Products & Solutions / Service Intelligence

SAS® for Service Operations Optimization

Transform your service organization from a tactical cost center to a strategic profit center

SAS Service Operations Optimization helps you monitor, analyze and optimize service operations. By optimizing resource utilization, SAS Service Operations Optimization reduces the overall costs of serving and managing existing customers. It also increases customer lifetime value by enabling more product and service cross- and up-sell opportunities.


  • Optimized customer satisfaction levels.
  • Increased productivity.
  • Quality improvement and management.

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  • Call pattern analysis.
  • Call resolution analysis.
  • Financial analysis.
  • Suspect claims analysis.
  • Performance ranking.

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SAS Supply Chain Intelligence Center


How SAS® Is Different

  • Forecasting:  As the leader in forecasting techniques that go beyond simple descriptive statistics, SAS provides accurate and granular call forecasting that yields meaningful analyses to precisely predict staff demand and training needs for optimum results.
  • Root Cause Analysis:  To reduce unwanted calls and improve the management of received calls, you must understand the root causes. SAS provides powerful filtering capabilities (along with Pareto, geographical, etc. views), so that early warnings of emerging issues help to save millions of dollars, protect brand image and increase customer satisfaction.
  • Supply Chain Intelligence Data Model:  Closely tied to sales, warranty and spares management where data may be stored in siloed IT and telephony systems, service demand analysis requires access to multitudes of data to improve service call forecast accuracy and the efficiency of root cause analysis. SAS provides a holistic view of KPIs and service operations through data integration and quality, combined with a comprehensive domain-driven data model for effective management.
  • Suspect Claim Analysis:  Suspect claim recovery can be significantly increased by providing better analysis on whom to audit and analytically reviewing all claims for differences from the norm (in both parts and labor). With SAS, automatically interrogate service provider failure and labor cost with comparison tests to identify potential provider issues and uncover miscoded and fraudulent claims before they are settled.


  • Optimized customer satisfaction levels. SAS for service operations optimization measures, identifies and optimizes customer satisfaction levels throughout the customer life cycle, including measuring the impact that individual agents have on satisfaction levels. The solution also identifies the root cause of customer dissatisfaction so you can quickly resolve both individual and systemic issues.
  • Increased productivity. SAS for service operations optimization monitors agent and technician performance to identify areas for improvement. By providing near-real-time, comprehensive performance dashboards, the solution allows agents and managers to quickly identify both individual and departmental issues for improved performance, optimization of staff resources and the elimination of unproductive activities.
  • Quality improvement and management. SAS for service operations optimization rapidly identifies quality issues and trends occurring at the individual, team or departmental level, allowing the quick resolution of any problems. Management and agents receive continuous feedback so they can improve the overall quality of the customer service experience.


Call pattern analysis.
  • Number of calls received in a period.
  • Call pickup time.
  • Call resolved on phone (not routed).
  • Call dropped or abandoned before pickup.
  • Call dropped or abandoned before resolving.
  • Top reasons for call.
  • Top products called for.
  • Calls not completed.
  • Calls repeated.
  • Maximum call hold period.
  • Average call hold.
  • First-call-complete percentage.
  • Number of products serviced in a day.
  • Number of calls handled in a day.
  • Technician and agent man-hours available.
  • Revenue per technician and agent man-hours.

Call resolution analysis.
  • First-call-resolution analysis.
  • Incomplete-call analysis.
  • Mean-time-to-repair analysis.
  • Repeat-failure analysis.
  • Skill analysis report group.
  • Deferral analysis report.
Financial analysis.
  • Cost-per-call analysis.
  • Cost-by-product analysis.
  • Cost-by-account-head analysis.
  • Cost-by-technician and -agent analysis.
  • Revenue-by-product analysis.
  • Revenue-by-account-head analysis.
  • Revenue-by-technician and -agent analysis.
  • New contract sale.
  • Revenue forecasting.

Suspect claims analysis.
  • By service provider.
  • Claim ID number.
  • High labor amount (suspect indicator).
  • High overhead amount (suspect indicator).
  • High material cost amount (suspect indicator).

Performance ranking.
  • Pareto analysis.
  • Trend charts.
  • Forecasting.
  • Analysis of means (ANOM).
  • Correlation analysis.
  • Geographical analysis.


SAS Supply Chain Intelligence Center

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Supply Chain Solutions Service

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System Requirements

Operating system requirements

SAS for service operations optimization leverages the SAS Service Intelligence 3.1 architecture. The server tier is implemented in SAS as part of the SAS 9.1 foundation.

Middle tier

The SAS Service Intelligence architecture middle tier component enables automatic optimization of data and can be installed in the following operating environments:

  • AIX
  • HP IPF
  • Solaris SPARC
  • Solaris on x64: Version 10, Update 3
  • Windows Server 2003 (32-bit)


The SAS Service Intelligence architecture client is a Java-based client application. This client can be installed in any 32-bit Windows environments and supports the following databases:

  • DB2 8.2
  • Oracle Database 10G

Please contact your SAS representative with any additional questions about technical requirements.


Ready to learn more?

Call us at 1-800-727-0025 (US and Canada) or request more information.