We highlight how analytics can enhance revenue, reduce churn and increase sales, thereby offering a telecom company several opportunities to outthink and outpace its competitors. We also explore the possible focus areas in terms of predictive analytics.
The consumer’s ever-changing priorities have made it increasingly difficult for telecom operators to remain profitable. Encouraged by their success in utilizing analytics to gain a better grip on revenue and operations, telecom companies are now adopting analytics across several functional areas.
The Business Value of Predictive Analytics
Analytics is a widely used blanket term and often includes most data warehousing, business intelligence, online analytical processing (OLAP) and predictive analytics development activities, and their underlying technologies. Our focus here is on predictive analytics, the activity of building statistical models to solve business problems.
Predictive Analytics Opportunities in Telecom
Marketing, sales and customer relationship management are some of the areas where the returns from analytics are the highest. Ideally, analytics-driven telecom companies must have predictive analytics embedded in all their business processes, thereby moving away from decisions based on gut feeling or intuition.
The four phases of a telecom customer lifecycle:
Each phase can have several embedded analytical models, which can enhance operations considerably and provide a strategic advantage.
Predictive analytics models include the following:
- Campaign analytics
- Churn modeling
- Cross-selling and up-selling
- Customer lifetime value analytics
- Customer segmentation
- Fraud analytics
- Marketing spend optimization
- Network optimization
- Price optimization
- Sales territory optimization
- Social network analytics
- Social media analytics
- Web analytics
Thus, predictive analytics can bring about a quantum change in the way various activities are viewed and executed, across functions and hierarchies. The key benefits that telecom companies reap out of predictive analytics are diverse, from identifying and seizing new opportunities and risk management to reducing cost and improving efficiencies.