When Verizon Wireless, followed closely by AT&T Wireless, launched data sharing last year, it changed the landscape for wireless plans and billing schemes. Almost immediately, Sprint began to attack shared data plans with fear and doubt regarding the pitfalls of sharing freely in a group. After all, its adverts suggested, won’t the kids devour all of Mom and Dad’s data anyway? Despite the fact that Sprint relented and rolled out its own shared data plans, it raised important questions regarding whether sharing really is good for consumers and operators. As it turns out, sharing is good, but it also asks billing environments to do complex things they were never designed to do and may never be able to support fully.
The sharing model embraces two key ideas; individual customers use multiple devices and they belong to groups, like families and companies, which share resources. Shared plans are good for consumers because they represent a real response to consumer demand. People don’t want to have separate wireless contracts for every device they use.
Verizon Wireless has also proven that sharing is good for mobile operators. The company reported a year over year revenue increase of 9.5 percent in Q4 2012 and cited its Share Everything plans multiple times as a factor (it even began reporting ARPA – average revenue per account – rather than ARPU – average revenue per user – in order to account for the new plan structure). Globally, operators are taking note and preparing to launch their own sharing options. But what’s taking so long? The fact is, sharing data services across multiple users and devices is actually quite complex, both technically and from a business perspective.
To avoid the ‘fear and doubt of shared consumption’ illustrated in Sprint’s anti-sharing campaign, there are considerations when rolling out any sharing scheme. Within a group, someone should have access to individual and group consumption reports and controls. There needs to be a means to see and allocate resources appropriately among users in order to prevent service abuse by any one member. Ideally, members of the group should be alerted when the group’s shared balance is running low. Individual members also should be alerted as they approach their individual limits. With overage charges in play, and a lack of usage alerts and controls, shared data’s upside can quickly fade away. To support this requires implementing real-time rating, charging and policy.
These types of business issues add complexity to what is already a complex technical challenge. First, policy and charging must work together to enable proper sharing; this kind of coordination has been problematic for operators who maintain separate charging and policy applications that don’t communicate with each other.
Second, it’s not easy to determine what the right amount and types of user control might be, much less to expose such control to users effectively. This goes beyond allocating data among family members, especially in business pooling scenarios, and wades into issues regarding how individual members of a group are treated once they’ve exceeded their limits – whether they are cut off, charged overage, or throttled back to a reduced data rate.
Third, charging and billing systems were not designed to handle the technical complexities of balance sharing. Specifically, real-time charging systems were designed to work like parking lots having one meter (balance) for each car (device). Data sharing plans require a fundamentally different architecture beyond these simple one-to-one relationships. They need to support sharing in scenarios where a single user has multiple devices, where a single account has multiple users who might also have multiple devices, and large enterprise sharing scenarios across hierarchies with thousands of employees. This critical change in system architecture can’t be fixed by adding features or code. Systems or solutions that were architected this way won’t be able to adapt.
While data sharing seems like a simple concept, it is often the case that what appears simple from the outside is actually quite complex under the covers. The fundamental complexity involved in data sharing exposes real weaknesses in legacy processes and systems. These systems were designed for very different business models; i.e. those that never accounted for multiple users or devices accessing a shared pool of services. Even when these systems have been modified to bill for these new service models, the time, cost and effort involved in modifying them significantly limits time to market for new services or modifications to the new plans.
As the industry adjusts to the multi-device reality and responds to consumer demand for shared service models, legacy systems and processes will need to give way to new technologies that enable operators to move quickly in order to compete with future service innovations that move the market as shared data has done.