More than a quarter (27%) of communications service providers (CSPs) have no strategy for ‘big data analytics’ in 2013, and the vast amount of information that CSPs hold about their subscribers has been largely untapped, according to new research.
The term ‘big data analytics’ is associated with capturing and analyzing consumer behavior using the Web, and can have huge benefits in terms of both revenue generation and customer loyalty.
Analysys Mason’s recently published report, titled Big data analytics: how to generate revenue and customer loyalty using real-time network data, finds that the volume of data on telecoms networks has increased a thousand fold in the last 20 years, and more data has been created in the last 2 years than the preceding 50.
“The data that CSPs are creating has four key attributes,” explained Patrick Kelly, lead author of the report and Research Director for Analysys Mason’s Telecoms Software research division. “The data has volume (there is lots of it), variety (from call logs to M2M sensor data, it is extremely varied), velocity (it can be gathered in real time) and value (if structured and analyzed correctly, it can be extremely valuable and profitable).”
However, the report also cautions that CSPs should strive to understand the outcomes for specific areas of their businesses before investing in big data and analytics. The report estimates that CSPs can increase their net profit margins by 12% with the right cross-marketing and sales promotions, and customer retention can be increased by 0.2% with effective loyalty programmes.
“Moreover, CSPs can use these insights to defer capital investments in the radio access network without degrading the service, saving hundreds of millions in capital spending,” added Kelly.
Importantly, the report indicates that only a fraction of the data that traverses telecoms networks needs to be captured for analysis. Three types of data are important for CSPs: customer data (usage, location, device, etc.), market intelligence (dimension, demographics, segmentation, etc.) and real-time network data (service quality, call centre efficiency, revenue optimization, etc.).