Monetising & Pricing Data by Network, Market & Segment
|出版商||Mobile Market Development Ltd||商品編碼||296685|
|出版日期||內容資訊||英文 40 Pages
|行動數據的收益化&費用資料:網路、市場、各市場區隔 Monetising & Pricing Data by Network, Market & Segment|
|出版日期: 2014年02月26日||內容資訊: 英文 40 Pages||
Today mobile data is at the core of mobile operators' strategies as their profitability is highly dependent on how they respond to the huge surge in demand for mobile broadband services.
MNOs need to deploy suitable pricing strategies for new data services if they want to succeed in reversing the downward trend in blended ARPU due to declining revenues in voice and traditional messaging services and the, to date, inability to match data demand volume growth with revenue growth.
The shift towards data-centric propositions poses significant monetisation challenges, as operators need to drive mass adoption of services whilst preventing price erosion and abuse of the service by a few users. The obverse of the challenges is the huge opportunity to use innovative data-centric propositions as the means to reverse years of revenue decline.
Operators need to develop tariff plans fit for the new data-centric environment and include pricing differentiation elements such as speed, service level, time, usage, device type, applications, personal customers, multi-line accounts, payment method and so on, as well as partnerships with OTT service providers to integrate their propositions within the core MNO offer.
In this report, we examine trends in mobile data usage and data spend across different markets and operators, identify key challenges faced in monetising mobile data services, analyse different pricing structures adopted by advanced operators for LTE services and assess their impact on KPIs. We also explore the link between different devices and data usage patterns and analyse the way customer segments are evolving in the new data-centric environment.
The report concludes with a set of recommendations that should be given urgent consideration by any mobile operator seeking to use data as a means to reverse overall revenue decline.