ECONOMIC-MATHEMATICAL APPROACH TO MEASURING THE IMPACT OF MARKETING ACTIVITIES WITH A FOCUS ON PROMOTIONAL PROMOTIONS

Abstract

This article addresses the issue of assessing the effectiveness of marketing activities based on an economico-mathematical approach, with a focus on promotional campaigns as one of the key tools for stimulating sales in today’s competitive business environment. In conditions of limited resources and intense market competition, companies must make marketing budget decisions based on clear quantitative justification. To this end, the article proposes the use of regression modeling to identify the relationship between sales volumes and various influencing factors, including product price, presence of promotions, advertising expenditure, and seasonal demand fluctuations. The study simulates a case of a beverage company operating throughout the year. A hypothetical dataset was constructed for 12 months, taking into account key sales drivers. A multiple linear regression model was developed and demonstrated a high level of adequacy, with statistically significant coefficients for the main variables. In particular, it was found that promotional campaigns lead to an average increase in sales of approximately 24 thousand UAH, while a price increase of 1 UAH results in a sales decrease of around 7 thousand UAH. Advertising expenses also showed a positive impact: each additional 1,000 UAH invested in advertising was associated with a sales increase of more than 2,000 UAH. Seasonality plays a notable role, especially during the summer months, when consumer demand tends to rise significantly. The obtained results are of high practical value for companies operating in promotion-intensive sectors such as FMCG. The findings enable data-driven decision-making regarding the planning and optimization of marketing efforts. The article also offers concrete recommendations for improving the allocation of marketing budgets, accounting for seasonal effects, and integrating economico-mathematical models into day-to-day business decision-making processes. Directions for future research include applying more advanced analytical techniques and working with real company data to refine and validate the proposed approach.