Cloud Computing

Using Big Data to Drive Customer Satisfaction

Pictured is a collage of graphs depicting the concept of Big Data.
A pearl of wisdom must be in the great sea of data.

Telcos have access to a gold mine of customer data. Whenever a customer interacts with the operator’s website, whenever they use any self-service options, or even when they call in to talk to a live operator – there is data that is generated that holds a lot of value for the telecommunications provider.

Telcos operate sophisticated networks. Whether Broadband, VoIP, Video, Wireless or others, these networks generate operational and consumer data any time they are powered up. This data has immense potential to inform the telecommunications operator of issues past and present.

The insights that can be gleaned from the deliberate analysis of data can be used to predict and prevent issues in the future, generate more revenue and increase customer satisfaction.

Customer satisfaction is a key predictor of a business’s success. Telecommunications providers must use any and all avenues available to maximize customer satisfaction. Analysis of customer behavior and correlation of network data with customer events can provide insights that can be used to improve customer satisfaction.

We explore two areas of data analytics that can help raise customer satisfaction.

Quality of Service (QoS)

Whether providing voice, video or data – Quality of Service defines customer satisfaction. However, Telcos don’t always collect data about the Quality of Service, or analyze it.

And it’s rare to connect the data and the analysis of historical data (including the detection of patterns) back into actions that can be taken to improve QoS. The network data that is generated by equipment from the core to the last mile and into the home can be correlated to determine QoS levels and improve them if needed.

As a commonly seen example, network devices generate data about utilization levels of ports on switches and routers – providing a unique view into the ebbs and flows of data through these ports. Analyzing the traffic patterns and utilization data allows the telecom operator to predict problem areas in advance, including times of day. Correlation with external events such as weather data allows for better insights into whether traffic peaks may be related to external events.

The customer service interaction

An oft-quoted issue for telecommunications providers (and Cable TV operators in particular) is the negative perception of customer service by unhappy customers. Customers call into a call-center where overworked and harried customer service agents are ill-equipped to be able to address the customer’s needs.

Customer care is a particularly appropriate area for the application of analytics. Analysis of historical consumer data (packages subscribed to, billing history, past issues, etc.) combined with knowledge of current events is the key to getting a complete view of the customer and being able to address the immediate issue.

Analysis of customer care call records yields data regarding the highest incidence of terms used in a call. Transcription of calls enables the analysis of call records, performed offline using open source big data technologies such as Hadoop or traditional proprietary technologies.

A customer service agent who is able to see the relevant patterns or issues a customer has faced, is better prepared to be able to help a customer. This translates into a measurable increase in customer satisfaction.


We have seen a couple of examples of how analytics can help a Telecom provider improve customer satisfaction. There are many other examples of the use of Big Data in Telecom, and we will share more such examples in future articles.

Big Data analytics techniques have matured to the level that telecommunications operators are able to apply them for low-cost and use them to improve customer satisfaction. Productized big data solutions are available from the Big Data industry and the Telecom industry, and can then be used to deliver long-term value, including raising customer satisfaction.

Author Kshitij Kumar, Contributing Editor

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