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Title

Optimizing Advertising Campaigns in Online Travel Agencies: Leveraging the LRFM Method for Customer Segmentation and Promotional Effectiveness

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Abstract

 The research focuses on exploring how Promotional activities impact customer purchasing behavior in the online travel agency industry using the LRFM method (Latency, Frequency, Value for Money, and Lifetime Value). By developing a segmentation model based on these dimensions, the study aims to optimize personalized targeting in advertising campaigns. The methodology involved implementing promotions like discounts, utilizing the LRFM method to categorize customers, and using clustering techniques to refine customer groups for personalized promotions. Statistical methods, including K-Means clustering, were used to analyze customer behaviors. The findings highlighted the complex relationship between promotions and customer behavior, showing how the LRFM model can significantly impact advertising campaign segmentation outcomes. Clustering based on refined LRFM criteria provided insights into the differential impact of promotions across customer groups. Implications of the research include academic contributions to understanding promotional strategies and LRFM dimensions in customer behavior and practical insights for optimizing advertising campaigns in Online travel agencies. The segmentation model and personalized promotions offer a foundation for future marketing strategies and more effective advertising campaigns in the online travel industry.

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