30%
Network expansion25%
Improved marketing effectiveness35%
Reduced payout discrepanciesOur client, a prominent prepaid mobile virtual network operator (MVNO) based in the United States, operates an extensive network of dealers and affiliated retail stores nationwide. These stores sell SIM cards and plans as well as provide services, like SIM activation and number porting. To complete transactions, the stores utilize multiple sales channels. While some stick to the client’s proprietary dealer portal, many others use various third-party platforms.
With transactions coming through various channels, the client struggled to gain a clear view of their business performance. Identifying which strategies, stores, and regions were driving success and which needed improvement was a challenge. To address this lack of visibility, the client partnered with TenUp to develop a solution. Their goal was to use data-driven insights for decision-making and ensure all stores remained active and sales increased.
Developing a centralized, data analytics solution to help our client in the telecom industry track sales performance across brands, regions, and channels—while uncovering trends, measuring marketing impact, and improving onboarding efficiency.
We developed a Telecom Data Analytics Solution with data visualization and CRM capabilities, providing a holistic view of business performance and streamlining process management across multiple sales channels.
Our custom-built, Telecom Data Analytics solution helped our client achieve the following business benefits:
Our custom-built Telecom Data Analytics solution turned fragmented data into actionable insights and helped our client gain complete visibility into multi-channel sales performance. Integrated analytics, automated reporting, and role-specific dashboards helped improve decision-making, expand their network, and reduce payout discrepancies, driving operational efficiency and business growth.
Data analytics enables telecom companies to derive actionable insights from massive volumes of customer, network, and usage data. This leads to improved operational efficiency, better customer segmentation, proactive service management, optimized resource allocation, reduced churn, and increased revenue. It also facilitates informed decision-making by offering a 360-degree view of business performance across regions, channels, and user groups.
Telecom data analytics faces several key challenges:
Predictive analytics enables telecom companies to reduce churn, prevent outages, and optimize network and customer operations. By analyzing past and real-time data, it forecasts demand spikes, detects fraud, anticipates hardware failures, and improves customer targeting. This leads to higher uptime, better service quality, smarter marketing, and cost savings—helping telcos act proactively instead of reactively across both technical and business functions.
Machine learning powers telecom analytics by detecting fraud, predicting churn, optimizing networks, and automating operations. It analyzes patterns in massive datasets—enabling real-time decision-making, predictive maintenance, and dynamic pricing. ML also enhances customer segmentation and personalized service delivery, improving both performance and user satisfaction while cutting costs. As data grows, ML helps telecoms scale insights and adapt rapidly to changing conditions.
Telecom data analytics uses a mix of tools for data processing, visualization, and machine learning. Key technologies include:
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