If there is one industry that raises data in all possible ways, it is telecommunications. The telecommunications industry serves billions of people every day, generating huge amounts of data. Although many telecom companies do not influence this data, the introduction of data science, deep learning and artificial intelligence in this industry is inevitable.
Based on a survey of best deep learning companies in USA, the Untapped Promise of Big Data found that most companies have not yet seriously impacted the data they have to maximize profits. And only 30 percent said they had already invested in big data.
While there is a definite debate among telecom companies as to whether the return on investment is worthwhile, there is no doubt that data science, deep learning (DL) and artificial intelligence (AI) are inevitable when it comes to the future of the industry. Those who figure out how to use these methods and techniques will thrive; Those who do not will be left behind.
Through the use of data science, deep learning development and artificial intelligence strategies, telecommunication companies improve four areas of their services. The importance of data science, ML, DL and Ai services in Virginia to the telecom industry is particularly evident in these four areas, which can be examined individually in this paper:
Customer service recommendation and business customization:
Service recommenders can also be used to boost existing services or to identify why users are not adopting some services and in turn, suggest value-added services to them based on their profile and choice. Additionally, they also predict churn based on previous churn usage patterns and changes in other user profiles.
The following figure illustrates a music recommendation system based on SVM (Support Vector Machine) that extracts personal information at the user level, time, location, activity records, along with the musical context to suggest suitable music services.
Music recommendation system: a vehicle alarm system used by telecommunications operators.
With customer-generated network data, it is easier to automate the process of grouping customers into segments, such as profiling customers based on their calling and messaging behavior.
Predictive maintenance through artificial intelligence applications:
Predictive analytics, powered by AI, enables telcos to leverage data, sophisticated algorithms, and advanced machine learning capabilities to forecast future results based on historical data. data science development company in USA and Artificial intelligence algorithms use data-driven techniques to monitor the current state of equipment and predict equipment failures based on the analysis of previous patterns. This makes it possible to proactively troubleshoot equipment such as power lines, data center services, cell phone towers, and also the various devices that are placed in customers' homes.
There is no doubt that machine learning and artificial intelligence will make the edge smarter and pave the way for next-generation telecom solutions. We have machine learning capabilities in the cloud, hardware, neural networks, and open-source frameworks.
Customer segmentation:
Clustering to segment customer profiles requires complex models based on multivariate time series analysis, which have limitations in terms of scalability and ability to accurately represent the temporal behavior sequences (TBS) of users, illustrated in the following figures. TBS can be short, noisy and non-stationary, where the LDA model serves as the best to represent the temporal behavior of mobile subscribers as compact and interpretable profiles, relaxing the strict temporal order of user preferences.
Using Preventive Maintenance to Help Customers:
Prevention management is effective not only on the network side but also on the customer side. Dutch telecom KPN analyzes notes generated by its contact center agents and uses insights designed to make changes to its Interactive Voice Response (IVR) system. KPN tracks and analyzes customers' home behavior — with their permission — such as changing channels on their modem, indicating a Wi-Fi problem. Once identified, KPN will address these issues in advance, making it more successful for technical teams.
Virtual assistants for customer support:
Another application of AI in telecommunications is conversational AI platforms. Also known as virtual assistants, they have learned to automate and scale one-to-one conversations so efficiently that they are projected to reduce business expenses by up to $ 8 billion annually by 2022, according to a Machine learning consulting company in USA Research. Telecommunications companies have turned to virtual assistants to help deal with the myriad of support requests for installation, configuration, troubleshooting, and maintenance, which often overwhelm customer service centers. With artificial intelligence, operators can implement self-service capabilities that show customers how to install and operate their own devices.
Conclusion:
In telecommunications, with the promotion and adoption of the SDN / NFV architecture, with the acceleration of the network cloud and the evolution of new systems and technologies, today's telecommunications systems face more and more challenges and place great pressure on the telecommunications operators. As the telecommunications network transforms into the telecommunications cloud, more and more opportunities open up.
AI can benefit from the resources brought by the telecom cloud and the big data generated by that cloud. Meanwhile, the best artificial intelligence company in Frisco can help telecom operators efficiently manage and optimize network resources, automate orchestration and operation. network functions and enrich different types of smart applications and cloud services. Artificial intelligence technology and cloud telecommunications infrastructure help and support each other, as well as develop and grow together. In telecom open source projects, we have seen some initiatives that leverage artificial intelligence technology for VNF operation and edge computing as for now, for example, Distributed Monitoring and Analysis (DMA), Distributed Analytics as a case ONAP4K8S usage, and so on.
We look forward to more AI initiatives and more innovations from the community during the transformation of telecommunications. We invited people to stop by our booth at KubeCon & CloudNativeCon North America 2019 and watch our AI closed-loop automation and network analytics demo.
USM Business Systems is one of the best mobile app development company in USA, whose service is to organize and manage the development of deep learning as a complete subdivision of artificial intelligence. It includes building and maintaining deep sensory links, using the most acceptable platforms and languages, and dealing with the most essential data and problems. When defining the application areas of deep learning, it is essential to say that the technology is used in important areas. USM Business Systems is aware of workspaces that provide a totally configured development environment for deep learning.
WRITTEN BY
Koteshwar Reddy
I am working as a Marketing Associate and Technical Associate at USM Business Systems. I am working in the Internet of Things and Cloud Migration Service Providers domain. I completed B.E. in Computer Science from MIT, Pune. In my spare time, I am interested in Travelling, Reading and learning about new technologies.
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