Proceedings of the 4th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2022, December 9-11, 2022, Chongqing, China

Research Article

Data Processing Based on Comparative Analysis and the Study of Consumers' Score on Cereal Under Different Influencing Factors

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  • @INPROCEEDINGS{10.4108/eai.9-12-2022.2327613,
        author={Weicong  Chen and Haowei  Yuan},
        title={Data Processing Based on Comparative Analysis and the Study of Consumers' Score on Cereal Under Different Influencing Factors},
        proceedings={Proceedings of the 4th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2022, December 9-11, 2022, Chongqing, China},
        publisher={EAI},
        proceedings_a={MSIEID},
        year={2023},
        month={3},
        keywords={cereals; customer satisfaction; data analysis; comparative analysis},
        doi={10.4108/eai.9-12-2022.2327613}
    }
    
  • Weicong Chen
    Haowei Yuan
    Year: 2023
    Data Processing Based on Comparative Analysis and the Study of Consumers' Score on Cereal Under Different Influencing Factors
    MSIEID
    EAI
    DOI: 10.4108/eai.9-12-2022.2327613
Weicong Chen1,*, Haowei Yuan2
  • 1: Henry Samueli School of Engineering University of California Irvine Irvine
  • 2: School of Software Southwest university Chongqing
*Contact email: weiconc1@uci.edu

Abstract

This research paper focuses on determining the combining effect of different factors that might affect customers’ satisfaction scores on cereal products. As the demand for breakfast cereals has been increasing dramatically during the COVID-19 period, knowing the taste of customer is vital in order to increase sales for companiees. We started our research for a solution to each of the three problems which are crucial for sales increase. In our research, we are using data that contains different characteristics of certain cereal products correlated with their specific customer satisfaction score. Then we will use different methods to determine the correlation of each factor with customer satisfaction score and the correlation of combinations of factors with customer satisfaction score. The methods that we used include unary linear regression, multiple linear regression, and nonlinear regression. In this exploration and analysis, we used SPSS and Excel related software to analyze the data and build a model, and obtained the coefficient before each independent variable in the model, so as to obtain the model.