Predictive marketing as a tool for anticipating consumer needs in real time in pharmacies

Authors

DOI:

https://doi.org/10.35381/r.k.v10i1.4871

Keywords:

Marketing, data analysis, consumer, behavior, pharmacology, (UNESCO Thesaurus).

Abstract

This article was written with the aim of implementing predictive marketing to anticipate consumer demands and behaviors, thereby optimizing marketing strategies. A mixed methodology was used, combining quantitative and qualitative approaches through surveys, document review, and direct observation. The results show that personalized recommendations improve the shopping experience, increase product relevance, and strengthen the relationship of trust with the pharmacy. It is concluded that predictive marketing allows establishments to differentiate themselves in a competitive market, optimize inventory management, and encourage loyalty through more effective and personalized sales strategies. In conclusion, predictive marketing provides a competitive advantage to pharmacies. By adopting these strategies, establishments or stores can differentiate themselves in a saturated market, combining personalized offerings with efficient inventory management and excellent customer service.

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References

Agencia nacional de regulación, control y vigilancia sanitaria. (2025). Registro Nacional de Establecimientos Farmacéuticos: Cifras de control y vigilancia. Quito: ARCSA. https://www.controlsanitario.gob.ec/

Altwaijri, A. (2025). The effect of marketing 5.0 on marketing performance: The moderating effect of customer resources. Decision Science Letters, 14, 113-122. https://n9.cl/rqwpo

Anwar, D., Faizanuddin, M., Rahman, F., & Dayal, R. (2025). Analyzing Consumer Behavior in E-Commerce: Insights from Data-Driven Approaches. Management (Montevideo), 3, 127. https://n9.cl/eww2f

Carvache, M., Carvache, W., & Víquez, A. G. (2024). Consumer Behavior in Marketing: Sociodemographic Aspects, Satisfaction and Loyalty. https://n9.cl/w24xmg

Csoban, E., Esqueda, S., & Ríos, A. (2024). Predicción del comportamiento de compra online: una aplicación del modelo SOR. Retos, 27, 21-33. https://n9.cl/23zys

Erazo, J., & Narváez, C. (2025). Marketing y gestión empresarial en la era del e-commerce. Cuenca: Fondo Editorial Perspectivas Globales. https://n9.cl/zdcgk

Fadel, K., & Konis, E. (2024). Analyzing the Influence of Marketing Strategies on Consumer Behavior in the Fast Fashion Industry: The Case of Zara in Cyprus. Revista de Gestão Social e Ambiental, 18(8), 1–21. https://n9.cl/3szdxh

García, G., & Herrero, P. (2025). Impacto del Marketing Predictivo basado en Inteligencia Artificial. Transformando Estrategias de Comunicación y Ventas en Pymes y Startups. Visual Review, 17(1), 165-178. https://n9.cl/0sl44

García, J., Aguilar, H., Vergara, H., Rivera, A., Armijos, F., & Lopez, J. (2025). Análisis predictivo en marketing digital: un enfoque de modelado estadístico para predecir el comportamiento del consumidor. 4, 1061. https://n9.cl/v8y8iz

Instituto nacional de estadística y censos. (2023). Ecuador en cifras: Estadísticas de Acceso y Uso de las Tecnologías de la Información y Comunicación. https://n9.cl/5t5zyg

Madrigal, F., Madrigal, S., & Martínez, M. d. (2024). Comportamiento del consumidor: cambios y tendencias en la sociedad contemporánea. Revista Venezolana De Gerencia, 29(106), 643–658. https://n9.cl/8bgrc

Ministerio de telecomunicaciones y de la sociedad de la información. (2022). Agenda de Transformación Digital del Ecuador 2022–2025. https://n9.cl/k271jk

Olmedo, C., Vela, J., & Ibarra, M. (2024). E-commerce y la dinámica del cliente en México: análisis de factores clave para optimizar la compra. Acta Universitaria, 34. https://n9.cl/tgzrw

Pachas, L., Calderón, H., & Cárdenas, F. (2023). Chatbot basado en Deep Learning para recomendar productos relevantes. Computación y Sistemas, 27(2), 511–523. https://n9.cl/dc3zt

Sánchez, J., Sánchez, D., Romero, E., & Macías, N. (2025). Inteligencia de negocios en la optimización de estrategias de marketing: Enfoque basado en el análisis predictivo. Revista De Ciencias Sociales, 31(2), 340–351. https://n9.cl/9ole5

Wang, L., Jing, Z., Li, H., Li, C., & Su, Y. (2025). The Influence of AI-Driven Personalization in Social Media Marketing on Consumer Purchase Decisions and Behavior. International Journal of Accounting and Economics Studies, 12(5), 438–444. https://n9.cl/4vp3lw

Published

2025-12-01

How to Cite

Cuásquer-Chicaiza, M. C., & Jácome-Ortega, M. J. (2025). Predictive marketing as a tool for anticipating consumer needs in real time in pharmacies. Revista Arbitrada Interdisciplinaria Koinonía, 10(1), 838–856. https://doi.org/10.35381/r.k.v10i1.4871

Issue

Section

De Investigación