Location Analytics Market by Component (Services, Software), Deployment Mode (Cloud, Hybrid, On-Premise), Analytics Type, Application - Global Forecast 2025-2032
Description
The Location Analytics Market was valued at USD 22.95 billion in 2024 and is projected to grow to USD 25.73 billion in 2025, with a CAGR of 13.23%, reaching USD 62.06 billion by 2032.
A clear and compelling introduction to location analytics that outlines foundational concepts, practical use cases, and organizational readiness considerations
Location analytics is reshaping how organizations interpret spatial data to drive operational efficiency, customer engagement, and strategic planning. At its core, location intelligence transforms coordinates and movement data into meaningful insights that influence decisions across industries from retail site selection to transportation routing. This introductory overview clarifies foundational concepts, highlights typical use cases, and frames the key technological enablers that practitioners must consider when integrating spatial analytics into enterprise workflows.
The discipline is supported by a convergence of sensors, connectivity, and analytics engines that enable organizations to capture both historical patterns and live signals. These capabilities make it possible to understand customer journeys, optimize logistics, and detect anomalous behavior in physical environments. In addition, growing regulatory attention on privacy and data governance requires that location programs balance commercial objectives with robust controls for consent, minimization, and secure processing.
As organizations move from pilots to production, they must evaluate deployment options, integration complexity, and organizational readiness. Strategic alignment across business units, clear definition of success metrics, and investment in skilled analytics talent are preconditions for realizing measurable outcomes. The introduction closes by setting the stage for deeper analysis of transformative shifts, regulatory influences, segmentation, regional dynamics, and tactical actions leaders can take to unlock the potential of location analytics.
An in-depth review of the major technological, regulatory, and operational shifts transforming the location analytics landscape and adoption dynamics
The landscape for location analytics is experiencing transformative shifts driven by evolving data sources, enhanced compute capabilities, and heightened expectations for immediate, actionable insight. Advances in sensor networks, ubiquitous mobile connectivity, and edge processing have increased both the volume and velocity of geospatial data, enabling a transition from static maps to dynamic, context-aware systems. This shift is further accelerated by matured machine learning models that fuse temporal and spatial features to recognize patterns and predict operational outcomes in near real time.
At the same time, privacy regulations and public sentiment are reshaping how organizations collect, store, and use geographic information. Practitioners must design privacy-preserving architectures that incorporate techniques such as differential privacy, secure multiparty computation, and strong anonymization to maintain utility while mitigating risk. Interoperability and standards adoption are also rising in importance as enterprises wish to integrate location intelligence with CRM, ERP, and IoT platforms, demanding open APIs and modular solutions.
Consequently, value is increasingly derived from the orchestration of data, analytics, and business process change rather than from isolated point solutions. Organizations that combine agile deployment models with rigorous governance and cross-functional stakeholder engagement will be best positioned to capitalize on this period of rapid technological and regulatory evolution.
A comprehensive analysis of how the cumulative effects of U.S. tariff adjustments in 2025 reshape supply chains, procurement choices, and the hardware-software balance in location analytics
Tariff policy changes announced in the United States for 2025 have introduced a range of implications for supply chain design, procurement strategies, and capital planning that indirectly affect location analytics programs. Increased levies on certain hardware components and imported devices can raise the total cost of ownership for sensor networks, edge computing nodes, and specialized GNSS-enabled equipment. This cost pressure prompts organizations to reassess sourcing strategies and lifecycle management practices for physical assets that underpin spatial data capture.
In addition, tariffs can influence vendor selection and partner ecosystems by creating incentives to localize production or to qualify alternative suppliers in regions with preferential trade terms. These shifts in vendor footprints are consequential for deployment timelines, warranty and support arrangements, and interoperability expectations. Moreover, procurement teams are revisiting contractual terms to address potential future trade disruptions and to build in flexibility for component substitution and retrofitting.
From a strategic perspective, the cumulative effect of tariff-driven changes is to increase emphasis on software-centric and cloud-native solutions that decouple value from hardware lifecycle constraints. Organizations are exploring hybrid approaches that balance on-premise control for sensitive operations with cloud services for scalability, while also investing in asset-agnostic architectures that facilitate rapid substitution and minimize the impact of trade policy volatility.
A granular segmentation-led perspective revealing how components, deployment choices, organization size, analytics modalities, and applications drive differentiated adoption and value
Segment-focused analysis reveals distinct adoption patterns when the market is organized by component, deployment mode, organization size, analytics type, and application. When examined by component, the market is studied across services and software, with services further subdivided into managed services and professional services; the managed services pathway tends to attract organizations seeking operational outsourcing, while professional services support bespoke integrations and advanced analytics implementations. Looking at deployment mode, the market is studied across cloud, hybrid, and on-premise options; cloud deployments enable rapid scaling and reduced capital expenditure, hybrid models balance latency and control requirements, and on-premise installations serve environments with strict data residency or regulatory constraints.
Organizational scale also drives requirements, and when segmented by organization size the market is studied across large enterprises and small and medium enterprises; larger entities typically demand enterprise-grade integrations and governance, whereas smaller organizations prioritize cost-effective, modular solutions that can deliver rapid operational wins. Analytics type further refines capability needs, as the market is studied across historical, predictive, and real-time approaches. The historical strand is focused on customer journey and traffic patterns to inform strategic planning, predictive analytics concentrates on demand forecasting and risk assessment to enable proactive decisions, and real-time analytics covers indoor and outdoor scenarios to support immediate operational control and incident response.
Finally, application-driven segmentation assesses use cases across banking, financial services and insurance; government and defense; healthcare; manufacturing; retail and e-commerce; and transportation and logistics, revealing that each vertical has unique telemetry mixes, regulatory considerations, and success metrics that should shape product roadmaps and implementation priorities.
A strategic regional analysis that contrasts regulatory, infrastructural, and commercial differences across the Americas, Europe, Middle East & Africa, and Asia-Pacific
Regional dynamics shape opportunity sets and competitive approaches, with the Americas, Europe, Middle East & Africa, and Asia-Pacific each presenting unique regulatory frameworks, infrastructure maturity, and commercial priorities. In the Americas, strong adoption of cloud-native stacks and a high tolerance for pilot-and-scale programs accelerates the integration of location analytics with retail and logistics use cases, and there is a robust ecosystem of integrators and analytics service providers that support rapid deployments. By contrast, Europe, the Middle East & Africa presents a mosaic of regulatory regimes and privacy requirements that encourage privacy-by-design architectures and demand careful alignment with local data protection standards.
Asia-Pacific exhibits rapid uptake driven by urbanization, smart city initiatives, and widespread mobile device penetration, creating fertile ground for real-time and indoor/outdoor analytics applications. This region also shows strong innovation in sensor hardware and edge computing, which complements cloud capabilities for latency-sensitive workloads. Transitioning between these regional narratives, it is clear that localization of product features, compliance approaches, and support services is essential for achieving sustained adoption. Leaders should evaluate regional partner networks, data residency requirements, and the availability of skilled technical resources when designing go-to-market strategies.
Taken together, these regional insights underscore the need for flexible commercial models and engineering practices that accommodate divergent regulatory expectations and infrastructure realities across global markets.
An analytical overview of the competitive landscape highlighting how platforms, specialists, integrators, and innovators collaborate to deliver differentiated location analytics solutions
Competitive dynamics in location analytics are defined by a mix of established platform providers, specialized analytics vendors, systems integrators, and nimble start-ups delivering verticalized solutions. Incumbent platform providers tend to compete on breadth of functionality, ecosystem integrations, and enterprise-grade governance features. Specialized vendors, meanwhile, focus on depth within particular domains such as indoor positioning, real-time event processing, or geospatial data enrichment, offering modular components that suit targeted operational needs. Systems integrators and global service firms play a critical role in bridging product capabilities with enterprise IT stacks, providing the implementation scale and project governance required for complex rollouts.
Start-ups contribute innovation velocity, particularly in areas such as sensor fusion, privacy-preserving analytics, and low-power positioning technologies. Partnerships among these different classes of organizations are increasingly common, as enterprises demand turnkey solutions that combine best-of-breed analytics with reliable deployment and managed support. Investment in developer ecosystems, robust APIs, and partner certification programs can therefore be significant differentiators. From a procurement perspective, buyers are prioritizing vendors with transparent data handling practices, clear SLAs, and demonstrable domain expertise relevant to their specific application contexts.
Actionable, phased recommendations for executives to align pilots, governance, procurement, and capability building to accelerate value from location analytics deployments
Senior leaders seeking to extract tangible value from location analytics should adopt a pragmatic, phased approach that balances quick wins with foundational investments in governance and architecture. Begin by aligning use cases with measurable operational objectives and by selecting pilot projects that address high-impact problems while requiring manageable integration effort. Simultaneously, invest in data governance frameworks that codify consent management, retention policies, and access controls to ensure compliance and build stakeholder trust. Interoperability should be prioritized through API-first design and adherence to open standards where practical, enabling future integration with enterprise systems.
Organizational capability building is equally critical. Upskill analytics and engineering teams on spatial data science techniques and on operationalizing models for real-time use. Where internal capacity is limited, consider engaging professional services for knowledge transfer during early deployments. Procurement strategies must account for potential supply chain volatility by favoring modular, hardware-agnostic solutions and by negotiating flexible terms that accommodate component substitution. Finally, adopt a metrics-driven culture by defining success indicators tied to business outcomes such as reduced travel time, improved queue management, or enhanced customer dwell conversion, and use these metrics to guide iterative improvements and broader rollouts.
A transparent mixed-methods research methodology that integrates primary interviews, technical review, and scenario analysis to validate practical insights and implications
This research synthesizes insights drawn from a mixed-methods approach that combines primary interviews, technical literature review, and structured analysis of deployment case studies. Primary research included in-depth conversations with practitioners across multiple industries, including IT architects, operations leaders, and analytics professionals, providing context on implementation hurdles, integration strategies, and measurable outcomes. Secondary sources comprised vendor documentation, standards bodies, white papers, and publicly available regulatory guidance to validate technical assumptions and to frame compliance considerations.
Analytic rigor was maintained through triangulation across multiple information sources, and findings were stress-tested with subject-matter experts to ensure practical relevance. Technical assessments evaluated solution architectures, data pipelines, and security controls, while commercial analysis examined procurement practices and partner ecosystems. Where applicable, scenario analysis was used to explore the implications of supply chain disruptions and regulatory shifts. Throughout the research, emphasis was placed on transparency of assumptions, clear documentation of methodologies, and replication of key analytical steps so that conclusions can be adapted to specific organizational contexts.
A concise conclusion that synthesizes strategic imperatives and operational priorities for translating location analytics capability into enterprise value
The cumulative narrative of this report emphasizes that location analytics is at an inflection point where technological capability, regulatory focus, and commercial demand converge to create meaningful enterprise opportunities. Realizing those opportunities requires deliberate architectural choices, robust governance, and a clear alignment between analytics initiatives and business outcomes. Firms that prioritize privacy-preserving design, modular architectures, and targeted pilots will be best positioned to scale solutions in a cost-effective and compliant manner.
Emerging pressures such as tariff-induced hardware cost increases and evolving regional regulatory frameworks underscore the value of flexible deployment models and vendor diversification. By approaching adoption with a pragmatic mix of cloud-native services, hybrid architectures, and selective on-premise control, organizations can balance performance, compliance, and resilience. Ultimately, success in this domain will be determined by the ability of leaders to translate spatial insights into operational decisions that measurably improve efficiency, customer experience, and risk management, and to institutionalize learning so that early wins are replicated across broader enterprise contexts.
Please Note: PDF & Excel + Online Access - 1 Year
A clear and compelling introduction to location analytics that outlines foundational concepts, practical use cases, and organizational readiness considerations
Location analytics is reshaping how organizations interpret spatial data to drive operational efficiency, customer engagement, and strategic planning. At its core, location intelligence transforms coordinates and movement data into meaningful insights that influence decisions across industries from retail site selection to transportation routing. This introductory overview clarifies foundational concepts, highlights typical use cases, and frames the key technological enablers that practitioners must consider when integrating spatial analytics into enterprise workflows.
The discipline is supported by a convergence of sensors, connectivity, and analytics engines that enable organizations to capture both historical patterns and live signals. These capabilities make it possible to understand customer journeys, optimize logistics, and detect anomalous behavior in physical environments. In addition, growing regulatory attention on privacy and data governance requires that location programs balance commercial objectives with robust controls for consent, minimization, and secure processing.
As organizations move from pilots to production, they must evaluate deployment options, integration complexity, and organizational readiness. Strategic alignment across business units, clear definition of success metrics, and investment in skilled analytics talent are preconditions for realizing measurable outcomes. The introduction closes by setting the stage for deeper analysis of transformative shifts, regulatory influences, segmentation, regional dynamics, and tactical actions leaders can take to unlock the potential of location analytics.
An in-depth review of the major technological, regulatory, and operational shifts transforming the location analytics landscape and adoption dynamics
The landscape for location analytics is experiencing transformative shifts driven by evolving data sources, enhanced compute capabilities, and heightened expectations for immediate, actionable insight. Advances in sensor networks, ubiquitous mobile connectivity, and edge processing have increased both the volume and velocity of geospatial data, enabling a transition from static maps to dynamic, context-aware systems. This shift is further accelerated by matured machine learning models that fuse temporal and spatial features to recognize patterns and predict operational outcomes in near real time.
At the same time, privacy regulations and public sentiment are reshaping how organizations collect, store, and use geographic information. Practitioners must design privacy-preserving architectures that incorporate techniques such as differential privacy, secure multiparty computation, and strong anonymization to maintain utility while mitigating risk. Interoperability and standards adoption are also rising in importance as enterprises wish to integrate location intelligence with CRM, ERP, and IoT platforms, demanding open APIs and modular solutions.
Consequently, value is increasingly derived from the orchestration of data, analytics, and business process change rather than from isolated point solutions. Organizations that combine agile deployment models with rigorous governance and cross-functional stakeholder engagement will be best positioned to capitalize on this period of rapid technological and regulatory evolution.
A comprehensive analysis of how the cumulative effects of U.S. tariff adjustments in 2025 reshape supply chains, procurement choices, and the hardware-software balance in location analytics
Tariff policy changes announced in the United States for 2025 have introduced a range of implications for supply chain design, procurement strategies, and capital planning that indirectly affect location analytics programs. Increased levies on certain hardware components and imported devices can raise the total cost of ownership for sensor networks, edge computing nodes, and specialized GNSS-enabled equipment. This cost pressure prompts organizations to reassess sourcing strategies and lifecycle management practices for physical assets that underpin spatial data capture.
In addition, tariffs can influence vendor selection and partner ecosystems by creating incentives to localize production or to qualify alternative suppliers in regions with preferential trade terms. These shifts in vendor footprints are consequential for deployment timelines, warranty and support arrangements, and interoperability expectations. Moreover, procurement teams are revisiting contractual terms to address potential future trade disruptions and to build in flexibility for component substitution and retrofitting.
From a strategic perspective, the cumulative effect of tariff-driven changes is to increase emphasis on software-centric and cloud-native solutions that decouple value from hardware lifecycle constraints. Organizations are exploring hybrid approaches that balance on-premise control for sensitive operations with cloud services for scalability, while also investing in asset-agnostic architectures that facilitate rapid substitution and minimize the impact of trade policy volatility.
A granular segmentation-led perspective revealing how components, deployment choices, organization size, analytics modalities, and applications drive differentiated adoption and value
Segment-focused analysis reveals distinct adoption patterns when the market is organized by component, deployment mode, organization size, analytics type, and application. When examined by component, the market is studied across services and software, with services further subdivided into managed services and professional services; the managed services pathway tends to attract organizations seeking operational outsourcing, while professional services support bespoke integrations and advanced analytics implementations. Looking at deployment mode, the market is studied across cloud, hybrid, and on-premise options; cloud deployments enable rapid scaling and reduced capital expenditure, hybrid models balance latency and control requirements, and on-premise installations serve environments with strict data residency or regulatory constraints.
Organizational scale also drives requirements, and when segmented by organization size the market is studied across large enterprises and small and medium enterprises; larger entities typically demand enterprise-grade integrations and governance, whereas smaller organizations prioritize cost-effective, modular solutions that can deliver rapid operational wins. Analytics type further refines capability needs, as the market is studied across historical, predictive, and real-time approaches. The historical strand is focused on customer journey and traffic patterns to inform strategic planning, predictive analytics concentrates on demand forecasting and risk assessment to enable proactive decisions, and real-time analytics covers indoor and outdoor scenarios to support immediate operational control and incident response.
Finally, application-driven segmentation assesses use cases across banking, financial services and insurance; government and defense; healthcare; manufacturing; retail and e-commerce; and transportation and logistics, revealing that each vertical has unique telemetry mixes, regulatory considerations, and success metrics that should shape product roadmaps and implementation priorities.
A strategic regional analysis that contrasts regulatory, infrastructural, and commercial differences across the Americas, Europe, Middle East & Africa, and Asia-Pacific
Regional dynamics shape opportunity sets and competitive approaches, with the Americas, Europe, Middle East & Africa, and Asia-Pacific each presenting unique regulatory frameworks, infrastructure maturity, and commercial priorities. In the Americas, strong adoption of cloud-native stacks and a high tolerance for pilot-and-scale programs accelerates the integration of location analytics with retail and logistics use cases, and there is a robust ecosystem of integrators and analytics service providers that support rapid deployments. By contrast, Europe, the Middle East & Africa presents a mosaic of regulatory regimes and privacy requirements that encourage privacy-by-design architectures and demand careful alignment with local data protection standards.
Asia-Pacific exhibits rapid uptake driven by urbanization, smart city initiatives, and widespread mobile device penetration, creating fertile ground for real-time and indoor/outdoor analytics applications. This region also shows strong innovation in sensor hardware and edge computing, which complements cloud capabilities for latency-sensitive workloads. Transitioning between these regional narratives, it is clear that localization of product features, compliance approaches, and support services is essential for achieving sustained adoption. Leaders should evaluate regional partner networks, data residency requirements, and the availability of skilled technical resources when designing go-to-market strategies.
Taken together, these regional insights underscore the need for flexible commercial models and engineering practices that accommodate divergent regulatory expectations and infrastructure realities across global markets.
An analytical overview of the competitive landscape highlighting how platforms, specialists, integrators, and innovators collaborate to deliver differentiated location analytics solutions
Competitive dynamics in location analytics are defined by a mix of established platform providers, specialized analytics vendors, systems integrators, and nimble start-ups delivering verticalized solutions. Incumbent platform providers tend to compete on breadth of functionality, ecosystem integrations, and enterprise-grade governance features. Specialized vendors, meanwhile, focus on depth within particular domains such as indoor positioning, real-time event processing, or geospatial data enrichment, offering modular components that suit targeted operational needs. Systems integrators and global service firms play a critical role in bridging product capabilities with enterprise IT stacks, providing the implementation scale and project governance required for complex rollouts.
Start-ups contribute innovation velocity, particularly in areas such as sensor fusion, privacy-preserving analytics, and low-power positioning technologies. Partnerships among these different classes of organizations are increasingly common, as enterprises demand turnkey solutions that combine best-of-breed analytics with reliable deployment and managed support. Investment in developer ecosystems, robust APIs, and partner certification programs can therefore be significant differentiators. From a procurement perspective, buyers are prioritizing vendors with transparent data handling practices, clear SLAs, and demonstrable domain expertise relevant to their specific application contexts.
Actionable, phased recommendations for executives to align pilots, governance, procurement, and capability building to accelerate value from location analytics deployments
Senior leaders seeking to extract tangible value from location analytics should adopt a pragmatic, phased approach that balances quick wins with foundational investments in governance and architecture. Begin by aligning use cases with measurable operational objectives and by selecting pilot projects that address high-impact problems while requiring manageable integration effort. Simultaneously, invest in data governance frameworks that codify consent management, retention policies, and access controls to ensure compliance and build stakeholder trust. Interoperability should be prioritized through API-first design and adherence to open standards where practical, enabling future integration with enterprise systems.
Organizational capability building is equally critical. Upskill analytics and engineering teams on spatial data science techniques and on operationalizing models for real-time use. Where internal capacity is limited, consider engaging professional services for knowledge transfer during early deployments. Procurement strategies must account for potential supply chain volatility by favoring modular, hardware-agnostic solutions and by negotiating flexible terms that accommodate component substitution. Finally, adopt a metrics-driven culture by defining success indicators tied to business outcomes such as reduced travel time, improved queue management, or enhanced customer dwell conversion, and use these metrics to guide iterative improvements and broader rollouts.
A transparent mixed-methods research methodology that integrates primary interviews, technical review, and scenario analysis to validate practical insights and implications
This research synthesizes insights drawn from a mixed-methods approach that combines primary interviews, technical literature review, and structured analysis of deployment case studies. Primary research included in-depth conversations with practitioners across multiple industries, including IT architects, operations leaders, and analytics professionals, providing context on implementation hurdles, integration strategies, and measurable outcomes. Secondary sources comprised vendor documentation, standards bodies, white papers, and publicly available regulatory guidance to validate technical assumptions and to frame compliance considerations.
Analytic rigor was maintained through triangulation across multiple information sources, and findings were stress-tested with subject-matter experts to ensure practical relevance. Technical assessments evaluated solution architectures, data pipelines, and security controls, while commercial analysis examined procurement practices and partner ecosystems. Where applicable, scenario analysis was used to explore the implications of supply chain disruptions and regulatory shifts. Throughout the research, emphasis was placed on transparency of assumptions, clear documentation of methodologies, and replication of key analytical steps so that conclusions can be adapted to specific organizational contexts.
A concise conclusion that synthesizes strategic imperatives and operational priorities for translating location analytics capability into enterprise value
The cumulative narrative of this report emphasizes that location analytics is at an inflection point where technological capability, regulatory focus, and commercial demand converge to create meaningful enterprise opportunities. Realizing those opportunities requires deliberate architectural choices, robust governance, and a clear alignment between analytics initiatives and business outcomes. Firms that prioritize privacy-preserving design, modular architectures, and targeted pilots will be best positioned to scale solutions in a cost-effective and compliant manner.
Emerging pressures such as tariff-induced hardware cost increases and evolving regional regulatory frameworks underscore the value of flexible deployment models and vendor diversification. By approaching adoption with a pragmatic mix of cloud-native services, hybrid architectures, and selective on-premise control, organizations can balance performance, compliance, and resilience. Ultimately, success in this domain will be determined by the ability of leaders to translate spatial insights into operational decisions that measurably improve efficiency, customer experience, and risk management, and to institutionalize learning so that early wins are replicated across broader enterprise contexts.
Please Note: PDF & Excel + Online Access - 1 Year
Table of Contents
182 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Real-time pedestrian footfall heat mapping integrating IoT sensors and mobile data analytics across urban centers
- 5.2. Privacy-compliant location intelligence solutions leveraging differential privacy to personalize customer experiences in retail environments
- 5.3. AI-driven predictive site selection platforms combining demographic microsegmentation with spatiotemporal mobility patterns
- 5.4. Integration of augmented reality spatial overlays with indoor positioning systems for enhanced customer navigation in large venues
- 5.5. Edge computing architectures for on-device location analytics reducing latency in critical asset tracking across supply chains
- 5.6. Geospatial big data fusion of satellite imagery and street-level IoT signals for dynamic environmental risk assessment
- 5.7. Cloud-based geofencing orchestration platforms enabling real-time automated marketing triggers based on cross-channel user location behavior
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Location Analytics Market, by Component
- 8.1. Services
- 8.1.1. Managed Services
- 8.1.2. Professional Services
- 8.2. Software
- 9. Location Analytics Market, by Deployment Mode
- 9.1. Cloud
- 9.2. Hybrid
- 9.3. On-Premise
- 10. Location Analytics Market, by Analytics Type
- 10.1. Historical
- 10.1.1. Customer Journey
- 10.1.2. Traffic Patterns
- 10.2. Predictive
- 10.2.1. Demand Forecasting
- 10.2.2. Risk Assessment
- 10.3. Real-Time
- 10.3.1. Indoor
- 10.3.2. Outdoor
- 11. Location Analytics Market, by Application
- 11.1. Banking Financial Services And Insurance
- 11.2. Government And Defense
- 11.3. Healthcare
- 11.4. Manufacturing
- 11.5. Retail And E-Commerce
- 11.6. Transportation And Logistics
- 12. Location Analytics Market, by Region
- 12.1. Americas
- 12.1.1. North America
- 12.1.2. Latin America
- 12.2. Europe, Middle East & Africa
- 12.2.1. Europe
- 12.2.2. Middle East
- 12.2.3. Africa
- 12.3. Asia-Pacific
- 13. Location Analytics Market, by Group
- 13.1. ASEAN
- 13.2. GCC
- 13.3. European Union
- 13.4. BRICS
- 13.5. G7
- 13.6. NATO
- 14. Location Analytics Market, by Country
- 14.1. United States
- 14.2. Canada
- 14.3. Mexico
- 14.4. Brazil
- 14.5. United Kingdom
- 14.6. Germany
- 14.7. France
- 14.8. Russia
- 14.9. Italy
- 14.10. Spain
- 14.11. China
- 14.12. India
- 14.13. Japan
- 14.14. Australia
- 14.15. South Korea
- 15. Competitive Landscape
- 15.1. Market Share Analysis, 2024
- 15.2. FPNV Positioning Matrix, 2024
- 15.3. Competitive Analysis
- 15.3.1. Alteryx, Inc.
- 15.3.2. Carto, Inc.
- 15.3.3. Cisco Systems, Inc.
- 15.3.4. Esri Global, Inc.
- 15.3.5. Foursquare Labs, Inc.
- 15.3.6. Google LLC
- 15.3.7. HERE Global B.V.
- 15.3.8. Hexagon AB
- 15.3.9. Inpixon, Inc.
- 15.3.10. Mapbox, Inc.
- 15.3.11. Microsoft Corporation
- 15.3.12. Oracle Corporation
- 15.3.13. Pitney Bowes Inc.
- 15.3.14. Precisely, Inc.
- 15.3.15. SAP SE
- 15.3.16. SAS Institute Inc.
- 15.3.17. TomTom International B.V.
- 15.3.18. Trimble Inc.
- 15.3.19. Zebra Technologies Corporation
Pricing
Currency Rates
Questions or Comments?
Our team has the ability to search within reports to verify it suits your needs. We can also help maximize your budget by finding sections of reports you can purchase.
