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A Study on data mining algorithms for churn prediction in an organization

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Employee churn and customer churn are two common problems for an organization as it indicates organization loss of resources and profits. Hence, implementing churn prediction helps to minimize those problems by detect potential churn employees or customers and apply targeted strategies to prevent them churning. By minimizing churn rate could maximize organizations revenue and productivity. This research presents churn prediction for human resource as a tool to prevent loss of valuable employees and for marketing as a tool to prevent customers’ loss in telecom industry. The author compared three common data mining algorithms on both datasets in predicting churn. The experiment result shows the overview prediction performance of each data mining algorithms in different unit of analysis. Overall, the best prediction was performed by Random Forest with PCC score 85.9% in employee churn and 95.4% in customer churn, F-Measure score 26.2% in employee churn and 82.2% in customer churn. However, Random Forest's F-Measure score in employee was not the highest as Neural Network has F-Measure score 47.1%. From this research, it can be concluded that Random Forest is a good data mining algorithm to perform churn prediction in different industries. Moreover, more comparison is recommended for real implementation in order to achieve best prediction model.

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Availability

B02497

(Rack Thesis)

Available

Detail Information

Series Title

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Call Number

2497

Publisher

: Swiss German University., 2017

Collation

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Language

English

ISBN/ISSN

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Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Data mining
IT
Random forest
Customer churn
Employee churn
Churnprediction
Telecom

Specific Detail Info

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Statement of Responsibility

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