Location Intelligence (LI) is transforming business operations and models through geospatial data analytics, AI integration, and real-time insights. With the market growing at 16.8% CAGR through 2030, 67% of data professionals now use location data for critical applications. LI drives efficiency gains across retail, banking, and telecom industries through optimised site selection, fraud detection, and service planning. Beyond efficiency, it enables new revenue models including subscription-based analytics platforms, collaborative data ecosystems, geo-targeted advertising, and usage-based insurance. This shift transforms static mapping into actionable intelligence, creating both enhanced operations and entirely new business opportunities.
“Location is the sole differentiator between mobile and traditional web” - Sam Altman, CEO of OpenAI
As the world integrates more technology into its ways of working and living, Location Intelligence (LI) is becoming a key enabler of new business models, products, and services. LI is “...the practice of collecting and managing customer location data, enriching it with other data sources, and analysing it for context to inform optimised actions, decisions, and customer experiences”, per a definition by leading Research firm Forrester. The technology integrates geospatial data with analytics, AI, and real-time insights to drive decision-making, with priorities like spatial analytics surging 62% year-over-year per recent surveys.
The industry roughly comprises providers of LI data products, LI execution technologies, GIS/spatial data analytics organisations, enterprise location data management services, and LI platforms. Some of the key ways LI providers can drive business value include:
- Analysis of large spatial and customer location data
- Democratization of advanced spatial analytics
- Using location to scale intelligent business decisions
Fueled by advances such as the Internet of Things (IoT), 5G network rollouts, and Machine Learning (ML) for predictive outcomes, LI has given a fillip to applications such as address validation (34% adoption), targeted marketing (27%), and fraud detection (24%) across different industries.
key trends in location intelligence
A survey of recent analyst reports highlights a rapid growth in the adoption of this technology, with the market projected at 16.8% Compounded Annual Growth Rate (CAGR) through 2030, driven by Artificial Intelligence (AI) and ML integration for dynamic geospatial analysis. Forrester emphasizes the importance of LI in insights-driven businesses generating trillions in revenue by enriching customer data for optimised actions.
The 2025 Data Integrity Trends and Insights Outlook by Precisely and Drexel University revealed a significant rise in the adoption of location-based intelligence to authenticate address data, detect fraud, optimise product delivery, and more. The report notes that 67% of their survey respondents — comprising data and analytics professionals from around the world — use location data in business critical use cases. Challenges such as privacy concerns (48% barrier) and poor address quality (37%) are being addressed by providers as adoption rises.
LI-based analytics and insights are not only driving improved efficiencies across a range of current businesses, but also ushering in new usage, revenue-share, and revenue models.
improving efficiencies
LI is improving efficiencies in a raft of industries. This is best understood by examining a few case studies from industry.
- Consider the retail and logistics sector
- Google Cloud's tools assist selection of sites by combining Point of Interest (POI) data of over 250 million businesses, demographics, and foot traffic, thereby enabling ROI for companies seeking to expand.
- Latin American ‘super app’ Rappi uses geolocation for transaction transparency to sell financial products and services.
- In the Banking and Financial Services and Insurance (BFSI) sector,
- A French mutual insurer with 11.5 million customers uses spatial analysis of climate, demographics, and portfolios to refine risk pricing for industrial sites.
- Across other companies in this sector, AI models have been deployed with geocoding and EV fleet route planning to optimize ATM strategies, detect fraud, and improve last-mile First Attempt Deliver Rates (FADR).
- In the telecom sector, one of the leading telecom operators in Romania has improved their service outage responses and leverages user-friendly analysis to offer better customer support using centralized coverage data on-premises.
morphing business models
Besides improving efficiencies across current service lines and products, geospatial intelligence also enables novel business models by enabling monetisation of spatial data through location-aware services, predictive platforms, and collaborative ecosystems. This shifts the dynamic from traditional mapping to revenue-generating insights such as usage-based insurance and dynamic logistics offerings.
Consider these new ways of monetisation:
- revenue from location services: Enterprises are now able to create subscription-based platforms that offer geospatial analytics as value-added services. For example, logistics firms can sell real-time dashboards that enable route optimisation and predictive rerouting, using AI to anticipate delays and charge per insight or API call. Retailers are able to create personalized, geo-triggered promotions and movement-pattern offers, boosting conversions while simultaneously packaging spatial data with demographics for third-party sales.
- collaborative data ecosystems: National agencies and private firms have collaborated to open data hubs. A prime example is Europe's Location Innovation Hub, which connects geospatial sources to co-create tailored services for small and medium businesses (SMEs). Such hubs generate revenue through downstream apps and shared infrastructure. Another instance is the Overture Maps Foundation, which provides stable open map data, enabling next-generation map-based products to be created. Mobile businesses can build location-based products using Overture data without proprietary costs.
- location-aware subscription service models:Emerging subscription models in the geo-spatial data industry now provide — for recurring fees — access to dashboards and advanced predictive tools that analyse movement patterns. Some telecom operators now charge for aggregated, General Data Protection Regulation (GDPR)-compliant data on subscriber trends, such as residential data usage areas or peak venue traffic. Platforms such as Placer.ai provide tiered subscription plans for foot traffic analytics data, chain market share tracking, and migration patterns. These support real estate and retail decision makers.
- advertising and partnerships:Geo-targeted advertisement services have created new revenue share models. Operators take a cut from the sales driven by LI. Premium attribution metrics, such as tracking post-event dispersal or campaign ROI, can command higher fees from advertisers targeting affluent groups through near-real-time insights. Ongoing partnerships with sectors like BFSI yield subscriptions for behavioral predictions, thereby enhancing customer loyalty programs.
- emerging usage models: Other usage models that are emerging include per-query fees for granular location graphs from data API calls, more advanced spatial forecasting data from analytics tiers, and custom insights such as tailored reports on demographics or mobility.
- other industry-specific business models include usage-based, geo-informed insurance policies such as low-risk customer policies based on flood or crime-risk modeling; or predictive analytics dashboards in logistics for route refinement. Financial inclusion platforms in Mozambique, for instance, map far-flung access points to expand their rural services. The retail sector leverages urban heat maps, socioeconomic characteristics and competitor analysis for site selection for retail outlets.
It is evident that these applications leverage geospatial data effectively to transform static data into actionable intelligence. The result: not just enhanced efficiency, but improved and new businesses, shaped by LI.
how Infosys BPM can help
With a range of AI-first geospatial data services, Infosys BPM can resolve clients’ business/IT problems across industry segments such as Utility, Oil & Gas, Telecom, Mining, Transport, and Services through the Infosys Global Delivery Model. Infosys BPM has a proven GIS Centre of Excellence (CoE), which offers services such as GIS consulting, spatial data management, spatial data analysis, and more.
