Telecom · Analytics ·

Telecom churn & retention

Analyze churn: causes, at-risk segments and retention-campaign effectiveness.

Illustrative preview of the SAP Analytics Cloud dashboard Telecom churn & retention for the Telecom industry: metrics Churn rate, Win-back rate, Customer acquisition cost, MNP (portability), analyzed by Plan / Package, Subscription channel, Tenure.
Illustrative preview of a possible rendering in SAC. Brand colors and structure; synthetic figures.

KPIs included

  • Churn rate
  • Win-back rate
  • Customer acquisition cost
  • MNP (portability)

Analysis dimensions

  • Plan / Package
  • Subscription channel
  • Tenure

About this template

Analyze churn: causes, at-risk segments and retention-campaign effectiveness. Designed for teams in the Telecom industry, the model pre-wires 4 key metrics — including Churn rate and Win-back rate — analyzable across 3 analysis axes (Plan / Package, Subscription channel, Tenure). You start from an already-bounded base (units, aggregations and business labels defined) rather than a blank sheet.

After downloading, import Telecom churn & retention into SAC Modeler (Files → New Model → Import data from a file), map the 3 dimensions and 4 measures, then build your Story. The provided dataset contains 720 to 960 rows with realistic values for the Telecom industry, available as .xlsx (multi-sheet workbook), .csv (flat table) and .package (ZIP bundle with model.json, data.csv and README).

FAQ

What is the "Telecom churn & retention" template for?

It provides a ready-to-use SAC structure to drive analytics in the Telecom industry. The standard business KPIs and dimensions are already defined, saving you the modeling phase.

Which KPIs are included?

The template includes 4 metrics: Churn rate, Win-back rate, Customer acquisition cost, MNP (portability). Each is computed across the dimensions Plan / Package, Subscription channel, Tenure.

How do I import it into SAP Analytics Cloud?

Download the .csv or .xlsx format, then in SAC: Files → New Model → Import data from a file. Map the columns (Dimensions then Measures), validate the types and build your Story. Allow 5 to 10 minutes for an operational model.