Assessment and Selection of Mathematical Trends to Increase the Effectiveness of Product Sales Strategy

This paper explores the application of a mathematical trend model to analyze product sales performance. A logistic trend model was utilized to analyze product sales performance, employing monthly sales data collected over three years. The model assessed impacts across various phases of the product life cycle. Significant sales trends were identified and modeled from historical data, demonstrating how sales dynamics mirror broader economic phenomena and consumer behaviors. In addition to logistic trends, linear and quadratic trends were also evaluated. To assess the significance of the sales trends for three products, the Mann–Kendall test was applied. The results indicate a statistically significant positive trend in the sales of product A. For evaluating the quality of data fit in model comparison, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were deemed appropriate. The analysis revealed that the logistic model effectively delineates different sales phases—from introduction to maturity—and highlights opportunities for optimizing strategic sales planning and customer satisfaction in alignment with market demands. The study’s findings are crucial for businesses seeking to enhance product lifecycle management and boost sales forecasting precision.