A TAXONOMY FOR ASSESSING FITNESS
`OF MOBILE DATA SERVICES
`IN US CONSUMER MARKETS
`
`By
`
`Michael Trupiano
`
`Submitted to the Department of Electrical Engineering and Computer Science
`
`In Partial Fulfillment of the Requirements for the Degree of
`
`Master of Engineering in Electrical Engineering and Computer Science
`
`At the Massachusetts Institute of Technology
`
`01 February 2001
`
`Copyright 2000 Michael Trupiano. All Rights Reserved.
`
`The author hereby grants M.I.T. permission to reproduce and
`distribute publicly paper and electronic copies of this thesis
`and to grant others the right to do so.
`
`Author
`
`Certified by
`
`Accepted by
`
`Departnent of Electfical Engineering and Computer Science
`01 February 2001
`
`Dr. Amar Gupta
`JTleois Supervisor
`
`Chairman, Department Committee
`
`Arthur C. Smith
`on Graduate Theses
`
`)60-
`
`MASSACHUSETTS INSTITUTE
`OF TECHNOLOGY
`
`JUL 3 1 2002
`
`LIBRARIES
`
`Google 1027
`U.S. Patent No. 9,445,251
`
`

`

`A TAXONOMY FOR ASSESSING FITNESS
`OF MOBILE DATA SERVICES
`IN US CONSUMER MARKETS
`
`By
`
`Michael Trupiano
`
`Submitted to the
`Department of Electrical Engineering and Computer Science
`
`01 February 2001
`
`In Partial Fulfillment of the Requirements for the Degree of
`Master of Engineering in Electrical Engineering and Computer Science
`
`ABSTRACT
`
`The market for mobile access devices is exploding as measured by conventional
`consumer electronics adoption standards. At rates outpacing consumer adoption of
`telephones, televisions, VCRs, and personal computers, mobile access devices are headed
`on the path of ubiquity in our culture over the next several years.
`
`A survey of the wireless market today shows that mobile access devices have
`certain characteristics, which allow for the creation of value-added services to the
`consumer. These characteristics include location-awareness, personalization, and
`immediacy. An examination of revenue models of information goods (as found on the
`World Wide Web over the last several years) reveals historically useful information that
`can help shape a taxonomy for assessing fitness of mobile data services in U.S. consumer
`markets. The ability to build an install base, to provide value-added (non-commoditized)
`products and services, and to manage the customer will be vital to the success of mobile
`data services firms.
`
`A framework originally described by Shapiro and Varian is employed as a
`measuring stick. A new framework and concomitant rubric are developed which measure
`a relative degree of opportunity and profitability for firms considering a foray into mobile
`data services. Mobile data service candidate companies are then measured against this
`rubric.
`
`Thesis Supervisor: Dr. Amar Gupta
`
`PAGE 2
`
`

`

`PAGE 3
`PAGE 3
`
`

`

`ACKNOWLEDGEMENTS
`
`I would like to express my gratitude to those who were supportive of the
`following study. I thank Dr. Amar Gupta of the Sloan School for his guidance and for
`the degree of autonomy he granted me over the course of the study. I thank Patrick
`Chung for unparalleled counsel. I thank Richard Bamwell of ZEFER and the
`management of ZEFER's Boston office for resources and professional guidance. Finally,
`I thank my family for standing behind me in all my academic and professional pursuits.
`
`Cambridge, Massachusetts
`01 February 2001
`
`PAGE 4
`
`

`

`
`PAGE 5
`PAGE 5
`
`

`

`ABSTRACT
`
`The market for mobile access devices is exploding as measured by conventional
`consumer electronics adoption standards. At rates outpacing consumer adoption of
`telephones, televisions, VCRs, and personal computers, mobile access devices are headed
`on the path of ubiquity in our culture over the next several years.
`
`A survey of the wireless market today shows that mobile access devices have
`certain characteristics, which allow for the creation of value-added services to the
`consumer. These characteristics include location- awareness, personalization, and
`immediacy. An examination of revenue models of information goods (as found on the
`World Wide Web over the last several years) reveals historically useful information that
`can help shape a taxonomy for assessing fitness of mobile data services in U.S. consumer
`markets. The ability to build an install base, to provide value-added (non-commoditized)
`products and services, and to manage the customer will be vital to the success of mobile
`data services firms.
`
`A framework originally described by Shapiro and Varian is employed as a
`measuring stick. A new framework and concomitant rubric are developed which measure
`a relative degree of opportunity and profitability for firms considering a foray into mobile
`data services. Mobile data service candidate companies are then measured against this
`rubric.
`
`Thesis Supervisor: Dr. Amar Gupta
`
`PAGE 6
`
`

`

`
`PAGE 7
`PAGE 7
`
`

`

`TABLE OF CONTENTS
`
`1
`
`INTRODUCTION .................................................................................................
`
`2 THE STATE OF THE INDUSTRY.......................................................................
`
`2.1
`
`2.2
`
`2.3
`
`THE WIRELESS INFRASTRUCT URE..............................................................................................................
`
`HANDHELD DEVICES: MOBILE PHONES AND PDAS ............................................................................
`
`A FOCUS ON THE UNITED STATES ..............................................................................................................
`
`3 W HAT M AKES M OBILE UNIQUE .....................................................................
`
`3.1
`
`LO C A TIO N -A W A R EN ESS ..............................................................................................................................
`
`3 .1 .1
`
`3.1.2
`
`E -9 1 1 .....................................................................................................................................................
`
`Location Determination Techniques............................................................................................27
`
`Network-Based Location Determination Techniques.......................................................................
`
`Handset-Based Location Determination Techniques.........................................................................
`
`Pros and Cons of Handset- and Network-Based Location Determination Techniques ...............
`Enhanced Location-Based Services Beyond E-91 1...................................................................31
`
`3.1.3
`
`3.1.4
`
`A Matter of Privacy: Location-Tracking v. Location Support...............................................
`
`3.2
`
`A FINER DEGREE OF PERSONALIZATION..............................................................................................
`
`3.2.1
`
`3.2.2
`
`Personalization on the World Wide Web ....................................................................................
`
`WWW Personalization vs. Mobile Personalization.................................................................
`
`How Traditional Internet Access and Mobile Internet Access Differ.................................................
`
`How to Adapt Personalization to Mobile Internet Access Devices.....................................................
`
`3.2.3
`
`Enhanced Personalization Via the Mobile Phone...................................................................
`
`3 .3
`
`3.4
`
`IM M E D IA C Y ....................................................................................................................................................
`
`H ITTIN G TH E SW EET SPO T ...........................................................................................................................
`
`4 M OBILE REVENUE M ODELS.............................................................................
`
`4.1
`
`4.2
`
`A CHIEV IN G A C RITICA L M ASS....................................................................................................................
`
`INFORMATION PRICING MODELS................................................................................................................
`
`4 .2 .1
`
`4 .2 .2
`
`4 .2 .3
`
`P erso n a lized P ricing ..........................................................................................................................
`
`V e rs io n in g ............................................................................................................................................
`
`G ro up P ric in g ......................................................................................................................................
`
`4.3
`
`LESSONS LEARNED FROM WWW REVENUE MODELS............................................................................
`
`11
`
`14
`
`15
`
`18
`
`21
`
`24
`
`25
`
`2 6
`
`27
`
`28
`
`29
`
`32
`
`33
`
`33
`
`39
`
`40
`
`42
`
`43
`
`4 6
`
`48
`
`51
`
`51
`
`55
`
`5 9
`
`6 1
`
`6 3
`
`67
`
`PAGE 8
`
`

`

`4.3 .1
`
`4.3.2
`
`4 .3 .3
`
`P o st-S a les F o llo w -Up .........................................................................................................................
`
`Customer Evaluation and Segmentation.....................................................................................72
`
`S tic k in ess D rive rs ...............................................................................................................................
`
`4.4
`
`RECOMMENDATIONS FOR MOBILE REVENUE MODELS........................................................................
`
`4 .4 .1
`
`4.4.2
`
`4 .4 .3
`
`T h e C ritic a l M a ss................................................................................................................................7
`
`Inform ation P ricing M odels.............................................................................................................82
`M etrics fo r S u cc ss .............................................................................................................................
`
`5 CASE STUDIES .......................................................................................................
`
`5.1
`
`5.2
`
`5.3
`
`5.4
`
`A SURVEY OF THE SERVICE OFFERING LANDSCAPE ...........................................................................
`
`A RUBRIC FOR MOBILE DATA SERVICE CANDIDATES........................................................................
`
`CASE STUDY: VERIZON W IRELESS ..........................................................................................................
`
`C A SE STU D Y : V IN D IG O ...............................................................................................................................
`
`6 CONCLUSIONS.....................................................................................................
`
`7 REFERENCES .......................................................................................................
`
`70
`
`7 4
`
`77
`
`9
`
`8 4
`
`86
`
`87
`
`89
`
`93
`
`97
`
`101
`
`102
`
`PAGE 9
`
`

`

`
`PAGE 10
`PAGE 10
`
`

`

`1
`
`INTRODUCTION
`
`In the late 1990's, much attention was paid to technologies such as wireless
`
`personal digital assistants (PDAs), global positioning system (GPS) in automobiles, and
`
`the boom in mobile telephone handsets. Prior to the technology stock fizzle in mid- 1999,
`
`tech analysts were frequently projecting near-unbridled growth in several of these high-
`
`tech sectors. The stock fizzle notwithstanding, the consumer adoption rate of some of
`
`these technologies is still far outpacing that of previous generations' consumer
`
`electronics.
`
`Perhaps the largest hurdle for companies hoping to break into or succeed in
`
`today's high-tech sectors is fixing on the appropriate strategies for pricing and revenue.
`
`While the topic of this study is decidedly revenue and pricing models, the context is
`
`unmistakably mobile data services. Identified as more than simply an ephemeral fad
`
`[17], mobile data applications find opportunity at nearly every turn. Examples are as
`
`diverse as the type of people who will use them; among those already market-tested are
`
`gateways to wireless Internet access, mobile online trading services, and short text
`
`messaging. Research trends indicate that access to mobile data services is occurring most
`
`frequently on mobile phones, following by PDAs [18]. The same report highlights
`
`analyst projections, which estimate that low-end smart phones will be the access devices
`
`of choice over the next several years. As such, the study will bias its focus toward
`
`service providers who aim to capture a significant fraction of these users.
`
`PAGE 11
`
`

`

`Mobile phones and handheld digital assistants, in particular, are pervasive in
`
`cultures around the world-ranging from the United States to Japan and Southeast Asia
`
`to Scandinavia. For a number of reasons-some technical, some social, some political-
`
`adoption dynamics have varied widely over these geographies [28]. As such, the study
`
`that follows will select one of these geographies-the familiar United States-and will
`
`explore the technical, social, and economic challenges presented in this geography.
`
`Additionally, mobile data services clearly have applications in both commercial and
`
`consumer sectors. As different business units commonly handle these two sectors, a
`
`focused study will choose one-the consumer sector-for in-depth analysis.
`
`The subsequent chapters in this study will examine more closely several of the
`
`issues central to the success of a mobile data services operations.
`
`-
`
`Chapter 2 (The State of the Industry) will delve deeper into the market
`
`segmentation issues addressed above, as well as explore the
`
`infrastructure supporting mobile data services. The key takeaway is that
`
`the existing infrastructure is advanced enough to build semi-robust
`
`applications for the types of access devices consumers will be using over
`
`the next several years.
`
`PAGE 12
`
`

`

`-
`
`-
`
`Chapter 3 (What Makes Mobile Unique) identifies the "mobile sweet
`
`spot," or the intersection of location-awareness, personalization, and
`
`timeliness. Each of these three elements demonstrates value-added to
`
`the wireless consumer. Taken together, they create a compelling value
`
`proposition and a useful framework for wireless functionality analysis.
`
`Chapter 4 (Mobile Revenue Models) focuses on generic information
`
`pricing models and, specifically, mobile revenue models. Strategies for
`
`pricing are accompanied by several examples. A history lesson is
`
`gleaned from companies' business experiences on the World Wide Web
`
`(WWW), and a series of recommendations for pricing and revenue
`
`models is laid out.
`
`-
`
`Chapter 5 (Case Studies) begins with a survey of mobile service
`
`offerings, follows with a qualitative rubric for evaluating mobile data
`
`service candidates, and concludes with two business cases as applied to
`
`this rubric.
`
`While admittedly not a survey of the complete mobile landscape, this sector- slice
`
`(i.e. U.S. consumer markets) is both personally relevant and a well-defined business
`
`challenge. It is the hope of the author that this study will make a strong case for mobile
`
`pricing and revenue model strategies in United States consumer markets.
`
`PAGE 13
`
`

`

`2
`
`THE STATE OF THE INDUSTRY
`
`This study focuses primarily on mobile data services revenue models. More
`
`exactly, it examines data services as an information good or as a service. The
`
`economics (e.g. market segmentation, market sizing, etc) of the infrastructure and
`
`consumer hardware will be discussed, but these economics will not be the focus of this
`
`research. As such, distinctions will be made over the course of this analysis between
`
`mobile data service revenue and "supporting role" revenue.
`
`At the conclusion of this chapter, the reader will better understand:
`
`- Delivery mechanisms will be assumed in the short run. At present, significantly
`
`more demand exists for low-bandwidth data services. The infrastructure will not
`
`pose a threat to the delivery of these types of services.
`
`- The mobile phone is the platform of choice. While other choices, such as the
`
`PDA, exist, most market analyses project that mobile phones will be the clear winner
`
`over the next five years. As such, companies looking to profit from data services
`
`should target this largest, most cost insensitive portion of the market
`
`- The United States is the geography of choice. While the US is not the leader is
`
`wireless deployment or applications, it provides an interesting and familiar context
`
`for this study. Adoption rates of mobile data services in the US will be affected by
`
`existing characteristics such as market structure of competitive goods and services
`
`and general economic welfare.
`
`PAGE 14
`
`

`

`Several of these related areas will be referenced; this chapter will provide the
`
`necessary introductions to them. Chief among these related topics are the existing and
`
`future delivery mechanisms (the infrastructure), the consumer hardware devices (mobile
`
`phones and PDAs), and geographies of the markets.
`
`2.1 The Wireless Infrastructure
`
`The wireless infrastructure, and its concomitant history, provides an important
`
`perspective for understanding the products and services that will excite United States
`
`consumer markets. As the infrastructure must be capable of meeting the demands of the
`
`services, some limitations will be found simply by examining characteristics of the
`
`network. This theme will be repeated in Section 2.2, where some limitations are
`
`imposed by the handsets accessing the network. This section will take a brief high-level
`
`look at the several "generations" of wireless networks. The take-away is that mobile
`
`data service providers should focus research and development efforts on the capabilities
`
`of today's 2.5G networks.
`
`I G
`
`2G
`
`2.5G
`
`3G
`
`Table 2A: Summary of the Generations of Wireless Networks
`Characteristics of Each Generation of Wireless Network
`- Analog processed data
`- Designed for voice
`- Capable of very little data
`- Large Coverage
`- Roaming in the United States
`- Low bandwidth
`- Digitally processed data
`- Designed for data, but primarily transmits
`- Capable of encoding and compressing
`voice
`data for increased efficiency
`- Low bandwidth
`- Built on top of 2G networks
`- Medium bandwidth
`- Capable of enhanced data services such as
`e-mail and Internet access
`- High bandwidth
`- Support for video and other broadband data
`- Not in place in U.S. markets
`Source: Original
`
`- Demand for technologies Is unclear at
`present
`
`PAGE 15
`
`

`

`First-generation (iG) wireless networks worked in conjunctions with analog-
`
`based cellular phones. The information transmitted is processed in a purely analog
`
`fashion; encoding and compression are not major players. The networks were
`
`historically limited with relatively small bandwidth, but managed wide coverage and
`
`penetration.
`
`Second-generation (2G) wireless networks were designed to transmit data.
`
`While voice was still the primary "data" to be transmitted, it was encoded and
`
`compressed to make more efficient use of the still-small bandwidth (ie. up to 9.6 kbps).
`
`More data support was added to 2G networks. Additionally, capabilities for
`
`international roaming were added to some 2G networks.
`
`Third-generation (3G) wireless networks aim to meet several objectives that
`
`have been gaffed in prior wireless networks. Included in these objectives are network
`
`universality (works everywhere), high bandwidth (up to 2 Mbps fixed), flexibility
`
`(accommodates various access demands), high quality of service (comparable to a fixed
`
`network), and service richness (support for simultaneous connections, video, integrated
`
`services, etc.) 3G networks are springing up in Japan and Scandinavia where cutting-
`
`edge technologies are being tested. While certainly flashy, consumer demand is not yet
`
`clear for 3G networks in the United States [17].
`
`PAGE 16
`
`

`

`Most of today's networks wireless networks, especially in the United States, are
`
`still a significant way off from 3G. It would, however, be unfair to classify the networks
`
`as 2G, either. Instead, today's U.S. networks fall into the category of 2.5G. 2.5G
`
`networks are characterized as voice-dominated, enhanced data networks capable of
`
`transmitting e-mail and accessing the Internet. Built on a 2G backbone with 3G
`
`ambitions, 2.5G networks are higher bandwidth and employ additional technologies (e.g.
`
`GPRS) not found in traditional 2G networks.
`
`As wireless networks ramp up toward 3G networks-and many IT research
`
`analysts predict they will be deployed within three to five years-service providers
`
`might be tempted to target the cutting edge data services these networks can support
`
`[26]. This will be a mistake. In United States consumer markets, the overwhelming
`
`majority of users do not have (and will not have) devices to take full advantage of 3G
`
`networks once they are deployed. As outlined in the next section, the expected mass
`
`adoption of handsets will occur in the low-end smart phone sector, which maps most
`
`closely with existing 2.5G networks. The rate of change of network status (from 2.5G to
`
`3G) is almost entirely unimportant if the access devices do not keep pace. As such,
`
`mobile data service providers should consider a range of services that will work on
`
`today's networks, and will continue to work (or, perhaps, work even better) on
`
`incrementally improved networks.
`
`PAGE 17
`
`

`

`2.2 Handheld Devices: Mobile Phones and PDAs
`
`For the past few years, much attention has been paid to the adoption rates of
`
`mobile phones vis-d-vis adoption rates of personal digital assistants (PDAs). While a
`
`more clear distinction used to exist between the two, the line is beginning to blur
`
`noticeably. Several PDAs from Palm are now internet-capable (via an add-on modem) or
`
`have wireless connectivity built in, as is the case with the Palm VII. Likewise, several
`
`mobile phone handsets are pushing the line of maintaining information more traditionally
`
`reserved for the PDA; among these features are:
`
`-
`
`-
`
`intelligent phone directories
`
`schedules
`
`- web browsers
`
`-
`
`alarms
`
`The two most identifying characteristics that distinguish the mobile phone from the
`
`PDA are form factor and usage pattern.
`
`On form factor, Dulaney [18] writes "Cellular-phone factors should remain stable,
`
`with the Nokia 2110 and Motorola StarTAC 8600 representing the extreme boundaries in
`
`acceptable form factors." PDAs, on the other hand, "while still vacillating between being
`
`a notebook replacement and an organizer, should become more organizer focused for
`
`form factors that are either belt-mountable or coat pocket size." Regarding usage,
`
`PAGE 18
`
`

`

`Dulaney continues, "Smart phones will become voice-first, data-second devices.
`
`Interactive PDAs will become data-first, voice-second devices." Limited largely by
`
`ergonomic constraints, spatial arrangement and methods of input will be built to suit
`
`these respective needs.
`
`Which, then, is it? Should service providers develop applications to deliver data
`
`and services to multiple platforms, or can they hedge their bets and maximize their
`
`investments by targeting just one? Laszlo takes a hard stance on the issues. His research
`
`firm, Jupiter Communications, projects that "the vast majority of handsets will fall into
`
`the category of low-end smart phones." Moreover, by 2003, he projects that "the US
`
`market for mobile devices will 75.9 million low-end smart phones, compared with only
`
`15.5 high-end smart phones and PDAs." Admitting that bandwidth considerations will
`
`Figure 2.1: Projected US Mobile Device Penetration, 2003
`
`Projected US Mobile Device Penetration, 2003
`
`80
`
`-o 70-
`
`2 60
`C.
`
`50
`
`40 -
`
`U)
`
`30 -
`
`20-
`0
`~10
`
`0
`
`Low end Smartphones
`Device Type
`
`High end Smartphones and PDAs
`
`Source: Jupiter Communications
`
`PAGE 19
`
`

`

`improve over time, he continues "but they will retain many of the interface and display
`
`limitations that plague today's web-enabled handsets."
`
`Jones [15] unequivocally states, "At some point between 2003 and 2005, we
`
`expect the number of mobile phones deployed worldwide to exceed 1 billion.
`
`Consumers will have more phones than PCs and will spend more time near a phone than
`
`a PC. The phone will be the dominant client device for consumers or mobile workers."
`
`The same research continues to describe that this massive number of phones will include
`
`multiple generations of technologies worldwide, from the most primitive to the most
`
`cutting-edge. Mobile data services may run the gamut, but those those will become most
`
`profitable will target the largest possible segment of this group. Further research by
`
`GartnerGroup indicates the following trends:
`
`-
`
`-
`
`digital phone technologies are expected to have replaced analog phone in developed
`
`countries by 2003
`
`the highest data rates and newest technologies are likely to be rolled out in urban
`
`areas with high populations
`
`Clearly, a trend is beginning to emerge. While the previous data were on a worldwide
`
`basis, the projections are in line with the others. A mobile data service provider would
`
`be foolish not to focus on the lion share of this emerging market-digital phones, low-to-
`
`medium bandwidth, urban locales.
`
`PAGE 20
`
`

`

`2.3 A Focus on the United States
`
`The United States is both a familiar backdrop and an interesting case study in
`
`wireless access device adoption. Adoption rates in the United States have not been
`
`nearly as eye-popping as adoption rates in Europe or Southeast Asia [26]. Several
`
`reasons exist which may explain this phenomenon. This section will begin by examining
`
`these reasons. It will continue by further segmenting the US market to help target mobile
`
`data service initiatives.
`
`The wireline infrastructure in both Europe and Southeast Asia is very poor
`
`compared to the wireline infrastructure in the US. Quality of service is much lower, and
`
`availability of service is non-existent in some locations. Wireless connectivity, then,
`
`provides a much cheaper means to achieve the same end as a high-quality wireline
`
`telephone system. Moreover, users in Europe have been conditioned to pay for telephone
`
`service differently that user in the US. In Europe, "local service" is metered. The cost
`
`structure for wireless service mirrors the cost structure for wireline service in Europe, so
`
`no leap of faith is required for the switch. In the US, users are conditioned on a flat fee
`
`local service. Using a mobile phone to place a call to a neighbor, then, is a fiscally
`
`irresponsible decision. When viewed together, these two obstacles pose a significant
`
`hurdle for US mobile service providers.
`
`PAGE 21
`
`

`

`Brooks [19] asserts that "Contrary to the current industry focus on the mobile
`
`professional, the largest opportunity associated with access outside of the home lies in
`
`tapping into the mass market. Between now and the end of 2005, over 27 million US
`
`households will come online; many will first use the Internet in an access point outside
`
`the home, presenting a key acquisition opportunity for Web ventures."
`
`In 1999, a mere five percent of all of US households were accessing the Internet
`
`with non-PC devices [20]. These numbers are expected to grow dramatically, as seen in
`
`Figure 2.2 below. This corroborates an earlier statement that a tremendous opportunity
`
`exists with a new customer base.
`
`Figure 2.2: Access Device Distribution of US Online Households, 1999 and 2005
`
`80%
`
`37%
`
`96-
`
`0PC
`
`0 Both
`M Non-PC
`
`100%-
`
`90%
`
`80%_
`
`70%
`
`60%
`
`50% -
`
`40%
`
`30% -
`
`20% -
`
`10%-
`
`0%
`
`0 0
`
`.
`
`1999
`
`2005
`
`Source: Jupiter Communications
`
`PAGE 22
`
`77:=
`
`

`

`Relevant to targeting the appropriate user groups, Brooks has also identified the
`
`following trends in growth of online users in the US market:
`
`- Households with incomes below $50,000 represent the lion's share (over 75 percent)
`
`of future new online users.
`
`-
`
`Low-income households are more likely to prefer access in currently under-targeted
`
`public areas, including post offices, supermarkets, malls, stores, and stadiums.
`
`Contrary, perhaps, to intuitive assumptions, it will be prudent for mobile data service
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`providers to consider heavily the opportunity that lies in lower-to-mid income
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`households. As many of these households are less likely to purchase PCs which cost
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`several thousand dollars, handheld devices may strike big in this sector. Regardless of
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`which income group or groups are eventually targeted, mobile data service providers
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`must be aware of the millions upon millions of new US households expected to jump on
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`the Internet and wireless bandwagon over the next several years.
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`In the preceding sections, a case has been built for examining mobile pricing and
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`revenue models in a specific geography (i.e. the United States) to a specific customer
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`segment (i.e. consumer's wielding low-end smart phones.) A case remains to be built for
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`the practicality of mobile applications. This will be the subject of Chapter 3, "What
`
`Makes Mobile Unique."
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`3 WHAT MAKES MOBILE UNIQUE
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`While it is clear that interest in mobile devices and mobile commerce has spiked
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`since late 1999, it remains unclear how to target the wireless audience comprised of
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`millions of potential consumers. Firms ranging from old standbys (e.g. IBM, Oracle) to
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`newer interactive agencies (e.g. Razorfish, ZEFER) are rapidly announcing and
`
`launching initiatives in mobile data access [21] [22] [23] [24].
`
`The foray into the wireless space is reminiscent of the World Wide Web's 1995
`
`explosion. Web sites and applications were designed quickly and often haphazardly as
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`the WVW audience grew. Early "killer apps" of web turned out to be portals (e.g.
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`Yahoo) and low-cost commerce (e.g. Amazon), while many other early web fads faded
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`quietly away. Developers and service providers alike should learn a lesson from the
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`web's boom and capitalize on those characteristics of mobile commerce which make it
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`unique. A sizable consumer base will be reached by providing services available on
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`today's 2.5G networks that hit the mobile "sweet spot." (See: 4.1 Reaching a Critical
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`Mass.)
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`In this chapter, the sweet spot is identified as the intersection of location-
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`awareness, personalization, and timeliness. First, each of these three components is
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`broken down to its elementary parts for examination. The sweet spot will then be
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`examined as a whole, and a framework will begin to appear. Revenue models discussed
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`in Chapter 4 will later be incorporated and a rubric created in order to analyze
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`opportunities in the U.S. consumer markets of mobile data services. The same sweet spot
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`will likely be the source of early mobile commerce revenues while bandwidth and form
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`factors are enhanced in the background.
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`3.1 Location-Awareness
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`"Location is the one thing that we have that no one else has on the Internet."
`
`Stephen Doyle
`Head of GPRS Solutions and
`Realizations at Motorola, Inc.
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`The ability of a device to know exactly where it is, within a few meters of
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`precision, opens up a world of opportunity for mobile service providers. Applications as
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`simple as "help me find the nearest gas station" or "I'm lost! Get me home!" are trivial
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`when a device knows its location and can hook into a powerful engine like MapQuest.
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`Location information, while not unique to mobile access devices, is especially
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`useful when the device moves with the consumer. Significant progress has been made in
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`mobile location determination as a result of the US Government's E-911 mandate, which
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`charged that 67% of all cell phone calls must be traceable within 410 feet by October
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`2001 [10]. E-911, however, is simply a bellwether of location-aware applications. While
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`E-911 will serve a decidedly noble and civic-minded function, the infrastructure will
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`enable myriad consumer-oriented applications that, if properly thought out and
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`PAGE 25
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`implemented, may yield significant revenue to service providers and those companies
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`building and maintaining the mobile infrastructure.
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`The remaining subsections will detail the 5-911 initiative, explore several
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`location determination techniques, consider enhanced location-based services beyond E-
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`911, and introduce the privacy question when location information is available.
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`3.1.1 E-911
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`Wireline 9-1-1 calls have historically been easy to locate as the telephone number
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`is physically tied to a street address. The Public Safety Answering Point (PSAP)
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`database is used as a lookup table, and routes the 9-1-1 call to the nearest PSAP dispatch
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`(i.e. 9-1-1 operator) along with relevant address information. As a result, the dispatch is
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`able to immediately proceed to the more urgent details of the 9-1-1 call.
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`In June 1996, the Federal Communications Commission first proposed a mandate
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`for enhanced 911 (E-911), which would provide a similar service for wireless 9-1-1 calls.
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`At the that time, wireless 9-1-1 calls were routed to a PSAP, which at times returned
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`useless or null data and was unable to further route t