巴鲁特安徽区品牌店12月份品类备货趋势数据

通过安徽区品牌店2020-2023年度12月份各品类销售数据分析，汇总预测每年度12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。通过安徽区品牌店2020-2023年度12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 constructed based on the sales data analysis of each product category in December of each year from 2020 to 2023 for brand stores in the Anhui region. The core objectives are to summarize and predict the monthly sales volume of each product category in December, guide the stock allocation direction for individual product categories of the brand, thereby improving the sell-through rate of each product category and enhancing the brand's overall competitiveness.

The dataset is derived from the actual sales proportion data of each product category in December across 2020, 2021 and 2022 of the Anhui regional brand stores. Two data processing methods are adopted:
1. Absolute averaging method: Let the sales proportion of each category in 2020 be X, that in 2021 be Y, and that in 2022 be Z. The formula for this method is (X + Y + Z) / 3.
2. Weighted averaging method: Assign respective weights to the sales proportions of each category in the three years, where the weight for 2020 is defined as p1, 2021 as p2, and 2022 as p3. The formula for this method is X*p1 + Y*p2 + Z*p3.

The forecasting results generated by the two aforementioned methods are more precise and valuable. The category stock allocation trend data is primarily utilized to analyze which product category of the brand will exhibit a favorable sales trend in the current month, providing targeted guidance and recommendations for the brand's stock preparation work.

搜集汇总

数据集介绍

特点

该数据集包含3034条记录，每年更新一次，适用于制造业中的服装行业。数据通过分析2020-2023年12月份各品类的销售数据，预测未来销售趋势，为品牌备货策略提供指导。