巴鲁特杭州区品牌店10月份品类备货趋势数据

通过杭州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：通过杭州区品牌店2020-2023年度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 is developed based on the actual sales proportion data of various product categories in October of each year from 2020 to 2023 at Hangzhou district brand stores. The goal of this dataset is to predict the sales volume of each product category in October of each year, guide the stock preparation direction for individual product categories of the brand, and thus improve the sell-through rate of each category and the brand's overall competitiveness. Data source: The dataset is derived from the actual sales proportion data of various product categories in October of each year from 2020 to 2023 at Hangzhou district brand stores. Data processing: The sales proportion of each product category in October 2020 is denoted as X, that in October 2021 as Y, and that in October 2022 as Z. The absolute averaging method is calculated as (X + Y + Z)/3. For the weighted averaging method, respective weights are assigned to the annual sales proportions of each product category for 2020, 2021 and 2022, where the weight for 2020 is defined as p1, the weight for 2021 as p2, and the weight for 2022 as p3. The formula of the weighted averaging method is X*p1 + Y*p2 + Z*p3. Data application: The results obtained through the statistics and calculations of the above two methods are more accurate and valuable. The category stock trend data is mainly used to analyze which product categories of the brand will have advantageous sales trends in the forecasted October, providing guidance and suggestions for the brand's stock preparation work.