Introduction
As in all industries, in order to win in a market and set an appropriate strategy, it is important to know as much as possible about that market and have at one's disposal tools that will provide insight and competitive advantage when properly, collectively, consistently, and timely applied. This paper presents a series of powerful, but easy to use and understand, analytical and operational tools that deliver insight and competitive advantage to the telecommunications professional. Moreover, it should be stated that as with all good tools, the tools and models as presented herein transition across industry lines and are not limited to the telecommunications industry alone.
background
Starting in the 1990s, the telecommunications market appeared to experience unprecedented and unbounded growth with the advent of The Telecommunications Act of 1996. This growth was paralleled by a growth in capital equipment purchases (CAPEX) by network operators (see Figure 1). However, by the early 2000s, we saw a major market correction and the collapse of many firms that caught many industry professionals, bankers, and investors by surprise. The economic dislocations caused by the failure of so many telecommunications network providers were enormous. Hence, an examination was undertaken to see if tools and models existed that could provide significant insight into changing market conditions. By examining these market dynamics and the fundamentals at play in the telecommunications space, it becomes apparent there are models and tools that provide insight as to the market's stage, and where it is likely to go next. Such a view is important to the investor, creditor, and operator alike in order to have a vision of the current and future market states so appropriate and timely decisions can be reached.
Figure 1. The revenue capital expenditure growth rate comparisons
analytical tools and models
Because of the turmoil experienced in the telecommunications industry over the past decade, it is useful to view tools that can assist the telecommunications professional with understanding the market(s) and the trends at play. Looking at the telecommunications market from 1996 to 2007, it can be seen that the market exploded in the first half of this period with a 26% cumulative annual capital expenditure growth rate (CAPEX CAGR), collapsing in the latter part of this period (Hilliard, 2007; Lehman Brothers, 2000).
When capital expenditures so far outstrip the gross revenue growth rate, one knows this situation cannot continue unabated, and a return to a more normal state must take place. In order to discern approximately when a return to a more normal state will come about, one may examine the underlying market drivers (Nugent, 2001, 2003). Market drivers will often signal the size, breadth, and depth of a market.
Market Drivers: During the period of 1996-2003 several large drivers were evident. The first was identified as the Y2K driver. Here many firms determined it to be better, easier, and less costly and risky to replace versus remediate infrastructure equipment. But here it was known this driver would be satiated by 2000. A second major driver was The Telecommunications Act of 1996 (www.fcc.gov). This Act brought about the creation of many new telecom competitors that raised billions of dollars in the equity and debt markets that went on a spending spree. However, most of these firms had flawed business plans, and through competitive thrusts by the incumbents in the form of administrative delay, regulatory appeal, and litigation, these new entrants were literally bled dry via the consumption of cash in non-revenue producing activities such as regulatory appeals and litigation, and doomed to failure (Nugent, 2001, 2003). Understanding how significant incumbents fight and how they use the most strategic weapons of all - cash position and cash flow - the demise of these new incumbents could be foreseen.
Figure 2. The life cycle curve
Another significant driver was the explosion in the number of wireless customers brought about by the "Digital One Rate" plan initiated by AT&T. Here wireless growth exploded from approximately 50 million subscribers to over 120 million in just several years. However, there are models that indicate this type of market satiates at approximately 50% of the overall population or 70% of the adult population (Nugent, 2003). In the United States, this satiation point is approximately 145 million narrowband voice subscribers - approximately where we are today. So this spending spurt on narrowband voice wireless Customer Premise Equipment (CPE) and infrastructure equipment could have also been estimated to end as the market approached satiation.
Hence, the telecommunications market downturn should not have been a surprise to anyone, as an understanding of the principal market drivers would have permitted an estimate of the market's size, breadth, depth, and duration.
Life Cycle Curve: Another important strategic understanding is the Life Cycle Curve. This Gaussian Curve (Bell Curve) is representative of all things, whether they are individuals, enterprises, nations, states, or civilizations. That is, each is created, grows rapidly, matures and passes. All such bodies, other than individuals, have the ability to change and adapt versus pass, but this almost never happens. Two companies that lasted longer than most, Stora Kopparbergs Bergslags AB, a Swedish trading company tracing its origins to the Middle Ages was acquired in 2002, and Kongo Gumi, in continuous operation since 560 AD, got into operating trouble in 2006 and was also acquired in November of that year. In fact, most of the leading companies last 40 years on average or less before the pass. This may be seen by looking back at any of the leading business lists of 40 years ago and seeing how many are still on the leading lists of today. Typically 80% of the entities on the first list will not appear on the second. Hence, an understanding of where one stands in its respective life cycle, as well as where its primary competitors, customers, and suppliers stand, is also important because leverage is changing all the time amongst this set. Moreover, to gain and sustain competitive advantage, one needs to constantly discern how and where to reallocate its assets. And the Life Cycle helps determine relative position.
The fundamentals of this model are that approximately 85+% of enterprises fail in the first five years of life, the Infant Mortality stage. The next two contractions represent major market consolidations (mergers and acquisitions). The first such consolidation takes place at approximately 50% market satiation; and the second consolidation at approximately 90+% market satiation. This understanding is important because it permits a right sizing of entity investments and business activity when the first market consolidation takes place, and provides clarity when one should exit its market if it cannot change the market. Moreover, it also hints that at and after the second consolidation point, an entity should only buy like kind assets at steep discounts versus large premiums. This is because in competitive markets at this point in time, dynamic growth and margins are gone.
Figure 3. The minute margin squeeze model for the interexchange carrier(IXC) market
Key: PPM = Price Per Minute; CPM = Cost Per Minute
Most recently we have seen several large 2nd Consolidation Stage purchases in the telecom arena where the industry is at the 90+% satiation point: AT&T's acquisition of Bell South and Cingular's acquisition of AT&T Wireless. In each case a large premium was paid for like kind assets late in the life cycle. This strategy is almost always costly in truly competitive markets, unless the enterprise can create a de facto duopoly where the two primary competitors no longer compete on price. The issue involves unit prices and unit costs, and the typical relationship between these functions. That is, as a function of time and competitive pressures, in competitive markets, unit prices decline faster than unit costs. A generic model of long distance unit prices and costs appears below:
This Unit Price Unit Cost Model above indicates that enterprise value, growth, and margins are greatest when the gap between unit prices and unit costs is greatest, The Profit Zone, and least when these functions (slopes) converge.
We see this same relationship in the United States narrowband wireless voice arena. Here, too, margin and growth are gone as the unit price/unit cost squeeze is on. Yet, we saw Cingular pay a large premium for AT&T Wireless late in life when margin and growth have abated. This will almost always create stress unless a de facto duopoly is created where the unit price degradation slope can be perverted as the two primary competitors no longer compete on price.
At a high level it is also important to understand where a market is today, and where it is going to be tomorrow. To help understand these conditions, a State, Gap, and Trend (SG&T) Analysis tool provides helpful insight (Hilliard, 2003; Wolford-Ulrich, 2004).
The development of a SG&T tool calls for a "one for one" transition (a "this to that" scenario over a period of time - there can be no ambiguities). Hence, a current and future state can be determined with some clarity.
An examination of this SG&T tool presented in Figure 5 indicates that the telecommunications world is moving from a fixed, tethered, narrowband, analog, circuit-based world, to one principally comprised of mobile, wireless, broadband, digital, packet-based communications. This transition portends significant issues for land-based carriers whose assets principally are in big physical plant (central offices, switching facilities, tethered trunks and circuits, etc.). Yet in the two large telecom acquisitions mentioned above, we saw premiums being paid for yesterday's technology where markets are already satiated. This model further indicates that land-based carriers' assets are probably depreciating significantly faster than their balance sheets indicate. Supporting this premise is the decline in the number of residential landlines from approximately 168 million lines in 2001 to approximately 140 million residential landlines today (www.fcc.gov).
Figure 4. Wireless minute unit prices and costs
Figure 5. State gap and trend analysis: Technology transition
The issue of yesterday's assets and liabilities and the value shown in the financial statements will become apparent in the first quarter of 2008 when most SEC reporting companies must start reporting under FAS 157, Fair Value Accounting. Under this requirement, enterprises must disclose the difference in value from the value indicated on the balance sheet and their current value. For instance, Verizon carries on its topics its wireless licenses at approximately $48 billion. In November, 2006 the FCC licensed approximately twice as much spectrum as Verizon holds for approximately $14 billion. This would indicate, under a market value approach, that Verizon's wireless licenses are worth significantly less than their carrying value by perhaps as much as $41 billion.
Moving from a macro model of market trends (SG&T) analysis in Figure 5, it can also be seen on a micro (tactical) level (Product Curve) what attributes successive telecommunications products must follow to win in future markets (Hilliard, 2006). Here, a Product Curve model is most helpful.
Figure 6. Product curve
The Product Curve demonstrates that devices (network and CPE) need to become smaller, consume less power, weigh less, give off less heat, cost less, be developed in faster and faster cycle times, and have less in sunk development costs, while at the same time do more. They need to operate at faster speeds and higher capacities while performing more functions to win in future markets. The Product Curve also portends troubles for land-line carriers as it can be seen in not too many years, the central office of today will be displaced by a laptop wireless broadband tool oftomorrow. The SG&T Analysis and the Product Curve shown in Figures 5 and 6, respectively, only highlight some important attributes. There other numerous others that may, and should be added for a fuller comprehension of the industry.
To see the Product Curve in action, a comparison of the original Motorola "Brick" cell phone may be made with the sleek small wireless communication devices we use today. Here, we can see that the devices have become smaller, weigh less, consume less power, cost less, give off less heat, but do more. This model would have also clearly shown that Iridium and ICO had to fail because they each fought the attributes of the Product Curve: long cycle development times, high costs and prices, large bulky equipment, consumed a lot of power, gave off a lot of heat, etc. And, because the development cycle times were so long, alternate market winning technologies were developed that did follow the Product Curve attributes - GSM wireless solutions in the main.
Mix Shift Analyses are another way to discern important market changes (Hilliard, 2006). Here, many consulting firms forecast where we are today and likely will be tomorrow.
MIX SHIFTS
These Mix Shift analyses indicate that the telecommunications industry will move from a tethered to a wireless world in the relatively near future, while at the same time the mix of telecommunications traffic will shift from principally voice to principally data. This mix shift does not mean that voice traffic will decline; rather it indicates that data traffic will grow dramatically compared to voice over the period indicated. It is presently estimated that data is growing at 2+ exabytes a year (2 X 10 to the 18th power). Moreover, voice will largely become data as Voice over the Internet Protocol (VoIP) becomes more the norm. This trend is highlighted in the SG&A analysis above where we see a shift from circuit to packet, and narrowband to broadband communications. Hence, when we again look at the recent acquisitions mentioned herein, where yesterday's technology was principally acquired at a large premium, it is likely that the enterprise will be challenged to deal with this situation going forward.
operational tools and models
Slope Analysis is the Converging/Diverging Gross Margin Analysis. Here, actual data from the Income Statement is plotted for several periods. Converging Gross Margins indicate increasing operational efficiency while diverging gross margins signify decreasing operational efficiency.
Table 1. Transport shift
| Transport \ Year | 2003 | 2008 | 2015 |
| Landline | 80% | 50% | 10% |
| Wireless | 20% | 50% | 90% |
Table 2. Mode shift
| Service \ Year | 1995 | 2020 |
| Voice | 90% | 10% |
| Data | 10% | 90% |
Figure 7. Converging/diverging gross margin analysis
Examining AT&T's Initial Public Offering (IPO) for its wireless unit demonstrates the importance of this tool. The IPO for AT&T's wireless unit was well received and the stock price climbed immediately. However, a reading of the offering document would have shown that the Gross Margin decreased (diverged) by over 50% in the preceding annual period. This indicated significant operational troubles. By discerning this diverging gross margin and drilling down to determine the reasons, one would have discerned that
AT&T's successful "Digital One Rate Offering" caught the company short, far short, of network capacity to support demand. Hence, AT&T had to go off-net and pay other carriers high fees to originate or terminate its traffic. This understanding would have highlighted additional issues facing AT&T Wireless: either take on more debt or dilute current shareholders further by issuing more stock in order to build more infrastructure.
Figure 8. AT&T Modified Z Score 1997 - 2001
One final operational tool is the Discriminant Function Algorithm (DFA) used to discern changes in corporate health on a prospective basis - Inflection Points. (Amdocs Corp., 2003; Slywotzky. Morrison, Moser, Mundt, & Quella 1999) This model uses Altman's Z Score algorithm for determining bankruptcy on a prospective basis (Altman, 1983). However, unlike Altman where he uses absolute scores, the DFA model only cares about changes in score - either positive or negative (Nugent, 2003). Moreover, the scale is changed in the DFA model from Altman's Bankruptcy Prediction model. As an example of what this tools yields on a prospective basis, AT&T is again viewed.
As can be seen, AT&T's Modified Z Score declined from over 5 in 1997 to under 1 by 2001. This is a dramatic negative decline. Yet, during much of this period, Wall Street was in love with this stock. Had one begun plotting this Z score in 1997, one could have discerned by 1999 that things were heading south long before others foresaw this decline. For instance, if Ericsson had been using this model in 1997 and beyond, it would have provided Ericsson early warning that it needed to re-address its marketing and sales strategy as its largest customer, AT&T, had a degrading corporate health.
future trends
From our examination of the past and the tools that may be employed to glean a view of the future, it is apparent that major transitions are underway at the strategic level; namely, voice to data, circuit to packet, narrowband to broadband, wired to wireless, and physical to virtual. Such transitions will require new networks to support these changes. Moreover, at the product or tactical level, we see that new solutions will be required to be smaller, lighter, less costly, consume less power, give off less heat, weigh less, and be developed in shorter time cycles if they are to be successful in subsequent markets.
conclusion
High level analytical and operational tools and models can assist the telecommunications professional in understanding the telecommunications market's characteristics, life cycles, trends, directions, limits, drivers, duration, and likely prospective performance. These tools demonstrate that wireless communications will follow the same life cycle characteristics as wired communications. Proper utilization of such tools collectively, consistently, and continually can lead to important and timely insights hopefully leading to competitive advantage based upon an early detection of changes in marketplace dynamics.
key terms
CAGR: Cumulative Annual Growth Rate - The percent of growth from one annual period to the next.
CAPEX: Capital Equipment Expenditures usually measured as a percent of gross revenue.
Converging/Diverging Gross Margin Analysis: A Slope Analysis Tool used to plot actual sales and gross margin data for several periods in order to discern trends and likely outcomes. Measure operational efficiency or inefficiency.
CPE: Customer Premise Equipment (end-user equipment).
Discriminant Function Algorithm: A term developed for Edward Altman in describing his Z Score analytical tool in determining the likelihood of an enterprise going into bankruptcy on a prospective basis. Used as a method for determining inflection points - changes in corporate health versus for bankruptcy prediction.
Inflection Points: Significant changes in corporate performance.
IXC: Interexchange Carriers (long distance companies that transport inter Local Access Transport Area (LATA) traffic).
LATA: Local Access Transport Area - a geographic area defined by the FCC.
Minute Margin Squeeze: Also known as Unit Price/Unit Cost Model - see below.
Mix Shifts: Shifts in the market between major components usually requiring different technology or solutions.
Product Curve: This tool takes a micro view of transi-tioning requirements or attributes successive solutions it must adhere to in order to win in the future market place.
Slope Analysis: The plotting and visualization of certain operating functions in order to discern trends, often before others see them, thereby permitting alteration of strategies in order to gain competitive advantage.
State Gap & Trend Analysis: A tool used to present in a structured format current market or technology states as well as future states. This analysis requires a "one for one" transition - a "this to that" view. This model calls for no ambiguities. The perilous part of this tool is determining how to transition the Gap - where one has to be by when, with what.
Unit Price/Unit Cost Model: A Slope Analysis Tool used to plot actual unit prices and unit costs for several periods in order to discern future trends and likely outcomes.