Proactive customer care is the biggest single driver of big data analytics uptake among telcos, according to a study published by Guavus on Tuesday.
A global survey of 81 respondents working at 65 network operators carried out by Heavy Reading on behalf of the big data applications maker showed that 87% of telcos have now completed or are implementing a big data strategy.
In terms of big data use cases, 57% cited proactive customer care, followed by revenue assurance (48%), targeted offers (47%), and service assurance (44%).
"It’s no surprise that proactive customer care is the top area for investment in big data. This is where we see the greatest interest from service providers that we work with," said Anukool Lakhina, founder and CEO of Guavus, in a statement.
Operators want to establish an "end-to-end view of their subscribers’ experience so they can quickly intervene to remedy any service degradations, prevent churn and raise customer satisfaction for increased loyalty and ultimately to grow revenue," he said.
When it comes to implementing a big data strategy, Guavus’ survey revealed that telcos are focusing their initial efforts on combining disparate silos of information spread throughout the organisation.
Combining online customer data, such as browsing habits, with billing history was listed as the primary focus area for respondents. This was followed by combining customer data with network operations data to establish the relationship between outages and subscriber behaviour.
"As big data strategies mature, the focus is shifting from simply trying to collect and analyse large data sets, to being able to derive actionable, operational intelligence," said Lakhina. "The findings show that operators have realised that the ability to fuse data streams and bridge the gap between business and operational data is essential to achieving this goal."
When it comes to the biggest challenges around implementing a big data strategy, 28% of respondents cited the inability to connect data silos. This was followed by poor data quality and management (25%) and finding people with the right skill set to handle big data projects (22%).
