Telecommunications

Cut churn. Kill support queues. Catch network faults before they page the ops team.

We build churn prediction engines, network monitoring dashboards, self-service subscriber portals, and billing transparency tools in 12 weeks. Software that stops the revenue leaks telecom operators can't see - retention, support, and capacity.

Get a telecom AI assessmentSee relevant work
11+

Telecom products

35%

Churn reduction

60%

Support cost savings

Overview

Churn is compounding faster than your retention team can keep up

Telecom software development at RaftLabs 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 cut cost-to-serve while protecting subscriber experience.

Telecom operators lose 1.5-3% of subscribers every month. Most don't see it coming until the cancellation request lands. The warning signs are there - billing complaints, repeated support calls, usage drop-offs - but they're scattered across BSS, OSS, CRM, and network tools that no human team can watch at scale.

Meanwhile, network ops teams fight fires instead of preventing them. Capacity planning runs on quarterly reports instead of real-time demand. Support agents answer the same 40 billing questions thousands of times a month while complex technical tickets pile up behind them.

Our churn models flag at-risk subscribers 3-4 weeks before they cancel. Network dashboards correlate RAN, transport, and core alarms into one view. Self-service portals handle 60-70% of support volume without an agent. Every product integrates with your existing BSS/OSS stack - Amdocs, CSG, Netcracker, Salesforce, Nokia, Ericsson. No rip-and-replace.

Experience Signal

11+ telecom products shipped with churn prediction engines, network monitoring dashboards, self-service subscriber portals, and billing transparency systems for mobile operators, broadband providers, and multi-region carriers. 12 weeks is our default, not our stretch goal.

11+

Telecom products

35%

Churn reduction

Industry Pain Points

What's broken in telecommunications

01

Monthly subscriber churn of 1.5-3% costs operators $50M-$200M annually in lost lifetime value, and retention teams work from lists updated quarterly

02

Call centers handle 4-6 million contacts per month with 40% of them being routine billing questions a self-service flow could resolve

03

Mean time to detect network issues runs 45-90 minutes because alarms aren't correlated across RAN, transport, and core

04

Capacity planning uses 6-month-old traffic models, driving 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

06

Field service dispatch relies on static routing, leaving technicians with 30-40% drive time and missed SLA windows on high-priority tickets

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

ML models score every subscriber's churn risk weekly using usage patterns, billing history, support interactions, and network quality per cell site. Triggers automated retention offers or routes high-value subscribers to dedicated save teams 3-4 weeks before cancellation.

02

Real-time network intelligence

Unified dashboards 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. Conversational assistant walks through plan comparisons, usage alerts, and billing explanations in plain language.

04

Capacity planning and optimization

Demand forecasting by cell site, time of day, and event calendar. Generates capacity recommendations that balance CapEx efficiency with QoS commitments and flags sites heading for congestion before subscribers feel it.

05

Billing transparency engine

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

06

Field service and technician dispatch

Dynamic dispatch that optimizes routes across technician skills, parts inventory, SLA windows, and live traffic. Cuts drive time and improves first-time fix rates by sending the right tech with the right parts to the right job.

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 1.4% industry benchmark. The 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 climbed 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 need a live agent. Average handle time ran 11 minutes per call.

What we built

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

Result

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

Network monitoring 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 was 52 minutes, and cross-domain correlation required manual war-room escalation.

What we built

We built a unified network intelligence layer that ingests alarms and performance data from all domains, correlates events automatically, and surfaces 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· Week 1-2

Subscriber and network data audit

We audit your subscriber data quality, BSS/OSS integration points, support ticket patterns, and network monitoring coverage. Revenue leakage from churn, support costs, and network incidents gets quantified using your actual numbers.

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· Week 2-4

Architecture and integration design

We design the product architecture and map every integration - billing, CRM, network management, provisioning, and data warehouse. Data pipelines get 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· Week 4-10

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· Week 10-12

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 as the system sees more data.

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 through self-service adoption
50-70% faster network incident detection and resolution
30-45% reduction in billing-related complaints through proactive transparency
25-35% improvement in field technician first-time fix rates through dynamic dispatch

Free Tools

Free tools for telecommunications

Calculators and assessments built from real project data. No signup required.

See all free tools

Pattern Transfer

RaftLabs first built anomaly detection and risk scoring models for insurance claims - flagging unusual patterns in high-volume transaction data. The same architecture - real-time feature extraction, probabilistic scoring, automated triage - now 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.

Get a telecom AI assessment
View case studies