Payment Switch’s Network and Security Engineer Revolutionizes Customer Value Prediction with Advanced Analytics

By Ugo Aliogo

In the competitive landscape of digital payments and financial services, understanding the long-term value of customers has become crucial for business survival. Edima David Etim, Network Operations Engineer at NIBSS PLC, is pioneering the use of advanced machine learning techniques to help organizations predict customer lifetime value with unprecedented accuracy, fundamentally changing how businesses approach customer relationship management.

“We’ve moved far beyond the days when you could segment customers based on simple demographics or transaction volumes,” explains Etim, whose unique position managing payment infrastructure has given him deep insights into customer behavior patterns across Nigeria’s diverse financial ecosystem. “The data flowing through our networks tells rich, complex stories about customer relationships that traditional analysis methods simply can’t capture.”

Etim’s interest in predictive analytics emerged from his hands-on experience managing Cisco ACI Infrastructure’s Data Centre technology integrated with Nutanix’ Hybrid cloud environments that process millions of transactions daily. Having implemented sophisticated network monitoring systems and worked extensively with cloud based hyperscalers like Azure, AWS, and GCP platforms, he recognized that the same data intelligence techniques used for network optimization could be applied to understanding customer behavior.

“When you’re monitoring network performance, you learn to identify patterns in seemingly random data streams,” Etim notes. “Traffic that looks chaotic actually follows predictable patterns once you understand the underlying behaviors driving it. Customer transactions work the same way – there are hidden patterns that reveal future value if you know how to analyze them properly.”
His approach leverages gradient boosting machines, a sophisticated machine learning technique that builds predictive models by combining multiple weaker prediction algorithms. This mirrors his network engineering philosophy of creating resilient systems through redundancy and intelligent failover mechanisms.

“In network design, we’ve learned that complex systems require sophisticated approaches,” Etim explains. “You can’t rely on simple linear relationships when you’re dealing with BGP routing policies, path selection, QoS implementations, and traffic engineering across multiple autonomous systems. Similarly, customer behavior involves complex, non-linear relationships that require advanced analytical techniques.”

Etim’s practical experience implementing and managing multi-homed BGP peering with upstream service providers such as Mainone Cables/Equinix and Swift as well as BGP Peering and prefix advertisement to Internet Exchange point of Nigeria (IXPN) for Low latency local content connectivity has taught him the importance of interpretability in complex systems. While his predictive models can achieve remarkable accuracy, he emphasizes that business stakeholders need to understand how these predictions are generated.

“It’s not enough to tell a business that Customer A has a lifetime value of ₦500,000 while Customer B has a value of ₦50,000,” he argues. “You need to explain why – what specific behaviors, transaction patterns, or engagement metrics drive those predictions. That’s where the business value really emerges.”
This cross-industry perspective, combined with his technical expertise in network automation using tools like Ansible, has led Etim to develop frameworks that can be customized for different business environments while maintaining consistent analytical rigor.

“The same principles that guide network automation apply to predictive analytics,” he explains. “You want systems that can adapt to changing conditions, learn from new data, and provide consistent performance across different environments.”

Etim’s research reveals that the most successful customer value prediction systems combine multiple data sources – transaction histories, engagement patterns, support interactions, and behavioral indicators – much like how modern network monitoring systems aggregate data from multiple sources to provide comprehensive visibility.

“In network engineering, we’ve learned that single points of failure are dangerous,” Etim notes. “The same applies to customer analytics. If your predictions rely on just transaction data, or just demographic information, you’re missing crucial insights. The real breakthrough comes when you can integrate multiple data streams intelligently.”

His work has practical implications for Nigerian businesses navigating digital transformation. Having earned additional certifications in business continuity management and project management, Etim understands that successful analytics implementations require careful attention to organizational change management and stakeholder adoption.

“The most sophisticated predictive model is worthless if business teams don’t trust it or don’t know how to act on its insights,” he emphasizes. “Success comes from building systems that enhance human decision-making rather than replacing it.”

As Nigeria’s fintech sector continues to mature, Etim’s approach to customer lifetime value prediction represents a significant evolution in how local businesses can compete with global players by leveraging advanced analytics while maintaining deep understanding of local market dynamics.