Use Network, Usage, and Billing Data to Reduce Churn.
Tie subscriber behaviour, network quality, and offers together to improve ARPU, cut churn, and target investment.
Churn Risk Alert
Segment: Enterprise Gold
Cell Cluster Congestion
Postpaid ARPU
Region: Metro North
Multi-SIM Detected
Household: Smith_Res
Cell Cluster Congestion
What Telecom Leaders Are Dealing With
OSS / NMS
PROBES & PERFORMANCE TOOLS
BSS / Billing
CHARGING & MEDIATION
CRM / Channels
SELF-CARE & PARTNERS
Yet core questions still stall decisions. If answering these requires three BI teams and manual CDR extracts, the analytics layer is a liability.
Architecture Pillars
Telecom Data Platform
Enterprise data backbone for subscriber, usage, and network signals. Consolidate CDRs, xDRs, network KPIs, billing, and CRM into a unified schema.
- Standardize hierarchies
- Lineage for audits
Decision Intelligence & BI
Reporting aligned to how the operator is run. Executive views for ARPU, AMPU, churn, and network performance on a single semantic model.
- Automated reporting packs
- Single semantic model
Applied AI & ML
AI applied where it shifts unit economics. Churn models, anomaly detection for fraud, and demand forecasting running on governed feature stores.
- Fraud anomaly detection
- Traffic forecasting
Capability Breakdown
Subscriber 360 View
Subscriber and Revenue Analytics
Objective: understand which subscribers, products, and channels actually contribute to sustainable ARPU.
Subscriber 360 and Segmentation
Subscriber, usage, recharge, and interaction history consolidated. Hybrid prepaid/postpaid, multi-SIM, and multi-product households resolved.
Impact: Targeted offers improve retention.
Churn and Win-back Analytics
Churn flags and propensity scores derived from usage, QoS events, recharge patterns, and complaints. Win-back strategies evaluated by cohort.
Impact: Stabilized revenue and subscriber quality.
Network and Operations Analytics
Objective: connect network performance directly to revenue risk, churn, and cost.
Network Quality (QoS / QoE)
Cell and cluster level KPIs (throughput, drops, latency) matched with subscriber metrics. Impact of poor experience on churn quantified.
Impact: Capex guided by commercial impact.
Capacity and Utilization
Utilization of RAN, backhaul, and core resources tracked over time. Hotspots and underutilized assets identified early.
Impact: Better capex timing and scope.
Commercial & Product
Tariff and Bundle Performance
Uptake, usage, and unit economics tracked. Unprofitable constructs identified.
Campaign Performance
Acquisition and upsell campaigns tracked from impression to retained revenue.
Enterprise Analytics
Contract profitability visible per customer. SLA performance linked to renewals.
Fraud & Risk
Revenue Assurance
Leakages identified around discounting, roaming, and interconnect. Direct recovery of revenue.
Fraud Detection Models
ML models for SIM fraud, subscription fraud, and usage anomalies. Fewer false positives.
Regulatory Reporting
Clear lineage from raw records to reported figures. Reduced compliance risk.
AI & ML in Practice
Objective: move from reactive dashboards to proactive and prescriptive operations.
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Churn and Next-Best-Action
Scores and recommended actions generated for high-risk, high-value subscribers. Outputs consumed by CRM.
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Network Forecasting
Traffic forecasts by cell, cluster, region, and product type. Inputs used for dimensioning and energy optimization.
Treat Telecom Analytics as
Network-Grade Infrastructure.
ARPU, churn, SLA performance, and regulatory metrics already sit under board scrutiny. A weak data stack adds silent risk. Rudder Analytics provides the architecture that holds under pressure.
