Data Analytics in the Telecom Industry

• Monitoring network performance KPIs (e.g., availability rate, throughput time).
• Detecting network issues and identifying their root causes (e.g., attributing latency to changes in router configurations).
• Site management and asset tracking analytics (e.g., monitoring energy usage; tracking asset performance and utilization).
• Network inventory monitoring and demand forecasting.
• Network load forecasting based on historical and real-time usage data.
• Predictive network maintenance.
• Interactive maps for network visualization and granular performance monitoring.

• Tracking customer-related metrics (e.g., CTLV, satisfaction rate).
• Identifying service and network usage patterns (e.g., preferred device and content type, geographic hotspots).
• User engagement analytics (e.g., active users per time period, session duration).
• Multidimensional customer segmentation (e.g., by demographics and service usage pattern).
• Customer churn prediction.
• Pinpointing cross- and upselling opportunities.
• Customer sentiment analysis based on data from communication logs, social media, review platforms for informed offering adjustment and development.
• ML/AI-powered recommendations for service personalization based on customer activity (e.g., to offer service bundling or subscription plan change).

• Monitoring service and product performance KPIs (e.g., service adoption rates, least and most used product features, app load times, streams or sales volume).
• Competitor pricing benchmarking.
• Dynamic pricing optimization.
• Predicting offering performance based on what-if modeling results and the historical performance of similar offerings.

• KPI monitoring (e.g., sales cycle length, conversion rate, revenue per salesperson).
• Customer journey and conversion path analysis (e.g., pinpointing drop-off points, attributing conversions to certain journey touchpoints).
• Monitoring the performance of marketing campaigns and loyalty programs (e.g., A/B testing results, program enrollment rate, return on ad spend).
• Lead scoring for sales efforts prioritization.
• Marketing content personalization for B2B and B2C clients based on demographic/firmographic and behavioral data.
• What-if modeling to test different campaign strategies and forecast their results.

For customer onboarding and support, field service

• Monitoring service delivery KPIs (e.g., service activation time, mean time to repair, field service efficiency rate, first call resolution rate).
• SLA analytics (e.g., real-time monitoring of SLA compliance, SLA performance reports for managers and clients).
• Service inquiries segmentation (e.g., by product/service category, channel, severity level, customer group).
• Issue and root cause detection (e.g., attributing long wait times to inefficient shift scheduling or workload distribution).
• Customer sentiment analysis, including in-call analytics and graphical representation of customer sentiment dynamics to help agents deliver positive customer experience in real time.

• ML/AI-powered detection of IRSF, subscription, and other fraud based on:
  • Network usage data (e.g., call duration, destination numbers, call volumes).
  • Customer data (e.g., device changes, subscription history, billing records).
  • Employee-related data (e.g., service activations, sales transactions).
• Automated fraud response actions (e.g., alerts to fraud management teams, additional verification requests to users, account blocking).

• Automated compliance checks (e.g., adherence to licensing and data protection requirements, spectrum usage limits).
• Alerting on compliance risks (e.g., unusual spikes in network usage, unauthorized access to sensitive data).
• Notifications on new regulatory requirements and changes in the existing regulations based on the analysis of external data (e.g., from regulatory bodies’ websites).
• Automated report submission to regulatory bodies in the required formats (e.g., FCC Form 911 for the US, CITC annual report for KSA, TRA annual report for the UAE).

• Financial KPI monitoring and multidimensional analysis (e.g., average revenue per user (ARPU), sales per product and region, net profit margin, operating cash flow).
• Benchmarking financial performance against user-defined goals and industry peers.
• Monetization analytics (e.g., attributing revenue to in-app purchases, premium features subscriptions).
• Investment analytics (e.g., capital expenditure evaluation for network upgrades and expansions, investment volatility calculation).
• Financial modeling and forecasting (e.g., for budget planning and variance control).

• Monitoring employee performance and comparing it with the provided compensation.
• Tracking employee satisfaction and identifying attrition reasons.
• Pinpointing skill gaps and suggesting employee-specific training programs.
• HR analytics (e.g., monitoring recruitment campaign performance and identifying the best talent attraction channels).
• Staffing and workload analysis (e.g., staff allocation modeling and forecasting, scheduling recommendations based on identified peak times).

• Role-specific dashboards.
• Clear visuals and interactive capabilities for drilling up and down, slicing and dicing.
• Scheduled and ad hoc report creation with automated submission to relevant parties, including regulatory bodies.
• Visualizing network assets on maps with capabilities for navigating to object details right from the relevant icon.