Customer Service · Analytics ·

Customer support performance

An overview of your support: ticket volume, resolution time and first-contact resolution rate (FCR).

Illustrative preview of the SAP Analytics Cloud dashboard Customer support performance for the Customer Service industry: metrics Ticket volume, Resolution time, FCR (first contact), Backlog, analyzed by Channel, Team, Request type.
Illustrative preview of a possible rendering in SAC. Brand colors and structure; synthetic figures.

KPIs included

  • Ticket volume
  • Resolution time
  • FCR (first contact)
  • Backlog

Analysis dimensions

  • Channel
  • Team
  • Request type

About this template

An overview of your support: ticket volume, resolution time and first-contact resolution rate (FCR). Designed for teams in the Customer Service industry, the model pre-wires 4 key metrics — including Ticket volume and Resolution time — analyzable across 3 analysis axes (Channel, Team, Request type). You start from an already-bounded base (units, aggregations and business labels defined) rather than a blank sheet.

After downloading, import Customer support performance 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 Customer Service 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 "Customer support performance" template for?

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

Which KPIs are included?

The template includes 4 metrics: Ticket volume, Resolution time, FCR (first contact), Backlog. Each is computed across the dimensions Channel, Team, Request type.

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.