each model comes with its pluses and minuses. 

1. Subscription models tend to be stickier (making revenues more predictable) but they offer less upside potential (it is difficult to grow subscription fees at high rates).
2. Advertising models scale up faster, since they require little in capital investment and adding new users is easier (since they free), but revenues are heavily driven by user intensity (how much time you can get users to stay in your ecosystem) and exclusive data (collected in the course of usage).
3. Transaction models are the riskiest, since they require users to use your product or service, but they also offer the most upside, since your upside is less constrained. Amazon Prime's value, in my view, does not stem primarily from the subscription revenues of $99/year but from Amazon's capacity to sell Prime members more products and services.

While no model dominates, picking the wrong revenue model can quickly handicap a business. For instance, using a subscription-based model for a transaction business, where usage varies widely across users, can result in self-selection, where the most intense users choose the subscription-based model to save money, and less intense users stay with a transaction-based model.

Differentiating across User-based Models

With the user-based framework in place, we can start distinguishing between user-based companies. Using existing user value and new customer acquisition costs as the dimensions, we can derive a matrix of companies that go from user-value stars to user-value dogs.

While the combination of high user value with low user acquisition costs may sound like a pipe dream, it is what network benefits and big data, if they exist, promise to deliver. 

• Network benefits refer to the possibility that as you grow bigger, it becomes easier for you to get even bigger, making it less costly to acquire new users. That is the promise of ride sharing, for instance, where as a company gets a larger share of a ride sharing market, both drivers and customers are more likely to switch to it, the former, because they get more customers and the latter, because they find rides more quickly.
• Big data, in a value framework, offers user-based companies an advantage, since what you learn about your users can be used to either sell them more products or services (if you are a transaction-based company), charge them higher premiums (if you are subscription-based) or direct advertising more effectively (if advertising-based). 

Many user-based companies aspire to have network benefits and to use data well, but only a few succeed.

The Pricing Game

As I look at user-based companies, some of which are being priced at billions of dollars, I am struck by how few of them are built to be long term businesses and how many of them are being priced on user numbers and buzz words. Using the framework from the last section, I would like to develop some common features that bad user-businesses seems to share in common and use one high profile examples, MoviePass , to make my case.

Mediocre User-based Companies
Given that so many young companies market themselves, based upon user and subscriber numbers, and that some of them can become valuable companies, are there signs that you can look for that separate the good from the mediocre companies? I think so, and here are a few red flags:

1. All about users, all the time: If the entire sales pitch that a company makes to investors is about its user or subscriber numbers, rather than its operating results (revenues and operating profits/losses), it is a dangerous sign. While large user numbers are a positive, it requires a business model to convert these users into revenues and profits, and that business model will not develop spontaneously. Companies that do not work on developing viable business models go bankrupt with lots of users.
2. Opacity about user data: It is ironic that companies that market themselves to investors, based upon user numbers, are often opaque about key dimensions on users, including renewal (churn) rates, user behavior and side costs related to users. The companies that are most opaque are often the ones that have user models that are not sustainable.
3. Bad business models: If having no business model to convert users to operating results is a bad sign, it is an even worse sign when you have a business model that is designed to deliver losses, not only in its current form, but with no light at the end of the tunnel. That is usually the consequence of having losses that scale up as the company gets bigger, because there are economies of scale. 
4. Loose talk about data: The fall back for many user based companies that cannot defend their business models is that they will find a way to use the data that they will collect from their users to make money in the future (from targeted advertising or additional products and services), without any serious attempt to explain why the data will give them an edge.
5. And externalities: Many user based companies argue that their "innovative" twists on an existing business will both expand and alter the business, leading to benefits for other players in that business, who, in turn, will share their benefits with the user based companies.

The bottom line is simple. It is easy to build user numbers, if you sell a product or service at way below cost, but if your objective is build a long-standing user-based companies, you need a pathway to profitability that is defined early and worked on continuously.

MoviePass: Too Good to be True? 

If you subscribe to MoviePass, for a monthly subscription of $10, you get to watch one theatrical movie, every day, for the entire month. Given that the average price of a theater ticket in the US is $9, this sounds like an insanely good deal, and for an avid movie goer, it is, and the service had two million subscribers in May 2018. MoviePass, though, pays the theaters for the tickets, creating a model that is more designed to drive it into bankruptcy than to deliver profits.

MoviePass Economics

When confronted by the insanity of the business model, Mitch Lowe, the CEO of MoviePass, argued that after an initial burst, where subscribers would see four or five movies a month, they would settle into watching a movie a month, allowing the service to break even. Since Mr. Lowe is a co-founder of Netflix and a former CEO of Redbox, I will concede that he knows a lot more about the movie business than I do, but this is an absurd rationale. If the only way that your service can become viable is if people don't use it very much, it  is not much of a service to begin with.  

In its early days, MoviePass seemed to be trying to build a viable business model, and acquired some high profile venture capital investors, but it was eventually acquired by Helios and Matheson, a data analytics firm, in a  transaction in August 2017. It is Helios and Matheson, intent on giving both data and analysis a bad name, that instituted the $10 a month for a movie-a-day subscription. The subscription worked in delivering users but it, not surprisingly, came with large losses. As MoviePass has continued to burn cash (more than $20 million a month by April 2018), the share price of Helios and Matheson has collapsed, in a belated recognition of its non-viable business model.

Adding to the sense that no one in this company has a grip on reality, Ted Farnsworth, the CEO of Helios and Matheson, argued that the service would continue and had acquired a $300 million line of credit. Since his backing for this line of credit was that he could issue the remaining authorized shares at the current market price, this indicates either extreme ignorance (potential equity issues don't comprise a line of credit) or unalloyed deception, neither of which is a quality that builds trust. Along the way, there have been other attempts to rationalize the model, including the possibility of using the data collected from subscribers to target advertising and the sharing of additional revenues generated by theaters and studios from more movie going. There is nothing exclusive about the data that will be collected from MoviePass subscribers and it is unlikely that theaters and small studios, already on the brink financially, will be willing to share their revenues. In short, this is a bad business model hurtling to a bad end, and the only question is why it took so long.

The Bottom Line

To build a good user-based business, you have to start with the common sense recognition that users are not the end game, but a means to an end. Unfortunately, as long as venture capitalists and investors reward companies with high pricing, based just upon user count, we will encourage the building of bad businesses with lots of users and no pathways to becoming successful businesses.

YouTube Video


Paper on User Based Value

1. Going to Pieces: Valuing Users, Subscribers and Customers

Blog Posts on User-based Value

1. Valuing Uber Riders
2. Valuing Amazon Prime Members
3. Valuing Spotify Subscribers
4. Valuing Netflix Subscribers

User/Subscriber Economics: Value Dynamics

In my last post, I tried valuing Uber by estimating how much an existing user was worth to the company and then using that number to extrapolate to the value of all existing users and the value added by new users. As always, I got many useful comments on what I was missing, what I could do better and what could be simplified, and I thank you (really). While I could spend this entire post rehashing assumptions, I don't intend to! To me, the most useful part of valuation is not the destination, i.e., the value that you get at the end, but the journey, i.e., the process of doing valuation, since it is the process that allows us to isolate the key drivers of value, which, in turn, focuses discussions on those variables, rather than on distractions. Consequently, I decided to revisit my Uber user-based valuation to see what I could eke out as implications for user or subscriber-based businesses.

Estimation versus Economic Risk
I will start by conceding the obvious. I made a lot of assumptions to arrive at the value of a user at Uber, but I will go further. There was not a single fact in that valuation, since every number was an estimate. That said, you could say that about the valuation of any company, with the divergence really being one of the degree of uncertainty you face, not in whether it exists. At the risk of restating points that I have made in my other writing, here are three general points that I would make about uncertainty in valuation.

1. Estimation uncertainty versus Economic uncertainty
To deal with uncertainty in a sensible way, you first have to categorize it. One of the categorizations that I find useful is to break the uncertainty you face when you are trying to value a business or an asset into estimation and economic uncertainty. Estimation uncertainty comes from incomplete, missing or misleading information provided by the company that you are valuing, whereas economic uncertainty is driven by forthcoming changes in the business that the company operates in, as well as macro economic factors. Estimation uncertainty can be reduced by obtaining better and more complete information but estimation uncertainty will remain resistant, no matter how much time you put in and what data analysis that you do. Using my Uber user valuation, it is true that some of the noise in the valuation comes from Uber being a private, secretive company and but most of the uncertainty comes from the ride sharing business being in a state of flux, as regulators and competitors work out how best to deal with shifting consumer tastes and changing technologies. This has two implications. The first is that even if you had access to more information, either because Uber decides to go public or you are an insider in the company, much of the uncertainty in estimated value per user will remain. The second is that your estimated value will change considerably over time, as the facts on the ground change, and that volatility in value cannot be viewed as a shortcoming of the model.

2. Uncertainty is an integral part of valuation
One critique that leaves me unmoved is that valuing a business or an asset, in the face of significant uncertainty, is pointless because you will be wrong. So what? Uncertainty is part and parcel of doing business and you cannot wish it, pray it or analyze it away. As I see it, you have two choices when it comes to uncertainty. You can deal with it frontally by making explicit assumptions or you can go into "denial" model and make implicit assumptions. When I tried to value a user at Uber, I made explicit assumptions about user life, renewal rates and a host of other variables, and I will cheerfully admit that I will be wrong on every one of them, but what is the alternative? When pricing a user by looking at what others are paying for users in similar companies, you are making assumptions about all of the variables as well, but those assumptions are implicit. In fact, they are hidden so well that you may not be aware of your own assumptions, a dangerous place to be when investing.

3. Uncertainty can (and should) be visualized 
Here is my response to uncertainty. Where data exists but I do not have access to that data, I will try to make my best estimates based upon the existing information, noisy, dated or second hand though it might be. Where I have access to data, I will check it against other data, common sense and economic first principles. Where there is no data, I will make my best estimates and to the extent that these estimates come with probability distributions, my value itself is a distribution, not a number. Illustrating this process, with the Uber user valuation:

Excel Add On: Crystal Ball (Oracle), Simulation Output

I have made distributional assumptions on four of my inputs: the portion of Uber's expenses that go to servicing existing users, the life time of a user, the proportion of expenses that are variable and the cost of capital (discount rate) to compute today's value.  Since these distributions are all centered on my base case assumptions, it should come as no surprise that the median value of a user ($414) is very close to my base case value ($410). However, there is a wide spread around that value, with the numbers ranging a low of $74, when the user life is short, the expenses of servicing a user are high, most of the costs are variable and the cost of capital is low, to a high of more than $1000 per user, when the opposite conditions hold. Note that at the current pricing of $69 billion, you are valuing each user close to $900, at the upper end of the distribution. 

User Economics: Cost Propositions
It is true that the end game for every business is to make money for its investors. That said, there is a tendency to over react, when a young company reports a loss, as was the case when Uber reported an operating loss of $2.8 billion for 2016, a few months ago. The pessimists on Uber viewed this as further evidence that the company was on a pathway to nowhere and that investors in the company must be delusional to attach any value to it. The optimists argued that it is natural for young companies to lose money and that Uber should be judged on other dimensions such as user growth and market potential instead. At the risk of angering both groups, I will use my Uber user valuation to argue that while I agree with the second group that losing money is typical at young companies, I will also take sides with the first group that you still need a pathway to profitability amidst the losses, for value to exist.

1. Servicing existing users versus acquiring new users
In my Uber user valuation, I started with the operating losses reported by the company ($2.8 billion), backed into the total operating expenses for the company ($9.3 billion) and then allocated that expense across three categories: servicing existing user (48.17%), acquiring new users (41.08%) and corporate expenses (10.75%). While I based this breakdown on the information (on increase in users and contribution margins in ride sharing) that I had on Uber in 2016, that information is dated, noisy and second hand. It is entirely possible that the actual break down of expenses is different from my estimate. If you are wondering why it matters, since the end result (that Uber lost $2.8 billion) is not changing, there are consequences that you can see in the table below:

Uber User Value: Existing User versus New User Costs

% of Operating Expenses spent on acquiring new users	Value of Existing Users	Value of New Users	Uber User Value	% of Value from Existing users
0%	$6,167	$18,147	$24,314	25.36%
20%	$10,619	$19,035	$29,654	35.81%
40%	$15,071	$19,923	$34,994	43.07%
60%	$19,523	$20,811	$40,334	48.40%
80%	$23,974	$21,699	$45,673	52.49%
100%	$28,426	$22,587	$51,013	55.72%

As you increase the proportion of the operating expenses that are spent on acquiring new users, the value of an existing user goes up because you are spending less money on providing service to that user, but the value of a new user also increases, as the net value added (the difference between the user value and the cost of acquiring a user) goes up. Ironically, as you spend more on acquiring new users and less on servicing existing users, the proportion of your value that comes from existing users increases.
User Value Proposition 1: A money-losing company that is losing money providing service to existing users/customers is worth less than a company with equivalent losses, where the primary expenses are coming from customer acquisitions.

This is, of course, neither profound nor surprising, and it explains why, left to their own devices and without any monitoring, young companies will claim that most or all of their expenses are for acquiring new customers. If you are investing in a young company, you will have to do your own assessment of whether managers are misrepresenting, by looking at expense growth over time versus new customers. If the number of total customers remains fixed and expenses keep rising, you should be skeptical about managerial claims (that most of the costs are for acquiring new customers).

2. Cost Structure
One reason that investors are willing to accept losses at young companies is because they believe that as the company grows its operations, there will be economies of scale. In income statement terms, this will result in expenses growing less quickly than revenues and improving operating margins. That said, you cannot take it on faith that this will always happen or that it will happen at the same rate for every company. To see the impact on user value of this dimension, I adjusted the portion of Uber's expenses that are variable (and will grow with revenues) and those that are fixed (and grow at a lower rate) and captured the value effect in this table:

Uber User Value and Cost Structure

% of current expenses that are fixed	Value of Existing Users	Value of New Users	Uber User Value	% of Value from Existing users
0%	$14,733	$15,250	$29,983	49.14%
20%	$16,412	$20,191	$36,603	44.84%
40%	$17,834	$24,373	$42,207	42.25%
60%	$19,040	$27,924	$46,964	40.54%
80%	$20,068	$30,949	$51,017	39.34%
100%	$20,947	$33,536	$54,483	38.45%

As the proportion of expenses that are fixed rises, the value of both existing and new users goes up but the latter goes up at a faster rate. Put simply, the economies of scale increase as you increase the rate at which you are adding scale.
User Value Proposition 2: A company whose expenses are primarily fixed (will not grow with revenues) will be worth more than an otherwise identical company whose expenses are variable (track revenues).

If unchallenged, young growth companies will always claim that they have massive economies of scale but that claim has to be backed up by the numbers. Specifically, investors should pay attention to the rate of change in revenues and expenses, since with large economies of scale, the former should change more than the latter. The caveat, though, is that having more fixed costs can increase risk, because it will increase the risk of failure at young companies and earnings volatility for more mature firms. As user growth levels off, having more fixed costs will reduce value rather than increasing it.

User Economics: Growth Propositions
For young companies, we generally view growth as good and while that is generally true, not all growth is created equal. In fact, even with young companies, there are some strategies that deliver growth in users or revenues, while destroying value. In a user or subscriber based model, there are two ways you can grow your revenues. One is to get existing users to buy more of your products or services and the other is by trying to acquire new users. While both can increase value, the former will be create more value, for two reasons. First, since it comes from existing customers, you don’t have to pay to acquire these users and it is thus less costly to the firm. Second, by increasing the value of a user, it increases the value of any new users as well, creating a secondary impact on value. Using my Uber user valuation, you can see the impact of changing the annual growth rate in revenues for an existing user in the chart below:

As revenue growth rate increases, the value of both existing and new users increases, with the value of Uber hitting $90 billion at high annual growth rates. If there is no growth in revenues, the value of Uber collapses as new users actually destroy value (because the cost of adding a new user exceeds the value of that user). Now consider how Uber's value is affected, if we hold existing user assumptions fixed and change the compounded annual growth rate (for the next 10 years) in the number of users:

While value increases with user growth rates, it increases at a lower rate than it did when we varied revenue growth from existing users.
User Value Proposition 3: A company that is growing revenues by increasing revenues/user is worth more than an otherwise similar growth company that is deriving growth from increasing the number of users/customers. 

Young companies face the question of whether to allocate resources to get new users or try to sell more to existing users is one of those. At least in the case of Uber, the numbers seem to indicate that the payoff is greater in getting existing users to use the service more than in looking for new users.

User Economics: Business Propositions
At the risk of stretching the user value model too far, it can be used to discuss business models in the space, from the networking benefits that so many companies in this space claim to possess to how the revenue model you choose (subscription, transaction or advertising) plays out in user values.

1. Competitive Dynamics and Networking Benefits

Is it better to operate in a business where the cost of acquiring a new user is low or high? Holding all else constant, the answer is obvious. A firm will maximize its value if can generate both high value per user and have a low cost of acquiring new users. That said, if everyone in the business shares these characteristics, one or another of these variables has to change. If the cost of acquiring new users is low for everyone, competition will drive down the value per new user, and if the value per user remains high, competition will drive up the cost of acquiring new users. The trade off is captured in the picture below:

User Value Proposition 4:  The exceptional firm will be the one that is able to find a pathway to high value per user and a low cost to adding a new user in a market, where its competitors struggle with either low value per user or high costs of acquiring users.
So how do the exceptional companies pull off this seeming impossible combination of high value per user and low cost per new user? I may be stretching, but it is at the heart of two terms that we see increasingly used in business, network benefits and big data.

• Network Benefits: If network benefits exist, the cost of acquiring new users will decrease as a company's presence in a market increases, reaching a tipping point where the biggest player will face much lower costs in acquiring new users than the competition, allowing it to capture the market and perhaps use its market dominance to increase the value of each user. In the case of Uber and ride sharing business, the argument for networking benefits is strong on a localized basis, since there are clearly advantages for both drivers and customers to shift to the dominant ride sharing company in any locality, the former because they will generate more income and the latter because they will get better service. The argument is much weaker on a global basis, though ride sharing companies are trying to create networking benefits by allying with airlines and credit care companies, and how this attempt plays out may well determine Uber's ultimate value.
• Big Data: While I remain a skeptic on the "big data" claims that every company seems to be making today, it is inarguable that there are companies that use big data to augment value. These companies collect data on their existing users/subscribers/customers and use that information to (a) customize existing products/services to meet user preferences, (b) create new products or services that meet perceived user needs and/or (c) for differential pricing. All of these increase user value by altering one or more of the inputs into the equation, with customization increasing user life and new products & differential the growth in revenues/user. In my view, the best users of big data (Netflix, Amazon, Google and Facebook) have used the data to increase their existing user value. Uber is still in the nascent stages, but its attempts at using data have expanded from surge pricing to differential pricing.

2. Revenue Models
In my version of user valuation, I look at revenues per user, drawing no distinction on how those revenues are derived. Broadly speaking, there are three revenue models that a user/subscriber based company can use, a subscription-based model where users or subscribers pay a subscription fee to continue to use the service or product, a transaction-based model where users or subscribers pay only when they use the service of product and an advertising-based model where users or subscribers get to use the product or service for free, but are targeted in advertising. Netflix operates on a subscription-based model, Uber is a transaction-based firm and Facebook generates its revenues from advertising. Some companies like LinkedIn have hybrid models, generating revenues from subscriptions (from premium members), transactions (from recruitments) and advertising.  There are other inputs into the valuation that will be affected by a company's revenue model and I have tried to capture them in the table below:

	Subscription	Transaction	Advertising
User Stickiness (User life & Renewal Probability)	High (High life & renewal probability)	Intermediate (Intermediate life & renewal probability)	Low (Low life & renewal probability)
Revenue per User Predictability (Discount rate)	High (Low Discount Rate)	Low Predictability (High Discount Rate)	Intermediate (Average Discount Rate)
Revenue per User Growth (Annual Growth Rate)	Low (Low growth rate in revenues/user)	Low (High growth rate in revenues/user)	Intermediate (Intermediate growth rate in revenues/user)
Growth rate in users (CAGR in # Users)	Low (Low CAGR in # users)	Intermediate (Intermediate CAGR in # users)	High (High CAGR in # users)
Cost of adding new users (Cost/New User)	High (High Cost/New User)	Intermediate (Middling Cost/New User)	Low (Low Cost/New User)

There is no one dominant revenue model, since each has its pluses and minuses. An advertising-based model will allow for much more rapid growth in a firm's early years, a subscription-based model will generate more sustainable growth and a transaction-based model has the greatest potential for revenue growth from existing users.
User Value Proposition 5:  The "optimal" revenue model may vary for a firm depending upon where it is in the life cycle and across firms depending on their product or service offerings and across investors, depending on whether they are focused on user growth, revenue growth or revenue sustainability.

3. Real Options
When valuing a company based upon its expected cash flows, there is a chance that you will under value the company, if it has control of a resource that could be used for other purposes in the future, even if that usage makes no economic sense today. That is why a technology or natural resource reserve that is not viable today can still have value, and this is the basis for the real option premium. In the context of a user-based business, optionality can become a component of value, to the extent that companies may be able to exploit their user bases to sell other products and services in the future. While the intuition of real options is simple, valuing real options is notoriously difficult and after much hand waving, most of us (including me) give up, but the user-based valuation model provides a framework to at least eke out some general propositions about optionality and value.

There should be no surprises in this picture, with the value of a real option in a user base tied to the inputs into an option pricing model.
User Value Proposition 6: The value of optionality from a user base will be greatest at firms with lots of sticky, intense users in businesses where the future is unpredictable because of changes in product/service technology and customer tastes. 

The Bottom Line
The most direct applications of a user or subscriber based model is in the valuation of companies like Uber, Facebook and Netflix. That said, more and more companies are seeing benefits in shifting from their traditional business models to user-based ones. Apple is a cash machine built around a smartphone but it is also accumulating information on more than a billion users of these phones, to whom it may be able to offer other products and services. Amazon started life as an online retail company but there is no denying the power of its seventy million Prime members in generating revenues for the company. I have used Microsoft and Adobe products for as long as they have been around, but with both companies, but my relationship with both companies has changed. I am now a subscriber (Office 365 and  Creative Cloud member) who pays annual fees, rather than a customer who buys and upgrades software on a discretionary basis. Understanding user economics and value is central to not only investors in these companies, when valuing and pricing them, but to managers of these companies, in their day-to-day business decisions. I will admit, without shame, that my knowledge of user-based companies is rudimentary and that my user-based model may be amateurish, in what it misses or mangles. That said, if you are an expert on user-based businesses, I hope that you can build on the model to make it more realistic and useful.

YouTube Video

Links

1. Crystal Ball (Simulation Add On for Excel)
2. My paper on dealing with uncertainty in valuation

Attachments

1. Uber User Valuation Spreadsheet
2. Uber User Value: Simulation Output

Blog Posts

1. Uber's Bad Week: Doomsday Scenario or Business Reset (June 2017)
2. User/Subscriber Economics: An Alternative View of Uber's Value

Uber's Coming out Party: Personal Mobility Pioneer or Car Service on Steroids?

After Lyft’s IPO on March 29, 2019, it was only a matter of time before Uber threw its hat in the public market ring, and on Friday, April 12, 2019, the company filed its prospectus. It is the first time that this company, which has been in the news more frequently in the last few years than almost any publicly traded company, has opened its books for investors, journalists and curiosity seekers. As someone who has valued Uber with the tidbits of information that have hitherto been available about the company, mostly leaked and unofficial, I was interested in seeing how much my perspective would change, when confronted with a fuller accounting of its performance.

Backing up!
To get a sense of where Uber stands now, just ahead of its IPO, I started with the prospectus, which weighing in at 285 pages, not counting appendices, and filled with pages of details, can be daunting. It is a testimonial to how information disclosure requirements have had the perverse consequence of making the disclosures useless, by drowning investors in data and meaningless legalese. I know that there are many who have latched on to the statement that "we may not achieve profitability" that Uber makes in the prospectus (on page 27) as an indication of its worthlessness, but I view it more as evidence that lawyers should never be allowed to write about investing risk.

Uber's Business
Just as Lyft did everything it could, in its prospectus, to relabel itself as a transportation services (not just car services) company, Uber's catchword, repeatedly multiple times in its prospectus, is that it is a personal mobility business, with the tantalizing follow up that its total market could be as large as $2 trillion, if you count the cost of all money spent on transportation (cars, public transit etc.)

Uber Prospectus: Page 11

While the cynic in me pushes me back on this over reach (I am surprised that they did not include the calories burnt by the most common transportation mode on the face of the earth, which is walking from point A to point B, as part of the total market), I understand why both Lyft and Uber have to relabel themselves as more than car service companies. Big market stories generally yield higher valuation and pricing than small market stories!

The Operating History
Uber went through some major restructuring in the three years leading into the IPO, as it exited cash burning investments in China (settling for a 20% stake in Didi), South East Asia (receiving a 23.2% share of Grab) and Russia (with 38% of Yandex Taxi the prize received for that exit). It is thus not surprising that there are large distortions in the financial statements during the last three years, with losses in the billions flowing from these divestitures. In the last few weeks, Uber announced a major acquisition, spending $3.1 billion to acquire Careem, a Middle Eastern ride sharing firm. Taking the company at its word, i.e., that the large divestiture-related losses are truly divestiture-related, let’s start by tracing the growth of Uber in the parts of the world where it had continuing operations in 2016, 2017 and 2018:

Uber Prospectus: Page 21

The numbers in this table are the strongest backing for Uber’s growth story, with gross billings, net revenues, riders and rides all increasing strongly between 2016 and 2018. That good news on growing operations has to be tempered by the recognition that Uber has been unable to make money, as the table below indicates:

Uber Prospectus: Pages 21 & 24

The adjusted EBITDA column contains numbers estimated and reported for the company, with a list of adjustments they made to even bigger losses to arrive at the reported values. I convert this adjusted EBITDA to an operating income (loss) by first netting out depreciation and amortization (for obvious reasons) and then reversing the company’s attempt to add back stock based compensation. The company is clearly a money loser, but if there is anything positive that can be extracted from this table, it is that the losses are decreasing as a percent of sales, over time.

The Rider Numbers
One of Uber’s selling points lies in its non-accounting numbers, as the company reported having 91 million monthly riders (defined as riders who used either Uber or Uber delivery at least once in a month) and completing 5.2 billion rides. To break down those daunting numbers, I focus on the per rider statistics to see the engines driving Uber’s growth over time:

Uber Prospectus: Page 21

There is good and bad news in this table. The good news is that Uber’s annual gross billings per rider rose almost 28% over the three year period, but the sobering companion finding is that the billings/ride are decreasing. Boiled down to basics, it suggests that the growth in overall billings for the company is at least partially driven by existing riders using more of the service, albeit for shorter rides. It could also reflect the fact the new riders for the company are coming from parts of the world (Latin America, for instance), where rides are less expensive.  Finally, I took Uber’s expense breakdown in their income statement, and used it to extract information about what the company is spending money on, and how effectively:

Uber Prospectus: F-4 (income statement in appendix)

I make some assumptions here which will play out in the valuation that you will see below.

1. User Acquisition costs: Using the assumption that user change over a year can be attributed to selling expenses during the year, I computed the user acquisition cost each year by dividing the selling expenses by the number of riders added during the year.
2. Operating Expenses for Existing Rides: I have included the cost of revenues (not including depreciation) and operations and support as expenses associated with current riders. 
3. Corporate Expenses; These are expenses that I assume are general expenses, not directly related to either servicing existing users or acquiring new ones and I include R&D, G&A and depreciation in this grouping.

The good news is that the expenses associated with servicing existing users has been decreasing, as a percent of revenues, indicating that not all of these costs are variable or at least directly linked to more rider usage. Also, corporate expenses are showing evidence of economies of scale, decreasing as a percent of revenues. The bad new is that the cost of acquiring new users has been increasing, at least over this time period, suggesting that the ride sharing market is maturing or that competition is picking up for riders.

More than ride sharing?
Uber is a more complicated company to value than Lyft, for two reasons. The first is that Uber is not a pure ride sharing company, since it derives revenues from its food delivery service (Uber Eats) and an assortment of other smaller bets (like Uber Freight). In the graph below, you can see the evolution of these businesses:

Uber Prospectus: Page 114

It is worth noting this table while suggests that while some of Uber’s more ambitious reaches into logistics have not borne fruit, its foray into food delivery seems to be picking up steam. Uber Eats has expanded from 2.68% of Uber’s net revenues to 13.12%. There is some additional information in another portion of the prospectus, where Uber reports its "adjusted" net revenue and gross Billings by business, and it does look like Uber's net take from Uber Eats is lower than its take from ride sharing:

Uber Prospectus: Pages 102 & 103

While it is clear that Uber's ride sharing customers have been quick to adopt Uber Eats, there are subtle differences in the economics of the two businesses that will play out in future profitability, especially if Uber Eats continues to grow at a disproportionate rate.

Unlike Lyft, which has kept its focus on the US and Canadian markets, Uber's ambitions have been more global, though reality has put a crimp on some of its expansion plans. While Uber's initial plans were to be everywhere in the world, large losses have led Uber to abandon much of Asia, leaving China to Didi and South East Asia to Grab, with India being the one big market where Uber has stayed, fighting Ola for market share and who can lose more money. The fastest growing overseas market for Uber has been Latin America, as you can see in the graph below:

Uber does not provide a breakdown of profitability by geographical region, but the magnitude of the losses that they wrote off when they closed their Chinese and South East Asian operations suggests that the US remains their most lucrative ride sharing market, in terms of profitability. 

The Road Ahead : Crafting a story and value for Uber

1. A Top Down Valuation
In valuing Lyft, I used a top-down approach, starting with US transportation services as my total accessible market and working down through market share, margins and reinvestment to derive a value of $13.9 billion for its operating assets and $16.4 billion with the IPO proceeds counted in. Using a similar approach is trickier for Uber, since its decision to be in multiple parts of the logistics business and its global ambitions require assessment of a global logistics market, a challenge. I did an initial assessment of Uber, using a much larger total market and arrived at a value of $44.4 billion for its operating assets, but adding the portions of Didi, Grab and Yandex Taxi pushed this number up to $55.3 billion. Adding the cash balance on hand as well as the IPO proceeds that will remain in the firm (rumored to be $9 billion), before subtracting out debt yields a value for equity of about $61.7 billion.

Download spreadsheet

The share count is still hazy (as the multiple blank areas in the prospectus indicate) but starting with the 903.6 million shares of common stock that will result from the conversion of redeemable convertible preferred shares at the time of the IPO, and adding in additional shares that will result from option exercises, RSUs (restricted stock units issued to employees) and new shares being issued to raise approximately $10 billion in proceeds, I arrive at a value per share of about $54/share, though  that the updated version of the prospectus, which should come out with the offering price, should allow for more precision on the share count. (Update: Based upon news stories today (4/26/19), it looks like the share count will be closer to 1.8 billion to 2 billion shares, which will result in a value per share closer to $31-$33/share).

2. A Rider-based Valuation
The uncertainty about the total accessible market, though, makes me uneasy with my top down valuation. So, I decided to try another route. In June 2017, I presented a different approach to valuing companies like Uber, that derive their value from users, subcribers or members. In that approach, I began by valuing an existing user (rider), by looking at the revenues and cash flows that Uber would generate over the user’s lifetime and then extended the approach to valuing a new user, where the cost of user acquisition has to be netted out against the user value. I completed the assessment by computing the value drag created by non-rider related costs (like G&A and R&D). In the June 2017 valuation, I had to make do with minimalist detail on expenses but the prospectus provides a much richer break down, allowing me to update my user-based valuation of Uber. The valuation picture is below:

Download spreadsheet

This approach yields a value for the equity of about $58.6 billion for Uber’s equity, which again depending on the share count would translate into a share price of $51/share. (Update: Based upon news stories today (4/26/19), it looks like the share count will be closer to 1.8 billion to 2 billion shares, which will result in a value per share closer to $30/share).

Value Dynamics
The benefits of the rider-based valuation is that it allows us to isolate the variables that will determine whether Uber turns the corner quickly and can make enough money to justify the rumored $100 billion value. The value of existing riders is determined by the growth rate in per-user revenues and the cost of servicing a user, with increases in the former and decreases in the latter driving up user value.  The value of new riders, in the aggregate, is determined by the increase in rider count and the cost of acquiring a new rider. One troubling aspect of the growth in users over the last three years has been the increase in user acquisition costs, perhaps reflecting a more saturated market. In the table below, I estimate the value of Uber's equity, using a range of assumptions for the growth rate in per user revenues and the cost of acquiring a new user:

Download spreadsheet

There are two ways that you can read this table. If you are a trader, deeply suspicious of intrinsic value, you may look at this table as confirmation that intrinsic value models can be used to deliver whatever value you want them to, and your suspicions would be well founded. I am a believer in value and I see this table in a different light.

• First, I view it as a reminder that my estimate of value is just mine, based on my story and inputs, and that there are others with different stories for the company that may explain why they would pay much more or much less than I would for the company. 
• Second, this table suggests to me that Uber is a company that is poised on a knife's edge. If it just continues to just add to its rider count, but pushes up its cost of acquiring riders as it goes along, and existing riders do not increase the usage of the service, its value implodes. If it can get riders to significantly increase usage (either in the form of more rides or other add on services), it can find a way to justify a value that exceeds $100 billion. 
• Third, the table also indicates that if Uber has to pick between spending money on acquiring more riders or getting existing riders to buy more of its services, the latter provides a much bigger bang for the buck than the former. 

Put simply, I hope Dara Khoshrowshahi means it when he says that Uber has to show a pathway to profitability, but I think that is what is more critical is that he acts on those words. In my view, this remains a business, whether you define it to be ride sharing, transportation services or personal mobility, without a business model that can generate sustained profits, precisely because the existing model was designed to deliver exponential growth and little else, and Uber, and the other players in this game), have only a limited window to fix it.

Refreshing the Pricing
Having spent all of this time on Uber's valuation, let me concede to the reality that Uber will be priced by the market, and it will be priced relative to Lyft. That is why Uber has probably been pulling harder than almost any one else in the market for the Lyft IPO to be well received and for its stock to continue to do well in the aftermarket. In the table below, I compare key operating numbers for Uber and Lyft, with Lyft's pricing in the market in place:

In computing the metrics, it is worth remembering that Uber and Lyft use different definitions for basic metrics and I have tried to adjust. For instance, Uber defines riders as those who use the service at least once a month and the closest number that I can get for Lyft is their estimate that they had 18.6 million active quarterly riders. Uber is bigger on every single dimension, including losses, then Lyft. I convert Lyft's current market pricing (on April 12, 2019) into multiples, scaling them to different metrics and applying these metrics to Uber:

Download pricing spreadsheet

In computing Uber's equity value from its enterprise value, I have added the cash ($6.4 billion of cash on hand plus the $9 billion in expected IPO proceeds) $ and Uber's cross holdings ($8.7 billion) to the value and netted out debt ($6.5 billion). To get the value per share, I have used the estimated 1175 million shares that I believe will be outstanding, including options and RSUs, after the offering. Depending on the metric that I can scale it to, you can get values ranging from $47 billion to $124 billion for Uber's equity, though each comes with a catch. If you believe that there are no games that are played with pricing, you should think again! Also, as Lyft's price moves, so will Uber's, and I am sure that there are many at Uber (and its investment banks) who are hoping and praying that Lyft's stock does not have many more days like last Thursday, before the Uber IPO hits the market.

Conclusion
I am sure that there are many who understand the ride sharing business much better than I do, and see obvious limitations and pitfalls in my valuations of both Uber and Lyft.  In fact, I have been wrong before on Uber, as Bill Gurley (who knows more about Uber than I ever will) publicly pointed out,  and I am sure that I will be wrong again.  I hope that even if you disagree with me on my numbers, the spreadsheets that are linked are flexible enough for you to take your stories about these companies to arrive at your value judgments.

YouTube Video


Spreadsheets (for valuation)

1. Uber Valuation - Top Down
2. Uber Valuation - User-based
3. Uber Pricing

Other Links

1. Uber Prospectus (April 2019)
2. My first and fatally flawed valuation of Uber (June 2014)
3. Bill Gurley's take down of my Uber valuation (July 2014)
4. My post on the future of ride sharing (August 2016)
5. My first user-based valuation of Uber (June 2017)