Employee Attrition AnalysisIdentify / Model key indicators of employee attrition for SanDisk employees based on publicly available data

4th Aug, 2015

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Contents

The Question1

Approach2

Analysis & Findings3

Predictive Model & Validation4

Conclusion and Client Feedback 5

The Question

Analyze patterns in profile information of past SanDisk employees to solve the following:

• Identify attributes that could be indicative of ‘risk of leaving’?

• Develop a model to predict employee attrition using the features identified

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Contents

The Question1

Approach2

Analysis & Findings3

Predictive Model & Validation4

Conclusion and Client Feedback5

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Approach: The problem involved a multi-staged approach of extraction meaningful information from raw data, identifying key factors impacting attrition, predictive modeling and validation

• Data included the

following aspects for

1300+ employees who

were past employees or

currently at SanDisk:• Location

• Industry

• Education

• Experience

• Meaningful variables

extracted from raw data

• Variables ranged from

time sensitive variables

like tenure at SanDisk to

static variables like

educational qualification,

level of position etc.

• Data exploration of

employees grouped in to

four buckets to identify

to factors that explain

attrition• Under 1.25 years of tenure at

SanDisk

• Tenure of 1.26 to 2.25 years

• Tenure of 2.26 to 6 years

• Tenure of more than 6 years

• Develop predictive

model using multiple

methods to predict the

likelihood of employee

attrition

• Validate the model using

test data

Unstructured Input Data

Feature Extraction Identify Patterns Predict Attrition

6 weeks

Feature Extraction: 50+ Relevant variables extracted from raw data

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Variables that are relevant from a business standpoint were extracted from the raw unstructured data by applying business rules, missing values were treated using standard methods

Modeling Approach

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- Techniques used include machine learning and statistical approaches like Logistic regression

50+ variables extracted from raw data

Multiple techniques used for building predictive model

Identified top variables that explain attrition

- < 10 variables were identified using data exploration methods including tree based statistical methods

- Odds of the factors being able to explain attrition better than the baseline is used to identify top factors

- Processing of raw data to extract and convert to variables conducive of modeling

- Categorization of data values used to create categories

Optimal model identified through testing and

evaluation

- 30% of the data was used for testing model performance

- Classification accuracy and mis-classification rates were used as key measures to compare and identify optimal models

$9,000 Solver Prize Money

https://www.crowdanalytix.com/contests/employee-attrition-analysis

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Contents

The Question1

Approach2

Analysis & Findings3

Predictive Model & Validation4

Conclusion and Client Feedback5

Data exploration indicated higher attrition being associated with lower tenure, attrition for tenures < 1.25 years is 5 times higher than employees with tenure of 6+ years

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Statistical validation of results with P-values using a decision tree

Further, data for employees under each bucket (defined based on tenure) are explored with additional variables – this shows tenure Factor Y and Factor X as important factors in explaining attrition

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… And for employees with higher tenure, Factor Z and Factor S explain attrition better

Summary of top factors identified as impacting attrition

• Various combination of factors and how much they explain the risk of employees leaving are shown

• Overall rate indicates a baselines rate upon which the Risk of leaving (explained by variables) is compared (can be assumed to be a random assignment)

• Multiple variables combinations are able to explain the risk of employees leaving at more than twice that of the random model, which explain the value of using these factors as predictors

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Contents

The Question1

Approach2

Analysis & Findings3

Predictive Model & Validation4

Conclusion and Client Feedback5

Top variables identified in exploration were further used to build predictive models using multiple methods

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• Total SanDisk jobs• SanDisk tenure

• Decision trees (rpart)• Random forest• Structured Vector

machine• Logistic regression

• Accuracy metric: Area under the curve (how good is the classification that employees will leave or not leave)

• False Negative rate

Top Variables identified from exploration

Types of Models used for prediction

Model evaluation metrics

While all models were comparable in terms of their accuracy, decision tree based model had the least misclassification (false negative rate)

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Receiver operating characteristic curves showing model performance comparison

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Contents

The Question1

Approach2

Analysis & Findings3

Predictive Model & Validation4

Conclusion and Client Feedback5

Insights from top performing model

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• Top performing model is able to classify

57% of churn accurately

• Lower the tenure higher is the risk of

attrition

• Higher likelihood of churn the company if

avg. tenure < 2.3 years

• For all employees with a lower tenure at

SanDisk (especially < 1.5 years), higher

the factor X is, higher likelihood there is

for them to churn

Outcome & Insights

Client Feedback

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“The greatest advantage of the crowdsourcing approach was the available talent ready to work on my

problem. I can almost guarantee that the crowdsourcing approach dedicated significantly more man-

hours on this project than I would ever been able to get through internal resources.”

Additional follow-up questions from client:

• Any noticeable change in patterns over the years, specifically in terms of attrition

• Additional factors that had been considered such as organizational performance, macro-economic environment

Thank You

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