Employee Attrition Analysis

Cohort B Team 4 Suraj Shah, Mengzhen Li, Xi Gong

Background

• SanDisk• Digital storage leader• Portable flash drives

• Problem: High Employee Attrition Rate

Employee Attrition• Loss of employees: retirement, resignation• Impact: cost to company

• training new and old employees• reputation

Data• Publicly available• CrowdANALYTIX• Unstructured

Objective

• Identify key indicators of employee attrition• Analyse patterns that could be indicative of ‘risk of

leaving’

• Example:• Software positions over 1.5 years: “high” risk of leaving• Manager positions over 3 years: “low” risk of leaving

Cleaning the Data

Structured Data

Unstructured Data

Identify SanDisk Employees

• Example:• Green: Exp1-Dummy=0, Exp2-Dummy=1, it means that

currently not working with SanDisk but previously were.

• Dummy variables : SanDisk = 1, Other = 0

Group Employees with Different Attributes

Percent of Males who left SanDisk 245/383 = 63.9%Percent of Females who left SanDisk 116/171 = 67.8%Percent of Missing Genders who left SanDisk 41/69 = 59.4%

Distribution Analysis - SPSS

Clustering

K-Means Kohonen

Discovering Attrition Patterns

• Tableau: Bubble Chart, Bar Chart• Inputs: Experience 2, Program 1, Average Duration-

Experience• Subgroups: • Experience 2: Engineering, IT, Sales & Marketing,

Operations, HR, etc.• Program 1: Computer Science, Electrical Engineering,

Marketing, etc.

Insights on Females – Cluster 1

Insights on Males – Cluster 2

Conclusion

• SanDisk can match any new joining employee’s characteristics with those in the visualization and make an intelligent guess as to how long an employee would stay

References

• www.CrowdANALYTIX.com• www.SanDisk.com

Thank You!