Customer experience increasingly determines a company’s competitive edge in their field – and the telecommunications industry is no exception. Indeed, telecoms are no longer characterized solely by the products they offer. In today’s consumer landscape, network performance is one of a company’s most valuable product offerings.
Data insights for enhancing network performance and customer satisfaction have driven a notable transition in the industry away from a traditional product-centric approach, towards network-centric service models. The growing importance of network performance belies the need for efficient big data analytics vis-a-vis AI algorithms to support strategic real-time decision-making and improve customer experience in mobile-first environments.
But the huge performance costs associated with running these systems remains a barrier for sufficiently integrating data analytics and AI into the telecom industry.
The cost-performance dilemma and its implications for decision-making are driving the conversation around data-driven operations as enterprises struggle to balance high-performance analytics with cost-effectiveness.
Accurate, efficient Machine Learning algorithms for garnering data analytics will prove to be a key path for telecom companies to successfully leverage their data.
By overcoming the technology gap with cost- and time-efficient analytics, telecoms can drive customer retention and optimize their network performance to position it as their primary offering.
A changing telecom industry
To future-proof telecom operations, we must first understand the shift that is already underway – prioritizing network performance over product offerings.
Telecom companies are transitioning their strategies to prioritize the performance and reliability of their overall network rather than their products and platforms. This reflects the recognition that superior network performance is a critical driver of customer satisfaction and loyalty.
The critical catalyst for this shift is a growing need for customer experience enhancement amidst the rise of digital services and increasing customer expectations. Telecom operators have realized that investing in high-quality network optimization and reliability is essential for enhancing overall customer experience and reducing churn rates (by as much as 15%). Most telecoms offer a similar array of products and services, so the differentiating factor is now delivering superior network experiences. By doing so, they can attract new customers and retain existing ones, ultimately driving long-term growth and profitability.
Telecom operators are increasingly leveraging AI and advanced analytics to gain deeper insights into performance metrics and customer behavior. For example, through AI-driven data analytics, telecoms can not only analyze geolocation data to determine user locations within predetermined regions, but they can run user engagement analysis on much larger data sets, enabling them to learn movement trends as far back as a years to date.
These ongoing changes all combine in what is ultimately an industry-wide move towards data-driven optimization – of network efficiency; of analytics and decision making; of customer experience. By analyzing vast amounts of data, operators can identify potential network issues proactively, optimize network capacity and prioritize areas for improvement based on customer usage patterns and preferences.
Price and performance in telecoms data
To keep pace with the data age, telecom companies must infuse their network data analytics with two key features: GPU-processing to address key data challenges and accurate SQL-powered Machine Learning algorithms for reliable functioning and user satisfaction. It’s a potent combination of capabilities that enables efficient data processing and analytics, machine learning optimization and cost effectiveness.
Speed is critical as telecom networks must operate continuously. Any delay in data processing can have severe consequences such as network outages or degraded service quality. Big data analytics systems must support analytics needs seamlessly and rapidly or telecom companies will be unable to sufficiently analyze network performance data and external factors to predict and prevent network issues.
Machine learning algorithms also play a crucial role in telecom network maintenance by helping operators detect anomalies, predict failures and optimize network performance. Big data analytics systems can optimize machine learning algorithms for GPU acceleration, enabling telecom companies to train and deploy accurate ML models faster and more efficiently.
Telecoms must therefore seek out big data analytics solutions that offer cost-effective solutions and deliver high performance and scalability. By harnessing the power of SQL on GPUs, these systems can offer exceptional cost-performance ratios, allowing telecom companies to maximize insights while minimizing costs. Alongside cost effectiveness, these systems can be easily integrated with telecoms’ existing infrastructure – they will be most likely to adopt new analytics solutions that seamlessly integrate with their current ecosystem.
Don’t phone it in
As the telecom industry prioritizes network performance and customer experience, the integration of GPU-accelerated analytics is rapidly becoming a necessity. Only by seamlessly processing data to predict and prevent network outages and leveraging analytics for personalized experiences can telecom companies remain competitive, attract and retain customers, and drive growth. Infusing efficient data processing with optimized machine learning will enable telecoms to meet evolving customer demands while maximizing profitability.
