Telecom companies have access to mass amounts of data that can be transported and captured in order to improve their performance. But with the use of sincere analysis and understanding of the given data, there is a way to present deeper insights on customers through processes. It is only recently that this data began being valued and used. Before, it was hard to analyze and store all this information, but as companies have evolved, easier ways to deal with such large influxes of data have emerged. The combination of big data technologies and streaming analytics come together to help telecommunications companies discover beneficial insights into their customers. In time, this will inspire a shift in the way telecommunication business is done.
Call Blocking and Labeling Visibility and Control
Every day consumers are the target of unsolicited calls, often originating from ‘Robo callers’. Telco is constantly implementing call-blocking techniques to prevent these unwanted calls, however, these fraudulent robot callers leverage technologies to spoof their caller identity and scam victims with threats from the Internal Revenue Service (IRS), loan offers, free travel, etc. A comprehensive solution is necessary to aid the FCC Robocall Strike Force with call blocking and labeling solutions not only to help put an end to fraudulent actors, but also to help with the unintended drop-in contact rates for legal call originators around the world.
Success Story:
Artha enabled Numeracle to address the problem of improper call blocking and labeling and lack of trust in voice communications
Fraud Detection
The telecommunication industry is particularly vulnerable to fraudulent activity due to its high traffic of customers and users. The most widespread fraud in the telecom area is illegal access and authorization, fake profiles, behavioral fraud, etc. There is a very apparent direct relationship between company and customer in terms of fraud.
Predictive Analytics
Telecommunication companies use predictive analytics in order to gather the information that will ease decision making. To better understand the customer, organizations should have knowledge about customers’ preferences and needs. Predictive analytics analyzes past and current data to predict the future, the better is the quality of historical data, the better is predictability.
Customer Segmentation
Segmenting the market and pinpointing specific groups helps companies stay relevant in a dynamic environment. This golden rule is relevant to various areas of business
There are four segmentation schemes of primary importance:
- Customer value segmentation
- Customer behavior segmentation
- Customer lifecycle segmentation
- Customer migration segmentation
Accurately targeting markets helps with identifying customer needs in order to enhance business performance. It enables enhanced business planning and targeting.
Network Management and Optimization
When customers seem to feel more engaged and interested in the offered functions, there is a much greater chance that partnerships will be successful for telecommunications companies. Network optimization and management give operations an advantage and help get to the root of the problem. By analyzing historical data trends, problems can be averted boosting sales and business in the future.
Resources
Implementation of Data Lake to perform cognitive analytics on Telecom customers
Learn how Artha assisted Asian telecom giants on their digital transformation journey
Data Science Analytics - Recommendation engine to attracts new customers using R
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