巴鲁特无锡区品牌店10月份品类备货趋势数据

通过无锡区品牌店前三年中每年10月份各品类销售数据分析，预测每年度10月份各品类产品的销售数量，对品牌单品类备货方向做出引导，进而提高品牌各品类的售罄率，提高品牌整体的竞争力。数据来源：通过无锡区品牌店前三年中每年10月份各品类实际销售占比得出数据，数据处理：20年各品类销售占比为X,21年各品类销售占比为Y,22年各品类销售占比为Z,绝对平均法计算公式为（X+Y+Z）/3:加强平均法，给20/21/22年每年份产品的销售占比计算出一个权重，20年销售占比权重定义为p1，21年销售占比权重定义为p2，22年销售占比权重定义为p3，加强平均法计算公式为X*P1+Y*P2+Z*P3;数据应用：通过以上两种方法的统计和计算得出的结果会更加的精确和有价值；品类备货趋势数据主要用来分析当月品牌哪个品类产品销售趋势会有优势，给品牌备货做出意见指导和建议。This dataset analyzes the sales data of each product category in October across the previous three years from brand stores in Wuxi District, aiming to predict the sales volume of each product category in October of each subsequent year, guide the stock preparation direction for individual product categories of the brand, thereby improving the sell-through rate of each category and enhancing the brand's overall competitiveness. Data Source: The data is derived from the actual sales proportions of each product category in October over the three preceding years (2020, 2021 and 2022) collected from brand stores in Wuxi District. Data Processing: The sales proportion of each category in 2020 is denoted as X, that in 2021 as Y, and that in 2022 as Z. The simple averaging method uses the formula (X + Y + Z)/3. For the weighted averaging method, weights are assigned to the sales proportions of each category across the three years: the weight for 2020 is defined as p1, 2021 as p2, and 2022 as p3. The formula for weighted averaging is X*p1 + Y*p2 + Z*p3. Data Application: The results calculated via the two above-mentioned methods are more accurate and valuable. The category stock preparation trend data is primarily used to analyze which product category of the brand will have a favorable sales trend in the target October, providing guidance and suggestions for the brand's stock preparation work.

搜集汇总

数据集介绍

特点

该数据集提供了巴鲁特无锡区品牌店10月份各品类销售历史数据及预测分析，旨在通过数据分析优化备货策略，提高售罄率和品牌竞争力。