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Category Archives: Financial

Apple, Amazon, Google, Microsoft

Last year we looked at the four companies and compared their business model over two quarters: Apple (hardware), Amazon (retail), Google (advertising), Microsoft (software). It struck me how far the integrated Wolfram Alpha technology has come in the last two years. It combines the symbolic computing capabilities of the Mathematica platform with curated data (for example financial data) and some pretty impressive linguistic analysis capabilities for free-form text input.

For example, in Wolfram Alpha, just type in the following query: “Googe vs. Amazon vs. Apple vs. Microsoft” The results are shown as a series of three screen-shots below:

ComparisonWolframAlpha1

ComparisonWolframAlpha2

ComparisonWolframAlpha3

Not only do you get the various data such as the fundamentals or the analysis of a mean-variance optimal portfolio displayed, but you get all the code needed to programmatically load such data. For example, if you want to get the breakdown of the analyst ratings, the system will expand it as follows:

AnalystRatings

So far we haven’t done any coding or bothered with integrating any data source. This amount of integration and automation is pretty impressive. I am often surprised how few companies are taking advantage of such advanced technology platforms.

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Posted by on April 28, 2013 in Financial

 

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Faceplant with Facebook?

With the Facebook IPO coming up this Friday there is a lot of attention around its business model and financials. I’m not an expert in this area, but my hunch is that a lot of people will lose a lot of money by chasing after Facebook shares. Why?

I think there are two types of answers. One from reasoning and one from intuition.

For reasoning one needs to look at a more technical assessment of the business model and financials. Some have written extensively about the comparative lack of innovation in Facebook’s business model and core product. Some have compared Facebook’s performance in advertising to Google – the estimates are that Google’s ad performance is 100x better than that of Facebook. Some have pointed out that many of Facebook’s core metrics such as visits/person, pages/visit or Click-Through-Rates have been declining for two years and go as far as calling this the Facebook ad scam. One can question the wisdom of the Instagram acquisition, buying a company with 12 employees and zero revenues for $1B. One can question the notion that the 28 year old founder will have 57% of the voting rights of the public company. One could look at stories about companies discontinuing their ad Facebook efforts such as the Forbes article about GM pulling a $10m account because they found it ineffective. The list goes on.

Here is a more positive leaning infographic from an article looking at “Facebook: Business Model, Hardware Patents and IPO“:

Analysis Infographic of pre-IPO Facebook (source: Gina Smith, anewdomain.net)

To value a startup at 100x last year’s income seems just extremely high – but then Amazon’s valuation is in similarly lofty territory. As for reasoning and predicting the financial success of Facebook’s IPO, people can cite numbers to justify their beliefs both ways. At the end of the day, it’s unpredictable and nobody can know for sure.

The other answer to why I am not buying into the hype is more intuitive and comes from my personal experience. Here is a little thought experiment as to how valuable a company is for your personal life: Imagine for a moment if the company with all its products and services would disappear overnight. How much of an impact would it have for you as an individual? If I think about companies like Apple, Google, Microsoft, or Amazon the impact for me would be huge. I use their products and services every day. Think about it:

No Apple = no iPhone, no iPad, no iTunes music on the iPod or via AppleTV on our home stereo. That would be a dramatic setback.

No Google = no Google search, no GMail, no YouTube, no Google maps, no Google Earth. Again, very significant impact for me personally. Not to mention the exciting research at Google in very different areas such as self-driving vehicles.

No Facebook = no problem (at least for me). I deactivated my own Facebook account months ago simply because it cost me a lot of time and I got very little value out of it. In fact, I got annoyed with compulsively looking at updates from mere acquaintances about mundane details of their lives. Why would I care? I finally got around to actually deleting my account, although Facebook makes that somewhat cumbersome (which probably inflates the account numbers somewhat).

I’m not saying Facebook isn’t valuable to some people. Having nearly 1B user accounts is very impressive. Hosting by far the largest photo collection on the planet is extraordinary. Facebook exploded because it satisfied our basic need of sharing, just like Google did with search, Amazon did with shopping or eBay did with selling. But the entry barrier to sharing is small (see LinkedIn, Twitter or Pinterest) and Facebook doesn’t seem to be particularly well positioned for mobile.

I strongly suspect that Facebook’s valuation is both initially inflated – the $50 per account estimate of early social networks doesn’t scale up with the demographics of the massive user base – as well as lately hyped up by greedy investors who sense an opportunity to make a quick buck. My hunch is that FB will trade below its IPO price within the first year, possibly well below. But then again, I have been surprised before…

I’m not buying the hype. What am I missing? Let me know what you think!

UPDATE 8/16/2012: Well, here we are after one quarter, and Facebook’s stock valuation hasn’t done so well. Look at the first 3 month chart of FB:

First 3 month of Facebook stock price (Screenshot of StockTouch on iPad)

What started as a $100b market valuation is now at $43b. One has to hand it to Mark Zuckerberg, he really extracted maximum value out of those shares. It turns out sitting on the sidelines was the right move for investors in this case.

 
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Posted by on May 16, 2012 in Financial, Socioeconomic

 

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Sankey Diagrams

Sankey Diagrams

Whenever you want to show the flow of a quantity (such as energy or money) through a network of nodes you can use Sankey diagrams:

“A Sankey diagram is a directional flow chart where the width of the streams is proportional to the quantity of flow, and where the flows can be combined, split and traced through a series of events or stages.”
(source: CHEMICAL ENGINEERING Blog)

One area where this can be applied very well is that of costing. By modeling the flow of cost through a company one can analyze the aggregated cost and thus determine the profitability of individual products, customers or channels. Using the principles of activity-based costing one can create a cost-assignment network linking cost pools or accounts (as tracked in the General Ledger) via the employees and their activities to the products and customers. Such a Cost Flow can then be visualized using a Sankey diagram:

Cost Flow from Accounts via Expenses and Activities to Products

The direction of flow (here from left to right) is indicated by the color assignment from nodes to its outflowing streams. Note also the intuitive notion of zero-loss assignment: For each node the sum of the in- and outflowing streams (= height of that node) remains the same. Hence all the cost is accounted for, nothing is lost. If you stacked all nodes on top of one another they would rise to the same height. (Random data for illustration purposes only.)

The above diagram was created in Mathematica using modified source code originally from Sam Calisch who had posted it in 2011 here. Sam also included a “SankeyNotes.pdf” document explaining the details of the algorithms encoded in the source, such as how to arrange the node lists and how to draw the streams.

I find these a perfect example of how a manual drawing can go a long ways to illustrate the ideas behind an algorithm, which makes it a lot easier to understand and reuse the source code. Thanks to Sam for this code and documentation. Sam by the way used the code to illustrate the efficiency of energy use (vs. waste) in Australia:

Energy Flow comparison between New South Wales and Australia (Sam Calisch)

Note the sub-flows within each stream to compare a part (New South Wales) against the whole (Australia).

Another interesting use of Sankey Diagrams has been published a few weeks ago on ProPublica about campaign finance flow. This is particularly useful as it is interactive (click on image to get to interactive version).

Tangled Web of Campaign Finance Flow

Note the campaigns in green and the Super-PACs in brown color. The data is sourced from FEC and the New York Times Campaign Finance API. Note that in the interactive version you can click on any source on the left or any destination on the right to see the outgoing and incoming streams.

Finance Flow From Obama-For-America

Finance Flow to American Express

Here are some more examples. Sankey diagrams are also used in Google Flow Analytics (called Event Flow, Goal Flow, Visitor Flow). I wouldn’t be surprised to see Sankey Diagrams make their way into modern data visualization tools such as Tableau or QlikView, perhaps even into Excel some day… Here are some Visio shapes and links to other resources.

 
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Posted by on May 14, 2012 in Financial, Industrial

 

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Quarterly Comparison: Apple, Microsoft, Google, Amazon

Quarterly Comparison: Apple, Microsoft, Google, Amazon

Last quarter we looked at the financials and underlying product & service portfolios of four of the biggest technology companies in the post “Side by Side: Apple, Microsoft, Google, Amazon“. With the recent reporting of results for Q1 2012 it is a good time to revisit this subject.

Comparison of Financials Q4 2011 and Q1 2012 for Apple, Microsoft, Google, and Amazon.

Market cap has grown roughly by 25% for both Apple and Amazon, whereas Microsoft and Google only added 5% or less. A sequential quarter comparison can be misleading due to seasonal changes, which impact different industries and business models in a different way. For example, Google’s ad revenue is somewhat less impacted by seasonal shopping than the other companies.

Sequential quarter comparison of financials

Apple and Microsoft seem to be impacted in a similar way by seasonal changes. For Amazon, which already has by far the lowest margin of all four companies, operating income decreased by 40% while it increased its headcount by 17%. This leads to much lower income per employee and with increased stock price to a doubling of its already very high P/E ratio. I’m not a stock market analyst, but Amazon’s P/E ratio of now near 200 seems extraordinarily high. By comparison, the other companies look downright cheap: Apple 8.8, Microsoft 10.5, Google 14.5

Horace Dediu from asymco.com has also revisited this topic in his post “Which is best: hardware, software, or services?“. What’s striking is that all three companies (except Amazon) now have operating margins between 30-40% – very high for such large businesses – with Apple taking the top near 40%. Over the last 5 years, Apple has doubled it’s margin (20% to 40%), whereas Microsoft (35-40%) and Google (30-35%) remained near their levels.

(Source: Asymco.com)

Long term the most important aspect of a business is not how big it has become, but how profitable it is. In that regard Amazon is the odd one out. Their operating income last quarter was about 1% of revenue. Amazon needs to move $100 worth of goods to earn $1. They employ 65,000 people and had revenue of $13.2b last quarter, yet only earned $130m during that time! Apple earns more money just with their iPad covers! Amazon’s strategy is to subsidize the initial Kindle Fire sale and hoping to make money on the additional purchases over the lifetime of the product. In light of these numbers, do you think Amazon has a future with it’s Kindle Fire tablet against the iPad?

But what really struck me about the extreme differences in profitability is this comparison of Apple and Microsoft product lines (source: @asymco twitter):

(source: @asymco twitter)

This shows what an impressive and sustained success the iPhone has been. And the iPad is on track to grow even faster. Horace Dediu guesses that Apple’s iPad will be a bigger profit generator than Windows in one quarter, and a bigger profit generator than Google (yes, all of Google) in three quarters. We will check on those predictions when the time comes…

 
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Posted by on May 2, 2012 in Financial, Industrial

 

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Probabilistic Project Management at NASA with Joint Confidence Level (JCL-PC)

Probabilistic Project Management at NASA with Joint Confidence Level (JCL-PC)

On the Strategic Project and Portfolio Management Blog by Simon Moore one can find many fascinating stories about project failures as well as a related collection of project management case studies. One entry there links to a project management method NASA is mandating internally since 2009 to estimate costs and schedule of their various aerospace projects. The method is called Joint Confidence Level – Probabilistic Calculator (JCL-PC). It’s a sophisticated method using historical data and insight into estimation psychology (like optimism bias) to arrive at corrective multipliers for project estimates based on project completion percentages with required confidence level. It’s also using Monte Carlo simulations to determine outcomes, leading to scatterplots of the simulated project runs on a Cost-vs.-Schedule plane. From there one can determine estimates with for example 70% confidence levels for what the cost and schedule overruns will likely be.

If you’re either already familiar with the method or if you are very good at abstract thinking the above paragraph will have meant something to you. If it didn’t, bear with me. In this post I make a brief attempt to explain what I understood about the method using the data visualizations from two sources (a 100+ page report and a 12 page FAQ). The report is fascinating on many levels, as it deals with the history of high-profile project overruns (Apollo program, Space Shuttle, Space Station) and the pervasive culture of under-estimation (optimism bias) through not accounting for project risks that are unknown, but historically evident.

JCL starts with historical observations of similar projects with regards to cost and schedule overruns. For example, the above cited report contains best fit histogram distributions for robotic missions.

Overrun Distributions of Cost and Schedule for Robotic Missions (Soucre: NASA)

The idea is to use a set of such distributions for probabilistic estimates of cost and schedule. The set of distributions needs to account for the fact that in the early stages of a project there are more unknowns and as such higher risk of overruns. From the report:

The JCL-PC estimating method is based on the hypotheses that in the beginning phases of a project there are many unknown risks – and over time the project will have a high probability of exceeding estimated costs and scheduled duration. … Work as it was initially planned will inevitably change. Quantifiable risks become clearer and NASA’s S-Curves will tend to lay down as the work goes forward. Keep in mind that it’s not the project that is becoming inherently riskier. It’s a matter of participants fully identifying the real work that was “out there” all along. Even though the scope of the work wasn’t fully perceived “back when” – progress has continued to identify the risks and quantify the corrective actions. History is written in real time and that history differs to a greater or lesser degree from what was anticipated. The JCL-PC helps us better plan for and manage that difference.

The JCL-PC method strikes a needed balance between subjectivity and anticipated risk variability leaving only one remaining probability influence factor to deal with. – namely, assigning the percentage complete of the subject project. This % complete factor includes both subjective and objective elements.

One of the key elements is the notion of a multiplier which implements this reduced-uncertainty-over-time as well as a so called optimism corrector and other project risk in line with historical aerospace project overruns. The multiplier is plotted below as a function of the project % complete parameter for different confidence levels:

Multiplier as function of project % complete for various confidence levels

The concept is illustrated via two charts of a fictitious $1m project (applied here to cost overruns, but equally applicable to schedule overruns): The first shows a point estimate and it’s S-curves (confidence bands) per project % complete.

The second shows the S-Curves after applying “the optimism corrector and some minor project risk, through a more typical project life cycle with project scope creep … As the project evolves the S-Curve moves slightly to the right and becomes more and more vertical.”

It would be great to have an interactive graphic where the S-Cruves are plotted in response to sliding the project % complete between 0% and 100%. The report lists the above multipliers in a numerical table spanning project % complete (in 1% increments) and four confidence levels (50%, 60%, 70%, 80%). Rather than copying the entire table I filtered this down to just 10% increments in project % complete. This table tells NASA officials at various confidence levels, how much money they will have to spend for a $1m project as a function of project % complete:

Cost Estimate Table with project % complete and confidence levels

The data point highlighted in yellow is described as follows:

When the project is 50% complete, you’ll notice that a 50% confidence level suggests that the project can be completed for the anticipated $1,000,000. However, if we adhere to the NASA standard of a 70% confidence level, we see that another $400,000+ will likely be needed to complete the project. No matter how well a project is managed, it rarely compensates for ultra- optimistic budget estimates that sooner or later return with a vengeance and overcome the most skillful leaders.

As a final illustration the FAQ document includes this scatterplot as JCL-PC output:

Scatter Plot of Monte Carlo simulation with JCL-PC

A Frontier Curve represents all possible combinations of cost and schedule that will give you a percent JCL. The plot shows the Frontier Curve for a 70% JCL in yellow. The green dots are simulated runs with outcomes below the selected cost and schedule (blue cross-hair, yellow labels). White dots have either cost or schedule overruns, red dots have both.

The report makes bold claims about the potential of JCL-PC, but also about the challenges inherent in attempting to change an entire management culture. I am not qualified to comment on these claims, but my impression is that such probabilistic project management methods will raise the bar in the field and should lead to more accurate estimates.

The more I think about such abstract concepts, the more I’m convinced that mental models are inherently visual. We remember some key visualizations or charts and anchor our understanding of the concept around those visual images. We also use them to communicate or teach the concepts to each other – hence the value of the whiteboard or even the napkin drawing. As such, the increasing computational ability to produce such visual images and ideally even interactive graphics is an important element of academic and scientific endeavors.

 
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Posted by on February 25, 2012 in Financial, Industrial, Scientific

 

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Side by Side: Apple, Microsoft, Google, Amazon

Side by Side: Apple, Microsoft, Google, Amazon

Ed Bott at ZDNet.com wrote a post with the title: Microsoft, Apple, and Google: where does the money come from? He looked at the quarterly reports of these companies (links to sources in the article) and displayed a pie-chart of the revenue mix for each of them. Inspired by that, I added a fourth company – Amazon (source: 10-K for 2011) – and aggregated those pie-charts into one graphic.

Revenue Mix for Apple, Microsoft, Google, and Amazon

All four are large consumer-oriented technology companies; like millions of customers, I use many of their products and services every day. They each operate with different businesses models:

  • Microsoft: Software
  • Apple: Hardware
  • Google: Advertising
  • Amazon: Retail

Yet as a consumer I rarely think about these differences. All of them use state-of-the-art technologies like cloud-computing and mobile devices to achieve integrated end-to-end experiences geared to increase revenues in personal computing (Microsoft), smart mobile devices (Apple), online search (Google) or shopping (Amazon). And arguably all of them derive major competitive advantage from their software, such as Apple’s iOS which introduced the touch interface.

Perhaps most surprising is Google’s almost singular reliance on advertising, which makes it a very different business model. They offer all their technology for free – from search to mapping to operating systems and social media – to grow and retain online attention as enabling condition for advertising revenue. For a business this big the near complete dependence on one source of revenue is unusual; perhaps its time for Google’s leadership to seriously consider a diversification strategy? Without it Google is arguably more prone to disruption (such as from Facebook) than the other companies. Speaking of disruption: Apple derives almost 3/4 of its revenue (73%) from iPhone and iPad, neither of which existed 5 years ago. As Ed Bott points out, those two products now drive an astonishing $33.5b revenue per quarter!

To compare the companies by their absolute numbers, here is a bar chart of market capitalization, revenue and profit: (all in billions of Dollars and for Q4 2011, market cap as of 2/3/12)

Market Cap, Revenue and Profit for Apple, Microsoft, Google, and Amazon

Market cap of these four companies combined is approaching $ 1 trillion. Much has been written about the differences in market valuations relative to revenue and most importantly profit. The markets undervalue Apple and overvalue Google and Amazon. Let’s compare these dimensions (and number of employees) in the following radar plot:

Relative business performance for Apple, Microsoft, Google, and Amazon

The plot shows the relative performance of all with the highest in each dimension normalized to 100%. Amazon shows by far the smallest profit in the last quarter. Given it’s retail nature, it’s profit margins have always been smaller; and CEO Jeff Bezos has long emphasized the strategy of investing in future growth at the expense of present profits. Microsoft continues to enjoy very solid profit margins in a large, well diversified business. Google has incredible talent and for now is the undisputed king of online advertising. But Apple leads in all three factors, and it achieves 2x Microsoft’s results with less than half the number of employees! Apple’s profit is 1.5 times that of the other three combined! And it makes more than 60% of the profit with less than 20% of the employees. In fact, Apple’s market capitalization is now higher than $10m per employee! It must feel pretty special to be one of them these days…

Postscript: On Feb-13 analyst Horace Dediu at Asymco.com published an article with time-series data for the above companies (except Amazon) over the last 18 quarters (since 2007). It shows the evolution over time as depicted in this chart:

Apple Microsoft Google - Revenue and Operating Income 2007-2011

The article is called “The World’s Biggest Startup“. It’s main point is this: Microsoft and Google both grew their businesses steadily, but did not change their type of business. Apple did some of that in its established business segments, but more importantly and disruptively it added new categories (iPhone and iPad) for dramatic growth. That’s what startups do. Just so happens that Apple – whose stock today for the first time hit $500 – is also the most highly capitalized company in the world (around $460B). If Apple is a startup now, what will they look like when they are fully established?

 
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Posted by on February 6, 2012 in Financial, Industrial

 

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TreeMap of the Market

TreeMap of the Market

SmartMoney has an interactive visual tool on their website called “Map of the Market”. It is an application of the TreeMap concept developed by Ben Shneiderman which I have blogged about before here.

The map lets you watch more than 500 stocks at once, with data updated every 15 minutes. Each colored rectangle in the map represents an individual company. The rectangle’s size reflects the company’s market cap and the color shows price performance. (Green means the stock price is up; red means it’s down. Dark colors are neutral). Move the mouse over a company rectangle and a little panel will pop up with more information.

Map Of The Market (Source: SmartMoney website)

For example, the above map shows the 26 week performance with the Top 5 Losers highlighted (hovered over RIMM). More information from the corresponding Map Instructions page.

This map is also quite similar in concept to the StockTouch iPad app which I covered here. StockTouch displays 900 companies, grouped into 9 sectors. The above Map of the Market is a free service, with an available upgrade to one showing 1000 companies for a subscription fee. While interesting in its own right, however, this is not about the business model of how to monetize the use of such information.

It might be interesting to put together a time-lapse video showing this map for every close of business day throughout one year. Not only would one see the up and down movement by color, but also the gradual shifts in the cumulative size of various sectors due to the area in the tree map.

Another fascinating set of tree map uses is on display at the Gallery of the Hive Group website. Their interactive tree map product HoneyComb has been used in many different industries. The Gallery shows many examples, ranging from sales performance to manufacturing / quality applications to public interest uses such as browsing Olympic Games results or data on Earthquakes. See the following example screenshot (click to interact on the Hive Group website):

TreeMap of Earthquakes (Source: HiveGroup)

While you won’t get the full benefit of seeing the details of all 540 items in one view, you can filter using the panel controls on the right or change the grouping and size and color attributes. This shows for example that the most powerful earthquakes are generally not the most deadly ones and vice versa.

Interacting with these sample tree maps again drives home the fundamental notion that interactive visualizations lead to quicker grasp and better understanding of data sets. This is similar to how walking around and seeing an object from different perspectives gives you a better idea of it’s 3-D structure than seeing it just in one 2-D picture. With multiple ways of interacting it feels almost as if you’re walking inside the data set to see it from multiple angles and perspectives. You have to do it yourself to appreciate the difference it makes.

Lastly, a good article on some of the pitfalls of tree map design with lots of links to good/bad examples comes from the folks at Juice Analytics in their Blog post titled “10 lessons in Treemap Design“.

 
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Posted by on October 29, 2011 in Financial, Industrial

 

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