Data is bread and butter for the telecom industry. An industry that generates huge volume of data has arguably had a difficult and limited success with analytics. The performance and data processing requirements for telcos is huge – handling millions of calls per second, billion of events per day and ability to manage millions of devices at a time – and to ensure high standards of service & network quality – with user expectations of zero downtime. Managing these infrastructure and customer expectations is a gargantuan task – and pose unique advance analytics and machine learning challenges.
The success of analytics in telecom hinges on the basic challenge of keeping customers happy. Analytics and insights have a key role in keeping customers satisfied through two key KPIs:
a. Increase Average revenue per customer (ARPU)
b. Improve customer satisfaction and reduce Churn
Any analytics strategy for telcos should ultimately lead to achieving the above two KPIs. And some of the solutions to achieve these are:
1. Customer Churn Analysis: Churn is in indicator of customer satisfaction, continued patronage and the growth prospects. Real-time data is most useful in churn prediction, as it is based on the most recent customer data rather than end of month churn reports. Deploying a churn prediction model based on real-time data, with right offers would allow retention to be faster and more effective.
2. Customer Satisfaction Scorecard: Customer satisfaction is not a static metric. It changes with each interaction a subscriber has with the telecom network. The customer satisfaction should therefore be generated in real-time – allowing telcos to identify subscribers whose satisfaction score is consistently going doing, and those below the acceptable threshold – to make the right intervention and reduce the risk of churn
3. Call Centre optimization: For customer care team to be successful, the support team should have access to subscriber data, as well as all data and resources required to address subscriber queries and complaints. Machine Learning based algorithms can be used to provide real-time feedback on an ongoing interaction, and generate recommendations and answers for the customer care agent to resolve issues faster and more effectively
4. Competitor Analysis: Telcos should not look at subscriber acquisition, retention and customer experience in isolation. Competitor activities, subscription plans and marketing activities need to be monitored, and strategy to counter competitors should be dynamic. Machine Learning based algorithms allow processing of unstructured data – campaign monitoring, subscription plan offers etc. to develop an effective competitor monitoring and insights strategy
5. Social Media Insights: Subscribers now leave a lot of footprints and crumbs of insightful data on social platforms – while interaction with friends or common interest based communities. Social Media should not be looked with a myopic view of managing brand reputation by responding to tagged subscriber complaints. The social platforms have a lot to offer – and telcos should leverage these platforms as a source of rich and valuable data
6. Personalized Targeting: A lot has been said about personalization. While being intuitive, it is not an easy task to achieve. Telcos need to develop a recommendation system by combining subscriber and subscription plan data, along with behavioral and demographic info to identify subscriber needs – and then reach out to them with targeted and personalized communication to show value, care and ability to serve them with a personal touch
None of these should come as a surprise. A customer is relaying her life story 24*7. Just imagine the richness of data and the endless possibilities it brings. All that remains to be done by telcos is to make use of such incredibly rich data.
Customer Analytics is the key to higher ARPU and reduced churn
Customer data and insights generated through these data are of no use if the customer is dis-satisfied or had already unsubscribed. Timing is the key, and this can only be achieved if you have proper data infrastructure and analytical solutions to generate insights at a quick pace. Telcos should not wait for the month end reports to act on customers – with advanced analytics and machine learning based real time decision systems – telcos can leverage the full potential of their data
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Xtage Labs is an advanced analytics and machine learning based decision insights company. We work with businesses to derive insights from data, and improve the decision making processes.
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