Fast-Tracking Operator Plans to Win in
the $5bn Location Insights Market
The Operators’ Guide to Launching Location Services
OCTOBER 2015
Anthony Dornan & Philip Laidler
STL Partners / Telco 2.0
Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Preface
Subscriber location information is a much-heralded asset of the telecoms operator. Operators have
generally understood the importance of this asset but have typically struggled to monetize their position.
Some operators have used location information to enable third party services whilst others have
attempted to address the opportunity more holistically, with mixed success.
This report updates and expands on a previous STL Partners study: “Making Money from Location
Insights” (2013). It outlines how to address the potential opportunity around Location Services. It draws
on interviews conducted amongst key stakeholders within the emerging ecosystem, supplemented by
STL Partners’ research and analysis, with the objective of determining how operators can release the
value from their unique position in the location value chain.
This report focuses on what we have defined as Location Insight Services. The report argues that
operators should first seek to offer Location Insight Services before evolving to cover Location Based
Services. This strategic approach allows operators to better understand their data and to build location
services for enterprise customers rather than starting with consumer-orientated location services that
require additional capabilities. This approach provides the most upside with the least associated risk,
offering the potential for incremental learning.
This report was commissioned and supported by Viavi Solutions (formerly JDSU). The research,
analysis and the writing of the report itself were carried out independently by STL Partners. The views
and conclusions contained herein are those of STL Partners.
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Executive Summary
What’s changed in the Location Insights Marketplace?
Operators’ location ‘assets’ are a much discussed source of value, potentially allowing operators to offer
or augment a multitude of services or to provide insight based on analysis of movement patterns coupled
with other relevant information. Operators have attempted to realize the value of their location assets
through different approaches, with mixed success.
In 2013 STL Partners (STL) analyzed the opportunity around Location Insight Services (LIS), defining
the marketplace and providing a strategic blueprint for operators. Whilst the LIS opportunity has not
materialized at the originally predicted rate, the marketplace and operators’ strategies to deliver this
service have evolved significantly, and we believe that Location Services remain an important
opportunity for operators. This report builds on the 2013 report and subsequent learnings, exploring:

The strategy for addressing Location Services & key learnings from operator & OTT case studies
(Telefónica, DTAG, Turkcell, Singtel, Vodafone, Tado, TomTom, Validsoft, Deveryware)

The size of the Location Insight Services marketplace: c.$5bn by 2020

The technology and strategy options for operators
Understanding Location Services
Location Based Services (LBS) are geared towards supporting business processes (typically
marketing-oriented) that are dependent on the instant availability of real-time or near real-time data
about an individually identifiable subscriber. Typically these services require an interaction with the
customer (e.g. push marketing) and therefore require compelling user interfaces and permissions.
Location Insight Services (LIS) do not require real-time data and, where insights are aggregated and
anonymized, can safeguard individuals’ privacy. The underlying premise is that identification of patterns
in location activity over time not only enables a much deeper understanding of consumer behaviour and
motivation, but also builds a clearer picture of the visitor profile of the location.
Figure 1: Location Insight vs. Location Based Services
Source: STL Partners
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Operators should start their Location Journey by
focusing first on LIS
When considering how to develop and monetize their location assets we recommend that operators
start with LIS. Whilst many operators are already engaged in LBS (e.g. enabling push-marketing) the
majority are not actually providing the service but are simply sharing data and enabling a 3rd party
service provider.
Starting with LIS has a number of strategic advantages:

It’s a big opportunity in its own right

Telcos (should) have a data capture/technology advantage for LIS over OTT players

LIS provides an opportunity to build & learn incrementally, proving value

Privacy risks are reduced (particularly with aggregated data)

LIS does not require 100% coverage of the population, unlike a number of LBS use cases

LIS can provide internal benefits and can bolster the Go-to-Market strategy for vertical specific
offerings
This report explores in detail the rationale for starting with LIS.
Where is the Opportunity for Location Insight Services?
The potential size of the opportunity for each sector varies; however, size is not the only characteristic
that determines sector addressability. Perhaps what is more important, at least initially, is the ease to
develop services (that will be adopted) for a particular sector.
Whilst the retail sector is often discussed as the main opportunity area for LIS, it is not necessarily the
best sector to address first. With new services such as LIS, it is important that there is early evidence of
success. These proof points will allow operators to scale-up and expand their offering.
Operators should therefore focus on sectors where they can most readily offer Location Insight Services
without requiring significant technology investment or complex commercial arrangements. Operators
should aim to bag the low-hanging fruit to prove value and then focus on improving their offering to
address other sectors.
Through conversations with operators who are exploring LIS, we have found that they are having the
most success with the Travel & Transport and Local Government sectors. Operators have found these
sectors have lower barriers to entry; operators are able to provide services harnessing footfall analytics
data that do not require highly precise location information.
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
STL’s revised analysis estimates that the Location
Insights Market is potentially worth $5bn globally by 2020
Figure 2: STL Partners’ Analysis of the value of Global Location Insight
Services (by 2020)
Source: STL Partners
We have revised down our estimates on the potential size of the opportunity from $11bn based on
further learnings on how the LIS market is evolving. We believe that the opportunity has not been
realized at the originally predicted rate for a number of reasons, including:

Un-realistic expectations about the ease of developing and delivering this service

LIS is still a nascent concept and an emerging marketplace

Operators have typically not approached the opportunity correctly, failing to adopt an agile
approach

The retail sector has not embraced the opportunity as quickly as expected

The overall LIS ecosystem is complex
We have therefore made more cautious assumptions regarding both take up of LIS and the value of
benefits (gains) achieved from the insights. Despite these challenges, we believe that LIS represents a
significant opportunity for operators, estimating the LIS marketplace to be worth $5bn globally by 2020.
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Understanding the Technology Options for Location
Figure 3: Analysis of location data acquisition technologies suitability for
Location Insight Services
Source: STL Partners & Viavi Solutions Analysis
We expect telcos to have a significant advantage over OTTs for the technology options that are a better
fit to LIS requirements; these technologies are primarily network-centric. Telcos run and manage
networks and already capture this location information (whether or not they use it). Whilst this
information may not be at a granular enough level to offer insights to all sectors and for all use cases, it
provides a strong platform to build from.
Based on our conversations with operators STL Partners recommend that in most cases operators
should start with existing technologies/capabilities and add greater precision to this over time.
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Expanding Beyond Insights
Both LIS and LBS create a strong foothold to engage in more digital and data activities. Establishing a
role as an LIS/LBS provider can serve as a stepping stone to becoming a Trusted Data Provider. This
role can apply to both enterprise services and consumer services.
Figure 4: The Strategy Beyond Location Insights
Source: STL Partners
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Contents
Location, Location, Location ............................................................................................................11
The Importance of Information ..................................................................................................11
Location Based Services vs. Location Insight Services ............................................................14
Choosing the Right Strategy ............................................................................................................20
Where are we now?...................................................................................................................20
Start with Location Insight Services ..........................................................................................20
Improve you LIS offering, transition towards LBS & position yourself as a Trusted Data
Provider .....................................................................................................................................21
Location Insights – Marketplace Overview ......................................................................................22
Where is the Opportunity for Location Insight Services? ..........................................................22
Which Sectors are most addressable? ......................................................................................23
Sizing the Opportunity ......................................................................................................................26
Why haven’t forecasts developed as quickly as expected? ......................................................26
Location Insights potentially worth $5bn globally by 2020 ........................................................27
Benchmarks ...............................................................................................................................28
Where does the value come from – the Location Insights ‘Stack’ ............................................29
Understanding the Technology Options ..........................................................................................30
The Technology Options for Location Data Acquisition ............................................................30
Technology Advantages for Telcos ...........................................................................................32
The Right Degree of Location Precision....................................................................................32
Other Advantages of Starting with LIS .............................................................................................33
Incremental Learning .................................................................................................................33
Addressing the Privacy Question ..............................................................................................33
Market Coverage .......................................................................................................................33
LIS can provide internal benefits and can bolster the Go-to-Market strategy for vertical specific
offerings .....................................................................................................................................34
Expanding Beyond Insights .............................................................................................................35
Addressing Location Based Services ........................................................................................35
Becoming a Trusted Data Provider ...........................................................................................38
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Practical Guidance to Launch Location Services ............................................................................40
Market Strategy .........................................................................................................................40
Data Management .....................................................................................................................41
An agile approach, partnering, orchestration and governance .................................................43
Conclusions ......................................................................................................................................44
Appendices ......................................................................................................................................45
Appendix 1: Location Acquisition Technologies in Detail ..........................................................46
Appendix 2: Opportunity Sizing Methodology ...........................................................................52
Appendix 3: About STL Partners and Telco 2.0: Change the Game ........................................53
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Table of Figures
Figure 1: Location Insight vs. Location Based Services .......................................................................... 3
Figure 2: STL Partners’ Analysis of the value of Global Location Insight Services (by 2020) ................ 5
Figure 3: Analysis of location data acquisition technologies suitability for Location Insight Services..... 6
Figure 4: The Strategy Beyond Location Insights ................................................................................... 7
Figure 5: The Explosion of Smartphones (2007-2014) ......................................................................... 11
Figure 6: ‘Non-Smart’ Data Insights Become More Important as More ‘Things’ are Connected .......... 12
Figure 7: Mapping the Telco Opportunity Landscape ........................................................................... 14
Figure 8: Four opportunity domains for operators ................................................................................. 16
Figure 9: Turkcell’s Smart Map Tool ..................................................................................................... 17
Figure 10: TomTom’s Fusion Engine to Analyze Real-Time Traffic Information .................................. 18
Figure 11: Tado’s Proximity Based Thermostat .................................................................................... 19
Figure 12: Expanding Beyond LIS ......................................................................................................... 21
Figure 13: Location Insights – Market Taxonomy ................................................................................. 22
Figure 14: Telefónica Smart Steps Location Analytics Tool .................................................................. 24
Figure 15: Motionlogic’s Location Analytics Tool .................................................................................. 25
Figure 16: The value of Global Location Insight Services by industry and sector (by 2020) ................ 27
Figure 17: The Location Insights ‘Stack’ ............................................................................................... 29
Figure 18: How well do different location data acquisition technologies support Location Insight
Services needs? .................................................................................................................................... 31
Figure 19: Real-Time vs. Near Real-Time Location Information ........................................................... 36
Figure 20: Deveryware’s Dynamic Permissions Tool ............................................................................ 37
Figure 21: Become a Trusted Data Provider ......................................................................................... 38
Figure 22: Analysis of App/OS based real-time location Technology ................................................... 46
Figure 23: Analysis of App/OS based data stored on device Technology ............................................ 47
Figure 24: Analysis of Emergency Services Location Technology ....................................................... 48
Figure 25: Analysis of Granular (building level) network based Technology ........................................ 49
Figure 26: Analysis of Coarse (cell-level) network based Technology ................................................. 50
Figure 27: Analysis of Indoor Technologies .......................................................................................... 51
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Location, Location, Location
The Importance of Information
Over the last 5-8 years we have seen an explosion in the amount of smartphones globally (Figure 5).
This has dramatically changed the communications landscape. Traditional telecoms services (i.e. voice,
SMS) have come under increased pressure through competition from Over-the-Top (OTT) players, with
OTT players also becoming the predominant drivers of usage and engagement with the device. Whilst
the smartphone has increased data revenues, it also has the potential to provide/catalyze a myriad of
new opportunities for operators.
Figure 5: The Explosion of Smartphones (2007-2014)1
No. of Smartphone Units Sold (2007-2014)
1,400
No. of Units Sold (million)
1,245
1,200
970
1,000
800
680
600
472
400
200
297
122
139
172
2007
2008
2009
-
2010
2011
2012
2013
2014
Source: Statista
Users of smartphones are continually generating data through their interactions with the device and its
applications. Understanding this information has potential value. App and OS providers are readily using
this information to build accurate behavioral profiles of their users, which in turn can lead to increased
advertising revenue on their platforms. However lots of this potential information is not being captured
or understood.
One piece of valuable information smartphones (and other devices) both generate and can potentially
harness is location. Advances in technologies have meant that location information can be captured
from devices in a number of different ways. An operator can build an understanding of a device’s location
through its interaction with the cellular network (e.g. RAN signaling) or through the device’s own sensors
(e.g. GPS, SSID). These can effectively capture location information outdoors, whilst other technologies
(e.g. WiFi, beacons) can offer indoor location insight. These different methods of capturing location
information present different challenges and benefits and need to be understood before attempting to
address opportunities around location.
1
Source: Statista
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Mobile devices do not only create new ways to capture location data but also new ways to harness the
value of the information. Applications on the device are able to use location information to alter the
service provided to the user (e.g. recommend services in the user’s area) whilst third parties can take
advantage of this information to ensure the validity of their service (e.g. only allowing the user to view
the content available in their area or reducing fraud by pairing the location of financial transactions with
that of the mobile device).
Whilst the smartphone has sparked interest and excitement in harnessing this data, capturing location
information is not limited to smart devices. For example, location information can be captured from
feature phones (or smart phones with some functionality disabled), through the mobile network. Whilst
non-smart devices generate less location traffic and offer less-compelling user interfaces, they can
potentially provide significantly increased population coverage, allowing wider insights to be extracted.
The opportunity around ‘non-smart’ data insights becomes even greater as more devices and things
become connected. It is estimated that there will be 4.9bn connected things in 2015, with this increasing
to c.25bn by 2020.2
Figure 6: ‘Non-Smart’ Data Insights Become More Important as More
‘Things’ are Connected
No. of Connected Things by 2020
No. of Connected Things (billion)
30
25.01
25
20
15
10
5
3.03
3.75
2013
2014
4.88
0
2015
2020
Source: Gartner
As more and more devices and things become connected and the Internet of Things (IoT) comes to
fruition the amount of data produced will vastly increase. Understanding this information and providing
context with the addition of location information has the potential to add significant value to new services.
Operators are well positioned to capitalize on this opportunity, as for many IoT products and services,
location (and other relevant) data can effectively be captured over networks (as the ‘things’ may not
have the required resources and/or functionality). As operators’ networks remain independent of the
potentially fragmented IoT ecosystems they are well-positioned to have greater ‘device/thing coverage’,
providing more comprehensive insights.
2
Source: Gartner
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Location information has the potential to augment a countless number of services and therefore has the
potential to be extremely valuable. Operators are not blind to this fact. They have wrestled with how to
unlock the value of their location asset for a number of years. Indeed, in STL Partners (STL’s) 2013
report: ‘Making Money from Location Insights’ we outlined how operators should address the opportunity
and discussed its potential size.
Despite industry-wide understanding that this is a significant opportunity, it has not been realized at the
originally predicted rate. This is due to a number of reasons which we will discuss in this report.
Nonetheless operators still appreciate that they possess a unique asset in this space and many are
currently addressing or are planning to address the opportunity around location.
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Location Based Services vs. Location Insight Services
In the 2013 report, STL made a clear distinction between different types of location services.

Location Based Services (LBS) are geared towards supporting business processes (typically
marketing-oriented) that are dependent on the instant availability of real-time or near real-time
data about an individually identifiable subscriber. These are provided on the reasonable
assumption that knowing an individual’s location enables a company to deliver a service or
make an offer that is more relevant, there and then. Typically these services require explicit
consent and an interaction with the customer (e.g. push marketing) and therefore require
compelling user interfaces and permissions.

Additionally there is an opportunity to derive and deliver Location Insight Services (LIS) from
connected consumers’ mobile location data. This opportunity does not necessarily require realtime data and where insights are aggregated and anonymized, can safeguard individuals’
privacy. The underlying premise is that identification of repetitive patterns in location activity
over time not only enables a much deeper understanding of the consumer in terms of behavior
and motivation, but also builds a clearer picture of the visitor profile of the location. Additionally
LIS has the potential to provide data that is not available via other routes (e.g. understanding
the footfall within a competitor’s store).
Figure 7: Mapping the Telco Opportunity Landscape
Source: STL Partners
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The framework in Figure 7 has been developed by STL Partners specifically with the mobile operator’s
perspective in mind. We have split out operator location opportunities along two dimensions:

Real-time vs. Non-real-time data acquisition

Individual vs. Aggregated data analysis and action
Real-time vs. Non-real-time
Applications requiring (near) real-time location are Location Based Services, typically supporting
automated business processes. Location can often be a “trigger” for such processes (e.g. by Geofencing). For such services where location or proximity is the “trigger”, the required level of accuracy
(and hence timeliness) will depend on the application (e.g. country-level triggering for offering travel
insurance, building-level for retail promotions, macro cell-level for parking). Immediately accessible but
approximate / delayed location data may be sufficient in cases where the “trigger” is not actually the
location (e.g. card payment fraud detection).
This distinction between real-time and near-real-time data for Location Services has implications for
operators’ technology strategies relating to Location Insight and Location Based Services.
Applications drawing on historical (non-real-time) applications are Location Insight Services. These
typically support personal, business and/or policy actions that are not immediate (e.g. locate a new
store, plan transport links, exercise more). These insights can provide considerable personal,
commercial and economic value.
For example, the UK government has already spent over £100m on consultants’ advice on planning
and evaluation for the new HS2 rail line and is expected to spend up to £450m on advisers and
management firms in total3. (The line itself is expected to cost £50bn to build). Understanding how
people travel (by different modes of transport), through an analysis of location data over time, has the
potential to provide greater insight into the value that will be created from the launch of this new rail line.
Individual vs. Aggregated
The other dimension in the framework in Figure 7 relates to the level of data aggregation, insight and
action. Location data that is captured, analyzed and supports actions at an individual level is typically
used for business processes relating to interactions with that individual. Proximity marketing is an
example of a service using individual real-time location data. Others include Google Now or proximity
based heating controls (see Tado example below). Non-real-time individual location data can be used
to infer individual attributes such as travel modes, home-zone and/or work-zone. These can provide
profiling and context that support services or business processes relating to the individual.
Non-real-time individual location data can also be used as part of an enterprise service (e.g. auditing
the past-location of an organization’s field force in the case of a complaint) or for analysis of the
propagation of infectious diseases (e.g. tracking the location history of declared individuals with
infectious diseases).
Where location data from many individuals is aggregated, this can be used to gain valuable insight into
locations and transport links. This could be real-time (e.g. traffic conditions) or non-real-time (e.g. weekly
commuting patterns, footfall). If location information is combined with other data and aggregated, this
can provide insight into not only the number of individuals and their relationship with a location, but also
3
Source: Express & Star
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the grouped profiles (e.g. zonal heat mapping of shopping mall catchment and how this evolves over
time for different social-economic groups of subscribers).
A big difference between these two levels of aggregation relates to privacy and permissions. Although
regulations vary by country, as a general rule, use of data at an individual level implies more explicit
consent, greater visibility and control by the individual user (other than for internal planning purposes
e.g. Network Planning and Network Control). For aggregated data, privacy can be protected, thereby
reducing the levels and nature of permissions. This is because, with the correct measures, aggregate
data cannot be used to identify or affect the experience of individual users.
The resulting four opportunity “domains” are summarized below.
Figure 8: Four opportunity domains for operators
1. Aggregated Location
Insight Services
2. Individual Location
Insight Services
• Using "Place" insights to make valued decisions
• Typical applications are around service planning and optimisation
• Using "Person" insights to inform and support
• Typical applications are around profiling, context & tracking of
people & things (e.g. field force audit, disease propagation)
3. Aggregated Location
Based Services
• Automated decisioning
• Typical applications are incident reporting, real-time routing,
smart city resource allocation & optimisation (e.g smart parking)
4. Individual Location
Based Services
• Person-level or "thing-level" business processes
• Typical applications are tagging, field asset tracking, security &
proximity "triggering" of personal notifications/marketing
Source: STL Partners
Examples of each of these domains are provided in the following analysis.
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Example of Aggregated Location Insight Services
Turkcell Smart Map (Akıllı Harita)4
An Insight Service that provides a “Heat Map” of the type of visitors to a given location (e.g. mall, station,
airport) with breakdowns by home location and profile (age, gender, market segment, loyalty program).
The service has been used to gain insight into traveler flows, target campaigns and site planning. Users
can access the service on-line, consuming “units” from a pre-paid account.
Figure 9: Turkcell’s Smart Map Tool
Source: Turkcell
Example of Individual Location Insight Services
Garmin Vivoactive
This is one of many fitness trackers now available. In this example, the data
is captured and managed directly by the device. The data is then
synchronized with a smartphone app that stores this information in the cloud.
Historic location can be made available (by the user) to third parties, including
social media. This data can then in turn be used to build profiling and context
of the user (e.g. knowing where the person usually exercises and what type
of exercise they enjoy).
4
Source: Turkcell
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Example of Aggregated Location Based Services
TomTom Traffic (Vodafone)
The TomTom Traffic service, operating via Vodafone's network, provides real-time
traffic reports and information on the best alternative routes to avoid congestion and delays. This service
generates its traffic information from real-time, anonymous data gathered from millions of users across
the Vodafone network as they move around the road network of each country.
Figure 10: TomTom’s Fusion Engine to Analyze Real-Time Traffic Information
Source: TomTom
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Example of Individual Location Based Services
Tado Smart Thermostat
A smart home thermostat that uses the proximity of residents (to their home) to set the heating (or
cooling). The service turns down the temperature automatically when nobody is at home and starts to
heat (or cool) as users start to come back home.
The location is currently determined by a smartphone app (although this requires that the app is installed
and GPS is turned on).
Figure 11: Tado’s Proximity Based Thermostat
Source: Tado
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Choosing the Right Strategy
Where are we now?
Most operators understand the potential value of their location asset and have attempted to monetize
their data. Some operators have used location to enable 3rd party services whilst others have attempted
to address the opportunity more holistically. Both have achieved mixed success for a number of reasons.
Most operators who are attempting to monetize location data have been drawn towards Location Based
Services, namely push-marketing and advertising. Whilst some operators have achieved moderate
success here (e.g. O2 Priority Moments5), most are acting as enablers for other services. They are
therefore addressing a limited part of the value chain and subsequently are not realizing significant value
from their data. We do not consider those that pursue this strategy to be Location Based Services
Providers, rather they are simply enablers.
Similarly a number of operators are addressing Location Insights, albeit with different approaches. Some
are partnering with analytics and insight companies (e.g. Telefonica and GfK), others are developing
services mostly on their own (e.g. SingTel’s DataSpark), whilst others are simply launching pilots.
In order to maximize the value that operators can secure through Location Services, we believe that
operators need to address the whole Location ‘Stack’, not simply enabling new services or providing
raw data. STL believe that the best way to do this is to start with Location Insight Services.
Start with Location Insight Services
When considering how to develop and monetize their location assets we recommend that operator’s
select to start with LIS. Whilst many operators are already engaged in LBS (e.g. enabling pushmarketing) the majority are not actually providing the service but are simply sharing data and enabling
a 3rd party service provider.
Starting with LIS has a number of strategic advantages:

It’s a big opportunity in its own right

Telcos (should) have a data capture/technology advantage for LIS over OTT players

LIS provides an opportunity to build & learn incrementally, proving value

Privacy risks are reduced (particularly with aggregated data)

LIS does not require 100% coverage of the population, unlike a number of LBS use cases

LIS can provide internal benefits and can bolster the Go-to-Market strategy for vertical specific
offerings
These advantages are explored in more detail further in this report.
5
O2 Priority Moments: Priority is a free service that provides O2 customers with exclusive offers. The service uses location
information to present ‘nearby’ offers to customers. A year and a half after its launch in 2012, Priority Moments had acquired
millions of customers, equivalent to 100% brand participation. This made it one of the fastest growing loyalty schemes in the
UK. Source: Marketing Society
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Improve your LIS offering, transition towards LBS &
position yourself as a Trusted Data Provider
As operators begin to establish their LIS businesses, they should look to extend their capabilities and
service offerings. Operators should enhance their LIS offering over time, adding additional technologies/
acquisition capabilities to increase the precision of their location insights. This will allow operators to
address more use cases, making the service potentially more appealing to various sectors and
organizations. Operators should also focus on improving their analytics component, allowing an operator
to ingest and analyse data from numerous sources, to draw valuable insight.
Developing additional capabilities (e.g. the ability to capture near-real/ real-time location information)
can allow operators to provide LBS services. Furthermore, the learnings from LIS and LBS and the
relationships formed can be leveraged to allow operators to take advantage of wider digital & data
opportunities.
Operators who have ambitions to address LBS will need to tackle 3 key challenges:

The Requirement for Real-time information

Complying with and Managing Privacy and Permissions Requirements

Creating Compelling User Interfaces
Both LIS and LBS create a strong foothold to engage in more digital and data activities. Establishing a
role as an LIS/LBS provider can serve as a stepping stone to becoming a Trusted Data Provider. This
role can apply to both enterprise services and consumer/individual services.
Figure 12: Expanding Beyond LIS
Source: STL Partners
The strategy to expand Location Services is discussed further in this document.
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Location Insights – Marketplace Overview
Where is the Opportunity for Location Insight Services?
STL Partners has set out the opportunity landscape for Location Insight Services in the taxonomy below.
This taxonomy maps out key industry sectors against functions, identifying the areas where we see most
opportunity for Location Insight Services.
Figure 13: Location Insights – Market Taxonomy
Research &
Strategy
Retail
Gov & Health
Transport &
travel
Telecoms
Leisure &
Hospitality
Utilities &
Services
Advertising
(Media)



















Audit &
Compliance
Operations


Site Planning


Marketing &

Advert.





Source: STL Partners
To illustrate this taxonomy, here are some examples where functional areas potentially map to sectors:

Research & Strategy for an airline: Turkcell built insight into traveler volumes between areas,
by time of day, by type of transport and age group  this helped to build the business case for
new routes and to assess potential impacts of pricing changes.

Audit & Compliance: Particularly important for sectors with significant regulation and/or public
accountability. For example, transport regulation compliance; goods vehicle operators can
demonstrate that heavy goods/coach drivers are not driving more than their permitted hours.

Operations for Retail: Understanding footfall analytics in-store can allow a retailer to optimize
the store layout for their customers. This is different to using location data to profile the footfall
visiting a retailer’s store versus a competitor’s store or to plan new retail store locations.

Site Planning for Telecoms: Prioritize network build out (e.g. small cells deployment) to
address predicted capacity needs. Use location and usage information to track when ‘valued
customer experiences’ (e.g. video streaming for particular users) are performing poorly and
prioritize network improvements accordingly.
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015

Marketing & Advertising: Understand who my customers are (collectively), select media
based on predicted footfall of target audience (e.g. out of home advertising), and validate
outcome of campaigns based on footfall of targeted customers.
Which Sectors are most addressable?
The potential size of the opportunity for each sector varies; this is explored in more detail in Figure 16
below. However, size is not the only characteristic that determines sector addressability. Perhaps what
is more important, at least initially, is the ease to develop services (that will be adopted) for a particular
sector.
Whilst the retail sector is often discussed as the main opportunity area for LIS, it is not necessarily the
best sector to address first. With new services such as LIS, it is important that there is early evidence of
success. These proof points will allow operators to scale-up and expand their offering.
Operators should therefore focus on sectors where they can most readily offer Location Insight Services
without requiring significant technology investment or complex commercial arrangements. Operators
should aim to bag the low-hanging fruit to prove value and then focus on improving their offering to
address other sectors.
Through conversations with operators who are exploring LIS, we have found that they are having the
most success with the Travel & Transport and Local Government sectors. Operators have found these
sectors have lower barriers to entry; operators are able to provide services, harnessing footfall analytics
data, that do not require highly precise location accuracy.
For example, for the Travel & Transport sector, LIS can track the number of people moving between
different locations (e.g. cities) and their mode of transport on a given day. This insight can be used by
transport companies to drive their pricing strategy. (E.g. a rail company can learn that there are lots of
commuters to a particular city, yet they do not take the train. They could then change their pricing
strategy or run a campaign to try and target this group of commuters).
Similarly, operators who are providing LIS are gaining traction with Local Government. Governments
are typically interested in the presence and flow of citizens across jurisdictions and as local
governments/cities undertake ‘Smart Cities’ initiatives this insight can become even more interesting.
Governments/cities can leverage this insight to better plan transport links (through an understanding of
commuting patterns), manage public energy expenditure (e.g. street lighting) and identify where to
improve services (such as healthcare) and amenities.
Additionally, operators that develop LIS will be able to sell this service to other telecoms operators (that
are not direct competitors).
Nevertheless, the operators we spoke with still believe that the retail sector represents the most
significant opportunity in the future. However, the use cases that are attracting interest in the retail sector
often require a level of granularity of location information that cannot easily be provided by the telecoms
operator. For example, shops would like to know where exactly people are standing/moving in their
store. This insight will allow them to better understand and refine their internal operations and store
display. LIS providers should aim to achieve this level of accuracy over time but should start with sectors
that are more readily addressable, without significant investment in new technologies.
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Key Learnings – Telefónica Smart Steps
Telefónica’s Dynamic Insights (its ‘big data’ arm) launched
Smart Steps in 2012. Smart Steps’ aim is to provide ‘insights
based on the behavior of crowds to help companies make
informed decisions.’6
The team attempted to address the retail sector first, as it deemed this to be the biggest opportunity and
the best match for the service they were providing. Despite some early success with a supermarket in
the UK, where Smart Steps helped them to more effectively target their customers (distributing coupons
based on an analysis of customer post codes), the product was not as effective as originally expected
at serving the needs of the retail industry.
Smart Steps found that the retail sector required a level of granularity that (at least initially) they were
not able to provide; they could provide macro-level information (through cell-id) but were unable to
provide location information about what is happening outside a particular retail store.
However, Smart Steps realized that they could still put this more ‘macro-level’ data to use. This
information provides deep insights into the journeys individuals take. Smart Steps was able to assess
the different routes that individuals take to travel between locations and to analyze the mode of transport
(through an analysis of location and speed).
Figure 14: Telefónica Smart Steps Location Analytics Tool
Source: Telefónica
Smart Steps pitched this data set to train operating companies and were able to sell this as a service to
3 companies who were bidding for the East Coast Line in the UK. It has since expanded the offering to
address other forms of transport, notably focusing on serving the road infrastructure public sector
markets. Outside of the transport sector, Smart Steps is applying the service to digital media and it also
6
Source: Big Data Monetisation in Telecoms
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
plans on assessing potential technologies that will allow it to capture the level of granularity required to
address the retail sector.
Other key learnings from Telefonica are that:

It is having more success with a ‘Managed Service’ model (as opposed to its initial ‘definedproduct’ approach). Its managed-service model allows it to design a service that solves the dataneeds of specific companies.

It used consultancies effectively as part of its Go-to-Market Strategy. Rather than going directly
to companies, it worked with consultancies that develop sector specific models.

A small, flexible team is more effective at developing the service, rather than establishing a large
product team. Once the ‘product’ is repeatable, scale can be added.
For more information on Smart Steps’ strategy please read this interview with the UK Country Director
of Telefónica Dynamic Insights.
Key Learnings – Deutsche Telekom’s Motionlogic
Motionlogic was established in 2013 and is a 100% owned subsidiary of
Deutsche Telekom. It operates across four countries (Germany, Poland, Czech Republic, Croatia),
providing in-depth analysis of traffic and movement flows across cities and countries based on
anonymous signaling data from mobile and WiFi networks.7
It recently launched an initiative with a German public transportation company to analyze traffic flow
around the city. Motionlogic was able to provide information on: population fluctuations across districts
at different times of the day (previously assessed based on highly uncertain statistical estimates); origindestination analysis of travelers; modes of transport used at particular times of day.
This information was used to optimize public transport, including: route planning, bus timetables, pricing
and connection management for delays.
Figure 15: Motionlogic’s Location Analytics Tool
Source: Motionlogic
7
Source: Motionlogic
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Sizing the Opportunity
Why haven’t forecasts developed as quickly as
expected?
Most analyst firms and operators expected the opportunity around Location Insights to take off relatively
quickly, yet the opportunity has not been realized at the rate predicted. Whilst part of this could be
attributed to hype, there are other underlying reasons as to why this opportunity is taking longer to come
to fruition.

Some un-realistic expectations about how easy it would be to first acquire data (in a usable
format) and then turn data into money: in practice “raw” telco data has rarely been found to be
that useful on its own. It needs “wrangling”, “blending, associating & combining”, “visualizing”
and “converting” into formats that support existing practices.

LIS represents a new emerging marketplace. It is therefore not well defined and those that
want to succeed will have to establish both the technical and commercial model to do so.

Operators have typically approached the opportunity incorrectly. STL, in the 2013 report,
indicated that to achieve significant value from LIS, players need to provide not just the data
but also the insights. Most operators have simply captured value through the provision of their
data and have not approached this opportunity from a strategic perspective.

The retail sector (estimated to biggest sector by potential revenue) has not embraced LIS (or
location) as expected. What has emerged is that many key use cases for the retail sector
require increased accuracy of location information, including indoor insights.

The LIS ecosystem is complex. As mentioned previously LIS is an emerging marketplace
and is not well defined. Whilst STL recommend that operators seek to address the whole
value chain of LIS, this still may require forming strategic partnerships. For example, operators
may need to partner with analytics technology companies, even if they themselves offer the
Insights Service.
Indeed STL, in the 2013 report, sized the total LIS opportunity at $11bn by 2016. Despite not being
realized as quickly as expected, STL still believe that this is not only a big opportunity, but also a
potentially strategic one. LIS provides telcos with the opportunity to position themselves effectively within
the digital ecosystem.
Furthermore, the majority of operators and analyst firms still expect location and location insights to be
a big opportunity for telcos. Big Data is bigger than ever and despite set-backs, operators are clearer
on the strategic need to offer more value to their enterprise and public sector customers.
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Location Insights potentially worth $5bn globally by 2020
Sizing a market’s potential such as that of Location Insight Services is fraught with difficulties associated
with evaluating any nascent service. We have approached this using a top-down approach for the UK
economy and then extrapolating our findings globally.
The methodology and assumptions behind the market sizing can be found in Appendix 2. In summary,
we have calculated UK market potential by applying a weighting to the published Gross Value Added
(GVA) within representative industry sectors. Our weighting is derived from an assessment of the
relative potential impact of LIS (cost savings or improved value from existing expenditure), starting with
the prioritized industry sector / function matrix as set out in Figure 13 of the report.
Extrapolated from UK data, we estimate that the Global market potential by 2020 for LIS is around $5bn
and the relative contribution of each sector/function is shown on the bubble graph in Figure 16. This is
about half the number we projected in our first study. Furthermore, this is for a date that is further out.
Since we undertook the original study, we have learned more about how the LIS market is evolving.
This has resulted in more cautious assumptions regarding both take up of LIS and the value of benefits
(gains) achieved from the insights.
Figure 16: The value of Global Location Insight Services by industry and
sector (by 2020)
Source: STL Partners
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Benchmarks
A number of other analyst firms and technology vendors have offered their perspectives on LIS,
providing their own definitions and sizing estimates. The approaches are very different and are therefore
not strictly comparable. Nonetheless they do provide indicative benchmarks. The opportunity sizing
outlined in this document is within the ranges found across these benchmarks.
For comparison purposes, revenue within (the broader market of) global location-based services is
expected to reach US$10bn in 2016, up from $2.8bn in 2010. Location-based advertising is the market
segment that is expected to show the highest growth — it will generate more than 50% of total locationbased services revenue in 20168
For benchmarking purposes, other industry analysts have assessed the market for Location Insights
and arrived at different indications of market size:
“Location analytics is expected to grow significantly and is expected to be a $9 billion market by
2016.” 9
“ABI Research forecasts direct carrier revenues to approach $2 billion by 2019 in a market with
far greater long-term potential.” 10
“The Location Analytics Market is estimated to grow to $11.84 billion in 2019, at a Compound
Annual Growth Rate (CAGR) of 11.6% from 2014 to 2019.” 11
Markets and Markets also estimates the location market as a whole, including location based
services, to be worth $39.9bn by 2019.
8
Source: Strategy Analytics - The $10 Billion Rule: Location, Location, Location
9
Source: Convergence Catalyst
10
Source: ABI Research
11
Source: Markets and Markets
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Where does the value come from – the Location Insights
‘Stack’
As discussed earlier, in order to capture value from LIS, operators must be willing to address the whole
Location Insights Value Chain or ‘Stack’. Simply harvesting and supplying data to a 3 rd party company
will not yield significant returns. There are 4 key components of the LIS Stack:
Figure 17: The Location Insights ‘Stack’
Acquisition
Analytics
Insight
Sales
Source: STL Partners
1. Acquisition: is the business of collecting data. This refers not only to location data but the
other data sets associated with location that will go on to form insight. The key options for
location data capture are covered in the next section of this report.
2. Analytics: is the method to discover patterns and information from the data. Analytics relies
on the simultaneous application of statistics, computer programming and operations research
to quantify performance. Analytics tools have become more advanced and powerful as
(collections of) data sets have become even more complex, through size, variety and velocity.
3. Insight: is the ability to interpret the information to inform a decision. A key part of the Stack,
Insight provides the ‘value-add’ to the relying organization. Insight refers to the interpretation
of the information to draw conclusions to help organizations make decisions.
4. Sales: is the process to provide/sell the Location Insight Service to relevant organizations.
Sales is the important final piece of the LIS Stack. As LIS is an emerging service and
marketplace, propositions and use cases are not well defined. An LIS provider will need to
have the ability to build and expand the insights service for a particular organization/sector.
Operators who have aspirations in this space should aim to address the whole LIS Stack; they should
not focus simply on acquisition. Building capabilities across the 4 areas will allow the operator to capture
the most value and to address this opportunity from a strategic point of view.
Some operators are choosing to partner across the different elements of the value chain, with varying
degrees of success. Through conversations with operators we have learnt that partnering can prove to
be challenging; often when partnering an operator ‘outsources’ some of the key capabilities required to
deliver the service, which in turn limits their ability to further develop the service.
STL recommends that operators (with significant aspirations in this space) should aim to play a central
role in delivering the insights. This does not necessarily mean attempting to do everything themselves,
but taking the lead in orchestrating the service with partners and bringing the service to market.
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Understanding the Technology Options
The Technology Options for Location Data Acquisition
The “ideal” characteristics of a location data acquisition technology will vary by requirements of the
location insights being sought. We have identified the following:

No need to install anything on devices. Having to install an app or client onto devices
creates an immediate obstacle and adds complexity, which can substantially reduce the
population coverage. Operators may be able to achieve reach without asking users by
installing clients over-the-air, but this requires working closely with device and OS players.

Geographic coverage is a key requirement for many applications. Technologies that are
patchy will need to be complemented with others to fill the gap.

Low device impact. Smartphone users have been sensitized to applications that consume
device resources, particularly those that drain the battery (notably GPS). They will disable or
uninstall those apps. This is also true for IoT where power can be a big challenge.

Good data continuity. This is the ability to secure large data samples/scale (have many
users/capture many events) over an extended time series (e.g. track “place” or “person” data
over long periods without gaps)

Low cost data capture. If the LIS provider incurs significant on-going cost in acquiring and
storing the data then this could make many LIS unviable. Solutions repurposing existing data
are thereby more desirable, since the cost of collecting and storing the data is already covered
through performance engineering, meaning that LIS providers only have to cover the marginal
cost of repurposing.

Population coverage. This refers to the population as a whole, and not subscriber coverage.
Partly a reflection of some of the other characteristics, this is important for LIS where a
sample, however large and/or representative, is never as good as the actual total population.

Location accuracy / precision. The importance of this varies with the application. However,
as a general rule, more precise is better.

Device Universality. This is related to population coverage although also encompasses
“things” as well as personal communication devices (e.g. works on devices other than
smartphones). It is a more forward-looking requirement for the IoT.
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Figure 18: How well do different location data acquisition technologies
support Location Insight Services needs?
Strong adherence to LIS
requirements
Moderate adherence to
LIS requirements
Poor adherence to LIS
requirements
Source: STL Partners & Viavi Solutions Analysis
The potential technology options have been grouped into 6 categories. These are set out in more detail
in Appendix 1.

App/OS based real-time location (GPS / SSID). This is essentially an OTT approach to
capturing location insights using device location resources (GPS, WiFi) and SSID mapping.
Google harvests considerable location data in this way which is used for LBS and LIS.
Although favored by many OTT players, it has a number of shortcomings from an operator
perspective, particularly regarding the potential impact on users (battery, data coms). Some of
these applications work through device-based geo-fencing which does not require continuous
connectivity, but still drain the battery.

App/OS based data stored on device (GPS / SSID). The main difference between this
technology and the one above is that location data is stored on the device (often as a byproduct and generally along with other data sets) and then synchronized with a central store
when needed (potentially not for several days).

Emergency Services Location. These technologies are designed to support automated
requests for precise locations. They use additional network resources and cannot be readily
extended to mass-collection of location time-series. In theory these are being rolled out in
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
many countries, however in practice implementation has been patchy and reliability is highly
variable.12

Granular (building level) network based. This essentially uses existing Radio-AccessNetwork (RAN) signaling to derive a location for all devices on the network. It does require
interaction between the device and the network (either using a service, for activity such as cell
hand-over, or regular checking-in with the network). This does not require GPS and will
typically work with all classes of phone, not just smart phones.

Coarse (cell-level) network based. This works by cell-id only which, depending on the size
of cells, can represent an area of several km 2. This limits its practicability as a source of
derived insights such as speed and direction.

Indoor Technologies. This is a collection of technologies including Bluetooth, beacons, WiFi
and cellular frequency device emission, sometimes in combination with device resources such
as motion sensors. These technologies are essentially geared to providing very accurate
footfall and individual pathway insights.
Technology Advantages for Telcos
For the technology options that are better suited to LIS (i.e. the ones that align with the ‘ideal’
characteristics) we would expect telcos to enjoy significant advantage over OTTs; these technologies
are primarily network-centric. Telco’s run and manage networks and already capture this location
information (whether or not they use it). Whilst this information may not be at a granular enough level to
offer insights to all sectors and for all use cases, it provides a strong platform to build from.
Additionally it is harder to start with LBS as it is better suited to on-device applications that have an
interface with the customer. Becoming a trusted provider of location information through the provision
of LIS will allow operators to claim a better foothold with LBS.
The Right Degree of Location Precision
Varying degrees of precision are required for different applications. For some analysis (e.g. longdistance travel) city or even country-level data may suffice. For others (e.g. Tado, Billboard audience
analysis), a more granular (building-level) view of location may be required. Very precise (accuracy
within a couple of metres) locations are required for indoor, “human flow” analysis such as indoor retail
footfall analysis to inform store design.
It is unlikely that a single location data acquisition approach can be made to work for all applications and
yet when approaching potential customers (such as retailers), LIS providers may wish to offer services
that require different levels of location precision. Providers should therefore remain open to using a
combination of location data sources, particularly when some of these can be introduced relatively
inexpensively for limited coverage (such as a shop floor).
Based on our conversations with operators STL Partners recommend that in most cases operators
should start with existing technologies/capabilities and add greater precision to this over time.
12
Source: USA Today
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Other Advantages of Starting with LIS
We have discussed in detail two of the key reasons why starting with LIS makes sense for operators:
the size of the opportunity and the technology advantage for operators in this space. There are however,
a number of other reasons why operators should think to start with LIS when attempting to monetize
their location asset.
Incremental Learning
Starting with LIS allows an operator to incrementally learn and build-out their location service. It can
afford them the opportunity to prove the value of their location asset and the service they are providing.
They can then enhance their offering over time, increasing the degree of accuracy of location
information, adding additional data sources to develop more insight and providing an avenue to offer
location based services.
This approach also allows operators to develop the service to meet the needs of the customer, rather
than attempting to build a product first and then take it to market. When a service is providing value to
a customer, then an operator can attempt to scale it (e.g. other countries, related industries)
This approach allows an operator to keep the investment at a minimum until the viability of the service
is proven. Practical guidance for how to launch Location Services is discussed later in this report.
Addressing the Privacy Question
Similarly LIS raises less regulatory and compliance issues than LBS. To provide individual LBS (in most
countries) requires that permission is captured from the user. For aggregated LIS, individuals are not
personally identifiable and hence explicit permissions do not necessarily need to be secured.
Starting with LIS therefore allows an operator to address the privacy question over time, ensuring that
they are able to develop the tools to effectively capture and manage user permissions before creating
services which require more-explicit permissions.
Market Coverage
For most individual and aggregated LBS and LIS services the ideal coverage is 100% of the target
users/market/devices. With the exception of enterprise users (who may all be using the same service),
it is very rarely possible to achieve 100% coverage as no single operator, app provider or device OS
player has 100% market share (unless the target market happens to be the users of a particular
device/app).
If only a small proportion of the target market can be addressed (e.g. for LBS), it reduces the attraction
for organizations looking to use the service. This can be resolved by aggregators (for example, Validsoft
aggregates location data from UK operators to provide LBS services to payment card issuers) or through
providers “pooling” services (e.g. Weve joint venture in the UK). Although achievable, these approaches
require considerable co-ordination and take time to deploy. Additionally, operating the service through
aggregators will result in lower value for providers as they are no longer addressing the whole ‘Stack'.
These models are therefore probably not the best places to start for operators with significant ambition.
For LIS (and particularly aggregated data), 100% coverage is also highly desirable. However, so long
as there is a very good understanding of the customer base and how it maps to the wider population, a
provider with a significant market share (e.g. >25%) should be able to extrapolate findings from their
customer base to the overall population. Potential LIS customers will need to be made comfortable with
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
the accuracy of extrapolated findings; it is critical that providers build convincing evidence of the
accuracy of these insights.
Mobile operators should be relatively well-positioned regarding market coverage compared to OTT
competitors (most of whom have small and highly-biased coverage). The possible exceptions are device
OS providers (Android, iOS, Windows) and major “always-on” apps such as Facebook or Twitter.
However, by their nature, these tend to rely on device-based data acquisition technologies which result
in far lower effective coverage rates (as users switch off data, GPS and WiFi to reduce battery drain).
Furthermore, their existing business models could potentially be disrupted by offering LIS services.
There is limited evidence that these players have any plans to enter LIS.
LIS can provide internal benefits and can bolster the Goto-Market strategy for vertical specific offerings
As indicated in the market sizing, Telecoms itself represents a sizeable LIS opportunity. Operators
should therefore seek to use LIS to improve their own business. This will:

Help them to understand potential customer needs and challenges

Further reduce the risk of inadvertent privacy violation

Help build credibility from “eating own dog-food”
However, based on our research, some operators have found it difficult to secure resources and funding
for internal “customers” and have therefore found that they prefer to “prove the case” for external
customers first.
A word of caution about how operators actually “monetize” LIS. We have found that Location Insight
Services are not always charged for as a professional service, but in a number of cases can be bundledin to enrich and differentiate wider services. For example:
During civil unrest in Egypt, the UK government asked Vodafone to provide
estimates for the number and location of UK holiday-makers within Egypt so that
planes could be chartered for their evacuation. Vodafone was able to provide an
estimate for all UK tourists based on its own customers. This ensured that the
evacuation could be planned far more accurately and efficiently. Vodafone did not
charge the UK government for the service, seeing it as part of the wider relationship
with an important customer.13
Turkcell has built a significant advertising and marketing business. Along with
delivering inventory & campaigns, it also provides marketing insights based on LIS.
Previously, these insights were charged for and therefore only taken up by some
customers. However, with a number of competitors choosing to bundle-in a range
of marketing insights, Turkcell has found that it needed to follow-suit to remain
competitive, even though some of Turkcell’s insights were unique.
13
Source: Vodafone, Analyst Tour 2012
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Expanding Beyond Insights
Addressing Location Based Services
As with LIS, operators with significant ambition within LBS should aim to address the whole value chain,
not simply acting as an enabler for other LBS providers. To effectively address LBS opportunities,
operators will need to add new capabilities. Three key additional capabilities are required:

The Requirement for Real-time information

Complying with and Managing Privacy and Permissions Requirements

Creating Compelling User Interfaces
The Requirement for Real-Time Information
As set-out earlier in Figure 7, Location Based Services are built on the premise of taking action based
on real-time information. Whilst not all LBS services require real-time information (some simply require
near-real-time information), operators will need to invest in technologies that are able to acquire and act
on information at faster speeds in order to provide new LBS services.
Proximity marketing use cases highlight the requirement for real-time information. Pushing a coupon
(for a particular shop) to an individual who has been in the vicinity (of that shop) is interesting to retailers.
However, pushing a coupon for a particular product to an individual at the exact time they are in front of
the product in the store is even more compelling.
Increasing the level of accuracy of the information, through the provision of real-time information and
more precise location data, generally increases the value of LBS. Whilst LBS is more directly applicable
to the retail sector, with over 50% of the estimated $10bn LBS market14 (by 2016) coming from locationbased advertising services, other sectors can take advantage of LBS with more real-time information.
For example, local government/councils could better manage the level of street lighting in particular
areas based on the number of people in a given area (conserving resources and minimizing light
pollution). Similarly, real-time accurate location information can improve transport, enabling real-time
traffic re-routing during periods of congestion.
Operators seeking to provide LBS can improve the precision (real-time and location accuracy) of their
service over time. As discussed earlier, the distinction between LIS and LBS can become blurred where
the LIS data can be used to maintain a readily accessible cache of “most recent” location. In this way,
technologies intended to support LIS applications can be extended to support real-time decisions.
For example, Validsoft provides a service to payment card issuers that checks (in a matter of a few
milliseconds) if a foreign card payment request matches with the location of card owner’s phone (based
on changes in HLR). If these do not match, the payment may be considered fraudulent and declined. If
they match, the payment won’t be blocked, helping to reduce the number of ‘false-positive’ international
card transactions. In this example, the business process demands a real-time response although the
location data acquisition may be delayed by a few minutes or even hours.
14
Source: Strategy Analytics - The $10 Billion Rule: Location, Location, Location
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Figure 19: Real-Time vs. Near Real-Time Location Information
Source: STL Partners (Companies illustrated are examples only)
Dynamic Permissions Strategy
In order to provide LBS (and some Individual LIS) some operators will need to capture new permissions
from customers. LBS typically uses an individual’s information (including location) to promote a particular
product or service. Depending on regulation in the local market, using this information to communicate
with the individual may require that new permissions are secured.
Most applications that currently use this type of data and push information to the user require a
mandatory acceptance of their conditions in order to access the app/service. For LBS, rather than simply
developing opt-in/opt-out tools for applications, operators should aim to develop dynamic permission
management tools that afford the user control over how they interact with the service.
Dynamic permission tools allow the user to change, at any given moment, how and what information
they are sharing. Most simply, users are afforded the control over turning on and off whether they are
sharing their information (e.g. location). Additional elements can be added to this tool. DeveryWare (a
geolocation technology company) has developed an intuitive ‘method and system for spatio-temporal
adjustment of geolocation permissions’.15 Its permissions management tool allows the user to share
geo-location data (at changeable degrees of accuracy) to a plurality of receivers. For example a user
could share his/her precise location with a particular friend whilst at the same time share their location
information with their employer at a much less precise level. At any time the user is able to dynamically
change:

The level of precision of location information

Whom they are sharing the information with

The times they are sharing information (e.g. off at night)

Where they will share information (e.g. off at home)
15
Source: Free Patents Online
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Figure 20: Deveryware’s Dynamic Permissions Tool
Source: Deveryware; Google Play
A dynamic permissions tool provides the user with much more control over the service, which in turn
helps the user understand the value of their location information.
Compelling User Interfaces
The majority of LBS are consumer/end-user focused (i.e. individual LBS). In order to provide these
services, operators will need to ensure that the delivery/communication is engaging for users. These
types of services lend themselves to smartphone apps with compelling interfaces and functionality and
OTT players are of course more established in this space. If operators have aspirations to compete in
LBS, they will need to develop the capabilities to build compelling user interfaces
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Becoming a Trusted Data Provider
Both LIS and LBS are services that can help operators establish a bigger role in the digital economy.
Building these new services develops the capabilities, skill sets and relationships to offer digital services
beyond connectivity. Operators should consider LIS/LBS from a strategic point of view and pursue them
with the intention of becoming a ‘trusted data provider’.
As a trusted data provider an operator can provide services to 2 main groups:

Enterprises

Individuals
Figure 21: Become a Trusted Data Provider
Source: STL Partners
Trusted Data Provider for Enterprises
LIS is a data analytics service for enterprises/governments. Operators can leverage and extend the
capabilities required to deliver LIS in order to provide additional enterprise analytics services.
As operators (and enterprises) become more comfortable with these services, operators should begin
improving the analytics component of their offering, transitioning their focus towards becoming a ‘big
data’ analytics company. Operators should develop the capabilities to ingest and analyze lots of different
types of data, becoming more independent of the acquisition of the data.
LIS allows an operator to demonstrate that they are able to handle this information (and comply with
privacy regulations), which potentially allows ambitious operators to take a larger role in becoming a
trusted data provider and analytics company.
Furthermore, as more and more devices and things become connected, operators can potentially secure
a greater role providing (location) context, analytics and insights from IoT. Operators are well positioned
to provide analytics and insight about things/devices; location and other relevant information can
effectively be captured over networks. Potential use cases include energy demand response (see
Connected Home: Telcos vs Google (Nest, Apple, Samsung, +…) report), insurance (e.g. SigFox),
warranty, equipment monitoring.
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Trusted Data Provider for Individuals
Operators also have the potential to become trusted data providers for individuals, with operators
attempting to manage the digital identity of individuals. The industry wide initiative, Mobile Connect, is a
stepping-stone in this direction. Mobile Connect uses the mobile phone number to log-in to digital
services. Having one tool to use multiple digital services potentially makes logging-in online safer, more
secure and simpler for individuals.
In addition to managing the digital authentication and identity of individuals, operators can leverage more
LIS/LBS capabilities to become trusted data providers for individuals. As operators develop LBS with
dynamic permission tools, they are affording more control to users to manage how their information is
shared. Further building on this role, operators can aspire to become custodians of personal data.
As a custodian, operators can manage an individual’s ‘Personal Data Store’. This includes managing
how individuals access and share information around: payments; consumptions (e.g. utilities), health &
wellness, social networks, communications.
Addressing the opportunity around Location is not only a tactical opportunity but also represents a
longer-term strategic opportunity for operators, where they can begin to position themselves as a
Trusted Data Provider, aiming to secure a greater role in the digital economy.
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Practical Guidance to Launch Location
Services
A number of operators have sought to build Location Insight Services as explicit initiatives. They have
adopted various approaches and enjoyed varying degrees of success (as measured against original
ambitions).
In attempting to provide practical guidance on how to launch such services we have drawn on the
experience of those operators and also the wider experience of operators in developing new innovative
lines of business. LIS are neither both pure “core” nor pure “digital” services, but span both. They require
a “hybrid” approach to innovation that borrows best practice from digital enterprise (e.g. Lean Start-up)
but with greater levels of participation from the existing business (technical teams, privacy compliance),
more in line with core product development.
It is dangerous to be too formulaic with guidance as operators are each unique and will have very
different circumstances which will influence their approach to launching LIS services, such as:

Size and composition of customer base

Market shares

Existing network and systems

Local maturity and concentration of target sectors (e.g. retail)
The following approach should therefore be taken as useful guidance, based on lessons from others,
rather than a universal formula. We have broken this guidance into 3 main areas:

Market Strategy

Data Management

Approach and Governance
Market Strategy
The first step for those tasked with launching LIS services will be to define the market strategy and initial
value proposition. In essence, this requires attempting to address the following questions:

What are the fundamental assumptions behind the opportunity?

What insights are we planning to bring and what will our clients do with these insights?


How do they do this today and why do we think that our approach will be fundamentally
different and better? Why do we think that this could represent sizeable opportunity?
What will the “press release” be when we eventually launch the service? This approach is similar
to that adopted by Amazon when developing a new service.
To answer these questions, the operator will draw on the team’s experience and dialogue with potential
customers (many of whom may be customers of existing core services). The operator should resist the
temptation to build a detailed Business Plan at this stage. Partly, this will take up a lot of time, partly this
will create expectations that may not be achievable and partly it will be plain wrong and potentially force
the initiative down a route that it shouldn’t go.
For inspiration, an entertaining account of how Google approaches such innovation can be found in this
presentation by Eric Schmidt on How Google Works.
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Data Management
The next step for the operator team will be to deal with the practicalities of defining and building its Data
Management Capabilities: how will it acquire, analyze, and develop insights from data. This is set out
below as a logical set of progressive steps. In practice, operators will need to adapt and pivot as they
learn more about the LIS market and their ability to serve it. This will be an iterative process.
Acquisition
Operators should start by reviewing what geo-data is currently collected, how it is collected and where
(which systems) it resides. For all the data sets, the team should establish:

Type of data (signaling, billing)

Nature and accuracy (of location parameters)

Latency / frequency of data collection
The next step will be to identify and prioritize the anticipated use cases (based on potential customer
needs). These should have already been identified at the Market Strategy stage. The use cases should
then be split into 2 groups:

Those that should be deliverable from existing data collection

Those potentially possible with additional data collection or fusion with other data
Based on this, the team will need to determine if there is enough value within the existing data to build
a minimum viable proposition or not. It may be necessary to attempt a pilot: extract some data, generate
some insight and validate the potential value with an actual (friendly) customer.
Once this is established, the next steps will be to complement the data acquisition capabilities:

Determine a roadmap for capturing additional data collection as necessary

Assess appropriateness of existing technology capabilities

Conduct a functionality gap analysis

Assess vendor/supplier capabilities, making sure that this captures in-house resource
availability and legacy system integration requirements
Analytics
As discussed previously, there is limited value in the “raw” data collection. The analytics is where much
of the value can be created. Operators should therefore seek to provide (or at least orchestrate) the
analytics layer of value.
Performing analytics is an iterative undertaking which relies on a range of tools for manipulating and
visualizing the data. Our research suggested that there are surprisingly few well-developed tools to
support this. The team will therefore need to:

Review how geo-data is currently analyzed

Tools and visualizations

Service delivery blackspots

Location/site centric

Context centric

Person centric
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015


Trend and/or pattern recognition

Predictive capability
Scope analytics resource requirements


Team resources

internal BI and/or data scientists

external consultants
Tools (hardware and software)

X-departmental data layer

Software: Data interrogation and visualization
As approaches to conducting analytics begin to emerge, the teams should start to work with colleagues
(security, privacy, compliance) to ensure data privacy regulations and subscriber permissions are in
place. STL’s research suggests that best practice is for operators to pro-actively engage the appropriate
data protection authorities at this stage to ensure compliance.
Through the analytics capability, it will then be possible to start addressing the “so what?” and progress
from interesting findings to actionable insight and valuable outcomes.
During the analysis stage consideration should be given at what stage the data can be partially
anonymized by replacing subscriber identities (such as IMSI) by an opaque identifier (e.g. cryptographic
hash of IMSI + periodically changing salt value). It is therefore not unusual for an analytics solution to
be able to associate together data for one subscriber (e.g. multiple journeys), but the system not be
aware of who that subscriber is. Complete anonymization can never be achieved through techniques
such as hashing identities – some level of aggregation will always be required.
Develop Insights
Delivering insights means undertaking the right analytics to produce (ideally unique) valuable findings
and communicating these in a compelling, usable way. Ultimately, insights will only create value when
they drive decisions or processes which would have otherwise resulted in a less valuable outcome.
Building insight requires people with a good understanding of the questions they are looking to address.
The starting point is to audit research insight requirements/capabilities. At least to start, the right
personnel will need to consist of a mix of Internal Business Intelligence and/or research specialists and
external consultants.
An “Agile” mind-set is also required along with the understanding that market LIS data requirements are
dynamic, nascent and developing. These skills will allow for a creative interpretation of data stories and
an understanding of the LIS customers’ challenges. This understanding should be person centric,
location centric or context centric; not network and service centric.
To build insight, the team may need to circle back and re-assess their needs in terms of Analytics tools
as some additional investment in geo-spatial visualization technologies – either bespoke or specialist
supplied – may be required.
Although the process of building insight should be creative, this should also be structured and
disciplined: productization should be sought early-on to maximize use of resources and maintain quality.

Data interpretation and commentary should be delivered in a standard report structure and
where possible similar charts and diagrams should be (re)used

For more bespoke (e.g. brand/advertiser specific) analysis – shorter ad-hoc reports will apply
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015

Data dashboard/analytics interface – software + aggregated data access subscription

Aggregated data access for DMP input – API + volume usage fee
Marketing and Sales
Our research has produced mixed findings regarding the “readiness” of operators’ existing account
teams to bring to market new LIS. On one hand, the existing account teams are unlikely to have the
right relationships, skills or understanding to bring such services to market, however they hold an entry
relationship with potential customers.
We would therefore expect the core LIS team to do most of the “selling” to initial customers of the service.
Then, once interest and value are proven, the responsibility for “selling” (to new customers) and
managing the relationship should transition to the account teams, with the LIS team retaining a sales
support role, focusing more on providing supporting marketing (case studies, references,
documentation, POCs) and training. Whilst this transition may be difficult, some operators have already
managed to effectively engage their enterprise account teams to proactively sell this service to their
existing customer base.
An agile approach, partnering, orchestration and
governance
As discussed previously, if operators limit their role to providing raw data, they are likely to find the
resultant revenues and margins disappointing. This does not mean that they should attempt to “go it
alone”, but rather they should build and orchestrate partnerships and drive the resulting insight.
Building new Location Insight Services requires an approach that combines best practice from pure
“digital” innovation and telecoms core service product development. It also involves partnering and
coordination. All this requires considerable agility.
Based on our research, operators have made the following recommendations:

Assemble cross-functional and cross-departmental project team

Identify and secure a senior-level “sponsor”

Set up a core team which includes a project “evangelist” and a project “pragmatist”



The project “evangelist” to motivate and inspire within the team, explain the strategic
rationale, and overcome objections from within the wider organization

The project “pragmatist” to prepare project plan, to manage cross-functional
communication, allocate and police responsibilities, deliverables and deadlines
Seek out a “friendly” pilot customer prepared to work closely and co-create an initial service

Do not spend too long documenting and defining requirements

Quickly define and build an initial analysis – propose potential insight and business
implications for the client

Take feedback, learn and iterate
Set out clear targets and metrics in a staged way

Initial phases should seek to demonstrate that there is potential value for target customers

Subsequent phases should aim to demonstrate that this value can be monetized
(profitably) in a scalable way

Only later phases should start to include financial targets (revenues and margins)
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Conclusions
LIS represents a potentially significant opportunity for operators; whilst it has not materialized at the
originally predicted rate, the marketplace and operators’ strategies to deliver this service have evolved,
and we believe that Location Services still remain an important opportunity for operators. We estimate
that the marketplace for LIS will be worth $5bn by 2020.
Operators are well positioned to succeed providing LIS. We would expect operators to enjoy a significant
advantage over OTT players as network-centric technologies are well-suited to the provision of LIS.
Telcos run and manage networks and already capture this location information (whether or not they use
it). Whilst this information may not be at a granular enough level to offer insights to all sectors and for
all use cases, it provides a strong platform to build from.
Based on our conversations with industry, we recommend that operators should:

Start with LIS when forming their Location Services strategy:

It’s a big opportunity in its own right

Telcos (should) have a data capture/technology advantage for LIS over OTT players

LIS provides an opportunity to build & learn incrementally, proving value

Privacy risks are reduced (particularly with aggregated data)

LIS does not require 100% coverage of the population, unlike a number of LBS use cases

LIS can provide internal benefits and can bolster the Go-to-Market strategy for vertical
specific offerings

Initially prioritize the ‘lower hanging fruit’, when launching LIS. Operators have evolved their
offering to best suit sectors where their service is more likely to be adopted. Sectors such as
Transport and Government (where less granular location data can provide valuable insights)
have readily adopted this service.

Aim to address the whole Location ‘Stack’: Data Acquisition; Analytics; Insights; Sales. This
will allow operators to develop the requisite capabilities to further develop this service, as well
as allowing them to capture significant value from the offering. Partner by all means, but take
the lead in orchestrating and taking the services to market.

Start with existing location data acquisition technologies/capabilities and add greater precision
to this over time.
The opportunities around LIS and LBS are potentially significant in their own right, yet they are also
strategic. Both LIS and LBS create a strong foothold to engage in more digital and data activities.
Establishing a role as an LIS/LBS provider can serve as a stepping stone to becoming a Trusted Data
Provider, allowing operators to secure a greater role in the digital economy.
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Appendices
Appendix 1: Location Acquisition Technologies in Detail
Appendix 2: Opportunity Sizing Methodology
Appendix 3: About STL Partners
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Appendix 1: Location Acquisition Technologies in Detail
App/OS based real-time location (GPS / SSID)
This is essentially an OTT approach to capturing location insights using device location resources (GPS,
WiFi) & SSID mapping. App and device OS providers harvest location data in this way which is used for
LBS and LIS. It has a number of shortcomings from an operator perspective, particularly regarding the
potential impact on customers (device battery, data consumption) and the fact that operators will find it
hard to differentiate here. To alleviate those shortcomings, some of these applications employ devicebased geo-fencing which does not require continuous connectivity, thereby reducing the battery drain.
Additionally, this method does not allow operators’ to use their network asset as means of differentiation.
Figure 22: Analysis of App/OS based real-time location Technology
Short description of the technology
Technical requirements device/apps
Technical requirements network
•
•
Device resources used to capture, store and report location.
Typically used for real-time location based services delivered via
apps, although data can also be “harvested” for LIS applications
•
Requires installation of software on the handset, either by end users
(e.g. app download) or over-the-air
•
Location captured by GPS, Cell-ID and/or SSID associated against a
location database (for WiFi, Cell-ID)
The WiFi SSID (and Cell-ID) approach requires building,
maintaining, and owning a large SSID directory including (estimated)
router locations. This is not legal in some countries.
•
•
•
Real-time location based advertising
Transport planning, Smart Parking/Cities (Streetline)
Google Maps
•
RT – High, NRT – High
Who are the typical providers of this
technology
•
•
•
Database platform: Cambridge Silicon Radio
WiFi: Ruckus Wireless, Purple WiFi,
Google Location Services
How mature is this technology?
•
High (established & proven with 5+ deployments over >3 years)
•
Depends on application and country. Contract needs to support
aggregated data use.
What are typical applications/case
studies of this technology
Relative applicability to real-time, near
real-time and non-real-time
Privacy and compliance implications
LIS Needs (Expansion of Figure 18)
Adherence to
LIS needs
Rating Explanation
•
Only if OS-based, otherwise not the case
High geographic Coverage (for users with
app or service)
•
Outdoors, if device resources are switched on
Low Device impact (eg. Battery, storage)
•
GPS, WiFi, Datacoms
Good Data continuity (24 X 7)
•
Users “switch-off” resources, GPS coverage
Low cost “Big Data” capture (to user or
provider)
•
Datacoms, need to run SSID data base
Population coverage
•
‘App’ centric
Location accuracy/precision
•
High Location accuracy outdoors
•
Smartphones and specialist devices
No need to install anything on device
Device Universality
Other
•
•
Real-time capture of location and communication with the network
Available for Indoor and outdoor applications
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
App/OS based data stored on device (GPS / SSID)
The main difference between this technology and the one above is that location data is stored on the
device (often as a by-product of operating another service and generally along with other data sets) and
then synchronized with a central store when needed (potentially not for several days).
Figure 23: Analysis of App/OS based data stored on device Technology
Short description of the technology
•
•
Individual tracking of data, used by enterprises, transport and health
related applications
Principally harvesting location and other type of information (health
monitoring etc)
Real-time data capture, but non-real-time syncing
•
Requires installation of software on the handset
•
The WiFi technology requires building, maintaining, and owning a
large SSID to the location central data base
•
Enterprise and healthcare applications
•
Tracking, Field force audit, Transport (Geopal)
•
RT – High, NRT – High, Non real-time- Mid
•
Garmin
•
High (established & proven with 5+ deployments over >3 years)
•
As some applications allow data sharing, which is a potential
geolocation data source, contract might be needed to support
aggregated data use
•
Technical requirements device/apps
Technical requirements network
What are typical applications/case
studies of this technology
Relative applicability to real-time, near
real-time and non-real-time
Who are the typical providers of this
technology?
How mature is this technology?
Privacy and compliance implications
LIS Needs (Expansion of Figure 18)
Adherence to
LIS needs
Rating Explanation
No need to install anything on device
•
Requires Installation of software on the handset
High geographic Coverage (for users with
app or service)
•
Only if device resources are switched on
Low Device impact (e.g. Battery, storage)
•
High Battery impact for GPS use
Good Data continuity (24 X 7)
•
Data stored on device
Low cost “Big Data” capture (to user or
provider)
•
Data downloading over “cheaper” means
Population coverage
•
‘App’ specific therefore limited to app use
Location accuracy/precision
•
Potentially good Location accuracy
Device Universality
•
Only available with smart phones / specialized
devices
Other
•
Often “comes with” other valuable data sets
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Emergency Services Location
Largely implemented to meet the requirement for accurate location for callers to emergency services,
this is available on many networks, particularly in the US. Such systems actively turn on additional
signaling to aid location, implying that it cannot be readily extended to mass-collection of location timeseries across as entire user base and would not be a viable means of doing so.
Figure 24: Analysis of Emergency Services Location Technology
•
Widely used with Emergency Response, network-triggered request
to the device generates signaling that enables “one-off” location
through a variety of means, including triangulation from multiple cells
•
Part of cellular standards
•
Requires additional wireless network infrastructure and uses
additional network resources when activated, meaning it cannot be
extended to mass collection
What are typical applications/case
studies of this technology
•
Emergency Responses
Relative applicability to real-time, near
real-time and non-real-time
•
RT – High, NRT – High
Who are the typical providers of this
technology
•
Network equipment providers (Ericsson etc), third parties, e.g.
Intrado
How mature is this technology?
•
High (established & proven over >10 years)
•
Compliant when used for Emergency responses situations. High
privacy implications if used for non-emergency related services
Short description of the technology
Technical requirements device/apps
Technical requirements network
Privacy and compliance implications
LIS Needs (Expansion of Figure 18)
Adherence to
LIS needs
No need to install anything on device
High geographic Coverage (for users with
app or service)
Low Device impact (eg. Battery, storage)
Good Data continuity (24 X 7)
Low cost “Big Data” capture (to user or
provider)
Rating Explanation
•
Part of cellular standards
•
Once triggered, ability to locate any connected
devices anywhere without need for GPS to be on
•
Uses less battery than GPS-only solutions.
•
Location only triggered when needed and not
practical to use continuously
•
Expensive Location mechanism due to demands
on resources
•
In practice cannot be extended to mass collection –
Only able to monitor a limited number of devices at
any point in time
•
Uses extra signaling traffic, and in some cases
GPS, enabling high accuracy
•
Can be used to locate any classes of phones
Population coverage
Location accuracy/precision
Device Universality
•
Other
(911 in the US, 112 in the EU)
Due to its cost, it is mainly used as an emergency response
application, locating individual calls to the Public Safety Answering
Point
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Granular (building level) network based
This essentially uses existing Radio-Access-Network (RAN) signaling to derive a location for all devices
on the network. It does require interaction between the device and the network (either using a service,
for activity such as cell hand-over, or regular checking-in with the network), but only as part of normal
service.
Figure 25: Analysis of Granular (building level) network based Technology
•
Uses existing RAN signaling data to derive and harvest building-level
data
•
None, but location reporting availability higher when phone in use /
mobile data is switched on
•
Depends on existing deployments, may require some additional
functionality to be introduced into network
What are typical applications/case
studies of this technology
•
Mobile network cell planning and optimisaiton
•
SoftBank
Relative applicability to real-time, near
real-time and non-real-time
•
RT – low (improving with 4G), NRT – mid (high over time)
Who are the typical providers of this
technology
•
Viavi Solutions, Teoco
•
High (established & proven with 5+ deployments over >3 years)
•
Depends on application. Contract needs to support aggregated data
use
Short description of the technology
Technical requirements device/apps
Technical requirements network
How mature is this technology?
Privacy and compliance implications
LIS Needs (Expansion of Figure 18)
Adherence to
LIS needs
Rating Explanation
No need to install anything on device
•
Uses existing RAN signalling data to derive
location
High geographic Coverage (for users with
app or service)
•
Enables location of all devices on the network all
the time
Low Device impact (eg. Battery, storage)
•
No impact on devices
Good Data continuity (24 X 7)
•
So long as devices connected, potentially longer
time gaps between updates where no phone
activity and static
Low cost “Big Data” capture (to user or
provider)
•
Relatively low cost, since uses existing data
Population coverage
•
Operator-specific, so would need
aggregation/collaboration for 100% cover
Location accuracy/precision
•
“Building” level
Device Universality
•
Can be used to locate any classes of phones
•
Other
•
Moving to near real-time and real time applications (with LTE
deployments )
Potentially vast data sets to process
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Coarse (cell-level) network based
This works by cell-id only which, depending on the size of cells can represent an area of several km.
This limits its practicability as a source of location insights, particularly vectors. Furthermore, in order to
acquire the data, the latter will be have to be extracted from the network which, for real-time acquisition,
will generate a material load on the HLR, or require a probe and storage.
Figure 26: Analysis of Coarse (cell-level) network based Technology
Short description of the technology
•
Uses current connected cell-ID as a proxy for location
Technical requirements device/apps
•
None
•
May require upgrading or use of probes
•
•
•
Outdoor Location (Indoor if micro cells used)
Nascar Sprint Cup (Partnership with Qualcomm)
Transport (inter-city) planning
Relative applicability to real-time, near
real-time and non-real-time
•
RT – High, NRT – High, Non real-time – High
Who are the typical providers of this
technology
•
Viavi Solutions, Qualcomm, Alcatel-Lucent
How mature is this technology?
•
High (established & proven with 5+ deployments over >3 years)
Privacy and compliance implications
•
Contract might be needed to support aggregated data use
LIS Needs (Expansion of Figure 18)
Adherence to
LIS needs
Technical requirements network
What are typical applications/case
studies of this technology
Rating Explanation
No need to install anything on device
•
No impact on the device
High geographic Coverage (for users with
app or service)
•
Enables location of all devices at cell level
Low Device impact (eg. Battery, storage)
•
No impact on devices
Good Data continuity (24 X 7)
•
Enables continuous data capture
Low cost “Big Data” capture (to user or
provider)
•
Data largely available already
Population coverage
•
Operator-specific, so would need
aggregation/collaboration for 100% cover
Location accuracy/precision
•
Only accurate when device in a small cell
•
Can be used to locate any classes of phones or
connected things
Device Universality
Other
•
Carriers often have poor records to where small cells are
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Indoor Technologies
This is a collection of technologies including Bluetooth, beacons, WiFi and cellular frequency device
emission, sometimes in combination with device resources such as motion sensors. These technologies
are essentially geared to providing very accurate footfall and individual pathway insights. However some
of the technologies (e.g. beacons) simply provide insight that an individual is in proximity to something
and do not deliver exact location insights.
Figure 27: Analysis of Indoor Technologies
•
Uses a combination of WiFi, Bluetooth Beacons, and devices
sensors to provide indoor location services
•
Varies. Some require apps to be installed, others only require
enabling Bluetooth and/or WiFi
•
Requires building and maintaining a data base to process the data
collected
•
B2C Proximity and location based marketing, footfall and pathway
tracking for retail layout optimisation
•
Retail (Walmart), events (SXSW)
Relative applicability to real-time, near
real-time and non-real-time
•
RT – High, NRT – high
Who are the typical providers of this
technology
•
Apple (iBeacons), Qualcomm,
Path Intelligence (Cellular), Navizon
•
Medium (Early adoption, growing number of deployments and use
case over > 1 year)
•
Depends on application. Contract needs to support aggregated
data use, or push content
Short description of the technology
Technical requirements device/apps
Technical requirements network
What are typical applications/case
studies of this technology
How mature is this technology?
Privacy and compliance implications
LIS Needs (Expansion of Figure 18)
Adherence to
LIS needs
Rating Explanation
No need to install anything on device
•
Requires software to be installed on the device,
or Bluetooth/WiFi to be activated
High geographic Coverage (for users with
app or service)
•
Geographic Coverage limited to specific locations
(eg. a single store)
Low Device impact (eg. Battery, storage)
•
•
Limited device impact for WiFi
Some battery impact for Bluetooth use
Good Data continuity (24 X 7)
•
Data collected when Bluetooth/WiFi activated –
limits continuous capture
Low cost “Big Data” capture (to user or
provider)
•
Data collection is an issue for telcos, as the data
is mostly transmitted through Bluetooth
Population coverage
•
Only captures users activating Bluetooth/WiFi
Location accuracy/precision
•
Provides highly accurate (<5m) footfall and
individual pathway insights
Device Universality
•
Requires connected smart devices
Other
•
•
•
Enables near real-time targeted advertising
High potential for site planning (indoor location)
Another form of “sniffing” data, similar to WiFi/GPS based solutions
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Appendix 2: Opportunity Sizing Methodology
Definition
The LIS market potential is defined as the total value that could be secured by all external LIS suppliers
from all potential buyers. The actual market will be lower (and/or slower to meet potential), reflecting
service adoption rates (penetration) over time.
LIS Opportunity sizing Methodology Overview
1.
Our approach was to first model the UK opportunity and then extrapolate this to a global estimate.
2.
From our research, we identified the business areas most likely to benefit from LIS: A matrix of 12
industry sectors (e.g. Retail, Local Government, Telecoms) and 5 functions within those sectors
(Research, Business Development, Operations & Planning, Site Planning, Sales & Marketing and
Advertising)
3.
For each of the 12 sectors we produced 2020 projections for their Gross Value Added (GVA) using
historical data from the Office for National Statistics (ONS) and sub-sector research where this was
not broken out by the ONS. (NOTE: GVA provides a ‘net’ monetary value for the goods and services
(G&S) produced by a sector. It is essentially the revenues less the cost of intermediary goods and
services bought in.)
4.
Using this framework, we next allocated the GVA for each sector between the 5 functions identified.
For example Site Planning is estimated as 3% of Telecoms’ GVA. These were derived using our
best understanding of each industry’s unique practices and cost structure. There are no published
figures on this and estimates are difficult to make given that companies may choose to outsource
part of these functions (thereby removing them from GVA).
5.
This gave us forecasted GVA Figures for each of the 5 functions in each of the 12 industry
sectors.
6.
We then estimated the potential percentage gain or uplift for each sector-function combination
from the introduction of LIS. Each function was rated for each sector as High (10% uplift),
Medium (5% uplift), Low (1% uplift) or none. For example, Strategy in Transport was given a
medium (5%) estimated uplift in GVA.
7.
We then made some further assumption on adoption levels of GIS for each function by sector.
Each function's level of adoption (percentage of industry using GIS services was judged on the
basis of industry concentration and data intensity. Levels of adoptions were set at either High (30%),
Medium (15%), Low (5%) or none. For example, Site Planning for Leisure are expected to have
high (30%) levels of GIS adoption.
8.
Recognising that only a proportion (25%) of this economic gain would be captured by third-party
LIS suppliers, we then derived the total potential value (potential revenue) for third-party LIS
suppliers by sector and function. Adding each of these sector-function combinations together gives
an aggregate potential value for the UK of £212m in 2020.
9.
Finally, we scaled this domestic figure up to a global figure. Firstly, we noted that the UK consists
of 3.4% of the global economy by spending. Secondly, we adjusted for the fact that the UK is an
advanced service-based economy, and therefore stands to create proportionally more value from
LIS than the average global economy. By increasing the UK’s share of global LIS to 6.8% (double
its share of GDP), we effectively halved our global potential market estimate for LIS. Thirdly, we
expressed the global figure in US $ by applying an exchange rate of $1.5 per £. (NOTE: The
calculation is therefore £0.21bn ÷ 0.068 × 1.5 = $4.7bn.)
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Fast-Tracking Operator Plans to Win in the $5bn Location Insights Market | OCTOBER 2015
Appendix 3: About STL Partners and Telco 2.0: Change
the Game
STL Partners delivers original and strategic research, consulting and advisory services for telecoms,
technology and media businesses that seek transformational success in the digital economy. It:

Helps clients create opportunities, make new connections, deal with threats, and drive strategy,
plans and effective actions.

Specializes in changing business models, driving innovation and growth, and is behind ‘Telco 2.0’
– the leading visionary benchmark for success in Telecoms.

Key practice areas include: Transformation; Disruptive Strategies in Communications, Content
and Commerce; Cloud and Enterprise ICT; and Future Networks.
To get involved, please call +44 (0) 247 5003 or email [email protected] to engage with us
through:

STL Partners Research, which includes the Telco 2.0 Executive Briefing Service, in-depth streams
on the key practice areas, and the widely read Telco 2.0 industry blog and newsletter

Bespoke Consulting and analytical services, typically helping clients evaluate opportunities,
develop new propositions and business models, and develop ‘go to market’ strategies.

Expert and interactive support for specific engagements with key market contacts and new
connections.
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