Introduction

With the increasing number of travelers, providing appropriate facilities has become essential. Modern hotels must ensure guest comfort and satisfaction through effective management, skilled personnel, and optimized workflows. Poor management can lead to customer dissatisfaction. In the tourism industry, human interactions play a crucial role in shaping visitor perceptions, and staff competence directly influences guest loyalty and hotel profitability. Effective communication and interpersonal skills contribute to positive customer experiences and enhance service marketing outcomes.
The rapid advancement of technology in service industries necessitates data-driven decision-making for competitive advantage. Business Intelligence (BI) helps organizations optimize operations, improve data accuracy, and support strategic decision-making. By leveraging BI, hotels can enhance efficiency, reduce costs, and increase customer satisfaction, ultimately improving profitability. BI integrates data analytics and predictive models, enabling businesses to assess market trends, customer behavior, and competitive positioning. It facilitates smart decision-making, fostering operational improvements and personalized customer engagement.
Customer Lifetime Value (CLV) has gained significant attention as a key marketing approach, allowing businesses to estimate the financial worth of customers and optimize customer relationship strategies. CLV is assessed based on spending amount, relationship duration, and purchase frequency. Understanding CLV helps businesses make informed marketing investments, prioritize high-value customers, and enhance long-term profitability. Relationship marketing and personalized services strengthen customer loyalty and satisfaction, reinforcing trust and engagement.
In the hotel industry, decision-making challenges arise from limited data access. Some managers rely on IT-generated reports, others on personal experience, while some embrace technology-driven insights. Implementing BI in hospitality enables better risk assessment and strategic decision-making. This study explores the relationship between BI and CLV in the hospitality sector of Hamedan, aiming to provide practical recommendations for improving hotel performance. It also highlights the role of BI in staff development and operational efficiency. The research seeks to answer how BI contributes to the transformation of the hotel industry.
 

Results

The findings reveal a strong positive correlation between Business Intelligence (BI) and Customer Lifetime Value (CLV) (r = 0.755, p < 0.001), indicating that hotels that effectively utilize BI tend to achieve higher CLV. Among the BI dimensions, "competitive advantage creation" emerged as the most influential factor in enhancing CLV.
Key results include:
All four BI components (effective decision-making, competitive advantage creation, efficiency enhancement, and time optimization) positively correlate with CLV and its subcomponents. Multiple regression analysis identified that "competitive advantage creation" was the most significant predictor of CLV, explaining a considerable portion of the variance in customer retention and profitability. Friedman ranking test revealed that within the CLV framework, the "network effect" dimension was ranked as the most critical factor in determining customer loyalty, followed by learning, base value, and growth. The study confirms that hotels integrating BI effectively into their operations are better positioned to optimize service quality, reduce customer churn, and improve long-term profitability.
 

Discussion

Customer Lifetime Value (CLV) is a crucial metric in marketing and business strategy, enabling organizations to assess the long-term profitability of their customers and make informed decisions regarding resource allocation. However, traditional CLV calculation models often fail to capture the complexity of customer behavior—particularly in service-oriented industries such as hospitality. This study seeks to bridge this gap by integrating Business Intelligence (BI) into CLV analysis, demonstrating how BI tools and processes can enhance customer retention and overall business performance.
The findings confirmed a significant positive relationship between BI and CLV, indicating that hotels effectively leveraging BI tend to achieve higher customer lifetime value. Among the various BI dimensions, "competitive advantage creation" had the strongest impact on CLV, underscoring the importance of strategic differentiation in retaining high-value customers. Hotels that utilize BI to offer personalized services, anticipate customer needs, and optimize operational efficiency are more likely to enhance customer loyalty and long-term profitability.
Furthermore, multiple regression analysis revealed that although all BI components positively contribute to CLV, "competitive advantage creation" stood out as the most influential predictor. This suggests that hotels should prioritize innovation, data-driven decision-making, and differentiation strategies to maximize customer value. Within the CLV framework, the network effect also emerged as a critical factor, reinforcing the significance of customer relationships and word-of-mouth referrals in sustaining long-term customer value.
 

Conclusion

Customer Lifetime Value (CLV) is one of the fundamental concepts in management and marketing literature, as organizations continuously strive to evaluate their profitability based on CLV calculations and make strategic decisions to enhance future profits. In typical CLV calculation models, various indicators—such as customer churn, customer migration, customer acquisition, and advertising costs—play a decisive role. The inclusion of these indicators depends on the specific formula being applied. However, some studies have indicated that existing CLV formulas in the hotel industry often fail to accurately reflect the true value of customer relationships. This highlights the need for incorporating more effective indicators into CLV models. Accordingly, this research introduces Business Intelligence (BI) as a means of identifying new and relevant indicators for CLV analysis, given the increasing importance of BI in the hotel industry. Establishing a clear connection between BI and CLV opens new avenues for more precise calculation of customer retention rates in hospitality settings.
The findings of the study revealed a significant relationship between BI variables and CLV, confirming that BI indicators can serve as reliable predictors of customer lifetime value. Moreover, the results underscore that creating competitive advantages has the most substantial impact on increasing CLV.
Therefore, it is recommended that hotels leverage business intelligence tools, conduct accurate analysis of customer data, and develop effective competitive strategies in order to enhance customer lifetime value and foster greater customer loyalty.
 
Acknowledgments
We are grateful to all the persons for scientific consulting in this paper.