One of the highest priorities for telecoms is to retain its customers; therefore, it is critical for communication service providers (CSPs) to monitor customer experience and churn around people who exhibit patterns that indicate they might leave the network. Customer retention costs are only 50% of the cost required to acquire a new customer, which means that CSPs can retain their existing subscribers at half the cost of acquiring a new subscriber.
CSPs are effectively using big data analytics to bring together various data points including – historical records, quality of service, network performance, billing information, call-centers call details and social media sentiment analysis to predict and prevent churn. Churn prediction models through big data allow telco’s to launch retention campaigns that identify and then address “at risk” customers via outbound channels.
Communications service providers can generate more revenue and create better customer experiences by tracking and analyzing customer clickstreams to understand their preferences and propensity to buy. They can optimize their website and social streams to search for specifying keywords or search terms and if clickstreams show a customer searching specific network plan, CSPs can promote targeted plans to that customer and introduce new tariff schemes for up-selling.
Big data analytics can be used to create tailor-made marketing campaigns that target customers by using click-stream data, location-based insight, and social networking data. Telecom service providers can better understand customer preferences and the likelihood of purchase, by tracking and analyzing customer click-stream data. They can then initiate targeted promotions or offers to that individual customer or target group. This can also help increase conversion and cross-sell opportunities.
Telecom product managers can glean valuable insights by analyzing the rich data generated from their customer’s mobile devices. This enables them to proactively present the right offer at the right time, in the right context to the right customer to improve conversion rates. Examples include personalized data top-up plans or up-sell recommendations based on data usage.
The big data ecosystem can be used to store huge volumes of historical data over a time span and correlate that with customer likes and dislikes through advanced analytics.