Problem Statement

HR or project teams would typically want to analyse utilisation analysis, and uncover opportunities to drive efficiencies. Typical insights focus on identifying attrition risks, workload management and optimise incentives with data driven reward & recognition schemes.

An example of Utilisation & Staffing data structure

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Please refer to this article for more information about preparing data.

Segments

A segment is a qualitative value, like names or categories. Here are some of the typical segments we find in utilisation & staffing data:

  • Employee attributes: Employee Name, Employee Location, Employment Status, Employee Position, Team, Service Line, etc.

  • Engagement/client attributes: Engagement Industry, Engagement Code Name, Client Code, Client Name, Engagement Profit Centre, etc.

Measures

A measure is a quantitative, numeric value. Here are some of the typical measures we find in support & service data:

  • Hours charged

  • Time standard

  • Revenue

What sort of insights can Auto Insights help me uncover?

Automate utilisation analysis

  • Hours charged by Engagement Industry

  • Hours charged by Employee Position

  • Hours charged by Team

  • Hours charged by Client

  • Actual recurring revenue by Engagement

Analyse staff productivity

  • Hours charged by Employee Position

  • Hours charged by Employee Position and Employee Location

  • Actual recurring revenue growth by Client last quarter

Uncover opportunities to drive efficiencies

  • Revenue by Team

  • Average Engagement Realisation Percentage by Service Line

  • Actual Recurring Revenue by Client

Related material

How to prepare data

How to upload data
Quick guide to Auto Insights

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If you have any questions, please contact us at support@hyperanna.com. 

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