In a constantly changingbusiness environment, the integration of artificial intelligence (AI) is becoming a fundamental direction in achieving increased revenues and sales volumes for companies. AI and its various applications contribute to identifying patterns in consumer choices, which at the same time contributes to the more effective formation of marketing and sales strategies of companies. Predictive AI (AI), which uses algorithms and data analytics to predict future trends and behavior, is now widely developed, enabling companies to make informed decisions, and overcome competitive challenges.The accuracy and reliability of forecasts depend on the quality of the data that enters the system.Thus, understanding the importance of data quality is vital for organizations seeking to take advantage of the broad capabilities of PAI.
The analysis of literary sources makes it possible to conclude that the implementation of AI algorithms in the work of marketing departments of companies is developing widely.At the same time, most of the known studies focus on marketing data. An important indicator when agreeing with a company and a client is the length of the period from the qualification of a potential buyer to the first real order.Such data in open sources is not enough. This research aims to analyze the impact of the use of PAI on the duration of the period of transition from the qualification of a potential client to his conversion, that is, on the duration of the conclusion of the agreement.
A study of the impact of PAI on the duration of deals showed an increase in the time for successful deals by 59.5% and an increase in the time to process losing deals by 62.3%.The correct implementation and use of forecasts and PAI effectively affect various indicators of commercial departments, including the length of the transition period from the qualification of a potential client to his conversion.Thanks to the use of PAI, the commercial department processes new inquiries and contracts much faster, and accordingly has more time to work with potential, new, and existing customers. The results of the analysis of PAI opportunities and its role in shaping the future of business are useful for ensuring its stable growth and success.
[1] O. Artun, D. Levin, “Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data”, John Wiley & Sons, 2015, 272 p. https://doi.org/10.1002/9781119175803
[2] M. A. Mediavilla, F. Dietrich, D. Palm, “Review and analysis of artificial intelligence methods for demand forecasting in supply chain management”, Procedia CIRP, 2022, 107, pp. 1126–1131. https://doi.org/10.1016/j.procir.2022.05.119
[3] G. Muruganantham, R .S. Bhakat,“A review of impulse buying behavior”, International Journal of Marketing Studies, 2013, 5(3), pp. 149–160. https://doi.org/10.5539/ijms.v5n3p149
[4] R. Dash, M. McMurtrey, C. Rebman, U.K. Kar,“Application of artificial intelligence in automation of supply chain management”, Journal of Strategic Innovation and Sustainability, 2019, 14(3), pp. 43–53. https://doi.org/10.33423/jsis.v14i3.2105
[5] M. H. Huang, R. T. Rust, “A strategic framework for artificial intelligence in marketing”, Journal of the Academy of Marketing Science, 2021, 49, pp. 30–50. https://doi.org/10.1007/s11747-020-00749-9
[6] P. Fang, Z. Gao, R. S. Tsay, “Supervised kernel principal component analysis for forecasting”, Finance research letters, 2023, 58(A), 104292. https://doi.org/10.1016/j.frl.2023.104292
[7] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, G. M. Ljung, “Time Series Analysis: Forecasting and Control”, Wiley, 2015, 720 p.
[8] G. P. Zhang, M. Qi, “Neural network forecasting for seasonal and trend time series”, European Journal of Operational Research, 2005, 160(2), pp. 501–514. https://doi.org/10.1016/j.ejor.2003.08.037
[9] M. Danilevsky, K. Qian, R. Aharonov, Ya. Katsis, B. Kawas, P. Sen, “A survey of the state of explainable AI for natural language processing”, Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing 2020, https://doi.org/10.48550/arXiv.2010.00711
[10] N. Patel, S. Trivedi, “Leveraging predictive modeling, machine learning personalization, NLP customer support, and AI chatbots to increase customer loyalty”, Empirical Quests for Management Essences, 2020, 3(3), pp. 1–24.
[11] J. Kazmaier, J. H. Van Vuuren, “Sentiment analysis of unstructured customer feedback for a retail bank”, ORiON, 2020, 36(1), pp. 35–71,https://orion.journals.ac.za/pub/article/view/668/594 . https://doi.org/10.5784/36-1-668
[12] A. Aljohani, “Predictive analytics and machine learning for real-time supply chain risk mitigation and agility”, Sustainability, 2023, 15, 15088. https://doi.org/10.3390/su152015088
[13] V. Sohrabpour, P. Oghazi, R. Toorajipour, A. Nazarpour, “Export sales forecasting using artificial intelligence”, Technological Forecasting and Social Change, 2021, 163, 120480. https://doi.org/10.1016/j.techfore.2020.120480
[14] S. Chen, S. Ke, S. Han, S. Gupta, U. Sivarajah, “Which product description phrases affect sales forecasting? An explainable AI framework by integrating WaveNet neural network models with multiple regression”, Decision Support Systems, 2024, 176, 114065. https://doi.org/10.1016/j.dss.2023.114065
[15] I. Vallés-Pérez, E. Soria-Olivas, M. Martínez-Sober, A. J. Serrano-López, J. Gómez-Sanchís, F. Mateo, “Approaching sales forecasting using recurrent neural networks and transformers”, Expert Systems with Applications, 2022, 201, 116993 https://doi.org/10.1016/j.eswa.2022.116993