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 l...
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4695 |
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| author | Marcela Malindzakova Gabriela Izarikova |
| author_facet | Marcela Malindzakova Gabriela Izarikova |
| author_sort | Marcela Malindzakova |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3756cab89abc4697be742a19c811ad38 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3756cab89abc4697be742a19c811ad382025-08-20T02:30:45ZengMDPI AGApplied Sciences2076-34172025-04-01159469510.3390/app15094695Assessment and Selection of Mathematical Trends to Increase the Effectiveness of Product Sales StrategyMarcela Malindzakova0Gabriela Izarikova1Institute of Logistics and Transport, Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Kosice, 042 00 Košice, SlovakiaDepartment of Applied Mathematics and Informatics, Faculty of Mechanical Engineering, Technical University of Kosice, 042 00 Košice, SlovakiaThis 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.https://www.mdpi.com/2076-3417/15/9/4695Gompertz curvelogistics trendthe product sale strategycompetitiveness of product sales |
| spellingShingle | Marcela Malindzakova Gabriela Izarikova Assessment and Selection of Mathematical Trends to Increase the Effectiveness of Product Sales Strategy Applied Sciences Gompertz curve logistics trend the product sale strategy competitiveness of product sales |
| title | Assessment and Selection of Mathematical Trends to Increase the Effectiveness of Product Sales Strategy |
| title_full | Assessment and Selection of Mathematical Trends to Increase the Effectiveness of Product Sales Strategy |
| title_fullStr | Assessment and Selection of Mathematical Trends to Increase the Effectiveness of Product Sales Strategy |
| title_full_unstemmed | Assessment and Selection of Mathematical Trends to Increase the Effectiveness of Product Sales Strategy |
| title_short | Assessment and Selection of Mathematical Trends to Increase the Effectiveness of Product Sales Strategy |
| title_sort | assessment and selection of mathematical trends to increase the effectiveness of product sales strategy |
| topic | Gompertz curve logistics trend the product sale strategy competitiveness of product sales |
| url | https://www.mdpi.com/2076-3417/15/9/4695 |
| work_keys_str_mv | AT marcelamalindzakova assessmentandselectionofmathematicaltrendstoincreasetheeffectivenessofproductsalesstrategy AT gabrielaizarikova assessmentandselectionofmathematicaltrendstoincreasetheeffectivenessofproductsalesstrategy |