In a world where connectivity has become essential for the economy and development, telecom operators must anticipate increasing demands for mobile services. They need to ensure the quality of their mobile networks while optimizing their investments. The necessity to predict and understand future traffic, as well as to manage congestion phenomena, has become paramount. It is in this context that Sofrecom was called upon to assist a major operator in optimizing its network capacity investments.
Customer's need
The operator sought to improve its understanding of traffic dynamics on its mobile network. The stakes were multiple: anticipating future congestions, prioritizing capacity addition actions based on their economic profitability, and thus ensuring an optimal user experience. In the face of these challenges, the operator wanted to rely on advanced methods, particularly the use of machine learning algorithms, to optimize its investments. The goal was not only to detect upcoming congestions but also to propose preventive solutions to address them while maximizing return on investment. Its objective was to implement innovative solutions to analyze mobile network data, forecast traffic trends, and make appropriate capacity adjustments. In summary, it was about transforming raw data into informed strategic decisions.
Methodology set up by our experts
To address this need, Sofrecom deployed a rigorous methodology tailored to the client's specifics. The steps of our approach that were implemented include:
1. Exploratory Data Analysis
We began with an exploratory data analysis using a datalab with Spark Scala. This step allowed us to analyze historical data and understand historical traffic trends on the mobile network and identify factors influencing congestion.
2. Traffic forecasting
To anticipate traffic trends, we used a forecasting model with Python, known for its ability to handle time series. This method allowed us to forecast traffic variations with increased accuracy. By integrating seasonal factors and exceptional events, we were able to establish precise forecasts on traffic evolution. This approach enabled us to project future network capacity needs.
3. Congestion detection
In collaboration with the RAN (Radio Access Network) teams in each country, we established a congestion detection methodology. This collaboration was essential to understand local dynamics and adapt our recommendations accordingly.
4. Capacity addition proposal
Based on traffic forecasts and congestion analyses, we proposed capacity adjustments. These recommendations were not only preventive but also strategic, aimed at alleviating network congestion before problems arose.
5. Estimation of gains and net present value
To evaluate the impact of capacity adjustments, we used classical regression models. This allowed us to estimate the net traffic gain generated by capacity additions. Finally, the methodology included a rigorous calculation of the NPV of the proposed investments. This process involved several steps: calculating the revenue generated by traffic gain, projecting the annual margin on the business plan, and integrating operational costs (OpEx), capital expenditures (CapEx), and the weighted average cost of capital (WACC) to obtain a clear view of investment profitability.
Benefits of Sofrecom's support
Sofrecom's support enabled the operator to achieve significant advancements in managing its mobile network. Among the observed benefits, we can mention:
- Improved understanding of network traffic: through in-depth data analysis, the operator gained a better understanding of traffic behaviors, allowing it to anticipate congestions and act accordingly.
- Prioritization of investments: based on the net present value of investments, the client was able to prioritize its projects. This allowed for better budget control and more efficient resource allocation.
- Cost optimization: by identifying the most profitable capacity additions, the operator was able to optimize its costs while improving the quality of service offered to its customers.
- Anticipation of congestions: the ability to proactively detect future congestions allowed the operator to implement preventive solutions, thus ensuring a quality user experience.
- Strengthening competitiveness: by optimizing its investments and improving network management, the operator strengthened its market position, enabling it to better meet customer expectations.
Sofrecom's support enabled the operator to tackle complex challenges related to managing its mobile network while optimizing its investments and improving customer satisfaction. Through a rigorous methodology and sharp expertise, we helped our client optimize its mobile network investments while enhancing the quality of its services.
Sofrecom expertise implemented in this project
In the context of this mission, multiple expertise from our teams were deployed:
- Data Science and Machine Learning: use of advanced algorithms for data analysis and forecasting.
- Data Analysis: expertise in exploratory analysis and big data processing.
- Economic Modeling: skills in estimating net present value and profitability analysis.
- Project Management: rigorous methodological approach to ensure the success of missions.
