Employee demission tendency detection method

Abstract

The invention discloses an employee demission tendency detection method, and aims at solving the problems existing in existing demission tendency judgment. The method comprises the following specificsteps: step 1, collecting original data of employees; step 2, integrating the original data, constructing internal features by adopting a UEBA method (a user and entity behavior analysis method), andestablishing a threat model; and step 3, establishing a user portrait system and a group analysis system according to the threat model and the original data to judge whether a threat exists or not, ifthe judgment result is that the threat or abnormity exists, outputting warning information to human resource management, and if the judgment result is that no threat or abnormity exists, stopping working. According to the invention, an internal employee behavior analysis method is established by using various intranet data such as OA system data, and the change of the work emotion and attitude ofthe employee is discovered in time, so that the departure tendency of the employee is analyzed and judged, the loss of talents and assets can be stopped in time, and the application prospect is wide.

Description

technical field [0001] The invention relates to the field of employee resignation tendency detection, in particular to a method for detecting employee resignation tendency. Background technique [0002] The normal flow of employees will undoubtedly bring positive impacts on the social economy, such as increasing the vitality of enterprises, stimulating competition for talents, and promoting the rational allocation of human resources. The business development strategy and corporate image cause significant losses, and sometimes even have disastrous consequences. The loss of employees is inevitable for the enterprise. For the employee resignation that has already occurred, what the enterprise can do is to minimize the negative impact of the employee resignation on the work. [0003] Human resource management is in its infancy, and enterprises have begun to build human resource platforms. In the early stage, this platform may be built by internal IT personnel of the enterprise,...

AI Technical Summary

Problems solved by technology

However, there is still no complete business data analysis model, and it is not possible to predict the hidden risks of enterprises based on data.

The human resources department is often not aware of employee resignation. They only rely on the relationship between employees, the frequency of leave, and the leader's stalking to judge the employee's work status, and judge whether the employee has resigned based on factors such as the entry time, employee age, and family. What's more, when the competition among enterprises is fierce, the company often fails to find out in time the batch of talents lost by "poaching". A large number of potential customer resources are often lost, and the loss outweighs the gain

[0005] At this time, digital human resources have become a hot topic in recent years. In traditional human resource strategies, HR relies more on intuition or past management practices; but in today's world where business forms, science and technology, and workforce profiles have all undergone tremendous changes Under such circumstances, the role of past experience or personal intuition is weakening day by day. This method makes human resource management often need to spend a lot of manpower and material resources to do some things that are based on facts. This approach cannot stop losses for the company in time, nor can it Retain talents in a timely manner. At the same time, if a misjudgment is made, it will also lead to a poor company atmosphere and a tense relationship with colleagues. What's more, during the resignation period, they can get away with doing things that harm the collective interests by using their positions, causing headaches for managers.

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