Telecom industry, probably, would be one of the highest, data producing industry in today's world. The amount of unstructured data produced and captured by the telecom service providers are humungous. And the size of data will keep increasing with time, as the number of individuals not using the services of telecom industry, directly or indirectly, is diminishing by the day.
Also, various challenges like Cost of Data Storage, Processing power of computers, and tools for Data analytics are being overcome, along with them getting cheaper and easily available as the time passes.
There are numerous Business Intelligence (BI) vendors like SAS, IBM, SAP, Tibco, and QlikTech. The software technology and rich library of analytics functionality provided by them is being used to its full potential for extracting meaningful insights from often incompatible data stores.
In the world that we live in, one of the most important area focused by any business is to improve user experience. To achieve this, they are creating complete profiles assembled from:
● voice, SMS and data usage patterns
● video choices
● customer care history
● social media activity
● past purchase patterns
● website visits, duration, browsing and search patterns
● age, address and gender
● type and number of devices used
● service usage
● geographic location
The results obtained from such deep analytics, allows the telecom providers to create individual customer centric services at each step of purchasing process. Some of the examples of this would be:
● NEC used facial recognition technology to identify the age and gender of pedestrians and created personalized messages to fit the demographics.
● IBM researchers aimed to pluck personal data (age, gender, shopping habits, etc.) from RFID chips embedded in mobile phones to create personalized advertising.
Telecom providers have also started using data analytics for Network Optimization, as, the loss is unstoppable when the network is down, underutilized, or is nearing maximum capacity. Companies analyze subscriber behavior and create individual network usage policies.
"You can love me or hate me but you cannot forget me" is a quote that suits best to the social media. Data scientists are harvesting data from reviews, rants and social feeds and subjecting this information to detailed sentiment analysis. Data scientists help telecommunication companies to:
● Improve or defend their brand image
● Track usage patterns
● Monitor the reaction to new products, offers and campaigns
● Tackle potential problems and ease customer concerns
● Identify new revenue streams
Location based initiatives are also on a fast pace, thanks to geo-fencing and sensor technologies. Telecom companies collaborate with other service providers to give best experience to their customers at a low cost.
One of the biggest challenge that telecom providers face, is customers jumping from network to network in search of a bargain. To prevent this loss of valuable customers, companies are hiring data scientists for creating real-time and predictive analytics that can in-turn help to:
● ombine variables (e.g., calls made, minutes used, number of texts sent, average bill amount) to predict the likelihood of change
● Know when a customer visits a competitor's website, changes his/her SIM or swaps devices
● Use sentiment analysis of social media to detect changes in opinion
● Target specific customer segments with personalized promotions based on historical behavior
● React to retain customers as soon as change is noted
The fact is that, the data acquired by telecom providers on daily basis is increasing at an astronomical rate. Having said that, the advances in the field of Data Science are also running parallelly. The future of any/all industry incorporating Data Science in the forefront is surely bright. But the telecom industry, especially, is going to be exciting to watch.