Machine Learning for Prediction of Business Company Failure in Hospitality Sector

Brito, José Henrique Silveira de; Pereira, José Manuel; Silva, Amélia Ferreira da; Angélico, Maria José; Abreu, António; Teixeira, Sandrina

doi:10.1007/978-981-15-2024-2_28

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…To attract the right set of customers, it is important to analyse the attributes and profile of the customers to accurately assess how closely those match with the product/service attributes, indicating a likelihood of a sale. Data mining and machine learning (ML) techniques are good candidates for predicting such likelihood, and their efficacy has been demonstrated in many similar types of tasks, e.g., predicting churn customers in telecommunication services [6] , food sale prediction in retail [7] , and prediction of company failure in the hospitality sector [8] . Both the domestic and global markets nowadays have become highly competitive, and the COVID-19 pandemic has forced many businesses to move to online electronic commerce platforms, making the businesses even more competitive and resorting to digital marketing to reach a wider base of customers.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

Kaisar,

Rashid,

Chowdhury

et al. 2024

Big Data Min. Anal.

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Telemarketing is a well-established marketing approach to offering products and services to prospective customers. The effectiveness of such an approach, however, is highly dependent on the selection of the appropriate consumer base, as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested ones. The introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base, and the existing literature in this direction shows promising results. However, the selection of influential features and the construction of effective learning models for improved performance remain a challenge. Furthermore, from the modelling perspective, the class imbalance nature of the training data, where samples with unsuccessful outcomes highly outnumber successful ones, further compounds the problem by creating biased and inaccurate models. Additionally, customer preferences are likely to change over time due to various reasons, and/or a fresh group of customers may be targeted for a new product or service, necessitating model retraining which is not addressed at all in existing works. A major challenge in model retraining is maintaining a balance between stability (retaining older knowledge) and plasticity (being receptive to new information). To address the above issues, this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately. A novel online learning method is proposed for model retraining when new samples are available over time. This newly introduced method equips the proposed approach to deal with dynamic data, leading to improved readiness of the proposed model for practical adoption, and is a highly useful addition to the literature. Extensive experiments with real-world data show that the proposed approach achieves excellent results in all cases (e.g., 98.6% accuracy in classifying customers) and outperforms recent competing models in the literature by a considerable margin of 3% on a widely used dataset.

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Section: Introductionmentioning

confidence: 99%

Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

Kaisar,

Rashid,

Chowdhury

et al. 2024

Big Data Min. Anal.

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The Business Failure Prediction Using Statistical Approach

Noprisson

2021

IJSRSET

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The topic of business failure is important because it can be used as a basis for policy making by stakeholders in a company or government. The results of business failure predictions can be used as company managers to take preventive measures for business failure. This study aim is to study the literature regarding the methods and results of predicting business failure from various sectors and regions. We used PRISMA (preferred reporting items for systematic reviews and meta-analyses) for conducting this research. As the result, we found twelve statistical methods for business failure prediction, including Hybrid Failure Prediction (HFP), Altman’s Z-score Model, Data Envelopment Analysis (DEA), Logistic Regression (LR), Neural Networks (NN), Support Vector Machine (SVM), Kernel Fuzzy C-Means (KFCM), IN01, IN05, Ohlson Model, Cart-Based Model and Cash-Flow-Based Measures. The highest result obtained by using cart-based model for dataset of financial indicators of Slovak companies with 92,00% accuracy.

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Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry

et al. 2020

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Using logistic regression technique and Deep Recurrent Convolutional Neural Network, this study seeks to improve the capacity of existing bankruptcy prediction models for the restaurant industry. In addition, we have verified, in the review of existing literature, the gap in the research of restaurant bankruptcy models with sufficient time in advance and that only companies in the restaurant sector in the same country are considered. Our goal is to build a restaurant bankruptcy prediction model that provides high accuracy, using information distant from the bankruptcy situation. We had a sample of Spanish restaurants corresponding to the 2008–2017 period, composed of 460 solvent and bankrupt companies, for which a total of 28 variables were analyzed, including some of a non-financial nature, such as age of restaurant, quality, and belonging to a chain. The results indicate that the best bankruptcy predictors are financial variables related to profitability and indebtedness and that Deep Recurrent Convolutional Neural Network exceeds logistic regression in predictive capacity.

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Machine Learning for Prediction of Business Company Failure in Hospitality Sector

Cited by 4 publications

References 18 publications

Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

The Business Failure Prediction Using Statistical Approach

Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry

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