I think the way Optimization teams (and other resources from Operators/Vendors) can reinvent themselves is by shifting (or at least learning) some data science (machine learning, artificial intelligence).
It’s time to adopt modern technologies and techniques to stay relevant. Learning Data Science can be good for any other area they may go (not only Telecom Optimization), as data science has proved its high value and efficiency. Nowadays data is a fuel needed for any successful company.
Telecom companies operate with vast communication networks and infrastructures with the intense data flow. Processing and analyzing this data with the help of data science algorithms, methodologies and tools find practical application.
Within the telecom industry data science applications are widely used to streamline the operations, to maximize profits, to build effective marketing and business strategies, to visualize data, to perform data transfer and for many other cases. Key activities of the companies working in the telecommunication sector are strongly related to data transfer, exchange, and import. The amounts of data passing through various communication channels are getting larger every minute. Therefore, old techniques and methods are no longer relevant.
Of course, Telecom companies have a big range of challenges (solve problems, control or even prevent from happening) where data science can be explored:
- Fraud detection
- Predictive analytics
- Customer segmentation
- Customer churn prevention
- Lifetime value prediction
- Network management and optimization
- Product development
- Recommendation engines
- Customer sentiment analysis
- Real-time analytics
- Price optimization
In following article you can see a little more detailed explanation for some of the most relevant and efficient data science use cases in the field of telecommunication (for the several use cases above, and how to get real benefits from each one):