Predictive Analytics for Telecommunications Companies
In an effort to better serve and retain customers as well as increase profits, telecommunications companies are utilizing powerful data mining, text mining, and advanced analytics techniques using STATISTICA Data Miner. These tools uncover interesting patterns and relationships that are used to set pricing, forecast demand, and improve customer relationships and loyalty. Recently, a consulting firm provided a successful strategy to a telecom company, based on results of a data mining project. The success story can be read here.
Some typical predictive analytic applications for the telecommunications industry include:
- Customer Relationship Management
- predicting customer retention and churn,
- Detecting relationships that aid with cross-sales, up-sales, and other marketing ventures,
- Predicting customer lifetime value to aid in acquisition strategies
- Customer segmentation
- Analyzing customer feelings and sentiment through Text Mining of
- social media analysis, Twitter and Facebook comments
- customer feedback and reviews, and
- inquiries to technical support
- Forecasting of network load factoring seasonal components and holidays, forecasting sales and growth, factoring in promotional campaigns,
- Using quality control charting to monitor customer wait time during a phone call or for service repairs or customer disconnects,
- Root cause analysis.
Cluster and link analysis can be leveraged for cross-sales and up sales. This analysis helps the telecommunications company to target the right customers for a particular marketing campaign or promotional deal.
STATISTICA Data Miner features several tools for forecasting such as Neural Networks Time Series, and traditional Time Series tools like ARIMA, Exponentially Weighted Moving Average, Fourier analysis, and many others. Using these tools, telecommunication companies can model the trends that affect network demands. Having a clear picture of future demand helps to devise a good strategy for marketing and resource management.
Areas of Application: Monitoring Processes with STATISTICA Enterprise QC and MAS
STATISTICA Enterprise QC monitors the various critical processes that are taking place within the telecommunications company, such as call volume and tower load. Immediate alerting of spikes can allow the company to react quickly so that the fewest customers are negatively affected by temporary outages.
STATISTICA Monitoring and Alerting Server (MAS) provides automated monitoring and dashboard summaries for highly automated processes within the telecommunications organization.
Anticipating Issues before they occur with STATISTICA Process Optimization and Root Cause Analysis
STATISTICA Process Optimization and Root Cause Analysis is an exceptional tool for monitoring a process at each step along the way, even anticipating quality control problems with unmatched sensitivity and effectiveness. By integrating cutting-edge predictive modeling and data mining techniques with the vast array of traditional quality tools including quality control charting, process capability analysis, experimental design procedures and Six Sigma methods, STATISTICA Process Optimization and Root Cause Analysis allows for complete process understanding, root cause analysis, and accurate predictions of quality outcomes during the manufacturing process.
STATISTICA Process Optimization and Root Cause Analysis allows you to take advantage of existing historical data and find patterns in the data that affect the process. Tower load, for example is a complex process, reliant on many factors and interactions. A traditional experimental design to find the driving factors is likely not feasible. Root Cause analysis uses your historical data to find factors and combinations of factors that affect the end product quality.
STATISTICA Process Optimization and Root Cause Analysis builds predictive models that reflect the relationship between inputs and outcomes of the process. The models can then be used to simulate runs, finding optimal settings and improving overall quality of the process.