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Telecommunications

Churn eats your subscriber base faster than acquisition replaces it. Fix retention, network ops, and billing before the bleed compounds.

We build churn prediction engines, network monitoring dashboards, self-service subscriber portals, and billing transparency tools for telecom operators. Our systems stop revenue leakage at the points that matter most - retention, support, and capacity.

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35%

Churn reduction

60%

Support cost savings

Overview

Subscriber churn is a compounding problem - and spreadsheets won't solve it

Telecom software development at 1Raft targets the three revenue leaks that cost operators the most: subscriber churn, support escalation volume, and network downtime. We bring patterns from 100+ products across adjacent industries to build churn prediction models, real-time network intelligence, capacity planning tools, and self-service portals - each engineered to reduce cost-to-serve while improving subscriber experience.

Telecom operators lose 1.5-3% of subscribers every month to churn, and most don't see it coming until the cancellation request hits. The signals are there - billing complaints, repeated support calls, usage drop-offs - but they're buried across disconnected systems that no human team can monitor at scale.

Meanwhile, network operations teams fight fires instead of preventing them. Capacity planning relies on quarterly reports instead of real-time demand signals. Customer support agents handle the same billing questions thousands of times a month while complex technical issues wait in queue.

We build software that connects these dots. Churn models that flag at-risk subscribers weeks before cancellation. Network dashboards that predict congestion before it degrades service. Self-service portals that handle 60-70% of support volume without agent involvement. Every product integrates with your existing BSS/OSS stack - we don't ask you to rip and replace.

Experience Signal

1Raft builds churn prediction engines, network monitoring dashboards, self-service subscriber portals, and billing transparency systems for telecom operators. Our engineering draws on patterns validated across 100+ products in adjacent industries - including anomaly detection models first built for insurance and healthcare.

35%

Churn reduction

60%

Support cost savings

Industry Pain Points

What's broken in telecommunications

01

Monthly subscriber churn rates of 1.5-3% cost operators $50-200M annually in lost lifetime value

02

Call centers handle 4-6 million support contacts per month, with 40% being routine billing inquiries an automated system could resolve

03

Network outages take 45-90 minutes to detect and diagnose because monitoring tools aren't correlated across layers

04

Capacity planning uses 6-month-old traffic models, leading to over-provisioning in some regions and congestion in others

05

Billing disputes account for 22% of all customer complaints, eroding trust even when the charges are correct

Solutions

Problems we solve in telecommunications

Each solution is built from patterns we've validated across 100+ products. No experiments on your budget.

01

Churn Prediction and Intervention

Machine learning models that score every subscriber's churn risk weekly using usage patterns, billing history, support interactions, and network experience data. Triggers automated retention offers or routes high-value subscribers to dedicated save teams before they cancel.

02

Real-Time Network Intelligence

Unified dashboards that correlate alarms, performance metrics, and customer experience data across RAN, transport, and core layers. Surfaces degradation patterns and predicts failures 30-60 minutes before they impact subscribers.

03

Self-Service Subscriber Portal

AI-powered portal and mobile app where subscribers manage plans, troubleshoot issues, pay bills, and upgrade services without calling support. Handles plan comparison, usage alerts, and billing explanations through conversational interfaces.

04

Capacity Planning and Optimization

Demand forecasting models that predict traffic patterns by cell site, time of day, and event calendar. Generates capacity recommendations that balance CapEx efficiency with QoS commitments.

05

Billing Transparency Engine

Real-time charge explanation system that breaks down every line item in plain language. Proactively alerts subscribers to unusual charges, overage risks, and better-fit plans - reducing billing disputes by 40-55%.

Use Cases

Real-world use cases

Churn Prediction for a Regional Mobile Operator

Problem

A mobile operator with 2.8M subscribers was losing 2.1% monthly - well above the industry benchmark of 1.4%. Their retention team worked from static lists updated quarterly, reaching at-risk customers weeks too late.

What we built

We built a churn scoring model trained on 18 months of usage, billing, support, and network quality data. The system flagged high-risk subscribers 3-4 weeks before likely cancellation and triggered personalized retention offers through SMS, app push, and agent queues.

Result

Monthly churn dropped from 2.1% to 1.4% within two quarters. The retention team's save rate improved from 12% to 31%. Annualized revenue impact exceeded $18M in preserved subscriber lifetime value.

Self-Service Portal for a Broadband Provider

Problem

A broadband provider's call center handled 380K calls per month. 43% were billing questions, plan changes, or troubleshooting steps that didn't require a live agent. Average handle time was 11 minutes per call.

What we built

We built a self-service portal with AI-driven troubleshooting, plain-language billing breakdown, one-click plan changes, and outage status. A conversational assistant guided subscribers through common issues with step-by-step resolution.

Result

Self-service adoption reached 61% of eligible contacts within 4 months. Call volume dropped by 148K calls/month. Annual support cost savings exceeded $6.2M while CSAT for self-service interactions scored 4.3/5.

Network Monitoring Dashboard for a Multi-Region Carrier

Problem

A carrier operating across 3 regions used separate monitoring tools for RAN, transport, and core. Mean time to detect (MTTD) network issues was 52 minutes, and cross-domain correlation required manual war-room escalation.

What we built

We built a unified network intelligence layer that ingested alarms and performance data from all domains, correlated events automatically, and surfaced root-cause hypotheses. Predictive models flagged degradation trends before threshold breaches.

Result

MTTD dropped from 52 minutes to 8 minutes. Cross-domain incident resolution time fell 64%. Subscriber-impacting outages decreased 38% in the first quarter.

Our Approach

How we approach telecommunications projects

1
Phase 1· Weeks 1-2

Subscriber and Network Data Audit

We audit your subscriber data quality, BSS/OSS integration points, support ticket patterns, and network monitoring coverage. We quantify revenue leakage from churn, support costs, and network incidents.

Deliverables

  • Revenue leakage analysis across churn, support, and network downtime
  • Data readiness assessment for ML model training
  • Prioritized opportunity list ranked by revenue impact and implementation speed
2
Phase 2· Weeks 3-4

Architecture and Integration Design

We design the product architecture and map every integration - billing platform, CRM, network management systems, provisioning, and data warehouse. Data pipelines are specified for real-time and batch processing.

Deliverables

  • Technical architecture with BSS/OSS integration specifications
  • Data pipeline design for subscriber and network telemetry
  • Phased delivery roadmap with milestone definitions
3
Phase 3· Weeks 5-10

Build and Controlled Pilot

We build in sprints and deploy to a controlled subscriber segment or network region first. Real subscriber interactions and network data validate model accuracy before wider rollout.

Deliverables

  • Working product deployed to pilot segment
  • Model accuracy and business impact metrics from real data
  • Iteration backlog based on pilot learnings
4
Phase 4· Weeks 11-14

Full Rollout and Continuous Optimization

We roll out across all subscriber segments and network regions with segment-specific tuning. Ongoing model retraining and monitoring keeps accuracy improving over time.

Deliverables

  • Full deployment across subscriber base and network footprint
  • Operational dashboards for churn, support, and network KPIs
  • Model retraining schedule and optimization plan

Outcomes

Measurable outcomes

20-35% reduction in monthly subscriber churn through predictive intervention
40-60% decrease in support ticket volume via self-service adoption
50-70% faster network incident detection and resolution
30-45% reduction in billing-related complaints through proactive transparency

Pattern Transfer

1Raft first built anomaly detection and risk scoring models for insurance claims - flagging unusual patterns in high-volume transaction data. That same architecture - real-time feature extraction, probabilistic scoring, automated triage - is exactly what powers our telecom churn prediction and network fault detection. When you've seen the pattern in one industry, you ship it faster in the next.

Services

Services for telecommunications

Frequently asked questions

We train ML models on your subscriber data - usage patterns, billing history, support contacts, network quality per cell site, and plan changes. The model scores every subscriber weekly and flags those with rising churn probability. Your retention team gets prioritized lists with recommended interventions, not just risk scores.

Next Step

Every month without churn prediction costs you 1.5-3% of your subscriber base.

One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.

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