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The Telecommunications industry faces explosive data growth, rising customer expectations, and constant cost pressures. For one major provider, these challenges created critical pain points: inefficient capacity planning, fragmented data, delayed upgrades, network congestion, and growing customer dissatisfaction.

With 5G, IoT, and cloud-native services reshaping the landscape, traditional planning methods proved insufficient. This case study explores how the provider transformed network planning with AI-powered analytics, scalable data infrastructure, and predictive forecast—delivering measurable gains in performance, efficiency, and customer satisfaction.

The Challenge: Breaking Through Data Silos and Planning Inefficiencies

Network performance directly affects experience, competitiveness, and revenue. The client’s traditional, manual methodologies no longer met modern demands.

Capacity Planning Inefficiencies and Data Fragmentation:

• Key data (NPIs, KQIs, usage, GIS, external signals) was scattered across legacy systems.

• Spreadsheet-driven planning turned real-time needs into weeks-long cycles.

• Upgrades were reactive rather than preventive due to limited predictive capability.

• Tools lacked robust what-if modelling across multiple scenarios.

• Diverse regional patterns complicated coordinated planning.

Delayed Infrastructure Upgrades and Network Congestion:

• Planning cycles of 3–6 months lagged changing network conditions.

• Resource allocation relied on outdated assumptions without real-time visibility.

• Peaks, events, and emergencies triggered congestion and degraded QoS.

• 5G slicing and dynamic allocation exceeded legacy approaches.

• Balancing cost and service quality lacked sophisticated modelling.

Customer Dissatisfaction and Competitive Threats:

• Peak-period slowdowns and drops harmed experience, especially in dense areas.

• Churn rose as customers moved to more reliable networks.

• Premium services underperformed due to inconsistent QoS.

• Social media magnified reputation risks.

• Meeting SLAs and regulatory requirements grew harder.

The Solution: AI-Powered Network Intelligence

Incremental fixed were insufficient, thus, an intelligent, data-driven ecosystem for real-time analysis, predictive modelling, and automated decision-making was designed.

Scalable Data Processing Infrastructure:

• Real-time ingestion of telemetry from base stations, core elements, and devices.

• Unified NPIs/KQIs, usage, GIS, weather, and event data.

• Elastic cloud compute scaled cost-effectively with workload.

• A governed data lake managed structured/unstructured data with lineage.

AI-Powered Demand and Capacity Forecast:

• Multi-horizon neural models forecasted minutes-to-months.

• Hierarchical forecasts spanned cell, regional, and national levels.

• Seasonal/event-aware modelling improved spike predictions.

• Deep learning handled non-stationary traffic better than traditional methods.

• Ensembles (LSTM, CNN, attention) improved accuracy and robustness.

Cell Congestion Forecast and Predictive Analytics:

• Continuous monitoring identified cells nearing thresholds.

• Predictive models scored congestion likelihood using real-time and external factors.

• Geo-spatial analytics revealed location- and demographic-driven patterns.

• Dynamic thresholds and proactive alerts included recommended mitigations.

Proactive Network Planning and Optimisation:

• Automated scenario modelling evaluated hundreds of capacity options.

• Investment optimisation aligned with predicted demand and ROI.

• 5G slice resource allocation optimized across services and segments.

• Predictive maintenance aligned upgrades with lifecycle needs and costs.