Month: January 2012

The effect of smartphone growth on share prices

Data on Q4 performance is starting to trickle in. Last week Motorola, as part of a profit warning, revealed their Q4 smartphone shipments were 5.3 million. RIM had reported 14.1 million units (quarter ending November). HTC has also warned that their shipments of smartphones (and tablets) totaled 10 million. Samsung provided guidance on overall sales but since they don’t report data on smartphone (or phone) sales, the estimates are ranging between 32 and 35 million.

Many have forecast Apple’s performance but there are many other companies which are not easily estimated (Sony Ericsson, LG, Nokia, ZTE, Others.) But rather than dwelling on the specific quarter, we can make some long-range estimates of performance based on the historic data.

One method I use is to look at patterns of growth. Growth is the first derivative of market performance and it sometimes shows patterns which the performance itself does not. Consider the following chart showing the growth pattern for RIM.

The blue line shows the absolute year-on-year quarterly growth based on reported numbers of smartphones shipped. I’ve also shown the growth “relative to market” which is unit growth with market growth subtracted. This orange line represents how much faster (or slower) the company is growing relative to the market. If the orange line is above zero, the company gained share. If it’s below zero, it lost share.

I also overlaid the performance of the share price (right scale).

Now compare that with another long-term incumbent in the smartphone market, Nokia.  Continue reading “The effect of smartphone growth on share prices”

Predictions for 2012

I have none to offer.

It may sound strange to hear me say that I don’t make predictions even though I often talk about how things will change and even provide some forecasts. The difference is one of degrees. A prediction to me is a very specific, time-sensitive and materially valuable recommendation. An observation about the future is an imprecise, intuitive hunch based on pattern recognition. It’s mushy. It’s theoretical. It’s the difference between saying a company is great and recommending to buy its stock with a price target in a time frame.

But it gets even weirder.

Continue reading “Predictions for 2012”

Estimating iPhone sales in the US during Q4

Verizon has been the first source of data on iPhone sales for Q4. They reported 4.2 million iPhones sold. The first quarter of Verizon iPhone sales had 2.2 million units, followed 2.3 million in Q2 and 2 million in Q3. The total for the calendar year came at 10.7 million.

The total for AT&T during the first three quarters has been 9.9 million. If we assume the same 2x sequential increase in Q4 relative to Q3 as observed by Verizon we obtain an estimate of 5.4 million iPhone for AT&T.[1]

The quarter also saw the addition of Sprint to the US iPhone distribution network. Sprint is rumored to have committed to buy 30.5 million iPhones over the next four years. My estimate for the first year was 7 million, placing first quarter sell-in at about 2 million.

An approximate total for Q4 US iPhone activations results in 11.6 million. The following chart illustrates these estimates: Continue reading “Estimating iPhone sales in the US during Q4”

[Sponsor] Carnegie Mellon University (with a history lesson)

It gives me great pleasure to have Carnegie Mellon University as a sponsor this week. This is because CMU holds a special, historic role in the development of the platform at the center of the disruption of mobile telecommunications.

I am referring to the kernel behind OS X and iOS: Mach.[1]

When I was a researcher at GTE Laboratories, I remember following the progress of this alternative kernel. As a research project it was one of the earliest microkernels and, along with virtual memory management, inter-process communication and control innovations, pioneered what became the basis of highly modular operating systems. Those innovations enabled an architecture which allowed complex systems to scale down to micro computers and eventually to devices.

There is a huge amount of lore around Unix and CMU’s efforts are deeply interweaved into it (as are Berkeley and AT&T). I strongly recommend a stroll down that memory lane. But I’ll keep it short here and say that original developers of Mach at CMU went on to be key executives at both Apple and Microsoft. It was really a spectacular success as far as academic research projects in computer science. A real inspiration.

So with that history, I want to thank Carnegie Mellon University for their sponsorship and I’m glad to see continuing innovation in their degree programs.

Today they are offering a Master of Information Systems Management  degree with a Business Intelligence and Data Analytics concentration (MISM-BIDA). This particular degree program is essentially cross-training in business process analysis and predictive modeling, two methodologies which deeply benefit from one another. Much of what I do for this blog is exactly this:  mapping, analytical reporting, segmentation analysis, and data visualization. I’m glad to see that his has been codified into a degree program.

Students in the MISM-BIDA program learn to integrate information filters and mining tools with applied business methods yielding insights that you see celebrated in the media every day. They do this with world-renowned faculty teaching a cohesive blend of data analytics, management, strategy, and IT courses.

I can only assume that this unique mix makes graduates highly valued by  financial service firms, consulting companies, technology agencies and start-ups.

If you like the results of this web site and would like to learn how it’s done “by the book”, consider the degree programs at Carnegie Mellon Heinz College.

Highly recommended.

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Notes:

  1. Carnegie Mellon also had a role in the development of Siri.

Apple's commoditization discount

When asked where Apple’s growth will come from, most analysts or observers will cite new products. As long as there are new products, then there is growth. Conversely, if there are no new products, then there will be no growth. This is such a commonly held belief that it’s axiomatic: Apple is being valued based on short-term foreseeable growth.

To be more precise, analysts value the wave of growth of every new product and heavily discount the post-growth phase assuming commoditization. There is no value assigned to Apple for extending market reach to the mass market.

Consider: Analysts currently forecast an operating income (or EBIT) of $43.3bn for 2012 and $49.7bn for 2013. That implies growth of 28% in 2012 and 15% in 2013. These growth rates are modest in light of Apple’s recent historic growth and especially 84% in 2011 on EBIT level. Much of this growth has been due to iPhone which quickly captured 4% market share in four years. To suggest 15% growth in 2013 is to suggest that Apple will not increase its phone market share by an appreciable amount. The implicit assumption in that growth figure alone is that Apple will remain a niche player.

Continue reading “Apple's commoditization discount”

Interactive Apple Analyst Data

As the quarter is now at an end, it’s time to once again review the performance of Apple’s most highly paid observers. The data set linked includes published quarterly forecasts starting quarter ending June 2008 to quarter ending September 2011. Thanks to Philip Elmer-Dewitt who collected and processed the data over several years.

Apple Analyst Data | asymco. (Requires Flash)

As with previous interactive data sets, this is based on Motion Charts “gadget” in Google Docs. Try selecting the Motion Charts tab at the bottom of the page and hit the play button.

The way this is set up now is that the origin (0,0) represents actual performance (labeled as “Oppenheimer”). Every bubble is an analyst’s performance measured as a distance from actual. The further the location of a bubble from origin, the worse the error. Placement above zero (or to the right) indicates over-estimation for Revenues (and EPS). Placement below zero (or to the left) indicates under-estimation. Color of bubble represents affiliation (professional or amateur). Size of bubble is iPhone unit error.

You can change the axes, chart type (tabs in upper right), size, colors, and track individual analysts’ performance (by checking a name checkbox). Or just download the data and analyze on your own by selecting from the Data tab.