Customer Segmentation for improving Marketing Campaigns in the Banking Industry

Ganar, Celine; Hosein, Patrick

doi:10.1109/acmlc58173.2022.00017

Cited by 1 publication

(2 citation statements)

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“…Lastly, decision trees can enhance customer satisfaction by identifying key purchasing factors, allowing businesses to tailor the customer experience and potentially increase sales and loyalty (Samarth 2023). Ganar and Hosein (2022) crafted a composite model to forecast whether a bank's customer is likely to switch to using online banking services. The authors compare two supervised learning algorithms: Decision Tree and Extreme Gradient Boosted (XGBoost).…”

Section: Related Studies On Decision Treesmentioning

confidence: 99%

“…The purpose is to predict clients' behavior when transitioning to an online banking platform. The results indicated that combining the K-Modes clustering algorithm and the XGBoost classification model yielded the best test accuracy, a remarkable 96.1% (Ganar and Hosein 2022). Wen (2023) analyzed customer churn in banks using logistic regression and decision tree models, with the latter proving more precise and unbiased.…”

Section: Related Studies On Decision Treesmentioning

confidence: 99%

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Time analysis of online consumer behavior by decision trees, GUHA association rules, and formal concept analysis

Pitka,

Bucko,

Krajči

et al. 2024

J Market Anal

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Data analytics plays a significant role within the context of the digital business landscape, particularly concerning online sales, aiming to enhance understanding of customer behaviors in the online realm. We review the recent perspectives and empirical findings from several years of scholarly investigation. Furthermore, we propose combining computational methods to scrutinize online customer behavior. We apply the decision tree construction, GUHA (General Unary Hypotheses Automaton) association rules, and Formal concept analysis for the input dataset of 9123 orders (transactions) of sports nutrition, healthy foods, fitness clothing, and accessories. Data from 2014 to 2021, covering eight years, are employed. We present the empirical discoveries, engage in a critical discourse concerning these findings, and delineate the constraints inherent in the research process. The decision tree for classification of the year’s fourth quarter implies that the most important attributes are country, gross profit category, and delivery. The classification of the morning time implies that the most important attributes are gender and country. Thus, the potential marketing strategies can include heterogeneous conditions for men and women based on these findings. Analyzing the identified groups of customers by concept lattices and GUHA association rules can be valuable for targeted marketing, personalized recommendations, or understanding customer preferences.

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Section: Related Studies On Decision Treesmentioning

confidence: 99%

Section: Related Studies On Decision Treesmentioning

confidence: 99%