Author: Y. Fareniuk, PhD Stud., ORCID ID: 0000-0001-6837-5042
Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Annotation: The article contains the results of applying marketing mix modeling based on Data Science technologies for FMCG companies.
The market share in packages (sales level) was modeled using regression analysis depending on the key elements of the marketing
complex (price, place, promotion), seasonality and media activity of the competitors in all communication channels. Econometric
modeling helps to assess the return of media investment by calculating the level of sales generated by media activity in each
communication channel and comparing it with the level of media investment, respectively. The influence of distribution on the
company’s position in the market and media efficiency has been studied in detail.
There is a connection between distribution and
media response: less distribution affects the decline in media performance, and vice versa. In conditions of low distribution, it is
important to increase the presence in regional communication channels through media pressure in critical sales regions for FMCG
brands and try to increase distribution levels nationally. The article contains an assessment of price sensitivity (elasticity) and
recommendations for optimizing pricing policy to increase market share by volume or by value depending on the company’s goals.
The price elasticity curve was determined by estimating the impact of the price index on the level of sales in packages and deals
in money using econometric modeling and simulations of sales levels depending on different options of the price index vs competitors.
Based on the research, recommendations for optimization of the marketing and media strategies to maximize sales of
FMCG companies are formed. Marketing mix modeling and Data Science provide the most efficient ways to achieve business KPIs.
Keywords: marketing mix modeling; Data Science; regression; FMCG; marketing strategy.
1st Revision: 24/06/2022
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