Planning and optimizing networks for profitability

Planning and optimizing networks for profitability

Phil Marshall/Tolaga Research

Traditionally network planning and optimization efforts have used approaches that target technical parameters, such as connection losses, ineffective connection attempts, average throughput and latency. While these parameters are important in ensuring overall network reliability, they do not optimize network resources to maximize return on invested capital (ROIC).   

Many operators have ROIC thresholds that are less than their weighted average cost of capital (WACC), which if not remedied will drive declining company valuations.

Planning and optimizing networks for profitability is complicated and requires the coordination and correlation of business intelligence from across an entire telecom enterprise. This might include profitability metrics from finance, customer experience from marketing, and network and service performance and planning information from technology and IT organizations. In some cases it also requires cooperation with other third-party ecosystem partners, such as cases the where OTT players own the customer relationships.   

Once the business intelligence needed for network planning and optimization has been collected, the data is analyzed using conventional techniques that are combined with other big data management and analytics capabilities, such as Hadoop and machine learning.   

Several notable activities that are being pursued by the industry include:  

  • Automated network planning and optimization solutions, such as self-optimizing networking (SON), which use advanced algorithms to ensure that networks are designed and optimized according to pre-determined operating criteria. Basic SON functionality for provisioning networks has already been widely adopted for femto cells, and companies like Amdocs/Actix, Cisco and AT&T are pioneering SON solutions aimed at optimizing operations in both small and macro-cellular environments.
  • Solutions that include financial information for individual users, user groups and service categories. This financial information is used when prioritizing network expansion and resource allocation initiatives. Companies like Amdocs, Ericsson and TEOCO are capitalizing on opportunities to combine their business support systems with network intelligence to develop network cost and profitability optimization solutions. Given that network resources are pooled and shared among large numbers of subscribers, network cost and profitability optimization solutions tend to be better suited to high-level design and operational initiatives, rather than those that are targeted toward individual subscribers or services.
  • Customer experience management (CEM) solutions that measure the experiences of individual customers, with the aim of allocating network resources to improve overall experience. These solutions, which are offered by companies like Huawei and Nokia, also target those customers who are likely to have a greater impact on overall market perception, using techniques such as net promoter scoring. We believe that other market research techniques such as factor, conjoint and sentiment analysis are well suited to complement existing CEM solutions by enabling operators to assess the potential outcome of alternative optimization scenarios.    

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