Advanced analytics as a tool for effective trade marketing in retail

Tatyana I. Sakhnyuk, Marina V. Korshikova and Pavel A. Sakhnyuk
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Tatyana I. Sakhnyuk: Moscow City Pedagogical University
Marina V. Korshikova: Stavropol State Agrarian University
Pavel A. Sakhnyuk: Financial University under the Government of the Russian Federation

Siberian Journal of Economic and Business Studies, 2025, vol. 14, issue 4, 214-228

Abstract: Background. In a highly competitive world in the tobacco market, companies face the need to optimize their data analysis and decision‑making processes. The main problem is the processing of ever‑growing volumes of sales data that were previously stored and analyzed in Excel, which led to slower analysis processes, calculation errors, and reduced effectiveness of marketing strategies. To solve this problem, the company's management decided to switch to using modern technologies. The relevance of the study is due to high competition in the tobacco market and the need to optimize data analysis and decision‑making processes. Previously, sales data was stored and analyzed in Excel, which slowed down analysis, led to calculation errors, and reduced the effectiveness of marketing strategies. Purpose: to develop and implement a machine learning‑based data analysis and sales forecasting system to improve the effectiveness of trade marketing. Methodology. The work uses machine learning methods, automation of analytical reporting, as well as tools for working with data (PostgreSQL, Power BI, Python). Airflow is used to manage the execution of data processing scripts and model training, monitor the updating of analytical reports, and integrate the system with CRM. Results. A data processing and analysis system has been developed; data has been transferred from Excel to PostgreSQL to solve encoding problems; automatic data loading and conversion mechanisms have been implemented; high‑quality data preparation for analysis has been carried out. Practical implications. The results of the study can be applied in companies working with large volumes of data; in the field of business analytics and working with big data; in industries with fierce competition and complex market conditions.

Keywords: trade marketing; machine learning; sales forecasting; big data; business analytics; PostgreSQL; Power BI; Python; XGBoost (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:cxm:rusebs:14:4:2025:214-228

DOI: 10.12731/3033-5973-2025-14-4-325

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