How Telecom Companies Can Improve Their Results With Big Data
If they want it, telecom organizations know everything about their customers; where they were when, with whom they connect frequently, what their daily habits are etc, all thanks to the growing amount of call detail records, location data, social media data and network traffic data. The global telecom industry experiences a massive growth in data, thanks to the rise of the smartphones and tablets, the next generation mobile networks and the developed world that becomes connected to the mobile internet. Those telecom companies that are able to use these vast amounts of data efficiently will outperform their peers, grow their market share and improve their bottom line results.
Improving the Customer Experience
Telco’s are collecting vast amounts of data among others because of the obliged storage of data by the European government. As such they can 'relatively easy' generate a 360-degrees view of their customers using their own data (call data, geo data, internet usage data etc.) and public data from social networks. With a 360-degrees view of their customers, they can start to create highly customized experiences with targeted promotional offerings. These intelligent mass-personalized multi-channel marketing campaigns can target the customer at the right moment at the right location with the right message. The integration of customer intelligence, behavior segmentation and real-time promotion execution can increase sales, increase promotional effectiveness, reduce costs and increase market share.
When all the relevant data is centrally stored in one platform that is accessible by call center representatives, it will be possible for them to modify subscriber calling plans immediately, thereby driving customer satisfaction and improving customer profitability. New personalized products or services can be offered to customers based on real-time usage patterns, which will reduce costs for the customer and increase customer satisfaction.
Innovate and Build Smarter Networks
Network traffic is increasing to double digits due to better positioning and the rollout of 4G worldwide. Understanding how, when and where customers are using the networks can lead to better networks that automatically adapt to high demands on the network. Algorithms could be used to monitor and analyze network traffic data in real-time, thereby optimizing routing and quality of services while decreasing outings and increasing customer satisfaction. These analyses can also be used to optimize the average network quality, coverage and deployment over time.
Real-time data from tracking all connected devices on the network can be combined with public data sets about events that happen in real-time. If an event occurs that drives internet or cellphone usage, a telecom organization could know it in real-time and take preventive action if required. Moreover, sensors in the network, for example at antennas, can monitor the equipment and notify if an action or maintenance is necessary.
Additionally, big data tools can be used to easily identify problems, perform real-time troubleshooting and quickly fix network performance issues, which will improve network quality and lower operating costs. When sensors in the network suddenly notice a high rate of drop-calls, immediate action can be taken thereby decreasing downtime and optimizing the network.
Although real-time deep packet inspection can be used to optimize traffic routing and steer network quality of service even more, in many countries, among others The Netherlands, it is forbidden. It even caused a stir when consumers found out that telecom organizations where monitoring what applications or which websites they were visiting from their mobile.
To Decrease Churn and Reduce Risks
In order to decrease churn rates, Telco's can start to better understand who of their customers are influencers and what their (latent) needs are. Understanding who the influencers are within large social and/or online networks can provide valuable information. If one of these influencers switches, it could cause a domino effect. Combined with billing analysis, drop-call analysis and sentiment analysis of their customers it can give Telco's the possibility to bring down churn rates by knowing upfront what is going to happen. Predictive analytics can automatically warn when action is required to prevent a customer from going to the competitor by offering a tailor-made deal just in time. A great example is how T-Mobile USA cut down churn rate by 50% in one quarter with big data.
Big data tools can also be used to reduce losses from customer or dealer commission fraud. Calls from the same number from two different locations could indicate a cloned SIM card and indicate fraud. Preventive measures can be taken immediately and automatically if required. Furthermore, excessive use of data can be easily detected with an outlier analysis. For example when a customer turns a cellphone into an unauthorized wireless hotspot to connect multiple users. In addition, historical payment data or call data records can be used to detect and identify fraudulent behavior in real-time.
Telecom organizations generate vast amounts of data and all that customer data can also be used to deliver relevant and timely location-based promotional offers and other services to third parties. Telco’s can sell their data to third parties or local governments. The sales of such (anonymous) customer insights could be a welcome source of additional revenue. There are many benefits for Telco’s, so they should develop a big data strategy to reap the benefits of the vast possibilities of big data.
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