Telecom companies are sitting on a gold mine, as they have plenty of data. But what they require is a proper digging and analysis of both structured and unstructured data to get deeper insights into customer behavior, their service usage patterns, preferences, and interests real-time. Here is where Big Data comes in.  Few use cases are listed below.

Network Analysis

Telecom operators have historically focused on managing the network with little visibility to the impact it has on the customer’s experience. The operator was forced to work with snapshots of network data in fragmented views or at a summary level in order to plan network capacity or provide information to customer care and marketing about customer transactions.

Analysis : With the network analytics solution from Big-Data, the service provider gets the measurements and metrics necessary to successfully manage their entire network end to end, optimize network spend and proactively address service issues and identify monetization opportunities. Benefits include:Identifying and resolving network bottlenecks in minutes Proactively managing customer experience and churn Managing and planning for capacity requirements to maintain and improve the quality of service Optimizing network investment to maximize impact for most lucrative customer segments

Location based services

The proliferation of smartphones presents new opportunities and challenges: consumers want the best deals for all purchases based on their real-time location while requiring the services provider to honor their privacy preferences and provide only relevant offers when requested/opted-in.

Analysis : Big Data enables service providers to analyze real-time location data over time for opt-in subscribers to understand subscribe lifestyle. Combining lifestyle and mobile profiles with subscriber usage and digital behavior allows service provider to create targeted offers for opt-in subscribers. This drives much higher response rates for marketing offers, resulting in higher revenue.

Benefits include:

Driving real-time contextual targeted marketing offers resulting in higher acceptance rate and revenue, increased customer loyalty and satisfaction and reduced cost for developing campaigns

Creating a foundation with location data to build out cross-industry solutions such as eHealth, mobile payments and ticketing, Smarter Cities (traffic management, disaster/emergency response) and vehicle telematics

Contact Center Text Mining

In many organizations the contact center channel data is analyzed typically from a SLA (Service Level Agreement perspective). For example TAT (Turnaround time), Average wait time etc. But the actual transcript of the conversation can yield powerful insights regarding telecom infrastructure usage.

Telecom providers are competing with each other to get greater ARPU (Average revenue per user) from data services as opposed to voice services. In this competitive environment a telecom provider launched a new but extremely viral gaming application on Mobile devices. A few days after its launch, it started observing a burst of calls to the call centers and on text mining the transcript Data Scientists found a sharp spike in the keywords alluding to performance. The specific intelligence regarding keyword burst and specific time of day at which this was encountered was shared with the infrastructure planning group which then put a plan in place to throttle the bandwidth dynamically based on usage.