國立虎尾科技大學 |

Predicting Customer Complaints in Mobile Telecom Industry Using Machine Learning Algorithms.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Predicting Customer Complaints in Mobile Telecom Industry Using Machine Learning Algorithms./
作者:	Choi, Chiyoung.
面頁冊數:	1 online resource (76 pages)
附註:	Source: Masters Abstracts International, Volume: 57-06.
Contained By:	Masters Abstracts International57-06(E).
標題:	Industrial engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780438011748

Predicting Customer Complaints in Mobile Telecom Industry Using Machine Learning Algorithms.
Choi, Chiyoung.

Predicting Customer Complaints in Mobile Telecom Industry Using Machine Learning Algorithms. - 1 online resource (76 pages)

Source: Masters Abstracts International, Volume: 57-06.

Thesis (M.S.I.E.)--Purdue University, 2018.

Includes bibliographical references

Mobile telecom industry competition has been fierce for decades, therefore increasing the importance of customer retention. Most mobile operators consider customer complaints as a key factor of customer retention. We implement machine learning algorithms to predict the customer complaints of a Korean mobile telecom company. We used four machine learning algorithms ANN (Artificial Neural Network), SVM (Support Vector Machine), KNN (K-Nearest Neighbors) and DT (Decision Tree). Our experiment utilized a database of 10,000 Korean mobile market subscribers and the variables of gender, age, device manufacturer, service quality, and complaint status. We found that ANN's prediction performance outperformed other algorithms. We also propose the segmented-prediction model for better accuracy and practical usage. Segments of the customer group are examined by gender, age, and device manufacturer. Prediction power is better for female, older customers, and the non-iPhone groups than other segment groups. The highest accuracy s ANN's 87.3% prediction for the 60s group.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438011748Subjects--Topical Terms:

679492
Industrial engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Predicting Customer Complaints in Mobile Telecom Industry Using Machine Learning Algorithms.
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520 $a Mobile telecom industry competition has been fierce for decades, therefore increasing the importance of customer retention. Most mobile operators consider customer complaints as a key factor of customer retention. We implement machine learning algorithms to predict the customer complaints of a Korean mobile telecom company. We used four machine learning algorithms ANN (Artificial Neural Network), SVM (Support Vector Machine), KNN (K-Nearest Neighbors) and DT (Decision Tree). Our experiment utilized a database of 10,000 Korean mobile market subscribers and the variables of gender, age, device manufacturer, service quality, and complaint status. We found that ANN's prediction performance outperformed other algorithms. We also propose the segmented-prediction model for better accuracy and practical usage. Segments of the customer group are examined by gender, age, and device manufacturer. Prediction power is better for female, older customers, and the non-iPhone groups than other segment groups. The highest accuracy s ANN's 87.3% prediction for the 60s group.
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