TRUGO CONSULTANCY SERVICES PVT. LTD.
Recruitment Workflow Management
How an HR firm prioritized open roles to minimize the time taken to filled open roles.
A leading European firm struggles to prioritize the open roles that they work at a given time, resulting in higher vacant positions. The agents pick any opening randomly and the hire rate was very bad as compared to the competitors.
A simple Python-based analytical model was put into place providing the ranking to each open vacancy to be worked upon.
Understand the Data & The Problem
Design a predictive model using python
Implement the solution and train the employees
Time to fill
The average time taken to fill a role is reduced by 28%
Implement the solution and train theBetter data visualisation using graphs led to quick decision making employees
A simple excel based solution was provided
The recruitment firm was not having any prioritization algorithm to work on open positions resulting in poor customer satisfaction.
One of the leading recruitment firms in Europe with offices in 3 continents is aggressive in increasing its market share. Recruitment agents used to fill roles which are primarily segmented into 3 levels:
Executive Level Roles
But there was no mechanism that workflow managers can use to guide the team around how to prioritize from a big list.
Historical data was analyzed to understand the time taken to fill open roles based on various characteristics like the level of the role, location of the role, and location of the office managing the open role. A regression model was fitted on the data. Time and Out of sample testing was done to gain confidence in the predictive power of the model. The ultimate solution showed clear bifurcation on the behavior of different segments.
Workflow managers who allocate the cases to the recruitment agents were given an excel based dashboard where they can find out which top positions they should work on based on simple data inputs.
Project type – Predictive Modelling
Industry – Recruitment
Technology – Python
Delivery Time – 2 weeks
Consultants worked – 01