The market for AI/ML and advanced analytics in the telecom sector is expected to reach $38.8 billion by 2031 (Source). The increased need for big data analytics for the telecom industry is primarily responsible for market growth. Machine learning and artificial intelligence methods are used by innovative telecom providers to optimize network performance, boost consumer satisfaction and retention, automate business processes for higher revenue, and much more. To realize the entire potential of Artificial Intelligence and Advanced Analytics in the long run, CSPs should prioritize investing in data analytics use cases in the telecom industry now in order to lay a solid base and platform for future AI and ML skills and capabilities.
Data science applications are widely utilized in the telecom sector to simplify operations, increase revenues, develop successful advertising and commercial strategies, exhibit data, execute data transfer, and for a variety of other purposes. Data transfer, exchange, and import are important operations for organizations in the telecommunications sector. The volume of data traveling via various communication routes is increasing by the minute. As a result, obsolete procedures and methodologies are no longer applicable. Hence, there is a need for the use of big data analytics in the telecom industry.
Major Use Cases of AI/ML and Advanced Analytics for Telecom Operators
Here is how AI/ML and data analytics help telcos sail success.
Predictive churn analysis
Customer churn prediction is crucial for telecommunications companies to efficiently retain consumers. It is more expensive to bring in new clients than it is to keep existing ones. As a result, huge telecommunications companies are attempting to create algorithms that can anticipate which consumers are most likely to shift and take the necessary steps.
The telecommunications sector is fraught with fierce rivalry among service providers for the highest subscriber market share. Having said that, product price is an important consideration for operators competing for new subscribers. Telecom operators may acquire precise data insights and build suitable pricing plans with the use of data analytics by analyzing consumers’ reactions to alternative pricing strategies, purchasing history, and competitor pricing. This can be achieved with AI/ML and advanced analytics.
Furthermore, telecom providers can maximize their ROI, determine the perceived worth of their product or service, and boost the efficiency of their sales team with the help of an advanced big data analytics solution for telcos and CSPs.
With the continuous global deployment of 5G, we are heading towards ever-increasing data consumption. Optimizing networks to resist such increased data traffic is quickly becoming one of the most important strategic choices in the telecom industry. This involves enhancing network and Internet connectivity. CSPs can use real-time AI/ML and advanced analytics to identify severely congested places where network traffic is approaching capacity thresholds and prioritize growth for additional capacity rollout. Telecom advanced analytics may also aid in the identification of deficiencies and ensure the functioning of network systems in a safe, reliable, and effective way.
A recommendation engine is a data filtering tool that uses machine learning algorithms to suggest the most relevant products or services to a certain user or client. It works on the basis of detecting patterns in consumer behavior data, which may be acquired either implicitly or explicitly.
Content-based filtering employs criteria that demonstrate the link between the customer profile and the product or service chosen by the customer. Conversely, collaborative filtering depends on data analysis based on the user’s preferences and behavior.
Product innovation and development
Product development is a complicated process that requires control and careful management from inception through continuous lifecycle management and maintenance. It is hard to ensure high-quality product performance in accordance with client expectations without the use of smart data solutions. Real-time data from numerous sources can be utilized to improve telecom products. They can also analyze customer usage to create novel and innovative products that meet the demands of users while saving money.
Big data solutions aid in understanding customer behavior by examining how they utilize telecom services. An in-depth analysis of purchasing history, service preferences, and feedback from consumers allows for personalized product offerings that target the appropriate audience at the right time.
This allows CSPs to create targeted offers for clients, keep an edge over others, sustain consistent growth, and boost conversion rates.
Attracting new subscribers
Telecom advanced analytics assists businesses in retaining consumers and attracting new subscribers by providing novel offerings and content. Advanced big data analytics solutions for telcos and CSPs enable businesses to create a customer profile and predict their preferences and requirements. The correct content and adaptable options retain existing consumers, attract new ones, and enhance the revenue of CSPs.
Telcos have always faced several obstacles originating from a wide range of issues. One of them is to protect their company and clients from any type of risk. Artificial intelligence in telecommunications has made it much easier to build up tools that can identify and respond to fraudulent network activity. Big data analytics has the potential to safeguard the telecommunications industry from such fraud. It can detect anomalies and intercept spam emails and phone conversations.
All these use cases are a reflection of how AI/ML and data analytics help telcos sail success.
Is your telecom business ready to leverage the benefits offered by AI/ML and advanced analytics in the telecom sector? When we talk about telcos, we typically notice a lot of easy opportunities for improving customer service, capacity planning, and network optimization. Telcos with broad and dispersed infrastructures are more likely to profit from scalable artificial intelligence or ML-powered solutions as well as big data analytics for the telecom industry while migrating outdated systems to more contemporary infrastructures.
While AI can aid in the optimization of a company’s operations, it is not always a simple solution to implement. To ensure the success of an AI project, extensive analysis, and managerial assistance are required. You will need to examine the existing data infrastructures and remain updated on telecom AI trends to determine if they align with your business goals. The data analytics use cases in the telecom industry make this possible.
6D Technologies’ Magik is an all-inclusive data analytics solution. This platform is empowered with AI/ML and provides advanced insight to CSPs to drive digital transformation that boosts business growth.