Introduction
Apple's "Limit Precise Location" feature represents the latest evolution in privacy restrictions that began with App Tracking Transparency (ATT) in iOS 14.5. While ATT fundamentally restricted how apps could share user data with third parties, this new feature targets the location information gathered by cellular networks when devices connect to cell towers. By reducing the precision of this network-level location data from street-level to neighborhood-level, Apple is extending privacy controls to the telecommunications infrastructure. Telcos using mobile network data for monetising mobility insights now face challenges similar to those app developers encountered with ATT— reduced data accuracy, disrupted business models, and the need for alternative approaches.
Unacast’s experience navigating through multiple waves of privacy restrictions has equipped us with proven methodologies and a clear roadmap for maintaining data utility in privacy-constrained environments. Privacy restrictions are not temporary obstacles to work around, but permanent shifts requiring fundamental changes in data architecture, sourcing strategy, and analytical capabilities.
This article examines what "Limit Precise Location" is and assesses the impact on products relying on mobile network data, and outlines mitigation strategies drawn from Unacast's experience navigating similar privacy transitions.
What "Limit Precise Location" Actually Does
When users enable "Limit Precise Location," location information made available to cellular networks will be limited. While Apple's documentation is intentionally vague about the specific technical implementation, the outcome seems clear: location determination shifts from street-address precision (typically 50-100m in urban environments) to neighborhood-level precision (potentially 500-2000 meters).
What remains unchanged:
- Emergency call location precision
- Signal quality and network performance
- Core network connectivity and service delivery
The Adoption of Apple Privacy Features
Understanding the adoption trajectory of previous privacy features is important for planning mitigation strategies and business impact. Below are the historical adoption patterns for other privacy features:
- App Tracking Transparency (iOS 14.5, 2021): ~25% opt-in within 6 months, ~35-40% long-term
- Hide My Email (iOS 15, 2021): ~15-20% adoption among eligible users
- Mail Privacy Protection (iOS 15, 2021): ~90%+ adoption (enabled by default for many users)
Key difference: "Limit Precise Location" is opt-in and requires manual activation, suggesting slower adoption than default-enabled features.
Projected Adoption Curve for "Limit Precise Location"
If we use the historical adoption data, we can make some rough estimates about the timeline and adoption rate. As this is an opt-in feature, initially only a small proportion of users will enable it with adoption rate picking up as awareness increases over the coming 18-24 month period. However, this can change depending on how Apple markets the feature and if in the future the feature is enabled by default.
Impact on Mobile Network Data Based Products
The shift from street-level to neighborhood-level precision will affect mobility data products telcos have developed using mobile network data. The key use cases for these data products have been to serve transport planning, retail intelligence, and out of home media. These products depend on spatial precision that neighborhood-level data cannot provide.
Transport and Urban Planning Analytics
- With neighbourhood-level data, it becomes difficult to distinguish between nearby stations or parallel routes. In dense networks, 500m precision cannot separate stations 300-400m apart affecting Origin-Destination datasets
- Neighborhood-level updates with sparse temporal resolution may make transport mode classification (walk/cycle, metro, rail, road) unreliable
Retail and Real Estate Analytics
- Footfall to high-street retail, shopping centers, and urban retail districts cannot be analysed with neighbourhood level data
- Site Selection intelligence shifts from venue-specific footfall and catchment analysis to neighborhood characterisation
Out of Home Media
- The shift to neighborhood-level precision renders geofencing to become too coarse to distinguish areas of interest from neighbourhoods
- Visit attribution possibly loses the granularity required to measure campaign effectiveness
Unacast's Proven Approach to Privacy-Driven Data Transitions
Unacast has successfully navigated major privacy shifts before, developing innovative solutions that maintain data utility while maintaining compliance requirements. When Apple introduced App Tracking Transparency (ATT) in iOS 14.5, fundamentally changing how apps could access and share user data, Unacast pioneered approaches that are directly applicable to evolving privacy landscapes.
Privacy-Safe Aggregation Architecture
Rather than relying on traditional data sharing models that expose clients to regulatory risk, Unacast developed privacy-safe aggregation methodologies that process location data within supplier and app-owned environments. By performing all aggregation, modeling, and anonymisation before data ever leaves the supplier’s environment, we eliminate the regulatory concerns associated with sharing precise location data. This approach ensures that customers receive only privacy-compliant, aggregated insights in line with GDPR, CCPA, and other privacy regulations.
This architecture means customers can access high-quality GPS-derived insights. The data delivered has already passed through privacy-enhancing technologies and aggregation layers, providing the precision needed while maintaining full regulatory compliance.
First-Party Relationships for Data Quality Assurance
When third-party data sharing became restricted under ATT and similar privacy frameworks, Unacast shifted strategy to establish direct first-party relationships with app publishers and SDK integrators. This approach provides several critical advantages:
- Verified consent mechanisms: Direct visibility into how user permissions are obtained and documented
- Data quality guarantees: Ability to validate accuracy and authenticity at the source
- Transparent provenance: Complete chain of custody from collection to delivery
- Compliance alignment: Direct contractual obligations ensuring privacy standards are met
First-party sourcing enables us to guarantee both the privacy integrity and quality of location data, with direct verification of consent practices and signal accuracy that's impossible with layered 3rd party aggregators.
Mitigation Strategies Based On Unacast’s Experience
Multi-Source Data Fusion and Machine Learning Enhancement
Based on our experience from previous privacy features, a twofold approach involving machine learning models and multi-source data fusion techniques will be needed to maintain and in many cases improve the quality and utility of mobility insights.
We have seen that combining First-party GPS signals through a pre-aggregation architecture with complementary data sources (POI databases, transportation network data, demographic information, temporal patterns, transaction data and more), and using that to power ML models with engineered features (our model currently has around 50-60 features) can deliver accurate location insights even when individual signals are sparse or imprecise.
Machine learning algorithms trained on billions of historical location patterns have the potential to:
- Infer missing location updates through trajectory modeling
- Correct data inaccuracies using map-matching and contextual signals
- Distinguish between similar nearby locations through behavioral patterns
- Predict visit attribution when precision is degraded
Key insight: When a large proportion of iPhone users enable "Limit Precise Location" and cellular network precision degrades to neighborhood-level, machine learning models trained on GPS data can still deliver street-level and venue-level attribution for transport planning, retail analytics, and network optimization.
This capability is the result of processing 60-70 billion location signals daily and continuously refining models based on ground truth validation. For telcos, it means that GPS-augmented analytics can actually outperform what was previously possible with cellular location alone—higher precision and richer behavioral context.
Summary and Recommendations
Unacast's experience navigating ATT and other privacy shifts provides a clear roadmap for Telcos facing iOS Limit Precise Location:
- Don't wait for full adoption: Build alternative data capabilities now while impact is <10%, so you're prepared when it reaches 25-40%
- Data fusion: combine with complementary data sources (We have had extensive experience from GPS)
- Prioritize privacy-compliant architectures: work with 1st party data providers and create a pre-aggregation data architecture
- ML-enhanced analytics: precision can be maintained through intelligent data modeling (eg. we created 50 features in our ML model to power mobility insights), not just raw signal volume
The telecommunications operators that will thrive through this transition are those that recognize privacy features as an opportunity to upgrade their location intelligence capabilities, not simply a loss of precision to be endured.
Want to understand what this means for your data strategy? Book a meeting with our team to talk through impact and mitigation options.
