DOI : https://doi.org/10.18517/ijaseit.14.6.11984

Leveraging Random Forest Algorithm for Enhanced Lead Conversion and Customer Retention

Xin Yi Tan (1), Siew Mooi Lim (2), Tong Ming Lim (3), Chi Wee Tan (4), Noor Aida Husaini (5)
(1) Faculty of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
(2) Faculty of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
(3) Faculty of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
(4) Faculty of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
(5) Faculty of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
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How to cite (IJASEIT) :
Tan , Xin Yi, et al. “Leveraging Random Forest Algorithm for Enhanced Lead Conversion and Customer Retention”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 6, Dec. 2024, pp. 2141-8, doi:10.18517/ijaseit.14.6.11984.
Citation Format :
This paper presents the development, implementation, and evaluation of a Random Forest-based Social Lead Scoring Model to address key business challenges in lead generation, customer retention, and optimization of lead management processes. The main goal is to create a strong, data-driven tool to precisely estimate lead conversion probabilities to guide better marketing and sales strategy decision-making. The model uses social metrics and past lead data to estimate conversion probabilities for every lead. The Tkinter library created a user-friendly interface that allows straightforward usage for non-technical business professionals. This model includes two fundamental functions calculate_probability and predict_conversion– which offer practical and pragmatic insights.  During the development phase, a thorough cross-valuation was conducted by the model trained on a large dataset, including several lead characteristics, to decrease overfitting and improve the model's predictive performance. Thus, the model scored 89.46%, which is higher than that of conventional lead-scoring techniques. However, there is still room for development, specifically in enhancing its predictive power and reducing overfitting risks on different datasets, although this model has great accuracy. The results highlight the need for data-driven strategies in raising conversion rates and show the possibilities of machine learning in lead management optimization. Including extra data sources and investigating advanced technologies such as deep learning should be conducted by future studies to improve model performance further. The increased accuracy in predictive analytics will give companies a competitive edge in their operations.

H.-P. Fu and T.-S. Chang, “An analysis of the factors affecting the adoption of cloud consumer relationship management in the machinery industry in Taiwan,” Information Development, vol. 32, no. 5, pp. 1741–1756, Jul. 2016, doi: 10.1177/0266666915623318.

M. Wu, P. Andreev, and M. Benyoucef, “The state of lead scoring models and their impact on sales performance,” Information Technology and Management, vol. 25, no. 1, pp. 69–98, Feb. 2023, doi: 10.1007/s10799-023-00388-w.

A. Powell, C. H. Noble, S. M. Noble, and S. Han, “Man vs machine,” European Journal of Marketing, vol. 52, no. 3/4, pp. 725–757, Feb. 2018, doi: 10.1108/ejm-10-2015-0750.

J. Bryan and P. Moriano, “Graph-based machine learning improves just-in-time defect prediction,” PLoS ONE, vol. 18, no. 4, p. e0284077, Apr. 2023, doi: 10.1371/journal.pone.0284077.

P. Kubiak and S. Rass, “An Overview of Data-Driven Techniques for IT-Service-Management,” IEEE Access, vol. 6, pp. 63664–63688, 2018, doi: 10.1109/access.2018.2875975.

V. Guerola-Navarro, H. Gil-Gomez, R. Oltra-Badenes, and P. Soto-Acosta, “Customer relationship management and its impact on entrepreneurial marketing: a literature review,” International Entrepreneurship and Management Journal, vol. 20, no. 2, pp. 507–547, Jun. 2022, doi: 10.1007/s11365-022-00800-x.

J. R. Dias and N. Antonio, “Predicting customer churn using machine learning: A case study in the software industry,” Journal of Marketing Analytics, Dec. 2023, doi: 10.1057/s41270-023-00269-9.

K. Zahoor, N. Z. Bawany, and S. Hamid, “Sentiment Analysis and Classification of Restaurant Reviews using Machine Learning,” 2020 21st International Arab Conference on Information Technology (ACIT), pp. 1–6, Nov. 2020, doi: 10.1109/acit50332.2020.9300098.

R. Katarya, A. Gautam, S. P. Bandgar, and D. Koli, “Analyzing Customer Sentiments Using Machine Learning Techniques to Improve Business Performance,” 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), pp. 182–186, Dec. 2020, doi:10.1109/icacccn51052.2020.9362895.

M. Rahman et al., “Revolutionizing Consumer Power Management: Unveiling Power Grid Feasibility Analysis Using Machine Learning,” 2023 10th IEEE International Conference on Power Systems (ICPS), pp. 1–6, Dec. 2023, doi: 10.1109/icps60393.2023.10428886.

I. Ullah, B. Raza, A. K. Malik, M. Imran, S. U. Islam, and S. W. Kim, “A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector,” IEEE Access, vol. 7, pp. 60134–60149, 2019, doi: 10.1109/access.2019.2914999.

D. Kilroy, G. Healy, and S. Caton, “Using Machine Learning to Improve Lead Times in the Identification of Emerging Customer Needs,” IEEE Access, vol. 10, pp. 37774–37795, 2022, doi:10.1109/access.2022.3165043.

B. S. A. S. Rajita, P. Tarigopula, P. Ramineni, A. Sharma, and S. Panda, “Application of Evolutionary Algorithms in Social Networks: A Comparative Machine Learning Perspective,” New Generation Computing, vol. 41, no. 2, pp. 401–444, Apr. 2023, doi:10.1007/s00354-023-00215-4.

D. Arkhipova and M. Janssen, “AI recommendations’ impact on individual and social practices of Generation Z on social media: a comparative analysis between Estonia, Italy, and the Netherlands,” Semiotica, Mar. 2024, doi: 10.1515/sem-2023-0089.

Y. Suh, “Machine learning based customer churn prediction in home appliance rental business,” Journal of Big Data, vol. 10, no. 1, Apr. 2023, doi: 10.1186/s40537-023-00721-8.

S. Zulaikha, H. Mohamed, M. Kurniawati, S. Rusgianto, And S. A. Rusmita, “Customer Predictive Analytics Using Artificial Intelligence,” The Singapore Economic Review, pp. 1–12, Aug. 2020, doi: 10.1142/s0217590820480021.

Y. Dai and T. Wang, “Prediction of customer engagement behaviour response to marketing posts based on machine learning,” Connection Science, vol. 33, no. 4, pp. 891–910, Apr. 2021, doi:10.1080/09540091.2021.1912710.

S. Koli, R. Singh, R. Mishra, and P. Badhani, “Imperative role of customer segmentation technique for customer retention using machine learning techniques,” 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), pp. 243–248, Jan. 2023, doi: 10.1109/aisc56616.2023.10085487.

S. A. Qureshi, A. S. Rehman, A. M. Qamar, A. Kamal, and A. Rehman, “Telecommunication subscribers’ churn prediction model using machine learning,” Eighth International Conference on Digital Information Management (ICDIM 2013), pp. 131–136, Sep. 2013, doi:10.1109/icdim.2013.6693977.

Ş. Dönmez, “Machine Learning-Based Merchant Churn Prediction in Banking,” 2023 8th International Conference on Computer Science and Engineering (UBMK), pp. 503–508, Sep. 2023, doi: 10.1109/ubmk59864.2023.10286753.

I. Jahan and T. Farah Sanam, “An Improved Machine Learning Based Customer Churn Prediction for Insight and Recommendation in E-commerce,” 2022 25th International Conference on Computer and Information Technology (ICCIT), pp. 1–6, Dec. 2022, doi:10.1109/iccit57492.2022.10054771.

A. H M, B. T, S. Tanisha, S. B, and C. C. Shanuja, “Customer Churn Prediction Using Synthetic Minority Oversampling Technique,” 2023 4th International Conference on Communication, Computing and Industry 6.0 (C216), pp. 01–05, Dec. 2023, doi:10.1109/c2i659362.2023.10430989.

N. Ali et al., “Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques,” Intelligent Automation & Soft Computing, vol. 31, no. 3, pp. 1671–1687, 2022, doi:10.32604/iasc.2022.019892.

P. Thamaraiselvi, J. Masih, P. Giri, J. Sridevi, I. A. Karim Shaikh, and M. V. R. Prasad, “Analysis of Social Media Marketing Impact on Customer Behaviour using AI & Machine Learning,” 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), pp. 1–6, Apr. 2024, doi:10.1109/iconstem60960.2024.10568805.

R. Vadakattu, B. Panda, S. Narayan, and H. Godhia, “Enterprise subscription churn prediction,” 2015 IEEE International Conference on Big Data (Big Data), pp. 1317–1321, Oct. 2015, doi:10.1109/bigdata.2015.7363888.

M. Cowan, T. Moreau, T. Chen, J. Bornholt, and L. Ceze, “Automatic generation of high-performance quantized machine learning kernels,” Proceedings of the 18th ACM/IEEE International Symposium on Code Generation and Optimization, pp. 305–316, Feb. 2020, doi:10.1145/3368826.3377912.

D. S. K. Nayak, S. P. Routray, S. Sahooo, S. K. Sahoo, and T. Swarnkar, “A Comparative Study using Next Generation Sequencing Data and Machine Learning Approach for Crohn’s Disease (CD) Identification,” 2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS), pp. 17–21, Aug. 2022, doi:10.1109/mlcss57186.2022.00012.

C. Paramasivan, D. Paul Dhinakaran, S. S. P. Selvam, S. Mukherjee, A. P. Juliet, and S. R. Devi, “Comparing Supervised and Unsupervised Learning Technologies for Customer Segmentation in Marketing,” 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), pp. 1–6, Apr. 2024, doi:10.1109/iconstem60960.2024.10568702.

A. M. Carrington et al., “Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 329–341, Jan. 2023, doi:10.1109/tpami.2022.3145392.

Y. Benhaddou and P. Leray, “Customer Relationship Management and Small Data — Application of Bayesian Network Elicitation Techniques for Building a Lead Scoring Model,” 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), pp. 251–255, Oct. 2017, doi: 10.1109/aiccsa.2017.51.

R. Nygård and J. Mezei, “Automating Lead Scoring with Machine Learning: An Experimental Study,” Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020, doi:10.24251/hicss.2020.177.

D. R. Hermawan, M. Fahrio Ghanial Fatihah, L. Kurniawati, and A. Helen, “Comparative Study of J48 Decision Tree Classification Algorithm, Random Tree, and Random Forest on In-Vehicle CouponRecommendation Data,” 2021 International Conference on Artificial Intelligence and Big Data Analytics, pp. 1–6, Oct. 2021, doi:10.1109/icaibda53487.2021.9689701.

G. N. Kumar and K. Hariharanath, “Designing a Lead Score Model for Digital Marketing Firms in Education Vertical in India,” Indian Journal of Science and Technology, vol. 14, no. 16, pp. 1302–1309, Apr. 2021, doi: 10.17485/ijst/v14i16.290.

A. Jadli, M. Hamim, M. Hain, and A. Hasbaoui, “Toward A Smart Lead Scoring System Using Machine Learning,” Indian Journal of Computer Science and Engineering, vol. 13, no. 2, pp. 433–443, Apr. 2022, doi: 10.21817/indjcse/2022/v13i2/221302098.

R. L. Marchese Robinson, A. Palczewska, J. Palczewski, and N. Kidley, “Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets,” Journal of Chemical Information and Modeling, vol. 57, no. 8, pp. 1773–1792, Aug. 2017, doi: 10.1021/acs.jcim.6b00753.

A. Lakshmanarao, A. Srisaila, and T. S. R. Kiran, “Heart Disease Prediction using Feature Selection and Ensemble Learning Techniques,” 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pp. 994–998, Feb. 2021, doi: 10.1109/icicv50876.2021.9388482.

J. B. G. Brito et al., “A framework to improve churn prediction performance in retail banking,” Financial Innovation, vol. 10, no. 1, Jan. 2024, doi: 10.1186/s40854-023-00558-3.

A. Manzoor, M. Atif Qureshi, E. Kidney, and L. Longo, “A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners,” IEEE Access, vol. 12, pp. 70434–70463, 2024, doi: 10.1109/access.2024.3402092.

A. Raj and D. Vetrithangam, “Machine Learning and Deep Learning technique used in Customer Churn Prediction: - A Review,” 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), pp. 139–144, Apr. 2023, doi: 10.1109/cises58720.2023.10183530.

P. Pulkundwar, K. Rudani, O. Rane, C. Shah, and S. Virnodkar, “A Comparison of Machine Learning Algorithms for Customer Churn Prediction,” 2023 6th International Conference on Advances in Science and Technology (ICAST), Dec. 2023, doi:10.1109/icast59062.2023.10455051.

T. Sunksuwan et al., “Utilizing Machine Learning to Enhance Profit Generation from Technical Trading Rules in Portfolio Construction within the Stock Exchange of Thailand,” 2023 International Conference on Machine Learning and Applications (ICMLA), pp. 1189–1193, Dec. 2023, doi: 10.1109/icmla58977.2023.00178.

M. A. Khan et al., “Effective Demand Forecasting Model Using Business Intelligence Empowered With Machine Learning,” IEEE Access, vol. 8, pp. 116013–116023, 2020, doi:10.1109/access.2020.3003790.

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