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Client story

Using AI in a Smart Capex solution to improve network planning and optimization

Telcom client
Region: Global
Industry: Telcom & Media

A Leading telecommunications company recognized the importance of leveraging data to improve their network planning and optimization.

Client Challenge: The client wanted guidance on how to optimize the timing and allocation of investments in their network infrastructure. Their previous strategies fell short of meeting business objectives, prompting the need for a partner with deep expertise in telecommunications, coupled with advanced capabilities in data science, big data, and AI implementations.
 
Solution: We supported the client by addressing challenges such as data quality issues, AI model implementation, and the geographical distribution of models, aiming to enhance network insights and decision-making.
Our AI-driven solution for mobile networks enables data-informed investment decisions by prioritizing sectors that most impact the customer experience (CXP). Dashboards provide clear correlations between customer contract value and traffic on network cells.



Benefits:

  • Mitigating Revenue Loss: aligning CapEx prioritization with revenue-generating hotspots to retain high-value customers.
  • Revenue and Traffic Insights: geographical and temporal distribution of revenue and downlink traffic utilization across sectors.
  • Vendor Migration Support: establishing a performance baseline for the current mobile network to facilitate smooth equipment migration.
  • Optimizing Network KPIs: identifying key network performance indicators (KPIs) most critical to customer experience (CXP) metrics.

Challenges:

  • Budgetary Challenges: Necessity to prioritize investments due to financial constraints.
  • Client Mobility Analysis: Mapping CXP, network, and financial metrics to address client mobility patterns.
  • 5G Metric Gaps: Lack of historical data on 5G networks and CXP.
  • 4G to 5G Transition: Complex and resource-intensive process of upgrading existing sites.
  • Cell Influence Framework: Defining a framework to quantify cell impact on potential financial loss.
  • Data Quality Issues: Challenges with data industrialization, implementation timelines, and integrity.
  • Post-COVID Variability: Shifts in cell latency patterns affecting load forecasting.