Abstract
Abstract Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka tool, is the analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey.
Highlights
Businesses use different technologies to perform analytics, such as simple reporting and dashboarding, which are just reports to inspect and study previous performance (Bumblauskas et al, 2017)
The increasing need of data mining applications based on classification in the home appliances after sales service field has revealed the need for practicing data-mining algorithms for better decision-making
The comparative analysis linked to classification measures involved recall, precision, F Measure, Matthews’s correlation coefficient (MCC), precision-recall curve (PRC), receiver operating characteristic (ROC) curve, false positive rate (FPR), and true positive rate (TPR) have been extracted
Summary
Businesses use different technologies to perform analytics, such as simple reporting and dashboarding, which are just reports to inspect and study previous performance (Bumblauskas et al, 2017). Businesses certainly use technologies as a means of data analytics in the perspective of digital transformation (OECD-BEIS, 2018). Businesses have a common aim to use digital technologies as an opportunity to improve decision-making competency along the business digital transformation journey (Schwertner, 2017). Advanced data analytics and artificial intelligence are drivers of deep analysis and change the businesses (West and Allen, 2018). As a confluence of statistics and machine learning (artificial intelligence), makes businesses predict future trends (Palmer et al, 2011; Sethi et al, 2016). Digitalization enables huge developments in after sales services; with prescriptive and predictive analysis opportunities, businesses reduce life-cycle costs and optimize costs (Rudnick et al, 2020)
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