Strategic workforce planning

Attrition management

Faced challenges

The leading automotive manufacturing company Continental wants to reduce the attrition ratio in Mexico. Together, we set up an analytics project to create transparency on attrition drivers, define target groups of attrition, and derive tailored, location-specific measures to reduce attrition.

„With the analytics of HRForecast, we were able to define location-specific measures tailored to our different employee clusters to achieve our attrition targets. Data Analytics is the future.“

Marco Galluzzi, HR Country Head Mexico

Questions to be clarified:

What are the main employee clusters affected by attrition and how can they be described? What is the financial impact of each of these clusters?

What are the drivers behind voluntary and forced attrition for each of the identified employee clusters?

How can the attrition hot-spots be described? What are the local differences throughout the country?

What are tailored measures that can be implemented to reduce attrition?

Project approach

1. Validation of available data

In a data workshop, we identified relevant data sources. The key sources of interest are primary data and unstructured information derived from exit interviews with leavers. Then, we set up a data model to integrate the data.

2. Build attrition model

Machine-learning algorithms enable the extraction of attrition drivers from the unstructured exit interviews. Analytics algorithms are then applied to bring transparency into the data set and derive location-specific insights on the questions.

3. Measures workshop

In a joint on-premise workshop with HR leaders across the country, suitable measures for each target group are discussed and allocated to the locations. Then we defined attrition targets follow-up activities for each site.

Key insights & value-adds for the client

The client:

used the analytics results to derive location-specific measures tailored for the attrition clusters
created action plans for follow-up activities
turned unstructured exit interviews into valuable insights
was able to simplify the understanding of different types of employees