Telecommunications | Lucid Sphere AI

In-Demand Telecom Data Analytics Features

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.