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Prediction and Analysis of Customer Complaints Using Machine Learning Techniques

Ghadah Alarifi et al.

International Journal of e-Business Research2023https://doi.org/10.4018/ijebr.319716article
AJG 1
Weight
0.59

What the paper says

Businesses must prioritize customer complaints because they highlight critical areas where their products or services may be improved. The goal of this study is to use machine learning approaches to anticipate and evaluate customer complaint data. The current study used logistic regression and support vector machine (SVM) to predict customer complaints, and evaluated the datasets using machine learning techniques after collecting five distinct length datasets from the Consumer Financial Protection Bureau (CFPB) website and cleaning the data. Both logistic regression and SVM can accurately predict customer complaints, according to this study, but SVM gives the greatest accuracy. The current study also found that SVM provides the highest accuracy for a one-month dataset and Logistic regression provides for a three-month dataset. In addition, machine learning codes were utilized to display and tabulate consumer complaints across many dimensions.

15 citations

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https://doi.org/https://doi.org/10.4018/ijebr.319716

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@article{ghadah2023,
  title        = {{Prediction and Analysis of Customer Complaints Using Machine Learning Techniques}},
  author       = {Ghadah Alarifi et al.},
  journal      = {International Journal of e-Business Research},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.4018/ijebr.319716},
}

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Prediction and Analysis of Customer Complaints Using Machine Learning Techniques

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Evidence weight

0.59

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.61 × 0.4 = 0.24
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.