scholarly journals Customer Satisfaction Measurement – Clustering Approach

2018 ◽  
Vol 66 (2) ◽  
pp. 561-569 ◽  
Author(s):  
David Schüller ◽  
Jan Pekárek
Keyword(s):  
Linear Regression ◽  
Customer Satisfaction ◽  
Hierarchical Clustering ◽  
Theory And Practice ◽  
Coefficient Of Determination ◽  
Individual Factors ◽  
Marketing Theory ◽  
Linear Regression Models ◽  
Satisfaction Measurement ◽  
Customer Satisfaction Measurement

The paper deals with the issue of customer satisfaction measurement. The aim of this study is to determine the importance of the individual factors and their impact on total customer satisfaction for multiple segments by using linear regression and hierarchical clustering. This study is focused on the market of café establishment. We applied hierarchical clustering with Ward’s criterion to partition customers into segments and then we developed linear regression models for each segment. Linear models for partitioned data showed higher coefficient of determination than the model for the whole market. The results revealed that there are quite significant differences in rankings of customer satisfaction factors among the segments. This is caused by the different preferences of customers. The clustered data allows to achieve a higher homogeneity of data within the segment, which is crucial both for marketing theory and practice. The approach i.e. partitioning the market into smaller more specific segments could become perspective for marketing use in different economic sectors. This attitude can allow marketers to target better on customer segments according to the importance of individual factors.

Maintaining Customer Loyalty by Intensifying Service Quality and Customer Satisfaction in Commuter Line Jabodetabek

2019 ◽  
Vol 10 (9) ◽  
pp. 902-909
Author(s):  
Umbas Krisnanto ◽  
◽  
Conny Marpaung ◽  
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Customer Satisfaction ◽  
Service Quality ◽  
Hypothesis Testing ◽  
Customer Loyalty ◽  
T Test ◽  
Coefficient Of Determination ◽  
Simple Linear Regression ◽  
Significance Level

This study aims to determine and analyze the influence of Service Quality and Customer Satisfaction on Customer Loyalty in Jabodetabek Commuter Line. The sample of this study was 50 people. Methods of collecting data by distributing questionnaires. Data analysis using the analysis used is simple linear regression, t test and coefficient of determination. The results showed 1) Service Quality has a positive and significant effect on Customer Loyalty in Jabodetabek Commuter Line, with a significance level of 0.048; and supported by the results of hypothesis testing with a t-count value of 4.433 > t-table value of 1.95, with a significance of 0.048 or < 0.05; 2) Customer Satisfaction positive and significant effect on Customer Loyalty in Jabodetabek Commuter Line, with a level significance of 0,000; and supported by the results of hypothesis testing with a t-count value of 4,969 > t-table value of 1.95, with a significance of 0,000 or < 0.05, 3) Service quality and Customer Satisfaction have a positive and significant effect on Customer Loyalty in Jabodetabek Commuter Line, with a significance level of 0,000. This means that the hypothesis H0 is rejected and Ha is accepted so that it can be concluded that service quality and customer satisfaction together have a positive and significant effect on customer loyalty in Jabodetabek Commuter Line.

scholarly journals Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 130
Author(s):  
Omar Rodríguez-Abreo ◽  
Juvenal Rodríguez-Reséndiz ◽  
L. A. Montoya-Santiyanes ◽  
José Manuel Álvarez-Alvarado
Keyword(s):  
Linear Regression ◽  
Vibration Amplitude ◽  
Regression Models ◽  
Engineering Problem ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Gray Wolf ◽  
Linear Regression Models ◽  
Machinery Condition Monitoring ◽  
Non Linear

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

scholarly journals Soybean Yield Estimation and Its Components: A Linear Regression Approach

Agriculture ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 348
Author(s):  
Marcelo Chan Fu Wei ◽  
José Paulo Molin
Keyword(s):  
Linear Regression ◽  
Mean Squared Error ◽  
Limiting Factors ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Validation Dataset ◽  
Learning Approaches ◽  
Linear Regression Models ◽  
Yield Estimation ◽  
Soybean Yield

Soybean yield estimation is either based on yield monitors or agro-meteorological and satellite imagery data, but they present several limiting factors regarding on-farm decision level. Aware that machine learning approaches have been largely applied to estimate soybean yield and the availability of data regarding soybean yield and its components (number of grains (NG) and thousand grains weight (TGW)), there is an opportunity to study their relationships. The objective was to explore the relationships between soybean yield and its components, generate equations to estimate yield and evaluate its prediction accuracy. The training dataset was composed of soybean yield and its components’ data from 2010 to 2019. Linear regression models based on NG, TGW and yield were fitted on the training dataset and applied to a validation dataset composed of 58 on-field collected samples. It was found that globally TGW and NG presented weak (r = 0.50) and strong (r = 0.92) linear relationships with yield, respectively. In addition to that, applying the fitted models to the validation dataset, model based on NG presented the highest accuracy, coefficient of determination (R2) of 0.70, mean absolute error (MAE) of 639.99 kg ha−1 and root mean squared error (RMSE) of 726.67 kg ha−1.

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