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Predicting Customer Complaints in Mo...

ProQuest Information and Learning Co.

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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Predicting Customer Complaints in Mobile Telecom Industry Using Machine Learning Algorithms./
Author:	Choi, Chiyoung.
Description:	1 online resource (76 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-06.
Contained By:	Masters Abstracts International57-06(E).
Subject:	Industrial engineering. -
Online resource:	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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245 1 0 $a Predicting Customer Complaints in Mobile Telecom Industry Using Machine Learning Algorithms.
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300 $a 1 online resource (76 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Masters Abstracts International, Volume: 57-06.
500 $a Adviser: Seokcheon Lee.
502 $a Thesis (M.S.I.E.)--Purdue University, 2018.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Industrial engineering. $3 679492
650 4 $a Information technology. $3 559429
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0546
690 $a 0489
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Purdue University. $b Industrial Engineering. $3 1181854
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