## 6 Conclusions  
Predictive models, crucial for this analysis, often carry inherent assumptions that may not align with dynamic market realities. Linear models assume constant rates of change, rarely seen in volatile markets, while nonlinear models, though flexible, might not capture unseen complex interactions that impact sales. More sophisticated models, such as the Gompertz model, complement more complex models, making it a permanent and versatile tool for tracking and forecasting growth in various fields, especially for processes that exhibit slowing growth patterns. Sales of product A are characterized by a slow start, accelerating growth in the middle stage, and reaching a peak due to market saturation. The Gompertz trend allows for the impact of saturation to be taken into account, making it a reliable tool for modeling and forecasting sales throughout the product life cycle. Using this model also helps companies develop more accurate business strategies, plan cash flows, and improve their overall resilience to market fluctuations. The Mann-Kendall test was applied to verify the presence of a trend in the sales data for product A. The resulting test statistic (Zs = 3.8242) confirms the existence of a statistically significant trend. At all three significance levels of 10%, 5%, and 1%, the test indicates that sales of product A exhibit a significant monotonic trend. Since the value of Zs is greater than zero, it can be concluded that this trend is positive, indicating a consistent increase in sales over the observed period. The integration of trend analysis with strategic alignment enhances the understanding of sales dynamics and supports more agile responses to evolving market conditions. In contrast, analysis of product B reveals a nonlinear trend, specifically a quadratic trend, accompanied by a significant decline in sales. This downward trend underscores the need to prioritize innovation related to product B. In the case of sales of product B, which is subject to significant competition and demand fluctuations, the quadratic model proves to be a more accurate tool for forecasting and analysis. It allows not only the prediction of future sales patterns, but also the adjustment of production and marketing strategies, thereby ensuring more efficient management of corporate resources. From the Gompertz trend for sales of product C, it is possible to assume a gradual slowdown in growth and subsequent stabilization at a certain level. A significant strength of the Gompertz trend is the inclusion of potential sales declines for product C after the peak in demand and the possibility of more accurately predicting changes in sales dynamics, especially in a competitive environment and with evolving consumer preferences. The flexibility of the Gompertz trend allows us to take fluctuations into account and provide more accurate forecasts. To verify the existence of a trend for the sales of product C, the Mann-Kendall test was applied. The test criterion (\(Z_{\text{s}}=2.0906\)) for the sales values of product C confirms the existence of a statistically significant trend at the 10%, 5% significance level. A properly applied Gompertz trend allows us to predict demand estimates and thus respond in a timely manner to market changes, optimize inventory levels and avoid overproduction, which ultimately contributes to competitiveness. The Gompertz trend appropriately captures sales fluctuations in the later stages of the product life cycle. Implementing product enhancements, along with improved promotional strategies, is expected to contribute to revitalizing sales and increasing the market appeal of this product category. The connection between trend analysis and matching strategy contributes to the understanding of sales dynamics and the ability to flexibly respond to changing market conditions. Ensuring these models' effectiveness on new datasets is essential for their reliability in predictive analytics.