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Today’s customers want competitive pricing, value for money and, above all, a high quality service. They won’t hesitate to switch providers if they don’t find what they’re looking for. So particularly in mature markets or where regulations and service dematerialisation makes ‘churn’ easier, it is absolutely crucial to put in place a sustainable and robust strategy for customer retention to preserve customer lifetime value. The telecoms market provides a good example of why the high acquisition costs and slim profit margins for each customer make churn analysis vital to help companies identify and retain the most profitable among them.
In this context, the paradigm change ‘more is more’ is in tune with the main aim of Big Data analytics. The uncovering of hidden value, through the intelligent filtering of low-density and high volumes of data, can become a real differentiating factor. The more data you have, and the more recent and accurate it is, the faster you can learn from it and the more predictive you can be.
The value of Big Data can then be exploited in two different directions: to decrease the capital expenditure (CAPEX) or operational expenditure (OPEX) associated with the computational infrastructure needed to address the huge amount of data used to feed predictive analytical models; and/or to increase the data sources used for the integration and leverage of new kinds of unstructured information, enabling companies to better describe and understand customer behaviour.
One method now emerging to enable an operator to move from reactive churn management to proactive customer retention is to use predictive churn modelling based on social analytics to identify potential ‘churners’, thereby enabling the operator to act on such predictions, rather than waiting for explicit trigger points (e.g. credit on prepaid card running down), by which time the churn is most probably inevitable, irrespective of any act or offer on the part of the operator. Big Data analytics offer the opportunity to process and correlate new data sources and types with traditional ones, to achieve better results more efficiently and receive insights that will set alarm bells ringing before any damage has been done, so giving companies the opportunity to take preventive measures.
Pricing analytics and ‘next best offer’ recommendations in particular are classic examples of how, by analysing structured data (such as CDRs) and unstructured or semistructured data types (such as log files, IVR tracked calls to call centres, clickstreams and, ultimately, text from e-mails), telecoms operators can provide more accurate, personalised offer recommendations.
Last but not least is the issue of timing. It is true that traditional business intelligence solutions have allowed enterprises to move forward by consolidating data sources into centralised data centres. However, this data is used ‘simply’ for reporting. We are now moving into a new era where information can and must be converted into realtime actionable insight, to enable the company to respond in real-time to behavioural changes in the customer mindset or to react quickly to threats on the competitive horizon. This is exactly why and where Big Data analytics can win the battle against ‘old’ BI tools.