Optimizing Bank Marketing Strategies Through Analysis Using Lightgbm

##plugins.themes.bootstrap3.article.sidebar##

PDF
Published: Jul 25, 2023
DOI: https://doi.org/10.60005/coreid.v1i2.11
Keywords:
Bank Marketing Campaign Strategy Optimization Machine Learning LightGBM

##plugins.themes.bootstrap3.article.main##

Erindra Reynaldi Diaz Aditya
Information System, UPN Veteran Jawa Timur
Dhian Kartika Yudha Satria
Information System, UPN Veteran Jawa Timur

Abstract

Marketing campaigns in a bank are one of the ways for the bank to achieve its organizational goals. Optimal marketing is a crucial factor for a bank's success in attracting and retaining customers. Therefore, if a bank's marketing campaigns are carried out suboptimally, it will be challenging to achieve the goals of those campaigns. In this case study, it can be observed that the number of customers who subscribe to fixed-term deposits is lower, with a proportion of 5289 customers making deposits and 5873 customers not making deposits. This research aims to optimize bank marketing strategies by applying analysis using the LightGBM algorithm, which is a highly effective and efficient Gradient Boosting Decision Tree algorithm. This approach facilitates the design of more optimal marketing strategies. The accuracy score of the predictive model generated is 0.8584, with an F1 score of 0.8564, including 974 true negatives and 943 true positives.

##plugins.themes.bootstrap3.article.details##

How to Cite
[1]
E. R. Diaz Aditya and D. K. Yudha Satria, “Optimizing Bank Marketing Strategies Through Analysis Using Lightgbm”, coreid, vol. 1, no. 2, pp. 58–65, Jul. 2023.
Issue
Vol. 1 No. 2 (2023): July 2023
Section
Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish articles in CoreID Journal agree to the following terms:

  1. Authors retain copyright of the article and grant the journal right of first publication with the work simultaneously licensed under a CC-BY-SA or The Creative Commons Attribution–ShareAlike License.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

References

I. Didy, “Marketing Campaign: Kunci Keberhasilan Bisnis yang Wajib Kamu Tahu,” Glints, 2021. [Online]. Available: https://glints.com/id/lowahttps://glints.com/id/lowongan/marketing-campaign-adalah/#.ZBf0hHZByUkongan/marketing-campaign-adalah/#.ZBf0hHZByUk.

M. Bugaj, K. Wrobel, and J. Iwaniec, “Model Explainability using SHAP Values for LightGBM Predictions,” in 2021 IEEE XVIIth International Conference on the Perspective Technologies and Methods in MEMS Design (MEMSTECH), 2021, pp. 102–106.

E. Al Daoud, “Comparison between XGBoost, LightGBM and CatBoost Using a Home Credit Dataset,” vol. 13, no. 1, pp. 6–10, 2019.

H. Alshari, A. Y. Saleh, and A. Odabaş, “Comparison of Gradient Boosting Decision Tree Algorithms for CPU Performance CPU Performansı için Gradyan Artırıcı Karar Ağacı Algoritmalarının Karşılaştırılması,” Erciyes Univ. J. Instutue Sci. Technol., vol. 37, no. 1, pp. 157–168, 2021.

G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree,” Adv. Neural Inf. Process. Syst., vol. 2017-December, no. Nips, pp. 3147–3155, 2017.

S. Moro, P. Cortez, and P. Rita, “A data-driven approach to predict the success of bank telemarketing,” Decis. Support Syst., vol. 62, pp. 22–31, Jun. 2014.

D. Pilendia, “Pemanfaatan Adobe Flash sebagai Dasar Pengembangan Bahan Ajar Fisika : Studi Literatur,” J. Tunas Pendidik., vol. 2, no. 2, 2020.

R. Khan, S. Akbar, and T. A. Zahed, “Flight Delay Prediction Based on Gradient Boosting Ensemble Techniques,” in 2022 16th International Conference on Open Source Systems and Technologies (ICOSST), 2022, pp. 1–5.

P. Septiana Rizky, R. Haiban Hirzi, and U. Hidayaturrohman, “Perbandingan Metode LightGBM dan XGBoost dalam Menangani Data dengan Kelas Tidak Seimbang,” J Stat. J. Ilm. Teor. dan Apl. Stat., vol. 15, no. 2, pp. 228–236, Dec. 2022.

J. Zhang, D. Mucs, U. Norinder, and F. Svensson, “LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity–Application to the Tox21 and Mutagenicity Data Sets,” J. Chem. Inf. Model., vol. 59, no. 10, pp. 4150–4158, Oct. 2019.

K. Sahoo*, A. K. Samal, J. Pramanik, and S. K. Pani, “Exploratory Data Analysis using Python,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 12, pp. 4727–4735, Oct. 2019.

J. DSouza and S. Velan S., “Using Exploratory Data Analysis for Generating Inferences on the Correlation of COVID-19 cases,” in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020, pp. 1–6.

S. Raschka, J. Patterson, and C. Nolet, “Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence,” Information, vol. 11, no. 4, p. 193, Apr. 2020.

P. Vadapalli, “Label Encoder vs One Hot Encoder in Machine Learning [2023],” upgrad, 2022. [Online]. Available: https://www.upgrad.com/blog/label-encoder-vs-one-hot-encoder/.

M. Wang, L. Yue, X. Yang, X. Wang, Y. Han, and B. Yu, “Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine,” Biomed. Signal Process. Control, vol. 68, p. 102630, Jul. 2021.

Y. Xu, W. Cai, L. Wang, and T. Xie, “Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM,” Shock Vib., vol. 2021, pp. 1–8, Dec. 2021.

E.-A. MINASTIREANU and G. MESNITA, “Light GBM Machine Learning Algorithm to Online Click Fraud Detection,” J. Inf. Assur. Cybersecurity, pp. 1–12, Apr. 2019.

Q. Li, Z. Wen, and B. He, “Practical Federated Gradient Boosting Decision Trees,” Proc. AAAI Conf. Artif. Intell., vol. 34, no. 04, pp. 4642–4649, Apr. 2020.

G. Jiao and H. Xu, “Analysis and Comparison of Forecasting Algorithms for Telecom Customer Churn,” J. Phys. Conf. Ser., vol. 1881, no. 3, p. 032061, Apr. 2021.

Q.-T. Bui et al., “Gradient Boosting Machine and Object-Based CNN for Land Cover Classification,” Remote Sens., vol. 13, no. 14, p. 2709, Jul. 2021.

H. Yun, “Prediction model of algal blooms using logistic regression and confusion matrix,” Int. J. Electr. Comput. Eng., vol. 11, no. 3, p. 2407, Jun. 2021.

N. D. Marom, L. Rokach, and A. Shmilovici, “Using the confusion matrix for improving ensemble classifiers,” in 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, 2010, pp. 000555–000559.

D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 697–711, 2021.

N. Hadianto, H. B. Novitasari, and A. Rahmawati, “Klasifikasi Peminjaman Nasabah Bank Menggunakan Metode Neural Network,” J. Pilar Nusa Mandiri, vol. 15, no. 2, pp. 163–170, Sep. 2019.

D. S. Y. Kartika, D. Herumurti, and A. Yuniarti, “Butterfly Image Classification Using Color Quantization Method on HSV Color Space and Local Binary Pattern,” IPTEK J. Proc. Ser., vol. 4, no. 1, p. 78, Jan. 2018.