How Apple is Spending its Cash

What happens when you become so rich that you don't know what to do with your money? You got yourself a nice black Benz with a heated steering wheel. You travelled to New Zealand to view the spectacular coral reefs. You even got a house maid. Now what?

I'm drawing an analogy, of course, but the fictional scenario above is a rudimentary representation of where Apple finds itself now. The company has too much cash, which is lying around unproductively. The main job of a corporation is to provide value to its stockholders. This value comes mainly in the form of stock appreciation and dividends, the latter of which Apple has been slowly increasing. As a company's growth slows, it tends to invest less heavily into the business, and instead spews off dividends to investors.

Tech writers almost always recommend Apple spend its mountain of cash in the form of Research & Development and hiring, but this advice is lacking an understanding of what money can buy. Apple has classically spent less on R&D than competitors, but this has never stopped them from releasing the most popular and innovative products. Just like with anything, there is quantity and quality. Spending billions on R&D because you have billions to spend is not a winning strategy - it's unlikely to provide adequate returns in the form of revolutionary products and it won't provide investor's with the value they seek. This is not the pharmaceutical industry and Apple is no longer a growth stock (it is too big).

Many industry mavens also eagerly advise Apple to staff-up, especially now, since the latest iOS and OS X releases have been riddled with bugs. They seem to think that Apple possesses a machine that turns dollars into productive employees. I assure you, this is not the case, because if it were, Apple would be far richer than it is now. While I agree that Apple should hire more qualified employees (particularly in the App Store, Search, Maps, and iCloud teams), what Apple should do is not what Apple could do. Staffing up teams quickly is a recipe for disaster, especially at a company with such a unique culture as Apple. It can take months, if not years, for an employee to be assimilated into the company. Let's also not forget that this employee will also need to be trained by current employees, taking their productive time away from work. Fortunately, Apple seems to be trying to solve this problem. From what I've seen anecdotally on Twitter and the web, many top-notch individuals have been pouched to join Apple in the last few years, and particularly this year. It's worth pointing out though - we don't know how many have left Apple in the same period.

Apple has also significantly accelerated its massive stock buyback program in order to put its cash to productive use. Stock buybacks are a divisive thing; ask 10 investors if it's a good idea and you'll hear 100 opinions. Buybacks are extremely situational, and they might work for some companies while not for others. In the case of Apple, however, I think they are a good idea because those acquired shares can be redistributed to employees, old and new. It's notoriously hard to recruit great engineers in the valley, and no better incentive exists than cash and stock.

It's easy to write advice about what Apple should do with all of its cash, but it would be foolish to think that Apple is unaware about all these possibilities. At the same time, management myopia is defintely something Apple should steer clear of, as it was that thinking that turned BlackBerry into BlackBerry.

Pop Goes the IPO

Lending Club, which is a peer-to-peer online lender, had an IPO this Thursday, December 11. After a full day of trading, shares closed at 56% above the initial IPO price. As often happens, many vociferous investment banking haters called this an IPO "pop", in which the banks stole millions of dollars from the company.

The truth is IPO's are a messy ordeal, and are full of estimations and guesswork by the bankers. Perhaps nobody can better explain what really goes on during an IPO than the notorious Epicurean Dealmaker. Here's an excerpt from the fabled Wall Street philosopher:

Now, you can see that this exercise is an art, not a science. Investment bank IPO pricing is the epitome of (very) highly educated guessing. We often get it wrong, but, on average, IPO pricing is normally pretty accurate. After all, it's our job, and we do it well. The picture gets complicated, however, when the company in question, like LinkedIn, does not have any comparable peers among listed public companies. Our guesses become much less educated and much more finger-in-the-air type things. There is no cure for this but to go to market and see what investors themselves tell you they are willing to pay.

For a thorough explanation of this sensitive topic, I recommend you read his full post on LinkedIn's IPO pop. The very same logic could be applied to last weeks Lending Club offering. Occam's razor taught this concept to us hundreds of years ago, so let's not blame the banks for the behavior of the market.

The Academic and The Professional

Business strategies are just like opinions, and opinions are just like you know what. Everybody has one. If you Google around, or read this blog, you will find a lot of conflicting advice that people give to companies. As with any advice, some of it is good, some bad, and some downright comical. You can categorize this advice into two umbrella categories: Academia and Experience.

Academia

Business strategies from the world of academia are distinctly different from strategies given by an experienced professional (I use the term experienced professional because it fits rather nicely, but you can substitute it for any word that implies real world knowledge). Academic strategies are often from PhD's and business school professors who attempt to explain the world with models. These models are imprecise, but they are general templates of how a business should operate to be successful. Probably the best known model is Michael Porter's Five Forces Analysis, which seeks to develop a framework to understand how intense competition in some industries is. This is a gross simplification of the Porter's model, but I bet if you asked him to pitch it to you in an elevator, that's precisely what he would say. The problem with this model, and any model really, is that it encourages you to fit a business into it even when it doesn't fit.

To illustrate the weaknesses of models, let us take Twitter and try to explain it through the Five Forces Model.

The Threat from New Entrants

This one is relatively simple to explain. It is easy to create a new social media startup, because the barriers to entry are so low. That said, this threat is mitigated somewhat by the difficulty of starting a successful startup that effectively competes with Twitter. Many other factors are present for this threat, such as economies of scale, customer loyalty, government policy, and a slew of others.

Threat of Substitute Services

This force is also simple to explain, and the model accurately reflects the realities of Twitter's business. How easy is it to create a substitute for Twitter? Well, creating the substitute is relatively simple. All you need to make is a service that lets you share 140 characters with the world. Many mimicking services have launched in the past few years, but literally all have failed. The switching costs to a new service are fairly low for Twitter users, since it's free and few people care about their past tweets being transferred to a new service.

Bargaining Power of Customers

This is where the model starts to break down quite a bit. Who are Twitter's customers? Are they the users of the service, or the customers the people who purchase ads on Twitter? The answer isn't clear and can be argued both ways. Incorrect use of the model can get a business in a lot of trouble, which you can say is the failing of the business and not the model. While this is probably true, business models are not like scientific models. People are must more open to accepting a business model such as Porter's Analysis when compared to a scientific model that sets forth a hypothesis. This results in business models that are often treated as gospel.

Bargaining Power of Suppliers

Traditionally, suppliers provided raw materials or some other basic ingredient/service to your company. You can see why Porter factored this into the model, since a powerful supplier could seriously undermine your business. But in the age of the internet, suppliers aren’t nearly as important to web companies like Twitter. This isn’t to say that Twitter doesn’t rely on any suppliers, because they probably rely on dozens of companies. It’s just that this factor is of such minuscule importance that it’s a nuisance to even include when evaluating most web companies.

Intensity of Competitive Rivalry

The problem with this factor is that it’s so comprehensive that a whole model can be built just to understand it. Surely Facebook is a rival of Twitter, since both are popular social networks. But than what about Path, Pinterest, LinkedIn, Yelp, Foursquare, Snapchat, and Tumblr? Isn’t the ultimate competition for the users time? If so, these companies (and many more) would be a competitor to Twitter. It’s worth noting that some are closer competitors (Facebook) and some farther (Foursquare), but my point remains: this factor is totally open-ended.

The goal of this exercise wasn’t to display my Porter Analysis prowess, but to walk you through its strengths and weaknesses. The greater goal, however, was to exemplify how models are used in the world of academia. Porter’s analysis is just one of many academic models, which are all somewhat similar to each other. That is, the world of academia attempts to create models that are comprehensive and general. They seek to explain whole market segments and industries, by citing common trends within these areas.

In a way, these models are naive. Human nature is such that it wants explanations for things, even when the explanation isn’t totally correct. While Porter’s model is extraordinarily helpful in understanding the rivalry within an industry, it suffers from being overly academic. His Five Forces fits for the industries he studied, but as we have seen, they don’t entirely fit Twitter. To try and wedge a web company like Twitter into his model would be to grind coffee with a knife. It might seem like I’m picking on Porter, but almost all of the academic models I have studied suffer from the same broad-stroke failings. The tech community has fully embraced Clayton Christensen’s disruptive innovation, but it is also too comprehensive for its own good. Academia follows the same thinking pattern that it teaches to its students. It would be silly to teach business school students specific strategies applicable in particular industries for distinct companies, so instead, professors focus on high-level theory. Unfortunately, the very same theory they teach becomes the strategic model they attempt to apply to a real-world company, and it simply won’t fit.

Experience

The second general category of business advice comes from the experienced professional. This is a person who has worked in an industry, or has specifically followed an industry, for years. Examples that come to mind are consultants, analysts, and C-suite executives who have held important roles within a company. These people usually take a different stance on business strategy - one that is much more nuanced and specific. Consequently, the experienced manager will give advice that is highly specific to one company, and this advice is usually never applicable to other companies. An experienced professional who works for Twitter might argue that keeping their API closed to third parties, effectively limiting the amount of complement products, to Twitter is a good idea. This would obviously be bad advice if applied to another company such as Dropbox, since they want to have their cloud storage available everywhere. The experienced manager does not build models, and instead circumvents them in order to get right to the problem. Business is a touchy-feely and free-flowing animal, while academia models are more rigid.

The experienced professional has his own failings too. Experience is usually narrow and applies to niches. Experience in one company might not apply to the next company the manager joins. It can lead to the overconfidence bias, which may actually hurt performance.

The Academic Professional

After everything I have written above, you could ask why doesn't the experienced professional just read some academia books to embrace the best of both worlds? And you would be right, since that is exactly what any business minded person should do. Both categories of business strategy have failings and biases, which can be circumvented by not fully buying into one way of thinking. Academics shouldn't be so confident about their models, because nothing in business is so black and white. A model that works during this decade might not work the next decade, and you often see businesses have trouble letting go of their old strategies that are no longer applicable. Similarly, the experienced professional should educate himself in the general trends some industries follow, but at the same time he must realize these models are just oversimplifications. Being able to maneuver and shape a model to the realities of the real-world is key to applying it right.

Some Spotify Charts

Sometimes it’s helpful to turn data into another medium. It is easy to get lost in the numbers, so I often find it helpful to view the information from a distance. Charts are great for that. I am still working on my Spotify analysis, but here are some preliminary findings. I will show the chart first, followed by my thoughts.

• As I have previously calculated, Spotify’s monthly growth is around 3.738%. We don’t have enough data to tell if this is a rapid rate, since no data is available from competitors. That’s why we can only compare Spotify with itself, at least for now. Paying subscribers are growing at nearly the same rate as active subscribers, which use the service for free. I would have predicted that free users would be growing much faster than paying subscribers, but the data seems to show this isn’t the case. Currently, roughly 25% of users pay for Spotify. This seems a little too good to be true, but I assume Spotify would like to convert even more users into paying subscribers. 

• Revenues and Cost of Revenues are an extremely basic measure of profitability. In fact, these two data points sum only to give us Gross Profit. That said, they are arguably the most important indicators of growth and scale. Revenue tells us growth, while Cost of Revenue implies scale. Spotify shifted from a negative Gross Profit in 2010 to a positive one in 2011 and on. In short, this means that Spotify is making more money than it costs to power the service (without taking into account R&D and SG&A). Many startups have higher Cost of Revenues than actual Revenues, implying that they are not operating at scale. Spotify is still not profitable since the company isn’t posting Net Profit’s, but it appears to be running efficiently, as far as most startups go. 

• 91% of Spotify’s revenue comes from subscriptions, while a measly 9% comes from advertising. This is a paltry amount, considering that 75% of users are free users. It makes you wonder how little Spotify pays out to artists for every play a free user makes. No wonder many musicians are hesitant to put their music on Spotify - it barely pays. 

I often like to gauge a company’s future success by a simple question. Would I invest in it? Currently, Spotify is unprofitable, but if they can spread their costs over an increasing amount of paying users, it’s very likely that Spotify can turn into a profitable business. 

Tensions in Social Media Valuations

Startups are extremely popular today, since the Internet and the post-PC era have allowed small companies to reach an audience of billions. Everybody carries a smartphone with them, which gives the potential access to your new idea to users. Assuming your startup idea is good, you can really hit it big. We have seen this happen to Facebook, Twitter, and Snapchat, who grew (and continue to grow) users at levels retail businesses would kill for. By the way, these services are free.

After reaching such a large audience, these social media companies require money to pay for the cost of doing business, which they first get from investors in angel rounds. The investors, in return, expect to be paid back more than their initial investment. Thus, a social media company (and all companies that require investment, really) must somehow start producing revenues and hopefully profits to pay their investors back. Since their service is free, online advertising has been the only way to generate these revenues. The way online advertising works is the more users see or click the ad, the higher revenues a social media company will get. Key performance indicators (KPI’s) such as monthly active users (MAU’s) are used to calculate a valuation on many such advertising companies.

As I’ve written in the past, these new valuation techniques are extremely young, and their results are to be taken with a grain of salt. My favorite view on this issue is from Aswath Damodaran, a finance professor at NYU Stern. His take says there are currently two major ways to valuate social media companies. The first is from the traditional investor, who looks at revenues, profits, and investments. The newer valuation technique looks at user growth and other user-focused metrics like MAU’s. He argues that as a company matures, it must transition from telling stories to showing meaningful results. These results are mainly focused on the fundamentals of business, like revenues and profits.

As a social media company grows, the way it is valuated shifts from the younger model, to the older, traditional model. Most of the successful social media companies are still too young to fully appreciate this valuation shift. In fact, most investors will probably shift their techniques unknowingly. They will notice how social media companies are maturing, and begin applying the traditional models. Stories will no longer be enough to take investor’s money, and results will have to be shown.