###### Abstract
Unforeseen economic scenarios, such as changes in VAT, various global and local crises, and evolving geopolitical landscapes, significantly influence consumer behavior. These shifts necessitate robust analytical tools to navigate and predict market dynamics effectively. Concurrently, continuous innovations within product technologies further compel the adaptation of advanced predictive models. Additionally, the evolution of market conditions and the introduction of new technologies demand adaptive forecasting models, as demonstrated in newer approaches to understanding technology lifecycles [6]. The Gompertz model serves as a crucial tool in aligning sales strategies with market demands by accurately forecasting the demand for new products and effectively managing the life cycles of existing products. By predicting the various stages of product adoption and saturation, the model enables businesses to tailor their strategies for maximum impact. The model's application in diverse sectors, such as broadband for predicting service adoption rates [7], illustrates its versatility and effectiveness in strategic planning. Furthermore, innovations in predictive modeling techniques are enhancing the accuracy and applicability of models like Gompertz in dynamic markets [8; 9]. The Mann-Kendall (MK) test is a widely used non-parametric statistical tool designed to detect monotonic trends in time series data. Its popularity stems from its robustness and minimal assumptions about data distribution. As [10] highlights, the \"Mann-Kendall test is one of the most popular non-parametric trend test based on ranking of observations\". The MK test does not require data to follow a normal distribution and is resilient against missing values and irregular time points, making it highly applicable across diverse domains [10]. The application of the Gompertz model provides top management with predictive insights that significantly influence strategic decision-making. By accurately forecasting sales growth and saturation points, the model informs decisions on expanding into new markets, where understanding the potential customer base and demand lifecycle is crucial. The model's insights into market dynamics and consumer behaviors enable strategic collaborations and multi-channel strategies, crucial for reaching broader markets [11]. Additionally, the continuous refinement of predictive models, adapting to modern market dynamics, underscores the evolving nature of strategic tools like the Gompertz model [12]. The Gompertz model equips top management with predictive insights for strategic decision-making, forecasting sales growth and saturation points crucial for market expansion and understanding customer demand cycles. It supports collaborative strategies by offering insights into market dynamics and facilitates multi-channel sales strategies to reach a broader customer base. Guided by these insights, companies can optimize market positioning and enhance their competitive advantage. Case studies detailing the successful application of the Gompertz model in the retail industry are presented. These examples illustrate the model's effectiveness in real-world settings, showing how businesses have applied the Gompertz curve to optimize marketing strategies and improve product lifecycle management. Sales strategies play a critical role in determining the success of both online and brick-and-mortar retailers. One significant challenge facing traditional retailers is \"showrooming, a phenomenon in which customers use brick-and-mortar stores to assess products and then purchase them from online retailers for lower prices\" [13]. This consumer behavior has led to declining sales in physical stores, prompting businesses to adopt competitive strategies such as price matching. By offering customers the same lower prices found online, price matching aims to \"prevent customers from practicing showrooming\".