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Study on Tourism Consumer Behavior Characteristics Based on Big Data Analysis.

Muyi Gan1, Yao Ouyang1

  • 1Shunde Polytechnic, Foshan, China.

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Summary
This summary is machine-generated.

Big data analysis reveals key factors influencing tourist numbers and spending in scenic spots. Understanding consumer behavior helps optimize marketing strategies for sustainable tourism growth.

Keywords:
Daming Mountain of Nanningbig datascenic spot analysis modelsecondary consumptiontourism consumer behavior

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Area of Science:

  • Tourism Management and Marketing
  • Big Data Analytics
  • Econometrics
  • Consumer Behavior

Background:

  • Scenic marketing increasingly relies on big data for precise targeting and operational efficiency.
  • Understanding the relationship between big data indicators and tourist influx is crucial for business growth.
  • Analyzing secondary consumption patterns reveals potential for increased revenue in tourist destinations.

Purpose of the Study:

  • To explore the relationship between various big data subsets and tourist numbers in scenic areas.
  • To investigate the differences and influences of consumption behavior on secondary items within scenic areas.
  • To identify opportunities for business growth and promote stable economic development in the tourism sector.

Main Methods:

  • Development of a multi-objective analysis model using econometric theories and multicollinearity diagnosis.
  • Application of a Data Envelopment Analysis (DEA) model to study consumption behavior differences among tourist types.
  • Econometric modeling incorporating variables such as internet protocol (IP) counts, Baidu index, weekend dummy variables, bounce rate, and air pollution to predict daily tourist numbers.

Main Results:

  • An optimized econometric model was established to predict tourist numbers based on identified explanatory variables.
  • Significant differences and influences in consumption behavior for secondary items were identified across different tourist demographics.
  • The study determined the contribution rate of various big data types to tourist numbers, aiding in targeted marketing efforts.

Conclusions:

  • Big data analytics provides valuable insights for precise marketing and operational strategies in scenic spot management.
  • Understanding diverse tourist consumption behaviors is key to maximizing revenue from secondary spending.
  • The developed models offer a framework for promoting continuous and stable growth in the scenic area's tourism economy.