Data pricing models have consistently featured on operators' shopping lists – particularly given their significant investments in mobile broadband spectrum and networks, which account for an increasing proportion of non-voice revenue, and the critical non-linear relationship between data usage and revenue. Specifically, innovative pricing models are becoming a clear focus in emerging markets as they go through the data adoption cycle, which typically occurs in three broad phases (see Figure 1).
Figure 1: Illustrative initiatives for driving the adoption of mobile data services, by market evolution phase [Source: Analysys Mason, 2012] Click to enlarge
Notes: Session data includes subscriber location, time of day, source of traffic (third-party versus internal), type of traffic (protocol, service type), active friends (IM). Subscriber data includes balance, usage patter, preferences, usage/spend limits, data plan/terms; network data includes current capacity and congestion levels.
Phase 1: Network investment
This phase typically lasts for about two years, is network coverage driven, and focuses strongly on network-based policy control and resource management. Data services are targeted towards the early adopters, have premium rate charges that are typically based on volume, and include a fair usage policy. Initiatives generally focus on optimising network coverage for serving small-screen demand, customer segmentation, streamlining back-end and customer support operations, and the opportunistic bundling of devices.
Phase 2: Market expansion
This phase lasts until the network reaches optimal utilisation, as the operator focuses on driving that utilisation, increasing traffic and encouraging service innovation leveraged by subscriber-level data. Price model innovation is critical in this phase for driving the adoption of data services, during which important pricing decisions focus on:
- differentiating and the point of convergence between 2.5G and 3G pricing
- service bundles versus a price play on pure data access
- reverse bundling versus operator bundling (and handset customisation)
- data device portfolio optimisation (small and large screen devices)
- validity versus rate trade-offs for data plans for various segments.
The pricing models include time-based plans daily plans, which are sometimes less expensive than monthly plans in order to target casual prepaid users. The strategy in this phase depends on whether the operator is a market leader or new entrant. Market leaders sometimes avoid price competition on data access and focus on the breadth of their offer, through bundles of SMS, voice services, data access, content and devices.
Phase 3: Revenue/traffic optimisation
The revenue/traffic optimisation phase is initiated after the network capacity utilisation begins to increase, and operators are required to invest in pure 3G capacity base stations, and focus is on optimising the data device portfolio (that is, USB modems, smartphones, feature phones and tablets). The price plans are restructured to ensure that the share of large-screen devices is reduced in comparison with that of small-screen devices to ensure better monetisation of network capacity. Also, the use of dynamic session data, in addition to network and subscriber usage parameters helps in the upselling and cross-selling of services through personalised plans, variable charging and loyalty programmes.
Understanding the data evolution cycle is key to driving service adoption
This trend and adoption cycle has been visible in markets such as Brazil, Indonesia and Malaysia, and operators that have made the right pricing decisions (in addition to addressing issues around device affordability, among others) in the market expansion phase have been able to convert a higher proportion of their user bases to 3G services. In markets such as India, where operators have recently rolled out 3G networks, the technology can be clearly seen to be going through such an evolution cycle. These markets are in the network investment phase now, in which operators have a strong focus on resource control and monetising premium customers. However, over time, these markets will also move into the market expansion phase, and an increasing emphasis on pricing models to drive mass-market adoption.
The key to driving data service adoption, yield and revenue is to understand the data evolution cycle and the important market variables, and to develop a clear roadmap for mobile data pricing. Mobile data pricing continues to be much more complex than voice pricing, and depends on a greater number of variables, but the potential upside of effective pricing in terms of adoption and yield is significant.