Skip to main content
SpringerLink
Log in

Unveiling Marketing Potential: Harnessing Advanced Analytics and Machine Learning for Gold Membership Strategy Optimization in a Superstore

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

This research paper presents a comprehensive case study conducted in a superstore, introducing a novel gold membership offer and employing sophisticated analytics and machine learning methodologies to identify potential customers. The primary objective of this study is to explore available data to discern the factors influencing customers’ responses to a new supermarket offering. Subsequently, a predictive model is developed to accurately gauge the likelihood of a favorable customer response. In pursuit of enhancing marketing strategies and bolstering sales, this study employs a suite of machine learning techniques, including decision trees, support vector machines, random forests, and XGBoost. Furthermore, the study incorporates metaheuristic optimization algorithms such as grey wolf optimization, slime mold algorithm, multi-verse optimizer, and particle swarm optimization to fine-tune hyperparameters of the machine learning models. These optimization algorithms serve as effective search mechanisms, facilitating the identification of optimal solutions and significantly improving classification performance in the context of the complex superstore problem. The research findings highlight the substantial impact of the metaheuristic strategy, specifically grey wolf optimization, on the performance of all machine learning models. Notably, the random forest model achieved the highest accuracy of 95% with the application of grey wolf optimization. Moreover, the decision tree model demonstrated remarkable improvement in accuracy following hyperparameter tuning with grey wolf optimization. Collectively, these results underscore the critical role of metaheuristic optimization in enhancing the performance of machine learning models for marketing strategies in the superstore industry.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Data Availability

All datasets used for supporting the conclusions of this article are available from the public data repository at the website of https://www.kaggle.com/datasets/ahsan81/superstore-marketing-campaign-dataset?resource=download

References

  1. Zhao B, Takasu A, Yahyapour R, Fu X. Loyal consumers or one-time deal hunters: Repeat buyer prediction for e-commerce, pp. 1080–1087 (2019). https://doi.org/10.1109/ICDMW.2019.00158

  2. Choudhary R, Gianey H. Comprehensive review on supervised machine learning algorithms, pp. 37–43 (2017). https://doi.org/10.1109/MLDS.2017.11

  3. Kiran J.S, Rao P.S.V.S, Rao P.V.R.D.P, Babu B.S, Divya N. Analysis on the prediction of sales using various machine learning testing algorithms. In: 2022 international conference on computer communication and informatics (ICCCI), pp. 1–6 (2022). https://doi.org/10.1109/ICCCI54379.2022.9740949

  4. Alagarsamy S, Varma K.G, Harshitha K, Hareesh K, Varshini K. Predictive analytics for black friday sales using machine learning technique. In: 2023 international conference on intelligent data communication technologies and internet of things (IDCIoT), pp. 389–393 (2023). https://doi.org/10.1109/IDCIoT56793.2023.10053454

  5. Akanksha A, Yadav D, Jaiswal D, Ashwani A, Mishra A. Store-sales forecasting model to determine inventory stock levels using machine learning. In: 2022 international conference on inventive computation technologies (ICICT), pp. 339–344 (2022). https://doi.org/10.1109/ICICT54344.2022.9850468

  6. Chang Y. Research on marketing data mining model based on support vector machine algorithm. In: 2022 international conference on artificial intelligence and autonomous robot systems (AIARS), pp. 350–354 (2022). https://doi.org/10.1109/AIARS57204.2022.00085

  7. Baržić M, Munitić N.-F, Bronić F, Jelić L, Lešić V. Forecasting sales in retail with xgboost and iterated multi-step ahead method. In: 2022 international conference on smart systems and technologies (SST), pp. 153–158 (2022). https://doi.org/10.1109/SST55530.2022.9954658

  8. Naik H, Yashwanth K, P, S, Jayapandian N. Machine learning based food sales prediction using random forest regression. In: 2022 6th international conference on electronics, communication and aerospace technology, pp. 998–1004 (2022). https://doi.org/10.1109/ICECA55336.2022.10009277

  9. Pane S.F, Putrada A.G, Alamsyah N, Fauzan M.N. A pso-gbr solution for association rule optimization on supermarket sales. In: 2022 seventh international conference on informatics and computing (ICIC), pp. 1–6 (2022). https://doi.org/10.1109/ICIC56845.2022.10007001

  10. Mallik R.S, Abhiram R, Reddy S.R, Jagadish R.M. A comprehensive survey on sales forecasting models using machine learning algorithms. In: 2022 fourth international conference on emerging research in electronics, computer science and technology (ICERECT), pp. 1–6 (2022). https://doi.org/10.1109/ICERECT56837.2022.10060168

  11. Odegua R. Applied machine learning for supermarket sales prediction. In: project: predictive machine learning in industry, p. 7 (2020)

  12. Choudhury A.M, Nur K. A machine learning approach to identify potential customer based on purchase behavior. In: 2019 international conference on robotics,electrical and signal processing techniques (ICREST), pp. 242–247 (2019). https://doi.org/10.1109/ICREST.2019.8644458

  13. Raza A. Superstore marketing campaign dataset (2023). https://www.kaggle.com/datasets/ahsan81/superstore-marketing-campaign-dataset

  14. Nguyen QH, Ly H-B, Ho LS, Ansari NA, Le HV, Tran VQ, Prakash I, Pham BT. Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Math Probl Eng. 2021;15:483. https://doi.org/10.1155/2021/4832864.

    Article  Google Scholar 

  15. Luque A, Carrasco A, Martín A, de Las-Heras A. The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Patt Recognit. 2019;91:216–31. https://doi.org/10.1016/j.patcog.2019.02.023.

    Article  Google Scholar 

  16. Anuradha Gupta G. A self explanatory review of decision tree classifiers. In: international conference on recent advances and innovations in engineering (ICRAIE-2014), pp. 1–7 (2014). https://doi.org/10.1109/ICRAIE.2014.6909245

  17. More A.S, Rana D.P. Review of random forest classification techniques to resolve data imbalance. In: 2017 1st international conference on intelligent systems and information management (ICISIM), pp. 72–78 (2017). https://doi.org/10.1109/ICISIM.2017.8122151

  18. Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A. A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing. 2020;408:189–215. https://doi.org/10.1016/j.neucom.2019.10.118.

    Article  Google Scholar 

  19. Kumar A. Support Vector Machine (SVM) Python Example - Data Analytics — vitalflux.com. https://vitalflux.com/classification-model-svm-classifier-python-example/. [Accessed 29-08-2023]

  20. Ogunleye A, Wang Q-G. Xgboost model for chronic kidney disease diagnosis. IEEE/ACM Trans Computat Biol Bioinform. 2020;17(6):2131–40. https://doi.org/10.1109/TCBB.2019.2911071.

    Article  Google Scholar 

  21. Kennedy J, Eberhart R. Particle swarm optimization. Int Conf Neural Netw. 1995;4:1942–19484. https://doi.org/10.1109/ICNN.1995.488968.

    Article  Google Scholar 

  22. Kang L, Chen R-S, Xiong N, Chen Y-C, Hu Y-X, Chen C-M. Selecting hyper-parameters of gaussian process regression based on non-inertial particle swarm optimization in internet of things. IEEE Access. 2019;7:59504–13. https://doi.org/10.1109/ACCESS.2019.2913757.

    Article  Google Scholar 

  23. Chauhan A, Shivaprakash SJ, Sabireen H, Md AQ, Venkataraman N. Stock price forecasting using pso hypertuned neural nets and ensembling. Appl Soft Comput. 2023;147: 110835. https://doi.org/10.1016/j.asoc.2023.110835.

    Article  Google Scholar 

  24. Li S, Chen H, Wang M, Heidari AA, Mirjalili S. Slime mould algorithm: a new method for stochastic optimization. Future Generat Comput Syst. 2020;111:300–23. https://doi.org/10.1016/j.future.2020.03.055.

    Article  Google Scholar 

  25. Guo WZ, Yuheng Li W. Application of support vector machine algorithm incorporating slime mould algorithm strategy in ancient glass classification. Appl Sci. 2023;13(6):3718. https://doi.org/10.3390/app13063718.

    Article  Google Scholar 

  26. Örnek BN, Aydemir SB, Düzenli T, Özak B. A novel version of slime mould algorithm for global optimization and real world engineering problems: enhanced slime mould algorithm. Math Comput Simulat. 2022;198:253–88. https://doi.org/10.1016/j.matcom.2022.02.030.

    Article  MathSciNet  Google Scholar 

  27. Mirjalili S, Jangir P, Mirjalili SZ, Saremi S, Trivedi IN. Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowl Based Syst. 2017;134:50–71. https://doi.org/10.1016/j.knosys.2017.07.018.

    Article  Google Scholar 

  28. Lin J, Zhu L, Wang Z-J. A hybrid multi-verse optimization for the fuzzy flexible job-shop scheduling problem. Comput Ind Eng. 2019;127:1089–100.

    Article  Google Scholar 

  29. Xu W, Yu X. A multi-objective multi-verse optimizer algorithm to solve environmental and economic dispatch. Appl Soft Comput. 2023;146: 110650. https://doi.org/10.1016/j.asoc.2023.110650.

    Article  Google Scholar 

  30. Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw. 2014;69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007.

    Article  Google Scholar 

  31. Aufa B.Z, Suyanto S, Arifianto A. Hyperparameter setting of lstm-based language model using grey wolf optimizer. In: 2020 international conference on data science and its applications (ICoDSA), pp. 1–5 (2020). https://doi.org/10.1109/ICoDSA50139.2020.9213031

  32. Zhang M, Wang S, Xie Y, Yang X, Hao X, Fernandez C. Hybrid gray wolf optimization method in support vector regression framework for highly precise prediction of remaining useful life of lithium-ion batteries. Springer Ionics. 2023;29:3597–607. https://doi.org/10.1007/s11581-023-05072-1.

    Article  Google Scholar 

  33. Grandini M, Bagli E, Visani G. Metrics for multi-class classification: an overview. ArXiv abs/2008.05756 (2020)

Download references

Funding

Not applicable.

Author information

Author notes

  1. Neeraj Garg, D. Saxena and Rajesh Kumar have contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, 302017, Rajasthan, India

    Vikas Ranveer Singh Mahala

  2. Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, 302017, Rajasthan, India

    Neeraj Garg

  3. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, 302017, Rajasthan, India

    Vikas Ranveer Singh Mahala, D. Saxena & Rajesh Kumar

  4. Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, 302017, Rajasthan, India

    Neeraj Garg

Authors
  1. Vikas Ranveer Singh Mahala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neeraj Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. Saxena
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rajesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The entire study and paper were conducted by the main author VRSM and NG. All the supervision and guiding were done by DS and RK.

Corresponding authors

Correspondence to Vikas Ranveer Singh Mahala or Neeraj Garg.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical Approval

Not applicable.

Human or/and Animal Rights

Not applicable.

Consent for Publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Emerging Applications of Data Science for Real-World Problems” guest edited by Satyasai Jagannath Nanda, Rajendra Prasad Yadav and Mukesh Saraswat.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh Mahala, V.R., Garg, N., Saxena, D. et al. Unveiling Marketing Potential: Harnessing Advanced Analytics and Machine Learning for Gold Membership Strategy Optimization in a Superstore. SN COMPUT. SCI. 5, 374 (2024). https://doi.org/10.1007/s42979-024-02700-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-02700-z

Keywords

Search

Navigation