Customer comeback: Empirical insights into the drivers and value of returning customers

Meire, Matthijs

doi:10.1016/j.jbusres.2021.01.017

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…Similarly, Qian et al [24] applied DT using the CHAID algorithm to predict air pollutant emissions and supply chain in China to help the government implement green supply chain management. Meire [25] examined different predictive and classification models, including DT (i.e., Random Forests) and Support Vector Machines (SVMs) to analyze the customer comeback rate and identify the main criteria and challenges of the customer comeback. The study revealed that social media data are significant determinants and predictors of customer comeback [25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Meire [25] examined different predictive and classification models, including DT (i.e., Random Forests) and Support Vector Machines (SVMs) to analyze the customer comeback rate and identify the main criteria and challenges of the customer comeback. The study revealed that social media data are significant determinants and predictors of customer comeback [25].…”

Section: Literature Reviewmentioning

confidence: 99%

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A Boosted Tree-Based Predictive Model for Business Analytics

Al-Omari¹,

Qutaishat²,

Rawashdeh³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Business Analytics is one of the vital processes that must be incorporated into any business. It supports decision-makers in analyzing and predicting future trends based on facts (Data-driven decisions), especially when dealing with a massive amount of business data. Decision Trees are essential for business analytics to predict business opportunities and future trends that can retain corporations' competitive advantage and survival and improve their business value. This research proposes a tree-based predictive model for business analytics. The model is developed based on ranking business features and gradient-boosted trees. For validation purposes, the model is tested on a real-world dataset of Universal Bank to predict personal loan acceptance. It is validated based on Accuracy, Precision, Recall, and F-score. The experiment findings show that the proposed model can predict personal loan acceptance efficiently and effectively with better accuracy than the traditional tree-based models. The model can also deal with a massive amount of business data and support corporations' decision-making process.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Boosted Tree-Based Predictive Model for Business Analytics

Al-Omari¹,

Qutaishat²,

Rawashdeh³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

LLMs in e-commerce: A comparative analysis of GPT and LLaMA models in product review evaluation

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Natural Language Processing Journal

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The impact of point policies on hotel “sleeping” guests: awakening model construction

Liu,

Guo,

Gao

2024

JHTI

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PurposeThe conventional customer lifecycle fails to acknowledge the “sleeping” stage between regular patronage and churn, particularly prevalent in the hospitality industry. This study constructs an awakening model to regain “sleeping” guests.Design/methodology/approach342 questionnaires from Macau using partial least squares-structural equation modeling (PLS-SEM) were analyzed. The model was compared across different membership levels through multigroup analysis.FindingsThe results indicate that the point policy can awaken “sleeping” guests by influencing their perceived value, regret, and integrated satisfaction with a shorter “sleeping” period. Two path coefficients showed significant differences among basic and elite members.Practical implicationsCompanies with loyalty programs should implement a transitional period before resetting points, leveraging altruistic point policies to awaken “sleeping” guests via direct communication. This strategy mitigates the negative impact of finite point expiration policies, enhancing customer re-engagement and point utilization.Originality/valueOur study focuses on a crucial facet of hotel marketing—customer regain strategies. By identifying customer segments who have not revisited the hotel group for more than twelve months, we confirm the concept of “sleeping” guests. This term offers a nuanced perspective, distinguishing “sleeping” guests from generic lost customers. The “sleeping” guest segment provides valuable insights for enhancing targeted and effective marketing activities in the highly competitive hotel industry.

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Customer comeback: Empirical insights into the drivers and value of returning customers

Cited by 3 publications

References 41 publications

A Boosted Tree-Based Predictive Model for Business Analytics

A Boosted Tree-Based Predictive Model for Business Analytics

LLMs in e-commerce: A comparative analysis of GPT and LLaMA models in product review evaluation

The impact of point policies on hotel “sleeping” guests: awakening model construction

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