Optimizing sales processes with artificial intelligence and machine learning: a scientometric analysis
DOI:
https://doi.org/10.51798/sijis.v5i4.870Keywords:
bibliometric analysis; sales automation; machine learning; artificial intelligence; sales processAbstract
This study explores the literature that evaluates how artificial intelligence (AI) and machine learning (ML) can affect the optimization of sales processes, using a scientometric and bibliometric approach. Through keyword co-occurrence analysis in the scientific literature, the main trends and patterns in AI and ML research applied to sales were identified. VOSviewer software was used to map the relationships between key terms and visualize the predominant focus areas in the field. The results reveal that the adoption of AI and ML technologies is highly correlated with improvements in the efficiency of sales processes, highlighting the growing importance of these technologies in the development of business strategies. However, limited participation of researchers from developing countries was observed in this cutting-edge field, underscoring the need for greater inclusion and international collaboration. This study provides a comprehensive view of the current state of AI and ML research in sales, identifying both the advances made and the gaps in the literature that require further attention. The findings provide a solid basis for future research seeking to delve into the practical applications of these technologies in different industrial and geographical contexts, as well as for the development of policies that promote a more equitable distribution of knowledge and resources in this emerging area.
References
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AlRyalat, S. A. S., Malkawi, L. W., & Momani, S. M. (2019). Comparing bibliometric analysis using PubMed, Scopus, and Web of Science databases. JoVE (Journal of Visualized Experiments), (152), e58494. https://doi.org/10.3791/58494
Auffarth, B. (2020). Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2. x and PyTorch 1.6. Packt Publishing Ltd.
Bejou, D., Wray, B., & Ingram, T. N. (1996). Determinants of relationship quality: an artificial neural network analysis. Journal of business research, 36(2), 137-143. https://doi.org/10.1016/0148-2963(95)00100-X
Butt, N. S., Malik, A. A., & Shahbaz, M. Q. (2021). Bibliometric analysis of statistics journals indexed in web of science under emerging source citation index. Sage Open, 11(1), 2158244020988870. https://doi.org/10.1177/2158244020988870
Chang, P. C., & Wang, Y. W. (2006). Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry. Expert systems with applications, 30(4), 715-726. https://doi.org/10.1016/j.eswa.2005.07.031
Chen, Z., Narayanan, N., Fang, B., Li, G., Pattabiraman, K., & DeBardeleben, N. (2020). Tensorfi: A flexible fault injection framework for tensorflow applications. In 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), 426-435). IEEE. https://doi.org/10.1109/ISSRE5003.2020.00047
Cui, R., Gallino, S., Moreno, A., & Zhang, D. J. (2018). The operational value of social media information. Production and operations management, 27(10), 1749-1769. https://doi.org/10.1111/poms.12707
Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., ... & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International journal of information management, 59, 102168. https://doi.org/10.1016/j.ijinfomgt.2020.102168
Eduardo Tasca, J., Ensslin, L., Rolim Ensslin, S., & Bernardete Martins Alves, M. (2010). An approach for selecting a theoretical framework for the evaluation of training programs. Journal of European industrial training, 34(7), 631-655. https://doi.org/10.1108/03090591011070761
Filgueiras, F. (2023). Designing artificial intelligence policy: comparing design spaces in Latin America. Latin American Policy, 14(1), 5-21. https://doi.org/10.1111/lamp.12282
Jiao, J., & Zhang, Y. (2005). Product portfolio identification based on association rule mining. Computer-Aided Design, 37(2), 149-172. https://doi.org/10.1016/j.cad.2004.05.006
Kühl, N., Schemmer, M., Goutier, M., & Satzger, G. (2022). Artificial intelligence and machine learning. Electronic Markets, 32(4), 2235-2244.
Kwok, L., & Yu, B. (2013). Spreading social media messages on Facebook: An analysis of restaurant business-to-consumer communications. Cornell Hospitality Quarterly, 54(1), 84-94. https://doi.org/10.1177/1938965512458360
Lacerda, R., Ensslin, L., & Ensslin, S. (2011). A performance measurement view of IT project management. International Journal of Productivity and Performance Management, 60(2), 132-151. https://doi.org/10.1108/17410401111101476
Loureiro, A. L., Miguéis, V. L., & Da Silva, L. F. (2018). Exploring the use of deep neural networks for sales forecasting in fashion retail. Decision Support Systems, 114, 81-93. https://doi.org/10.1016/j.dss.2018.08.010
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937-947. https://doi.org/10.1287/mksc.2019.1192
Muro, E. D. A., Álvarez, L. A. S., Rodriguez, V. H. P., Lucana, F. R. V., Rojas, L. M. H., Benavides, A. M. V., & Salazar, C. A. H. (2024). Fostering Equity in Rural Education: a Literature Review on Student Dropout and Retention Strategies. Revista De Gestão Social E Ambiental, 18(1), e04922. https://doi.org/10.24857/rgsa.v18n1-083
Olano, M. D., de la Cruz, A. S. V., Rodriguez, V. H. P., Cruz, L. D. C. S. S., Benavides, A. M. V., Salazar, C. A. H., … Reategui, J. A. (2024). The Need for Innovation in Financial Education: A Study of Household Indebtedness in Peru. Revista De Gestão Social E Ambiental, 18(1), e04919. https://doi.org/10.24857/rgsa.v18n1-081
Pagani, R. N., Kovaleski, J. L., & Resende, L. M. (2015). Methodi Ordinatio: a proposed methodology to select and rank relevant scientific papers encompassing the impact factor, number of citation, and year of publication. Scientometrics, 105, 2109-2135. https://doi.org/10.1007/s11192-015-1744-x
Park, B., & Bae, J. K. (2015). Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert systems with applications, 42(6), 2928-2934. https://doi.org/10.1016/j.eswa.2014.11.040
Policarpo, L. M., da Silveira, D. E., da Rosa Righi, R., Stoffel, R. A., da Costa, C. A., Barbosa, J. L. V., ... & Arcot, T. (2021). Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review. Computer Science Review, 41, 100414. https://doi.org/10.1016/j.cosrev.2021.100414
Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today's academic world. Publications, 9(1), 12. https://doi.org/10.3390/publications9010012
Puican Rodriguez, V. H., Suárez Santa Cruz, L. D. C., Salazar Asalde, A., Alcántara Suyón, A., & Camacho Delgado, F. M. (2024). The effect of taxes and tax refunds on the economic activity of the energy industry in Peru. International Journal of Energy Economics and Policy, 14(4), 36-47.
Rafiei, M. H., & Adeli, H. (2016). A novel machine learning model for estimation of sale prices of real estate units. Journal of Construction Engineering and Management, 142(2), 04015066. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001047
Ramirez-Soto, A. N., Ríos, J. E. S., Rodriguez, V. H. P., Vite, I. P. Y., & Castaneda, P. E. R. (2024). Models of Strategic Management in Smes in The Period of The Covid-19 Pandemic in Metropolitan Lima. Revista de Gestão Social e Ambiental, 18(1), e04936-e04936.
Rodriguez, V. H. P., Aguilar, H. E. V., Delgado, F. M. C., Santa Cruz, L. D. C. S., Benavides, A. M. V., Salazar, C. A. H., ... & Suyón, A. A. (2024). Challenges in the Relationship between Liquidity and Profitability: Perspectives from a Literature Review. Revista de Gestão Social e Ambiental, 18(1), e04923-e04923. https://doi.org/10.24857/rgsa.v18n1-084
Rufasto, A. M., Lucumí, N. P. R., & Rodríguez, V. H. P. (2024). SIRE: Catalyst for Improvements in Accounting and Tax Processes. Journal of Ecohumanism, 3(7), 928-937.
Rui, H., Liu, Y., & Whinston, A. (2013). Whose and what chatter matters? The effect of tweets on movie sales. Decision support systems, 55(4), 863-870. https://doi.org/10.1016/j.dss.2012.12.022
Santa Cruz, L. D. C. S., Rodriguez, V. H. P., López, D. I. F., & Olivera, J. J. I. (2024). Electricity Industry Strategies in Ecuador and Peru: Their Impacts on Energy Efficiency and Prices. International Journal of Energy Economics and Policy, 14(5), 464-478. https://doi.org/10.32479/ijeep.16713
Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. Data mining and knowledge discovery, 5, 115-153. https://doi.org/10.1023/A:1009804230409
Smith, K. A., & Gupta, J. N. (2000). Neural networks in business: techniques and applications for the operations researcher. Computers & Operations Research, 27(11-12), 1023-1044. https://doi.org/10.1016/S0305-0548(99)00141-0
Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial marketing management, 69, 135-146. https://doi.org/10.1016/j.indmarman.2017.12.019
Thomassey, S. (2010). Sales forecasts in clothing industry: The key success factor of the supply chain management. International Journal of Production Economics, 128(2), 470-483. https://doi.org/10.1016/j.ijpe.2010.07.018
. Production and operations management, 27(10), 1749-1769. https://doi.org/10.1111/poms.12707
Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., ... & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International journal of information management, 59, 102168. https://doi.org/10.1016/j.ijinfomgt.2020.102168
Eduardo Tasca, J., Ensslin, L., Rolim Ensslin, S., & Bernardete Martins Alves, M. (2010). An approach for selecting a theoretical framework for the evaluation of training programs. Journal of European industrial training, 34(7), 631-655. https://doi.org/10.1108/03090591011070761
Jiao, J., & Zhang, Y. (2005). Product portfolio identification based on association rule mining. Computer-Aided Design, 37(2), 149-172. https://doi.org/10.1016/j.cad.2004.05.006
Kwok, L., & Yu, B. (2013). Spreading social media messages on Facebook: An analysis of restaurant business-to-consumer communications. Cornell Hospitality Quarterly, 54(1), 84-94. https://doi.org/10.1177/1938965512458360
Kühl, N., Schemmer, M., Goutier, M., & Satzger, G. (2022). Artificial intelligence and machine learning. Electronic Markets, 32(4), 2235-2244.
Agarwal, A., Durairajanayagam, D., Tatagari, S., Esteves, S. C., Harlev, A., Henkel, R., ... & Bashiri, A. (2016). Bibliometrics: tracking research impact by selecting the appropriate metrics. Asian journal of andrology, 18(2), 296-309. https://doi.org/10.4103/1008-682X.171582
Ali, A., Abd Razak, S., Othman, S. H., Eisa, T. A. E., Al-Dhaqm, A., Nasser, M., ... & Saif, A. (2022). Financial fraud detection based on machine learning: a systematic literature review. Applied Sciences, 12(19), 9637. https://doi.org/10.3390/app12199637
AlRyalat, S. A. S., Malkawi, L. W., & Momani, S. M. (2019). Comparing bibliometric analysis using PubMed, Scopus, and Web of Science databases. JoVE (Journal of Visualized Experiments), (152), e58494. https://doi.org/10.3791/58494
Auffarth, B. (2020). Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2. x and PyTorch 1.6. Packt Publishing Ltd.
Bejou, D., Wray, B., & Ingram, T. N. (1996). Determinants of relationship quality: an artificial neural network analysis. Journal of business research, 36(2), 137-143. https://doi.org/10.1016/0148-2963(95)00100-X
Butt, N. S., Malik, A. A., & Shahbaz, M. Q. (2021). Bibliometric analysis of statistics journals indexed in web of science under emerging source citation index. Sage Open, 11(1), 2158244020988870. https://doi.org/10.1177/2158244020988870
Chang, P. C., & Wang, Y. W. (2006). Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry. Expert systems with applications, 30(4), 715-726. https://doi.org/10.1016/j.eswa.2005.07.031
Chen, Z., Narayanan, N., Fang, B., Li, G., Pattabiraman, K., & DeBardeleben, N. (2020). Tensorfi: A flexible fault injection framework for tensorflow applications. In 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), 426-435). IEEE. https://doi.org/10.1109/ISSRE5003.2020.00047
Cui, R., Gallino, S., Moreno, A., & Zhang, D. J. (2018). The operational value of social media information. Production and operations management, 27(10), 1749-1769. https://doi.org/10.1111/poms.12707
Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., ... & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International journal of information management, 59, 102168. https://doi.org/10.1016/j.ijinfomgt.2020.102168
Eduardo Tasca, J., Ensslin, L., Rolim Ensslin, S., & Bernardete Martins Alves, M. (2010). An approach for selecting a theoretical framework for the evaluation of training programs. Journal of European industrial training, 34(7), 631-655. https://doi.org/10.1108/03090591011070761
Filgueiras, F. (2023). Designing artificial intelligence policy: comparing design spaces in Latin America. Latin American Policy, 14(1), 5-21. https://doi.org/10.1111/lamp.12282
Jiao, J., & Zhang, Y. (2005). Product portfolio identification based on association rule mining. Computer-Aided Design, 37(2), 149-172. https://doi.org/10.1016/j.cad.2004.05.006
Kühl, N., Schemmer, M., Goutier, M., & Satzger, G. (2022). Artificial intelligence and machine learning. Electronic Markets, 32(4), 2235-2244.
Kwok, L., & Yu, B. (2013). Spreading social media messages on Facebook: An analysis of restaurant business-to-consumer communications. Cornell Hospitality Quarterly, 54(1), 84-94. https://doi.org/10.1177/1938965512458360
Lacerda, R., Ensslin, L., & Ensslin, S. (2011). A performance measurement view of IT project management. International Journal of Productivity and Performance Management, 60(2), 132-151. https://doi.org/10.1108/17410401111101476
Loureiro, A. L., Miguéis, V. L., & Da Silva, L. F. (2018). Exploring the use of deep neural networks for sales forecasting in fashion retail. Decision Support Systems, 114, 81-93. https://doi.org/10.1016/j.dss.2018.08.010
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937-947. https://doi.org/10.1287/mksc.2019.1192
Muro, E. D. A., Álvarez, L. A. S., Rodriguez, V. H. P., Lucana, F. R. V., Rojas, L. M. H., Benavides, A. M. V., & Salazar, C. A. H. (2024). Fostering Equity in Rural Education: a Literature Review on Student Dropout and Retention Strategies. Revista De Gestão Social E Ambiental, 18(1), e04922. https://doi.org/10.24857/rgsa.v18n1-083
Olano, M. D., de la Cruz, A. S. V., Rodriguez, V. H. P., Cruz, L. D. C. S. S., Benavides, A. M. V., Salazar, C. A. H., … Reategui, J. A. (2024). The Need for Innovation in Financial Education: A Study of Household Indebtedness in Peru. Revista De Gestão Social E Ambiental, 18(1), e04919. https://doi.org/10.24857/rgsa.v18n1-081
Pagani, R. N., Kovaleski, J. L., & Resende, L. M. (2015). Methodi Ordinatio: a proposed methodology to select and rank relevant scientific papers encompassing the impact factor, number of citation, and year of publication. Scientometrics, 105, 2109-2135. https://doi.org/10.1007/s11192-015-1744-x
Park, B., & Bae, J. K. (2015). Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert systems with applications, 42(6), 2928-2934. https://doi.org/10.1016/j.eswa.2014.11.040
Policarpo, L. M., da Silveira, D. E., da Rosa Righi, R., Stoffel, R. A., da Costa, C. A., Barbosa, J. L. V., ... & Arcot, T. (2021). Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review. Computer Science Review, 41, 100414. https://doi.org/10.1016/j.cosrev.2021.100414
Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today's academic world. Publications, 9(1), 12. https://doi.org/10.3390/publications9010012
Puican Rodriguez, V. H., Suárez Santa Cruz, L. D. C., Salazar Asalde, A., Alcántara Suyón, A., & Camacho Delgado, F. M. (2024). The effect of taxes and tax refunds on the economic activity of the energy industry in Peru. International Journal of Energy Economics and Policy, 14(4), 36-47.
Rafiei, M. H., & Adeli, H. (2016). A novel machine learning model for estimation of sale prices of real estate units. Journal of Construction Engineering and Management, 142(2), 04015066. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001047
Ramirez-Soto, A. N., Ríos, J. E. S., Rodriguez, V. H. P., Vite, I. P. Y., & Castaneda, P. E. R. (2024). Models of Strategic Management in Smes in The Period of The Covid-19 Pandemic in Metropolitan Lima. Revista de Gestão Social e Ambiental, 18(1), e04936-e04936.
Rodriguez, V. H. P., Aguilar, H. E. V., Delgado, F. M. C., Santa Cruz, L. D. C. S., Benavides, A. M. V., Salazar, C. A. H., ... & Suyón, A. A. (2024). Challenges in the Relationship between Liquidity and Profitability: Perspectives from a Literature Review. Revista de Gestão Social e Ambiental, 18(1), e04923-e04923. https://doi.org/10.24857/rgsa.v18n1-084
Rufasto, A. M., Lucumí, N. P. R., & Rodríguez, V. H. P. (2024). SIRE: Catalyst for Improvements in Accounting and Tax Processes. Journal of Ecohumanism, 3(7), 928-937.
Rui, H., Liu, Y., & Whinston, A. (2013). Whose and what chatter matters? The effect of tweets on movie sales. Decision support systems, 55(4), 863-870. https://doi.org/10.1016/j.dss.2012.12.022
Santa Cruz, L. D. C. S., Rodriguez, V. H. P., López, D. I. F., & Olivera, J. J. I. (2024). Electricity Industry Strategies in Ecuador and Peru: Their Impacts on Energy Efficiency and Prices. International Journal of Energy Economics and Policy, 14(5), 464-478. https://doi.org/10.32479/ijeep.16713
Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. Data mining and knowledge discovery, 5, 115-153. https://doi.org/10.1023/A:1009804230409
Smith, K. A., & Gupta, J. N. (2000). Neural networks in business: techniques and applications for the operations researcher. Computers & Operations Research, 27(11-12), 1023-1044. https://doi.org/10.1016/S0305-0548(99)00141-0
Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial marketing management, 69, 135-146. https://doi.org/10.1016/j.indmarman.2017.12.019
Thomassey, S. (2010). Sales forecasts in clothing industry: The key success factor of the supply chain management. International Journal of Production Economics, 128(2), 470-483. https://doi.org/10.1016/j.ijpe.2010.07.018
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