Editor’s Note: Welcome to our weekly Reader Forum section. In an attempt to broaden our interaction with our readers we have created this forum for those with something meaningful to say to the wireless industry. We want to keep this as open as possible, but we maintain some editorial control to keep it free of commercials or attacks. Please send along submissions for this section to our editors at: [email protected].
If you’ve read Pt. 1, “Wi-Fi offloading key to weathering data storm,” then you’re undoubtedly aware that mobile network operators are facing an impending congestion storm due to the converged device market’s increasing demand for premium data services and bandwidth-hungry applications. Today’s users expect seamless network access and a consistent, quality experience across their devices and applications. Consequently, network operators are faced with the two-pronged challenge of first ensuring data coverage for their customers; and second, improving network data capacity to ensure delivery of data services at the speed customers need for quality service.
For operators who want to optimize network resources in a cost-effective manner, on-device analytics helps them understand, at the device level, the interactions between access technologies as well as the impact of heterogeneous network topologies on their offloading strategies.
Given the growing complexity of today’s data networks, analytics is an essential tool for operators that need the ability to provide more control than simple Wi-Fi offloading. On-device analytics provides the intelligent feedback that operators need to understand and evaluate their Wi-Fi offloading practices. Furthermore, analytics gives operators the ability to see, measure, and understand usage patterns so that they can customize policies that reduce churn, improve network traffic flow and optimize user experience.
Analytics at the device level provides operators with a unified view of the network and device, which reflects the actual end user experience in a quantitative way. Operators gain visibility across all access technologies, including 3G, WiMAX, LTE and Wi-Fi. This visibility allows operators to measure and understand usage patterns about access technologies, time-of-day, device, user group and Wi-Fi specific usage metrics.
With the on-device client, operators can measure their subscribers’ Wi-Fi connection success rate and compare Wi-Fi service providers versus home or office Wi-Fi usage. By ensuring that service level agreements with Wi-Fi partners are being maintained, operators have the ability to improve the quality of Wi-Fi service.
Operators need to optimize policies and measure effectiveness to alleviate network congestion and improve user satisfaction. A device that uses an intelligent client with on-board analytics provides information about service-impacting issues. Consider the scenario where subscribers’ devices consistently report decreased throughput in a particular region at two different peak periods during the weekday. Based on the usage trends, an operator can publish a new offload policy targeted to offload traffic from those congested cell sites to preferred Wi-Fi hotspots during the peak time periods. Further analysis of the data also helps operators measure the effectiveness of the new policy and whether the Wi-Fi offloading in that area is sufficient to meet the service level expectations of the subscribers.
On-device analytics creates an opportunity for operators to target subscriber segments based on device, application or usage information for services, and expand the capabilities of Wi-Fi offload cases. Operators realize little to no revenue being generated by subscriber consumption of over-the-top content and services such as YouTube and Netflix, yet it can have considerable impact on network traffic. In this case, a policy might intelligently match OTT applications to a Wi-Fi network that would provide the optimal performance based on analytics.
The analytics client must collect non-personally identifiable information on the device for intermittent upload to a server based on policy, as opposed to “real time” analytics, which would further exacerbate the network congestion problem. The data collected is anonymous; it does not contain any individual user identification, so it does not violate privacy policies. Operators can subsequently mine the data to help build decision models for improving the subscriber experience or for shaping traffic policy.
So, why do operators need it? On-device analytics enables operators to build flexible and intelligent network and device policies. While the challenge of consolidating and collating potentially massive amounts of device usage data may seem daunting, the benefits are clear. Establishing baseline metrics is a crucial first step to developing successful offload strategies, which need to be flexible enough that they can be continuously refined to meet the evolving demands of data consumers and their devices.
Today’s customers demand a high quality experience, whether it’s using voice, video or data. It is no longer enough to simply offer bandwidth and connectivity. With on-device analytics, operators have access to important information that provide network and subscriber intelligence. This intelligence then enables the operator to shape and manage bandwidth to deliver the required quality of service, thus optimizing network efficiency so that the transition across the different access technologies is a smooth and seamless experience for the subscriber.
