Transforming Telecom with Advanced Data Analytics Solutions
Empower your business with multi-vendor, Multi Cloud, cross-domain, Telecom Analytics that convert large-scale data into actionable insights.
2023, the $3041.8 billion telecom sector focuses on enhanced connectivity, performance, competitiveness, and sustainability, driven by 5G monetization and edge computing. With IoT devices projected to exceed 50 billion by 2030, investment in 5G infrastructures is increasing which will considerably impact sectors like manufacturing, healthcare and more.
The year's telecom services are defined by high-value bundles combining mobile, home internet, and entertainment. Simultaneously, businesses are looking to adopt 5G edge technologies for improved efficiency, prompting greater collaboration.
Telecom analytics plays a pivotal role, providing data-driven insights for strategic decision-making and complex service comprehension, aiding in monetization. These insights influence competitive offers, churn management, deal recommendations, customer information, network reliability enhancement, and quality monitoring
How SCIKIQ Approaches
Telecom Analytics
Telecom analytics is a set of data analysis tools specifically designed for the needs of telecom companies. Telecom analytics draws data from various data sources, including network data, Business Support Systems (BSS), Operations Support Systems (OSS), and other internal and external IT applications, Multiple Clouds, to arrive at information which can be worked upon to derive actionable business insights.
SCIKIQ leverages artificial intelligence and machine learning (AI/ML) to enhance telecom analytics, shifting from simple reactive steps to forward-thinking strategies and predictive abilities. AI/ML helps automate various tasks, from network improvement to custom customer messaging, boosting efficiency and providing a smoother, tailored customer experience - a crucial part of high-quality user experience.
Telcos need a Data analytics partner which not only understands Data and Business but also the adoption of the new age technology as they evolve so as to allow the Telcom operators can innovate relentlessly.
Telecom Analytics Process
Telecom companies gain valuable insights to enhance customer service, optimize network performance, detect and prevent fraud, predict maintenance needs, and ensure revenue assurance with the right data analytics team.
Customer Services
- Personalizing customer service: Telecom companies can analyse customer data to understand preferences and needs, enabling personalized interactions. This includes tailored recommendations and faster issue resolution.
- Enhancing self-service options: Data analytics improves self-service by providing accurate information and easier troubleshooting for customers.
- Providing proactive support: Through data analytics, telecom companies can offer proactive support, such as notifying customers about potential issues and providing incentives to prevent churn.
Network Performance
- Identifying and resolving network problems: Analysis of network data helps identify congestion and underperforming areas, leading to network enhancements like adding cell towers and improving signal strength.
- Optimizing network resources: Data analytics enables efficient resource allocation and predicts network service demand, ensuring optimal use of bandwidth.
- Planning for network expansion: By leveraging data analytics, telecom companies can identify areas requiring new cell towers and predict demand for new network services.
Fraud Detection and Prevention
- Identifying fraudulent activity: Telecom companies analyze customer data to identify patterns of fraudulent behavior, such as unusual usage or multiple accounts under the same name.
- Preventing fraudulent activity: Data analytics helps prevent fraud by blocking suspicious transactions and implementing additional verification for high-risk activities
Predicting maintenance
- Identifying end-of-life equipment: Analyzing equipment data enables telecom companies to schedule preventive maintenance or replace aging equipment before it fails.
- Identifying patterns of equipment failure: Data analytics helps identify patterns of equipment failure, allowing adjustments to maintenance procedures or equipment upgrades to prevent future failures.
Revenue assurance
- Identifying and resolving billing errors: Analysis of billing data helps identify and rectify errors like incorrect or duplicate charges, improving accuracy and customer satisfaction.
- Identifying and preventing revenue leakage: Data analytics identifies customers using services without payment or unauthorized services, preventing revenue loss.
Process of Data Analytics in Telecom
The CPCQ process of data analytics in the telecom industry encompasses collecting, processing, cleaning, analyzing, and quality before visualizing, and utilizing data to drive informed decisions and catalyze business improvements. Each stage plays a vital role in transforming raw data into actionable insights that positively impact various aspects of the industry, propelling it towards remarkable success and innovation
Process
SCIKIQ CPCQ Process Framework
Collection
Processing
Cleaning
Quality
Use Cases in Telecom
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- Personalized product recommendations: Increase subscriber satisfaction and revenue by offering tailored product or subscription options based on subscriber behaviours and preferences.
- Retailer dormancy prediction: Analyse retailer activities to predict and manage the risk of inactive retailers, proactively maintaining a healthy distribution network.
- Fraud detection and prevention: Utilize machine learning and anomaly detection to identify and prevent fraud in recharge and voucher systems.
- AI-powered customer support: Integrate data analytics with AI chat agents for efficient support, cost reduction, and improved customer satisfaction.
- Subscriber churn prediction: Predict subscriber churn using advanced analytics, allowing targeted retention strategies and customer loyalty.
- Dynamic pricing optimization: Implement competitive pricing for recharges by monitoring customer behaviour and market trends.
- Network performance analytics: Monitor and optimize resource allocation to ensure seamless connectivity for subscribers.
- Sentiment analysis and social listening: Monitor social media to gauge subscriber sentiment and address concerns, leveraging positive feedback for brand enhancement.
- Inventory and supply chain optimization: Analyse data to optimize inventory and supply chain processes, reducing costs and ensuring availability.
- Revenue assurance and leakage prevention: Detect and address potential revenue leaks through transaction analysis, protecting revenue streams and market reputation.
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