JSB Market Research : Capex Optimization Strategies - Operator Best Practices for Making the Most of 3G Network Capex


'Capex Optimization Strategies: Operator Best Practices for Making the Most of 3G Network Capex,' a Telecom Insider Report by Pyramid Research, presents a selection of 17 techniques for managing 3G networks in a capex-efficient way.

These techniques have resulted from the application of the MASI, PYR's proprietary framework for analyzing and optimizing capex, to the capex profiles of a wide variety of operators.

In this report, we explain each of these capex-effective techniques for managing networks. Examples from the Middle East and Africa as well as from some developed-market players are also provided. Operators can already harness these best practices to control and reduce their 3G capex.

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Key Findings

 Our methodology for developing techniques of saving on capex produced a number of levers for controlling and optimizing capex dedicated to 3G networks.

Differentiated capacity margins and dynamic capacity allocations, for instance, drive forward efficiencies in the capacity delivery process, and as a result curb 3G capex.

Converged or heterogeneous architectures empowered by cloud-based services and dynamic pricing produce capex synergies, maximize the monetization of the available capacity and thus generate further savings.


“Capex Optimization Strategies: Operator Best Practices for Making the Most of 3G Network Capex,” a new Africa and Middle East Telecom Insider by Pyramid Research, starts by noting that operators today continuously need to manage their network capital expenditures in an efficient way, whether their investment priorities lie in broadband, innovation, improved coverage or QoS. To help them make the most of their network capex, Pyramid Research has developed the MASI framework. MASI provides a structure for the operator to systematically plan and optimize capex, a mission-critical aspect of any operator's daily life.

This Insider presents a selection of 17 techniques inspired by MASI for managing 3G networks in more capex-efficient ways. We discuss techniques related to capacity management, cloud-based services, converged and heterogeneous architectures as well as yield management and network sharing. We explain how to harness these best practices in order to control and reduce 3G capex and thus free up resources for innovation and building next-generation networks. These best practices are then illustrated using examples of implementation from the Middle East and Africa as well as from some developed-market players, such as Verizon, Orange, Zain, MTN, Etisalat, Korea Telecom, STC and Deutsche Telekom. The defined techniques are built on a common-sense, time-tested foundation of aiming to maximize profits through the more efficient use of available assets.

With an insightful application of these techniques, doing more with less can become an achievable goal, a reflex built into the operator's DNA, turning its modus operandi into a lasting competitive advantage.

Reasons To Buy

This Telecom insider helps executives optimize their 3G capital expenditures and thus free up resources for innovation and building the next generation networks in order to establish a long-lasting competitive-advantage.

The report offers a wealth of 3G capex optimization techniques related to the capacity planning and delivery process, network architectures and pricing strategies.

Each of the techniques and their benefits are detailed and include implementation examples by major MEA and global telcos and vendors, enabling readers to gain the insight they need.

The broad but detailed perspective will help operators manage their challenging capex constraints.

The report is designed for an executive-level audience, boasting presentation quality that allows it to be turned into presentable material immediately.

Table Of Contents


2.Capex optimization for 3G networks

 Optimizing the capacity planning and deployment process

 Optimizing the network architecture

 Leveraging network sharing

 Implementing dynamic pricing and dynamic capacity allocatio

 3Conclusion: Key findings and recommendations

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