Significant benefits for anticipating product sales in the e-commerce industry can be offered to businesses by using artificial intelligence models, ensuring accuracy, reliability and flexibility.
Original work prepared by a working group of DOTSOFT AE and National Research Center & id=”T2″>Technological development (EKETA), in the context of the project “development” (code KMP6-0078774).
In a comprehensive review of the existing literature on sales forecasting algorithms, the authors of the paper presented their findings at the 28th Panhellenic Conference on Progress for Progress in Computers and Computing (PCI 2024), which was held recently in Athens with international participation. The conference was organized by the Hellenic Society of Scientists and Professionals of Informatics & Communications and the University of Western Attica.
Utilizing a set of data from a distinguished online marketer in Greece, the work contributors applied various models, based on artificial intelligence, to predict sales performance.
The results they reached demonstrated the effectiveness of artificial intelligence algorithms in recording complex patterns within the scope of available data, leading to improved prediction accuracy over conventional methods. This is much more true, as found, when various data from exogenous sources, such as weather conditions and searches from Google Trends, are integrated into the field of analysis. In this way, the competitiveness and efficiency of e-commerce businesses can be enhanced.
As mentioned, future research could expand the value of the work done in several ways, such as by incorporating a wider range of exogenous features (macro-economic indicators, consumer sentiment data and social media trends) and exploring Architectural deep learning solutions, such as Long-Term Memory (LSTM). Thus, sales forecasting models can be further improved and data-driven strategic decisions can be supported in the rapidly evolving online retail landscape.
The a participated in the research on behalf of DOTSOFT SA. Manos, D. E. Filippidou, N. Pavlidis, G. Vakhtanidis and G. Karanasios, while on behalf of EKETA I. Mallidis, o. Tsolakis and G. Aifantopoulou. The survey was conducted in the context of the project “Development” (KMP6 code KMP6-0078774) in the framework of the “Innovation Investment Plans” of the Operational Program “Central Macedonia 2014-2020”, which It is co-financed by the European Regional Development Fund and National Resources.
This text has been translated automatically from the original Greek. In case of any discrepancy or inaccuracy, the original Greek text prevails. The translation is provided for information purposes only.