Modern buildings are expected to deliver seamless mobile connectivity as a core utility - just like electricity, water, and HVAC. Yet many commercial and mixed-use properties still struggle with in-building coverage and capacity issues that frustrate tenants, reduce productivity, and impact property value.
This case study explores how the use of crowdsourced data from WePlan, incorporating real end-user experience metrics and advanced calculated insights - including Z-axis (floor-level) intelligence - enables building owners, mobile network operators, and DAS/in-building solution providers to identify and resolve coverage and capacity challenges with precision.
By leveraging real-world user experience data, stakeholders can move from reactive troubleshooting to proactive, data-driven in-building connectivity optimization—driving measurable value across the ecosystem.
The Challenge: In-Building Connectivity Blind Spots
A commercial building in a major metropolitan area was experiencing persistent complaints from tenants about poor mobile performance. Issues included:
- Dropped calls
- Slow data speeds during peak business hours
- Inconsistent coverage in conference rooms and shared spaces
- Poor signal strength in some levels
The building had previously relied on operator-provided outdoor coverage maps and occasional RF walk tests. However, these methods lacked:
- Continuous, real-world performance data
- Visibility into actual end-user experience
- Granular floor-by-floor (Z-axis) insights
Operators, in turn, had limited visibility into how their network was performing inside the building environment.
DAS providers were running blind, not having a performance indicator for that particular building.
The result: uncertainty, delayed investment decisions, and misaligned expectations between stakeholders.
The WePlan Approach: Crowdsourced, Experience-Driven Intelligence
How WePlan’s crowdsourced data platform helps to identify issues and deploy solutions.
WePlan aggregates anonymized, real-world mobile network data from end users. This includes:
Real End-User Experience Metrics
- Signal strength and quality (RSRP/RSRQ)
- Throughput (download/upload speeds)
- Latency
- Packet loss
- Technology distribution (4G/5G)
- Call performance indicators
These metrics reflect actual user experience - not theoretical coverage predictions.
Calculated and Modeled Metrics
In addition to raw data, WePlan applies advanced analytics to generate:
- Congestion heatmaps
- Z-axis (floor-level) differentiation
The Z-axis dimension is important for some use cases. Rather than treating a building as a flat 2D footprint, WePlan’s intelligence differentiates performance by floor level - revealing vertical performance variations often invisible in traditional tools.
Insight Discovery: What the Data Revealed
After analyzing several months of crowdsourced data, clear patterns emerged.
Floor-Level Coverage Gaps
Z-axis analysis showed:
- Some floors had significantly weaker signal strength due to macro network
- Mid-level floors experienced inconsistent performance depending on orientation within the building.
Capacity Constraints During Peak Hours
Even in areas with acceptable signal strength, throughput dropped sharply between 11:30 AM and 2:00 PM and again between 4:30 PM and 6:00 PM.
Operator Variability
Different mobile operators exhibited different performance profiles across floors, highlighting opportunities for:
- Multi-operator neutral-host solutions.
- Targeted small cell deployments.
- DAS optimization strategies.
These insights transformed the conversation from anecdotal complaints to data-backed investment decisions.
Impact for DAS and In-Building Solution Providers: Smarter Sales and Better ROI
For DAS and in-building system integrators, WePlan’s data significantly enhanced the sales and deployment lifecycle.
Accelerated Sales Cycle
Rather than beginning with costly exploratory RF surveys, providers entered conversations with:
- Verified user-experience heatmaps.
- Floor-level performance insights.
- Congestion and capacity indicators.
This shortened time-to-proposal and improved credibility with property owners.
Precision Deployment Design
Z-axis data enabled:
- Targeted antenna placement by floor.
- Prioritized zones for capacity relief.
- Optimized equipment sizing based on measured demand.
Design decisions were grounded in actual usage patterns—not generalized assumptions.
Measurable ROI Validation
Post-deployment, continued crowdsourced monitoring allowed providers to:
- Demonstrate improvements in signal strength.
- Validate return on investment for stakeholders.
This closed the loop between design, deployment, and performance validation - strengthening long-term customer relationships.
Impact for Mobile Network Operators: Protecting Brand and Retention
For operators, poor in-building performance translates directly to churn risk.
WePlan’s insights allowed operators to:
Identify High-Impact Buildings
Crowdsourced analytics flagged the building as a performance outlier compared to nearby structures. This allowed operators to prioritize remediation based on real customer impact—not just RF models.
Optimize Network Strategy
Instead of overbuilding outdoor macro capacity, operators could:
- Deploy targeted indoor solutions.
- Adjust antenna tilt or sector configuration.
- Strategically integrate small cells or DAS nodes.
Improve Customer Experience
With in-building solutions deployed:
- Call drops decreased.
- Data throughput stabilized during peak hours.
- QoE scores improved measurably.
The result: improved NPS, reduced churn risk, and stronger enterprise customer relationships.
Implementation Outcome
Following data analysis:
- The DAS provider reached an agreement with the building owner and a neutral-host solution was deployed.
- High-rise floors received capacity-optimized antenna distribution.
- Operator integration was coordinated using shared performance benchmarks.
Within three months post-deployment:
- Call drop rates significantly decreased.
- QoE scores improved across all major operators.
- Tenant complaints reduced dramatically.
Continuous crowdsourced monitoring ensures ongoing optimization and supports future expansion planning.
Conclusion: A Data-Driven Future for In-Building Connectivity
This case illustrates a fundamental shift in how in-building connectivity challenges are identified and solved.
By leveraging WePlan’s crowdsourced data - including real end-user experience metrics, calculated performance indicators, and Z-axis floor-level intelligence - stakeholders gain unprecedented visibility into actual in-building network performance.
The result is a virtuous ecosystem:
- Building owners increase asset value and tenant satisfaction.
- Operators protect brand reputation and reduce churn.
- DAS and in-building providers accelerate sales cycles and deploy with precision.
- End users receive the seamless mobile experience they expect.
In a world where connectivity is mission-critical, data-driven insight is no longer optional—it is the foundation of smarter investment, optimized deployment, and measurable ROI. Discover how we turn data into actionable insights — Explore our solutions here.
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