At MAG, we are deeply involved with companies in the telecommunications and networking space enabling operators to automatically optimize network quality based on traffic information by region and time zone.
AI in the telecom industry uses advanced algorithms to look for patterns within the data, enabling telecoms to both detect and predict network anomalies. Our solutions enable CSPs to proactively fix problems before customers are negatively impacted.
AI in the telecom market is increasingly helping CSPs manage, optimize, and maintain infrastructure and customer support operations. Network optimization, predictive maintenance, virtual assistants, RPA, fraud prevention, and new revenue streams are all examples of telecom AI use cases where the technology has helped deliver added value for enterprises.
Implementing intelligent strategies for telcos that drive customer success
Read AllIndustry Problem:
In the telecom space, always providing impeccable network reception to customers is a big challenge as it involves a lot of variables that are sometimes not withing a telco’s control. It can lead to an experience that is frustrating for customers even prompting them to move to a different provider.
Since the rollout of 5G, customer expectations have risen exponentially with users always expecting fast and reliable connectivity. This means telecom providers need to make significant efforts to ensure that user experience is not impeded in any way.
Industry Problem:
Companies often struggle to provide fast and reliable service in a lot of areas due to several infrastructural and topological issues. Actively monitoring all the infrastructure at their disposal can be a big task for then.
AI-driven predictive analytics can help telecoms provide better services by utilizing data, sophisticated algorithms, and ML techniques to predict future results based on historical data. This means operators can use data-driven insights to monitor the state of equipment and anticipate failure based on patterns.
Industry Problem:
Companies often struggle to retain good support staff who can offer customers personalized help for when they need it most. However, this can prove to be cost prohibitive for companies very quickly as building a good support team that is large enough to cater to millions of paying customers is challenging.
Virtual assistants can efficiently automate and scale one-on-one conversations providing customers with the support they need, right when they need it with no waiting time. It can be substantially cost effective from an implementation perspective as well.
Industry Problem:
Targeted instances of scamming people via telecommunication channels have been on the rise and is one of the biggest issues for telcos today. With industry estimates indicating that 90% of operators are targeted by scammers daily – amounting to billions in losses every year – AI applications are especially timely for CSPs.
Industry Problem:
CSPs have vast numbers of customers engaged in millions of daily transactions, each susceptible to human error. Robotic Process Automation (RPA) is a form of business process automation technology based on AI.
RPA can bring greater efficiency to telecom functions by allowing telcos to manage their back-office operations and large volumes of repetitive and rules-based actions more easily.
