Common Analytical & Reporting (CARS)
Service providers(let’s call this CSPs) are sitting on terabytes of data including usage transactions, network performance data, cell-site data device information and other information spread across the network and back-office systems, Despite this treasure trove of information on customer behavior, most operators do not have the real-time, end-to-end view of their subscribers that can help maximize their revenue potential from each subscriber (APRU). In order to exploit the full potential of this stored data service providers must have solutions that can help them correlate, process and decipher actionable information that will help the operators to profile subscribers to achieve timely and accurate insights
The Wavenet CARS (Common Analytical & Reporting System) is a robust and scalable platform for CSPs which allows for product/solution transactional reporting and monitoring. Wavenet CARS provides valuable insight into application usage data analytics based on intelligence modules.
The platform architecture is designed to capture real-time analytics using data gathered from multiple data sources including other integrated Wavenet applications, third-party applications, and operator’s network elements.
The vision of CARS is not only limited to comprehensive reporting. but also to serve as a real-time analytics engine and a powerful tool to generate meaningful, contextual information that can be used for monetization. Furthermore, CARS equipped with machine learning tool/integrations to optimize the operational excellence of CSPs while giving a higher subscriber engagement.
In addition to data processing and hosting capabilities, the platform includes a set of industry-standard APIs, through which data can be exposed and consumed for effective backend operations.
Wavenet offers consultation services for CSPs and operators on data governance, policy management, data maintenance and management in the telecommunications sector.
Diagram (below): Wavenet CARS integration capability extends to multiple technology stacks including visualization, analytics,
database, computing, storage, distribution and data warehousing
Use Case Example:
Using Machine Learning to Leverage Telco Data for Call Categorization at Call Centers
In an instance where a CSP’s subscriber requires customer support from the Mobile Network Operator’s (MNO) customer service call center to obtain a new subscription service, the subscriber will attempt to connect to the Operator’s call center via a messaging or voice service.
The Wavenet system, using the words or text presented by the customer will first obtain the input. Using Wavenet’s Intent Matching Engine, the system will identify the customer’s exact intent of requiring a subscription service and will direct the customer’s call as appropriate to the relevant Customer Service Agent at the CSP’s subscription unit.
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