The telecom world is changing rapidly and organisations are challenged to not only expand but to merely stay relevant. To face the fast-changing environment, organizations need to be at a precise level of maturity where the decision-making process must move from gut feelings to the number and reasoning-based practices. The mandate here is clear: Organisations has to become data-driven, or risk being left behind.
However, the truth is spoken considering that almost 100% of enterprises want to become more data-driven, but only less than a 3rd has accomplished that goal. The primary point to being data-driven starts with having the ability to assess where one stands, and which is the best way to move ahead to a certain objective, a conception that continues to remain challenging for organizations.
Almost all organizations have been into the woods of data analytics consulting with the purpose to make a change and get the best out of the huge volumes of data they have. Sometimes they ask questions like what others (either in the same industry or across industries) are doing & how they could replicate it or what new changes can be done that no one else has yet. To compare the complete data analytics practice to the workings of an engine, an engine can be only as good as the sum of its machine parts.
The parts must slot in well, the oiling mechanisms should help in friction reduction, the oil supply must be timely and of course, the sparks need to be perfect. As many say, data is indeed the new oil & data analytics is fast becoming the engine (in this case, of growth).
Transformation is crucial, but to confirm true competitive advantage, organizations must transform themselves in a very planned phase. The approach to analytics can't be stochastic because it would lead to more troubles than benefits. Organizations must traverse one stage at a time. Not only defining those stages is the need of the hour but also knowing what steps one must take at a certain level of point to move from one stage to another. In this scenario, an Analytics Maturity Assessment becomes a necessity.
Big Data Are Employed In Various Use-Cases In The Telecom Industry:
Operational Intelligence
Telecom can benefit a lot from big data analytics by gaining a deeper and sharp understanding of switching, frequency utilization, and capacity use for capacity planning and management. Analyzing consumption of services and bandwidth in specific regions or areas helps with planning locations for infrastructure investment. Recording and analyzing data produced by the infrastructure and by sensors can easily accelerate troubleshooting information about the network. The Map R Converged Data Platform can efficiently collect and store tons of sensor data and scale as data grows.
Customer Churn Analysis
Accurate diagnosis of customer churn and enabling of alerts when a customer exhibits behavior that suggests imminent defection is a critical requirement for telecom. By observing at multiple factors, like comments on social media and declining usage, as well as historical data that show patterns of behavior that suggest churn, companies can predict when a customer is at risk of defecting. The Map R Converged Data Platform helps in compiling customer transaction data and communication streams from customers in real-time which will show how customers feel about their service. This is critical for detecting & understanding customer satisfaction issues in real-time.
Fraud Detection
CSPs need to safeguard their customers and their bottom line by proactively detecting fraudulent activities. They can monitor usage data, area-specific data and customer account data in real time to model baseline “normal†behavior. The MapR Converged Data Platform can enable building of predictive models that can flag anomalous phone calls that might indicate theft or hacking, both in business-to-business and business-to-consumer environments.
Click stream Analysis
Communications service providers can generate more revenue and make better customer experiences by tracking and analyzing customer click-streams to grasp their preferences and propensity to buy. For example, if click streams show a customer is interested in researching on a specific products, then CPS can show up targeted promotions or offers to that individual customer. They can optimize websites to extend conversion including cross-sell opportunities. The Maps Converged Data Platform helps ingests data faster, enables streaming writes to update models & target customers quicker.
Network Management/Optimization
Communications service providers can optimize quality of service and routing by analyzing network traffic in real-time. This enables them to reply to fluctuations in traffic & reallocate bandwidth as needed. They can also use the Map R Converged Data Platform to spot and solve network bottlenecks, manage capacity to plan for infrastructure investments and maintain quality of service, and optimize the network for their most potential customers.
Analytics play an important role in any industry. With reference to telecom industry, there are many ways that analytics can be really helpful. Some of the interesting areas could be related to:
Big data promises to accelerate growth and boost efficiency & profitability throughout the entire telecom network analytics.
- Analyzing call records in real-time to spot fraudulent behavior on the spot
- Letting call center agents to change subscriber plans easily and profitably
- Customizing marketing campaigns to individual customers by location-based & social networking technologies
- Using insights into customer taste and preferences to develop new products
Data analytics in telecom can even open up new sources of revenue, like selling insights about customers to 3rd parties. The analysis of Big Data in telecom also contributes to reducing the CAPEX or OPEX which is associated with the business operations.
Follow Up
Polestar Solutions has been working with customers across the world in helping them assess their Analytics Maturity, to define the business objectives and carve out an Analytics roadmap towards said objectives, through our Analytics Maturity Models.
Polestar defines those stages & also the steps between those stages, through an assessment across People, Process, Technology and most significantly Data.
To know more about why an Analytics Maturity Assessment is crucial, and how it can help your organization, Schedule a Demo with us and our Subject Matter Experts will get in touch with you.
