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Telco analytics that runs inside your own environment. AI text-to-SQL BI for high-volume CDR and network data, churn, ARPU, and uptime, with full data residency and no per-seat pricing. Book a demo.
By Anusha Maduri, Marketing & Content Specialist, Analytify AI · Updated June 10, 2026
Telco analytics is business intelligence for carriers, mobile operators, and internet service providers, where the data arrives in billions of records a day, the questions are about churn and uptime in the same breath, and where the data lives is a real constraint. Analytify gives telcos, MVNOs, and broadband providers an AI-powered platform that runs entirely inside their own environment, so analysis of call detail records, network telemetry, and subscriber behavior happens without that data ever leaving the operator's perimeter. It is the rare combination of generative analytics and full data residency that a high-volume carrier can actually deploy.
Every large analytics vendor will sell a carrier a cloud dashboard. Far fewer will let the operator keep petabyte-scale CDR data in its own infrastructure, run open-source software its own teams can audit, and still deliver plain-English, AI-driven answers. That gap, between what carriers are offered and what their network and data teams can actually run at scale, is exactly what a self-hosted telecom BI platform closes.
The distinction from ordinary SaaS analytics is the scale and the speed. A carrier does not analyze thousands of rows a day, it analyzes billions, and a slow answer about a degraded cell site or a churning segment is worth far less than a fast one. Telco analytics is the category that treats high data volume and real-time analytics as the starting point, not an upgrade.
Three forces make telecom analytics its own discipline. The data volume is enormous: a single operator's stream-processing pipeline can handle around 6 billion call detail records per day, and large carriers unify petabytes daily. The commercial stakes are high: roughly 30% of consumers switch providers each year, which makes churn and ARPU the metrics that decide a quarter. And fraud is a constant drain: the Communications Fraud Control Association estimates global telecom fraud losses at about 41.82 billion US dollars, roughly 2.5% of industry revenue.
The takeaway for a CDO or VP of network operations: the upside of analytics in telecom is measurable, but only if the platform can ingest carrier-scale data where it already governs it. A tool that forces a cloud upload of every CDR is a non-starter for the highest-volume, most sensitive workloads.
Average revenue per user by plan, segment, and region, margin by product, and revenue assurance that reconciles what was used against what was billed. This is the commercial core of carrier analytics, and it benefits directly from cohort analysis that tracks how revenue per subscriber moves over the life of each acquisition cohort.
Identify at-risk subscribers before they port out, model churn drivers across usage, billing, and support history, and measure the impact of save offers. With around 30% of consumers switching providers a year, a working churn model is the difference between holding a base and bleeding it, and predictive analytics is what turns a backward-looking churn report into an early warning.
Availability and uptime by cell site, region, and technology, dropped-call and packet-loss rates, latency, and capacity utilization. A real-time embedded analytics view lets network operations see degradation as it happens rather than in a next-day report.
Tie network quality of experience to satisfaction, track NPS and first-call resolution against service events, and see which network problems are driving which complaints. This is where network data and CX data finally meet on one network dashboard.
Minutes of use, data consumption per subscriber, plan mix, and overage patterns, so product and pricing teams can design plans against real behavior instead of guesswork.
Detect International Revenue Share Fraud, Wangiri callbacks, SIM-box bypass, and subscription fraud by scoring traffic patterns in real time. With fraud costing the industry tens of billions a year, this use case often pays for the platform on its own.
A strong network dashboard tracks the metrics the network team, the CFO, and the CX organization all watch. These are the core ones.
| KPI | What it measures | Why it matters |
|---|---|---|
| ARPU | Average revenue per user | Core commercial health of the base |
| Churn rate | Subscribers lost over a period | Retention and lifetime value |
| Network uptime / availability | Share of time the network is in service | Service reliability and SLA compliance |
| Minutes of use (MOU) | Voice usage per subscriber | Engagement and capacity planning |
| Data usage per subscriber | Average data consumed per sub | Plan design and network load |
| Net Promoter Score (NPS) | Subscriber willingness to recommend | Experience and loyalty signal |
| First-call resolution | Issues solved on the first contact | Support efficiency and CX |
| Subscriber growth | Net adds over time | Top-line momentum of the base |
Real time is where telco analytics earns its budget. A carrier that learns about a churn spike or a degraded sector a day late has already lost the chance to act. Instead of batch jobs that summarize yesterday, a streaming-capable platform scores CDRs and network events as they arrive, so fraud, service degradation, and revenue leakage surface in minutes. Two capabilities make this work at carrier scale. A semantic layer keeps definitions of ARPU, churn, and uptime consistent across every team and dashboard, so the network view and the finance view agree on the numbers. And a proper KPI dashboard built on that layer turns billions of raw records into the eight or ten metrics the business actually steers by.
This is the section every other telco analytics page skips, and it is the one that decides the deal. Analytify is a self-hosted BI tool. It runs on-premises, in your private cloud account, or in a hybrid footprint, so subscriber and CDR data stays inside your perimeter and never transits a vendor's cloud. For an operator weighing data-residency rules and the sheer cost of moving petabytes of telemetry off-network, that is not a preference, it is a requirement.
Because it is open source, the operator's own security and data teams can examine the code directly rather than trust a black box. Combined with full data residency, this is the deployment posture that lets a carrier adopt AI-driven analytics without inheriting cloud-egress cost or risk. The same self-hosted approach already underpins our work for managed service providers and across energy and utilities, two more industries where data volume and residency rule the decision.
Self-hosting does not mean giving up modern AI. Analytify brings generative BI and AI-powered business intelligence inside your environment, so a network or revenue analyst can ask a question in plain English and get a governed SQL query in return, all without the data leaving the operator's infrastructure.
Pairing AI text-to-SQL with carrier-scale data residency is the combination no large incumbent leads with, and it is the most defensible thing a telco analytics stack can offer. It also opens self-service to people who never wrote SQL, which is the point of a self-service analytics platform.
The incumbents are capable and well known. Cloudera reports that 80% of the top 100 telcos run on its platform, Amdocs and Ericsson are deep in the OSS and BSS stack, and Microsoft, Google, and the general BI vendors all push cloud-first telecom analytics. They are strong, but for a carrier the deciding factors are hosting, data volume, auditability, and cost.
| Capability | Cloudera / Amdocs / Power BI / cloud BI | Analytify |
|---|---|---|
| Self-hosted, on-prem, or hybrid by default | Often cloud-first or managed | Yes, by default |
| Open source and auditable code | No or partial | Yes |
| Data residency, no CDR cloud egress | Frequently requires vendor cloud | Data stays in your environment |
| High-volume CDR and network data | Yes, often at platform cost | Built for carrier-scale volume |
| AI text-to-SQL inside your environment | Cloud-based AI | Runs in your infrastructure |
| Licensing | Per seat or large platform contract | Platform license, no per-seat pricing |
For specific side-by-sides, see Analytify vs Tableau, Analytify vs Power BI, and Analytify vs Qlik Sense, or review pricing. Carriers standardizing on a warehouse can also see our Snowflake and BigQuery integrations.
It is business intelligence software carriers, mobile operators, and ISPs use to analyze subscriber behavior, network performance, revenue, and fraud. It must handle extreme data volume from CDRs and network telemetry and support real-time decisions.
Yes. Analytify is self-hosted and can run on-premises, in a private cloud account, or in a hybrid footprint, so subscriber and CDR data never leaves your environment or transits a vendor cloud.
It models churn drivers across usage, billing, and support data, scores subscribers by risk before they port out, and measures save-offer impact. Operators using closed-loop analytics have reported up to a 20% drop in churn alongside ARPU gains.
Yes. A streaming-capable platform scores call detail records and network events as they arrive, so fraud, degradation, and revenue leakage surface in minutes rather than in a next-day batch report.
ARPU, churn rate, network uptime and availability, minutes of use, data usage per subscriber, NPS, first-call resolution, and subscriber growth.
It scores traffic patterns in real time to catch International Revenue Share Fraud, Wangiri callbacks, SIM-box bypass, and subscription fraud. With global telecom fraud near 41.82 billion US dollars a year, this often justifies the platform on its own.
Yes, and the open code is an advantage. Network and security teams can inspect exactly what the software does, while self-hosting keeps high-volume subscriber data inside the operator and avoids the cost of moving petabytes off-network.
Incumbent telecom analytics tools are typically per-seat or large platform contracts. Analytify uses a platform license with no per-seat pricing on infrastructure you already run, which is usually far lower in total cost.
Book a walkthrough and we will show Analytify against a stack like yours, self-hosted, with no per-seat pricing.