Marketing Strategy Optimization: A Case Study Based on SARIMA And Genetic Algorithm

Kaiyuan Li; Jingyang Bao; Minyue Yang

doi:10.54097/21zgzn15

Authors

Kaiyuan Li
Jingyang Bao
Minyue Yang

DOI:

https://doi.org/10.54097/21zgzn15

Keywords:

SARIMA, Genetic Algorithm, Big Data.

Abstract

As societal living standard elevates, a heightened demand for enhanced quality of life, food quality, and freshness is emerging. In the fiercely competitive market economy, businesses are motivated to devise sensible pricing strategies and optimize their replenishment methods to maximize profits. In pursuit of profit maximization, this paper proposes a novel fusion optimizing-forecasting model based on SARIMA and a Genetic Algorithm for determining the optimal daily replenishment volume and pricing strategy in two steps: considering vegetable categories and considering vegetable items. In the case of vegetable sales volume forecasting and sales strategies, the applied model produces a 7-day prediction of sales volumes and the recommended pricing strategy selecting 27 products, resulting in a maximum profit of 1243.9 yuan. The outcome holds considerable significance for supermarkets in formulating profit-driven pricing strategies.

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