Customer support performance
An overview of your support: ticket volume, resolution time and first-contact resolution rate (FCR).
Ticket volumeResolution timeFCR (first contact)
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Customer Service · SAP Analytics Cloud
SAP Analytics Cloud templates for Customer Service
4 ready-to-use SAC and SAC Planning templates for Customer Service — each with the KPIs, dimensions and realistic data your function actually tracks. Import in minutes, or generate a tailored one. No sign-up.
KPIs we cover for Customer Service
Tickets openedResolution timeFCR (First Call Resolution)Satisfaction rate (CSAT)NPSBacklogCost per ticketEscalation rate
Analysis dimensions
Channel (Mail/Phone/Chat)PriorityTicket categoryTeamSupport tierRegion
4 Customer Service templates
Each opens with a live preview and a one-click download (.xlsx, .csv, .package).
SLA monitoring
Monitor service-level commitments: first response time, resolution times and SLA breaches.
% SLA metFirst response timeAverage resolution time
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Voice of the customer (VoC)
Analyze satisfaction, NPS and customer effort (CES). Categorize verbatims to spot pain points.
CSATNPSCES
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Contact center planning
Plan your call-center staffing: FTEs required, occupancy, schedule adherence and volume variance.
FTE requiredAgent occupancySchedule adherence
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How to model Customer Service in SAP Analytics Cloud
A solid Customer Service model starts with the right grain. Build your dimensions first (Channel (Mail/Phone/Chat), Priority, Ticket category), then add the measures that matter (Tickets opened, Resolution time, FCR (First Call Resolution)). The trap is aggregation: amounts and volumes sum, but rates and percentages must use AVERAGE, and stocks or balances must use a LAST exception on the time dimension — otherwise your yearly totals come out wrong.
Set those aggregations once in the Modeler, import your data, and build a Story from the KPIs above. For the full method, see our guides on importing a CSV into SAC and choosing the right aggregation.