Developing a Framework for Future Mobile Data Pricing

Abstract

Purpose: The revenues for mobile data transmission overtook the revenue of voice calls for the first time in 2014 in the USA. It can be observed that demand for mobile data -largely driven by video and cloud -is increasing exponentially, while overall data revenue is rising only moderately. This will lead to insufficient revenues stream to increase investments into mobile networks and ensure quality service. Consequently, hereof network performance will deteriorate sharply. At the heart of the problem is the current global pricing regime of fixed multiple MB/GB bundles, irrespective of time of the day, intensity of usage (e.g. video vs. email) and underlying economic value of the data. A new framework is proposed as to optimize and align network capacity and implicit data value/utility, which is crucial to ensure customer satisfaction and access justice.Methodology/Approach: The fundamental differences in pricing voice and data in voice and/or data centric networks are analysed in detail. Information has been synthesized as to develop insights into the impact of different devises and type of digital traffic for the overall performance of mobile networks. Based hereupon, a new framework for mobile data has been proposed to address the increasing misalignment between network capacity, usage and underlying data value/utility. Initial solutions have been proposed and discussed.Findings: While voice calls are easily quantifiable and are largely predictable in its occurrence and network load implications, mobile data traffic shows very large variations depending on type of traffic. While social media messaging by many customers consumes very little capacity, consumption of video streaming by relatively few customers can lead already to network saturation.Research Limitation/implication: Carriers set prices for a fixed amount of data -irrespective of intensity and time of data traffic -which leads to sharp spiky type of traffic patterns essentially signalling sharp overuse during busy hours coexist with large period of underused times.

‘Data must fall’: mobile data pricing, regulatory paralysis and citizen action in South Africa

“…In this regard, different pricing models seem to inform different data markets (Allen, 2016;Paetsch et al, 2017). In the early stages of data pricing development, MNOs adopted a fairly straightforward peak/off-peak pricing model especially for metropolitan-based networks who had limited reach and subscribers (Paetsch et al, 2017). However, this proved both expensive for consumers and networks alike, and hence unsustainable (Allen, 2016;Paetsch et al, 2017;Yaiparoj et al, 2008).…”

“…In the early stages of data pricing development, MNOs adopted a fairly straightforward peak/off-peak pricing model especially for metropolitan-based networks who had limited reach and subscribers (Paetsch et al, 2017). However, this proved both expensive for consumers and networks alike, and hence unsustainable (Allen, 2016;Paetsch et al, 2017;Yaiparoj et al, 2008). Different other models have been explored in different markets, including 'convenience-based data pricing,' where consumers are charged for the quality of data services that they receive; usage-based pricing, where pricing is based on specific preferred usage linked to user needs; and customer longevity-based pricing which rewards longstanding and loyal customers with lower rates and discounts (Allen, 2016).…”

“…These have often been used concurrently in different markets, with varied results. However, there is widespread acknowledgement that each country should devise a data pricing model that suits the needs of its citizens and the economic circumstances obtaining in that country (Allen, 2016;Jong, 2019;Paetsch et al, 2017).…”

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