Financial Service Provider D
Which personal characteristics, work and organizational conditions lead to absence? Read how AnalitiQs tackled this absence analysis.
Our client is a financial services provider, operating in more than 20 countries. The Netherlands is an important market for this company.
The Dutch branch has around 4,500 employees and more than half of the company’s costs are staff related. The employees are a key to success, but also the company’s major expense.
The organization considers the employees to be of great importance and wishes to make their HR processes more data-driven.
AnalitiQs received the request to analyse the current situation around data-driven HR, to formulate an ambition together with the HR management team, to turn this ambition into a roadmap and to assist with the first implementation phase of the roadmap. This phase included an HR-analytics pilot.
To start with, we studied the business questions that had come up during the baseline assessment, to see if any of them were suitable for the HR-analytics pilot. We eventually decided to use a classic HR-theme: absence. Reasons for this were on one hand, to keep the stakeholder landscape simple and on the other hand, to limit the risk factors during the start-up phase of the data-driven organization.
Once the theme was selected, we established a small project team. Our client provided the HR experts and the Data owners for the team. AnalitiQs provided a Data scientist and a Project manager. The entire HR-analytics process (determining business question, turning business question into analysis questions, collecting data, preparing data, analysing and generating insights) was managed with an agile project approach in combination with a few brief in-depth sessions.
The purpose of the analysis was to explain absenteeism. In other words, which personal characteristics, work and organizational conditions were leading to absenteeism? The concept of absenteeism was divided into absence frequency and absence duration and special attention was given to long-term absence from work.
The necessary data were obtained from various operational HR systems. Additional data from an employee survey were used, too. The total data set contained around 150 variables. After compiling all the data, we generated insights using a number of different statistical methods, including:
- Variance Analysis
- Regression Analysis
- Random Forest Analysis
- CHAID Analysis
The software we used included Excel, SPSS, R and Tableau.
The most important end product was an MS PowerPoint document. This document described causes and risk groups for absence frequency and duration as well as for long-term absence. It included overviews for the company as a whole, as well as for certain subgroups such as departments and operational employees compared with managers.
The end product gave the financial service provider a tool based on facts, which will help them develop interventions and policies aimed at specific groups. The analysis helped the organization in understanding if they were doing the right things to reduce absenteeism and will also help in using their budgets in a more focused way. Ultimately, this should lead to a significant reduction of absenteeism and consequently, a reduction of non-productive working hours.