To get real customer visibility and to consistently deliver a higher quality experience, MNOs must have access to real-time location awareness.

Alexander Haväng, Chief Technical Officer, Procera Networks

January 12, 2015

4 Min Read
Location Awareness Key to Mobile Intelligence

The key to increasing customer retention is keeping customers happy. For customer care teams this means being able to solve your subscribers' problems by having real-time insights into their experience when they contact your call center, which requires visibility into the network perspective as well as the user perspective.

Solutions that combine real-time radio access network (RAN) 3rd Generation Partnership Project (3GPP) signal data with IP quality of experience (QoE) data to provide location-based intelligence will have the edge.

Most MNOs are able to measure only rudimentary key performance indicators (KPIs) based on the signaling between the devices and the handset such as measuring basic connectivity (i.e. the dropped call) and activity heartbeats (data going to the handset). Video stalls, slow page load times, social networking feeds not loading or slow sync times for cloud services -- the KPIs that really matter to subscribers -- are completely beyond the ability of most operators to diagnose or resolve today. To achieve real visibility and to consistently deliver a higher QoE, MNOs must have access to real-time location awareness.

But getting access to near real-time RAN data is a challenge. One traditional way is to integrate with network vendors and receive the data through an operations support system (OSS), which is often costly or the data is not received in real time. Plus most operators have multiple vendors and technologies (2G/3G/LTE) making up their networks, which makes capturing RAN data even more cumbersome. Another solution has been to install probes in the network, but this requires a very large footprint to collect the relevant data and to cover the complete multi-vendor/multi-technology network. A third option is to install an app on the phone's operating system -- less than ideal, because the subscriber has to be involved, making it impossible to reach most M2M devices and USB modems.

The key to ensuring a high QoE for mobile subscribers is to have real-time location awareness that can be placed in context with the RAN 3GPP signal quality and IP QoE metrics experienced by the subscriber. In fact, 3GPP location and signal strength information already reside on SIM-enabled devices and could easily be used to improve the subscriber experience or offer location-based services. Examples of clever location-based services that can be enabled using combined RAN and IP data include:

  • Mobile operators can use under-utilized residential LTE network cell towers to offer "home" residential mobile broadband services to monetize these investments. The subscriber gets a higher data volume and a lower cost when they are in their home cell tower (either by tethering or a mobile hotspot device) and they get access to potentially more bandwidth than a typical DSL offering.

  • Innovative location-based services such as location-based charging (e.g. everyone attending a sporting event at a certain stadium gets free data usage from the closest towers), happy hour programs or even "turbo button" offers based on current network states. What customer wouldn't appreciate a speed boost if network conditions permit for that location?

  • Real-time location updates -- Mobile operators can manage congestion and service plans by a customer's location, including activating premium services and fair usage policies. This way, optimization can be homed in to specific geographic locations, rather than optimizing all subscribers across the board.

  • Location awareness also offers Internet of Things providers fine-grained control over where and which SIM-enabled devices can transmit data. For example, a vending machine doesn't need the capability to drive down the street streaming Netflix, so the operator might limit that system to a specific cell tower and only a specific type of traffic.

  • The possibility for "closing the loop" or automatically resolving the subscriber's issue. For example, in the case of congestion, the issue can immediately be resolved in a system once it knows which part of the network is congested. In other cases, the issues can be resolved by feeding detailed data into the operator's existing self-optimized network (SON) solution.

MNOs exist in a highly competitive environment where churn is costly, network operations are complex and visibility into subscriber's location and experience can be the key to success. Real-time location-based network intelligence can be invaluable to MNOs across all their departments. Uses include network planning and capex for the C-level, the creation of new revenue generating services for marketing and to assist with customer care and call center applications.

Wouldn't it be nice to receive a proactive communication (SMS, email, etc.) from your mobile provider acknowledging a poor mobile experience and offering a form of compensation while they address the underlying technical issue? Something as simple as that could stop subscribers from churning if delivered at the right time. Of course, increasing customer retention and expanding profitability is likely the more important outcome for MNOs.

-- Alexander Haväng, CTO, Procera Networks

About the Author(s)

Alexander Haväng

Chief Technical Officer, Procera Networks

Mr. Haväng is the CTO of Procera Networks and is responsible for the company's strategic technology direction. Haväng is widely known and a respected authority in the open source community, and is the lead architect for Procera's industry-recognized, deep packet inspection-based network traffic and service management solution, PacketLogic™. Earlier in his career, Haväng was one of the chief architects for the open source streaming server software Icecast, along with the secure file transfer protocol GSTP. He spent the early part of his career at IDA systems, an IT solution provider for the Swedish government, along with a stint in the Swedish military. Haväng studied computer science at the Linköping University in Sweden.

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