3 August 2023


The company we are taking as an example is facing a significant issue. It is experiencing approximately 5% to 20% of staff turnover per year. The two main challenges for the company are:

  • to understand why employees are leaving
  • to remedy the causes – where possible – in order to better manage its workforce

To achieve the above, the company could use surveys and interviews with employees that are leaving to identify the reasons for their departure. The company could also – but this is more difficult – use replies to the surveys to monitor the mind-set and wellbeing of employees and identify trends that could point to imminent departure.

The surveys, usually sent out by the Human Resources (HR) department, are often not completed very well or not at all by employees. Often, the employee concerned does not give any reasons for this, and does not necessarily want to comment on the subject. This can lead to misinterpretations or a lack of conclusions for both the employee and the company. To ensure that the survey is completed, the employer must specify that it is voluntary, and that it is important to understand the reasons for the employee’s departure so that the organisation can evolve. Employers must provide a framework and a medium that everyone would be able to use, and they need to consider what kind of survey (written, video or audio) would be easiest for the employee to complete. There is no right answer, as it depends on how tech savvy the employees are, their age group, and the geographical area. Furthermore, the survey and all related information must be secure and protected. Then the collected data has to be analysed. For a company that manages few departures a year, this is not an issue. However, it becomes more complex for a multinational with thousands of employees, different cultures and geographically scattered sites. What solutions best address these issues?

How can all the data be analysed in a reasonable amount of time in order to identify the causes of departures and detect areas of improvement?

OPPSCIENCE’s Intelligence Analysis Management (IAM) solution is composed of several AI modules that analyse and semantically process surveys on employees’ departure reasons. In the case of audio or video surveys, OPPSCIENCE’s solution analyses the text transcription of recordings. The IAM platform uses natural language processing algorithms to extract information from processed texts. This lets the user discover relationships and patterns hidden in textual data. In addition, it provides a graphical user interface to view analysed data and identified relationships. On a text medium, the platform is able to detect keywords, subjects and the feelings of employees. Next, the platform creates visual elements so that the user can clearly grasp the results. This means that the user can comprehend large volumes of textual data that could not be processed by humans or compared with other similar data.

The analysis of the departure reasons allows the user to extract details of the multiple varied causes, which may be similar to those of another employee having left a few months earlier. With this, potential triggers can be identified and measures can be taken to avoid a similar situation in the future. It also enables the company to improve its HR policies and procedures to ensure better working conditions for employees and improve the overall employee retention.

In conclusion, the intelligence provided by the IAM of OPPSCIENCE with regard to certain business subjects such as the analysis of staff resignations increases the quality of the data used to plan HR strategies. IAM helps businesses to understand the data landscape quickly, provide the right resources to the right people, and eventually make the right decisions. IAM enables companies to identify areas of improvement and implement processes that reduce the risk of errors in staff management.