- Telecom companies generate huge amounts of data -phone calls, sensor data from network, browsing data etc. can all provide actionable insights provided the data is ingested and replicated in real-time -which is precisely what BryteFlow does. It ingests data in varied formats from multiple sources and provides analytics-ready data that can be used to drive business insights.
- Data from the telecom network and network sensors can be analyzed to help pinpoint and fix faults and bottlenecks so the performance of the network improves.
- BryteFlow can provide a single view of the customer by ingesting data from varied sources like customer calls, subscription information, helpdesks etc. so telecom companies can institute new targeted offers, promotions and packages and get more out of sales analytics.
Building a responsive telecom network
Increasing consumption of data along with the insatiable demand for online entertainment and proliferation of connections like 3G, 4G, and even 5G, is putting pressure on telecom networks with sudden spikes that data operators find difficult to handle. BryteFlow ingests data from multiple sources and prepares it in real-time for analytics. Analysing data consumption patterns and network performance helps telecom companies to better allocate network resources and create predictive capacity models to deliver a smooth performance.
Creating better customer experiences with predictive analytics
Most telecom operators find it challenging to gain a complete view of their customers owing to difficulty in integrating data from different sources. BryteFlow helps by ingesting and preparing customer data from multiple sources to present a complete view of the customer. This helps analysts create predictive models that can indicate churn, increase wallet share by individualizing products – making for a focused and improved customer experience. Data can also be used to predict customer lifetime value to predict discounted revenues that will aggregate from a customer in future. This takes into account customer buying behavior, services subscribed, and average customer value. This affords real-time insights into customer segmentation and future revenues.
Managing online security with data analytics
Machine learning algorithms can be applied to vast amounts of customer and operator data to spot discrepancies in patterns that differ from normal traffic patterns. Thse anomalies through data visualization techniques are presented as real-time alerts to analysts warning them of suspicious activity -be it theft or fake profiles, illegal access, cloning, fraud, etc. BryteFlow ingests and prepares analytics-ready data in real-time so companies can act swiftly to curb the menace.