A reality, and a recently growing pain in organizations, is that of job turnover rates that are lived in Latin America, where there are average ranges that go from 13 to 33% in countries such as Argentina, Paraguay, Peru, Ecuador, Brazil and Mexico, according to the International Labor Review published by the International Labor Organization.
The staff turnover It is a common pain for companies, a movement that comes to represent a cost of up to 400% of the salary of the employee who leaves the company, according to a Deloitte study. Seeking to solve this situation through an analytical approach allows us to better identify and understand the problem, especially establishing an action plan based on the data, focusing and attacking the root causes.
A few years ago we faced this pain in my company. A team dedicated to analyze and identify the factors that were motivating the voluntary departure of employees. In my case, it was in the Logistics Operations area where we had a staff turnover of 38% in delivery positions.
The first step was to formulate our guiding question of the projectand that helped us and allowed us to communicate to our sponsors and main interest groups the progress and discoveries of the project: How can we reduce the voluntary turnover of the delivery team?
Second, the challenge was to estimate the direct cost of turnover based on the information that was available at that time: cost of recruitment and cost of boarding. Naturally, this gave us a value of the impact that our project could have and started conversations with the various actors involved in the logistics process to find out their hypotheses or beliefs as to why people decided to leave the company. I had about eight interviews between leaders of the logistics area, human capital managers and commercial teams to capture the different perspectives of the problem and list the different sources of information and data to collect to start the analysis.
One challenge, which probably many have faced or have faced, is to have the largest amount of data necessary to analytically discuss each of the various hypotheses that were built and that will allow us to corroborate whether they are true or not supported by the data. Of the 40 hypotheses proposed, and considering the available data, it was possible to work with 15 of them; another five were more partially analyzed.
The result of this first analysis brought us the first discoveries on the factors that tended to motivate the departure of collaborators:
- street hours. The greater the number of street hours, the risk of leaving increased by 20%.
- operational training. The lower the number of hours of operational training, the risk of leaving increased by 15%.
- Distance from work. For people who were more than 30 kilometers away from the workplace, the risk of leaving increased by 25%.
The generation and selection of the main insights analytical allowed to co-create an action plan together with the leaders of the operation and human capital to define a pilot audience and execute the actions of the plan, monitoring the progress of the turnover indicator in certain locations and thereby validating the causality and replicating the improvement actions in the rest of the centres.
After three months of carrying out the action plan in the pilot hearings, we were able to confirm that two of the three main factors identified presented a direct link to staff departure: street hours and operational training. This allowed us to collaborate with the functional area to optimize the process of creating delivery routes and thus reduce people’s hours on the road, as well as redesign the operational training program in order to ensure learning and understanding of the operation of the new income, obtaining a 10% reduction in turnover at the end of the year. This motivated us to continue iterating new analysis cycles to continue providing better results, services and experiences to our people.
I ask you: What will be your next initiative in human capital supported in an analytical “lens”? Here are some ideas to start and promote a data-driven culture:
- Predict who will resign
- Link store revenue to people engagement
- Check the effectiveness of employee training
- Dealing with employee absenteeism
- Measurement of employee engagement
- Active listening during a process of change
- Make compensation and benefit packages more flexible
As I have said in this series of texts on people analyticsa solid analytical culture will strengthen decision-making for the good of people and organizations.