Future-ready Telecom Analytics : 2021
Using data analytics, telecoms can adopt a data-driven approach to decision-making. Dashboards and widgets based on big data telecom sources can help the company figure out which customers are having the most technical issues (quality of experience), which infrastructure needs to be replaced quickest (quality of service), and which geographic areas have the most potential for market share growth. Analyze data from customer satisfaction sources in real-time to come up with new efforts to lower churn and keep consumers satisfied.
Cognitive Analytics with 5G
AI/ML networks and apps that are closed-loop, self-optimizing, and self-healing will be required for 5G service requirements to monitor user behaviour and service designs. Telcos will need two essential solutions needed to keep up with 5G networks, namely Anomaly Detection and Forecasting demand and behaviour. Technology, which is frequently referred to as cognitive analytics (and also referred to as AI analytics or augmented analytics) are critical enablers to manage complexity and meet the critical service expectations needed to serve game-changing technologies such as autonomous vehicles, remote surgery, and massive IoT. 5G networks and user applications/content are going to bring forth billions of time series, logs, event data, and only AI/ML can handle it.
Anomaly detection will make sure that the inherent network and customer support systems can self-heal and self-organize. Anomaly detection helps meet the high level of service requirements (low latency, high reliability) required to serve important business applications.
Forecasting demand and behaviour will be critical for service providers to automate network slice generation to support changing usage patterns. Forecasting user demand ensures that adequate network resources are allocated, produced, and scaled in advance of demand. The key value proposition for 5G service providers will be to move from service production to service consumption in order to fulfil shifting consumer demands.
Usage patterns, billing information, purchase history, device preferences, demographic data, location, customer journey, customer interactions, quality of service, network infrastructure, device logs, SLAs, and other forms of data are all evaluated in AI analytics. To automate the entire forecasting process, AI analytics platforms imitate the responsibilities of skilled data scientists. CSPs can forecast what will happen and how likely it is, as well as analyze the elements that influence results and analyze which actions will have the most impact on their KPIs and how.
One of the telecom’s most difficult tasks is to reduce churn while maintaining market uniqueness. Improving customer experience is the key to achieving this. CSPs may determine their next best offer and execute a micro-segmented and personalized campaign to a consumer at the correct time, depending on their own preferences and history, using AI data. Providers must examine data from a variety of sources for churn prediction to function, and only AI can make sense of such a large volume of data.
Operators can use data analytics toolkits to capture not just data, but genuine consumer insights in order to create services that promote growth. This telecom data analytics system provides insights on customer experience and behaviour that may be used to drive marketing, customer service, operations, and planning choices (and automate activities).
Use Cases: Leverage cross-domain, end-to-end data sources from any vendor’s network nodes or systems with productized use cases that break down data silos. A versatile platform with a powerful SDK and APIs can support new, unique apps and use cases.
- Real-time, near-network correlation: Real-time, near-network correlation, combined with our unique, proven, and patented algorithms, data models, and business rules, provides actionable insights into consumer symptoms, root causes, and next best actions.
- Patented Service Level Index (SLI): A patented service level index (SLI) is a metric that measures how well Customer satisfaction is measured subjectively, and the Net Promoter Score (NPS) is predicted for each customer.
- Advanced traffic analysis: Get proactive assurance for encrypted traffic that might be impossible to decipher otherwise.
IoT data consists of datasets generated by sensors, which are now both inexpensive and powerful enough to support an almost infinite number of use cases. The ability of sensors to collect data about the physical world, which can then be examined or coupled with other types of data to find patterns, is a massive advantage. The ability of sensors to understand physics discloses some of the real-time context surrounding a specific person, which may then be paired with the expressiveness of social media data to offer a deep understanding of an individual or a group of individuals. If done correctly, this can open up a slew of new options for consumers.
Why is it Important?
Telecom analytics can assist service providers to increase profitability, improve customer happiness. The ideal telecom analytics platform should be able to solve even the most difficult customer experience problems while also offering insights that can be used to drive a variety of essential choices and activities, such as:
- Creating more competitive packages and offers
- During the browsing and ordering process, recommending the most appealing offers to subscribers.
- Keeping members informed about their usage, expenditure, and purchasing options
- Setting up the network to provide more dependable services
- Quality of Experience (QoE) is being monitored in order to prevent any potential difficulties.
All of these actions contribute to a better user experience, leading to a satisfied customer. CSPs are able to convert unhappy customers into long term profitable customers.
Bridging the Business and Analytics Trust Gap
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