Artificial intelligence in potential customer segmentation: machine learning approach

Authors

DOI:

https://doi.org/10.56294/dm2024305

Keywords:

Artificial Intelligence, Technology, Segmentation, Potential Client, Machine Learning

Abstract

Integrating artificial intelligence (AI) into sales processes at a business level, specifically, in the segmentation of potential customers, is currently a very important issue for the promotion of your products and services. The present study focused on the analysis of the effectiveness of the machine learning approach used in mass consumption companies for the segmentation of potential customers. To achieve this objective, a systematic review of the literature will be carried out with a qualitative approach and supported by the PRISMA methodology. The results achieved in the review carried out showed that machine learning algorithms present better results compared to other approaches; Furthermore, regarding customer segmentation, this can be done through grouping, which is one of the most recognized machine learning techniques. It is concluded that it is necessary to expand the methods provided by this approach, using them to extract knowledge from unstructured, monitoring, and network data to achieve descriptive, causal, and prescriptive analyses; In addition, to outline the journey that customers take when purchasing and deploy decision support capabilities. All these benefits, at a business level, are provided by machine learning, reason enough for the proposed marketing strategies to be based on the information it offers

References

1. Olivar, N. El proceso de posicionamiento en el marketing: pasos y etapas. RAN - Revista Academia & Negocios; 2021, 7(1). https://doi.org/10.29393/RAN6-5PPNO10005

2. Li, L. & Zhang, J. Research and Analysis of an Enterprise E-Commerce Marketing System Under the Big Data Environment. Journal of Organizational and End User Computing, 2021, 33(6). DOI: 10.4018/JOEUC.20211101.oa15

3. Munnia, A., Nicotra, M., Romano, M. Big Data, Predictive Marketing and Churn Management in the IoT Era. In: Cunningham, J., Whalley, J. (eds) The Internet of Things Entrepreneurial Ecosystems, 2020, Palgrave Pivot, Cham. https://doi.org/10.1007/978-3-030-47364-8_5

4. Khrais, LT. Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce. Future Internet, 2020, 12(12): 226. https://doi.org/10.3390/fi12120226

5. Adikari, A., Burnett, D., Sedera, D., de Silva, D., Alahakoon, D. Value co-creation for open innovation: An evidence-based study of the data driven paradigm of social media using machine learning. International Journal of Information Management Data Insights, 2021, 1(2),100022. https://doi.org/10.1016/j.jjimei.2021.100022.

6. Vargas, A., Monje, A. N. Optimización de carteras de renta variable con machine learning optimización de carteras de renta variable con Machine Learning. Revista Investigación &Amp; Desarrollo, 2024, 23(2). https://doi.org/10.23881/idupbo.023.2-2e

7. Pitt, C, Bal, A. S., Plangger, K. New approaches to psychographic consumer segmentation: Exploring fine art collectors using artificial intelligence, automated text analysis, and correspondence analysis. European Journal ofMarketing, 2020, 54(2), 305-326. https://doi.org/10.1108/EJM-01-2019-0083

8. Mandapuram, M., Srujan, S., Reddy, M., Bodepudi, a. Application of Artificial Intelligence (AI) Technologiesto Accelerate Market Segmentation. Global Disclosure of Economics and Business, 2020, 9(2), 141-150. https://i-proclaim.my/journals/index.php/gdeb/article/view/662/613

9. Álvarez-Indacochea, A., Figueroa-Soledispa, M., Peñafiel-Loor, J. La importancia de la mercadotecnia y sus componentes en las organizaciones. Revista Científica FIPCAEC. Polo De Capacitación, Investigación y Publicación (POCAIP), 2020, 5(5), 62-87. https://doi.org/10.23857/fipcaec.v5i5.281

10. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC… et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 2021,372(71). doi: 10.1136/bmj.n71. PMID: 33782057; PMCID: PMC8005924.

11. Rivas, F. Cómo publicar un artículo original en revistas científicas con factor de impacto. Pediatría Atención Primaria, 2017, 19(Supl. 26), 101-109. http://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S1139-76322017000300014&lng=es.

12. Yi, S., Liu, X. Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review. Complex Intell. Syst. 2020, 6, 621–634. https://doi.org/10.1007/s40747-020-00155-2

13. Ma, L., Sun, B. Machine learning and AI in marketing-Connecting computing power to human insights. International Journal of Research in Marketing, 2020, (), S0167811620300410. DOI: 10.1016/j.ijresmar.2020.04.005

14. Joung, J., Kim, H. Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews. International Journal of Information Management, 2023, 70, 102641. https://doi.org/10.1016/j.ijinfomgt.2023.102641

15. Wu, S., Yau, W. -C., Ong, T. -S., Chong, S. -C. Integrated Churn Prediction and Customer Segmentation Framework for Telco Business. IEEE Access, 2021, 9, 62118-62136, DOI: 10.1109/ACCESS.2021.3073776

16. Rezazadeh, A. A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine-Learning Approach. Forecasting, 2020, 2(3), 267-283. https://doi.org/10.3390/forecast2030015

17. Chagas, B. N. R., Viana, J., Reinhold, O., Lobato, F. M. F., Jacob, A. F. L., Alt, R. A literature review of the current applications of machine learning and their practical implications. Web Intelligence, 2020, 1–15. DOI:10.3233/web-200429

18. Vermeer, S., Araujo, T., Bernritter, S., Noort, G. Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media. International Journal of Research in Marketing, 2019, 36. https://doi.org/10.1016/j.ijresmar.2019.01.010.

19. De Lima Lemos, R., Silva, T., Miranda Tabak, B. Propension to customer churn in a financial institution: a machine learning approach. Neural Computing and Applications, 2022, 34:11751–11768. https://doi.org/10.1007/s00521-022-07067-x

20. Sharma, R., Kumar, A., &t Chuah, C. Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer, International Journal of Information Management Data Insights, 2021, 1(2). https://doi.org/10.1016/j.jjimei.2021.100050.

21. Chambi Condori, P. Segmentación de mercado: Machine Learning en marketing en contextos de covid-19. Industrial Data, 2023, 26(1), 275-301. https://dx.doi.org/10.15381/idata.v26i1.23623

22. Suh, Y. Machine learning based customer churn prediction in home appliance rental business. J Big Data, 2023, 10(41). https://doi.org/10.1186/s40537-023-00721-8

23. Van Leeuwen, Rik., Koole, G. Data-driven market segmentation in hospitality using unsupervised machine learning. Machine Learning with Applications, 2022, 10, 100414. https://doi.org/10.1016/j.mlwa.2022.100414.

24. Amutha, R. Khan, A. Customer Segmentation using Machine Learning Techniques. Tuijin Jishu/Journal of Propulsion Technology, 2023, 44(3). https://www.propulsiontechjournal.com/index.php/journal/article/view/653/476

25. Vilaginés, J. Predecir el comportamiento del cliente con la lealtad de activación porperiodo. Del RFM al RFMAP. Esic Market Economics and Business Journal, 2020, 51(3), 639-667. DOI: 10.7200/esicm.167.0513.4

26. Bratinaa, D., Faganelb, A. Using supervised machine learning methods for rfm segmentation: a casino direct marketing communication case. Market-Tržište, 2023, 35(1), 7-22. http://dx.doi.org/10.22598/mt/2023.35.1

27. Martínez, I. J., Aguado, J. M. Sánchez, P.H. Smart Advertising: Innovación y disrupción tecnológica asociadas a la IA en el ecosistema publicitario. Revista Latina de Comunicación Social, 2022, 80, 69-90. https://www.doi.org/10.4185/RLCS-2022-1693

28. Monalisa, Siti., Juniarti, Y., Saputra, E., Muttakin, Fitriani., Khairil, T. Customer segmentation with RFM models and demographic variable using DBSCAN algorithm. TELKOMNIKA Telecommunication Computing Electronics and Control, 2023, 21(4), 742-749. DOI: 10.12928/TELKOMNIKA.v21i4.22759

29. Lone, H., Warale, P. Cluster Analysis: Application of K-Means and Agglomerative Clustering for Customer Segmentation. Journal of Positive School Psychology, 2022, 6(5),7798–7804.

30. Metilda, M., Vishnu, R.S, Agarshana, P. A Study onCustomer Segmentation Using K-Means Clustering forOnline Shoppers. Rivista italiana di Filosofia Analitica Junior, 2023, 14(2). https://rifanalitica.it/index.php/journal/article/view/339/274

31. Zúñiga, F. G., Mora, D. A. y Molina, D. P. La importancia de la inteligencia artificial en las comunicaciones en los procesos marketing. Vivat Academia, 2023, 156, 19-39. http://doi.org/10.15178/va.2023.e1474

32. Carrasco, M. Herramientas del marketing digital que permiten desarrollar presencia online, analizar la web, conocer a la audiencia y mejorar los resultados de búsqueda. Revista Perspectivas, 2020, (45), 33-60. http://www.scielo.org.bo/scielo.php?script=sci_arttext&pid=S1994-37332020000100003&lng=es&tlng=es.

33. Rodríguez, J., Bermeo, A. Análisis de datos profundo mediante herramienta de inteligencia artificial para la generación de un Dashboard Gerencial. Sapientia Technological, 2023, 4(1). https://doi.org/10.58515/010RSPT

34. Rivera-Montaño, S. Impacto de la inteligencia artificial (IA) en la efectividad de las estrategias de marketing personalizado. Revista Científica Anfibios, 2023, 6(2), 70-81. https://doi.org/10.37979/afb.2023v6n2.138

35. Ospina Usaquén, M., Medina, V., Rodríguez, J. Integración de la Inteligencia de Negocios, la Inteligencia de Mercados y la Inteligencia Competitiva desde el análisis de datos. RISTI, 2020, E34, 609–619. https://acortar.link/BZmEt5

36. Alghamdi, A. A Hybrid Method for Customer Segmentation in Saudi Arabia Restaurants Using Clustering, Neural Networks and Optimization Learning Techniques. Arabian Journal for Science and Engineering, 2023, 48:2021–2039. https://doi.org/10.1007/s13369-022-07091-y

37. Mitchell, T. Machine Learning. McGraw-Hill Science/Engineering, 1997.

38. Goodfellow, I., Bengio, Y., Courville, A. Deep Learning. The MIT Press, 2016.

39. Mena, A., de Oliveira, N., Xavier, C., de Lima, I. Técnicas de machine-learning utilizadas en estudios psicológicos con adolescentes: Una revisión sistemática. EDUPSYKHÉ. Revista de Psicología y Educación, 2022, 20(3), 23-37.

40. Mahesh, B. Machine Learning Algorithms-A Review. International Journal of Science and Research (IJSR), 2020, 9(1). https://doi.org/10.21275/ART20203995

41. Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., Aljaaf, A.J. A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. In: Berry, M., Mohamed, A., Yap, B. (eds) Supervised and Unsupervised Learning for Data Science. Unsupervised and Semi-Supervised Learning, 2020, Springer, Cham. https://doi.org/10.1007/978-3-030-22475-2_1

42. Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. SCI, 2021, 2, 160. https://doi.org/10.1007/s42979-021-00592-x

43. Pérez, A. R., Villegas, C. J., Cabascango, J. C., Soria, E. R. Inteligencia artificial como estrategia de innovación en empresas de servicios unvisión. Revista Publicando, 2023, 10(38), 74-82. https://doi.org/10.51528/rp.vol10.id2359

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Published

2024-01-01

How to Cite

1.
Eduardo Rafael Jauregui Romero ERJR, Alca Gomez J, Vilca Tantapoma ME, Orlando Tito Llanos Gonzales OTLG. Artificial intelligence in potential customer segmentation: machine learning approach. Data and Metadata [Internet]. 2024 Jan. 1 [cited 2024 Dec. 21];3:305. Available from: https://dm.ageditor.ar/index.php/dm/article/view/303