贵州地区京东平台品牌服装销售分析数据
收藏资源简介：
通过平台调取品牌店铺在贵州地区商品销售数据，可评估该地区不同季节不同产品销售情况，并通过客户颜色偏好度、各尺寸占比数据判断，了解客户喜好和该区域客户群体尺寸的分布情况，优化营销计划，实现生产订单的合理安排和区域针对性前期铺货。数据收集：收集销售相关的数据，包括订单来源店铺、颜色、尺码、季节、省、市、区、品类等。 数据处理：对数据进行清洗、删除重复数据，处理缺失值，识别和处理异常值，确保数据的准确性和一致性。 数据分析：对数据进行统计分析，根据季节变化，统计贵州区域内每季产品销售量的top6，然后通过字段“颜色”、“尺寸”分别进行数据分析，对排名前6品类的颜色偏好度和尺寸比例分别进行统计，颜色偏好度=该品类该颜色订单量/该品类订单总量，尺寸比列=该品类该尺寸订单量/该品类订单总量。计算出该区域内各个季节，客户群体偏向的品类，该品类顾客颜色的偏好和尺寸的选择比例，通过自建模型，使用BI实现分析结果的可视化展示。 数据应用：将分析结果应用到运营场景中，帮助品牌店铺更好了解该区域内各季节客户的偏好和销售尺寸的选择，从而制定更精准的营销策略，指导企业合理安排生产和铺货。
This dataset is obtained by extracting commodity sales data of brand stores in Guizhou region via the platform, which can be used to evaluate the sales performance of different products across seasons in this region. By analyzing customer color preference and size proportion data, we can understand customer preferences and the size distribution of the local customer group, optimize marketing plans, and achieve reasonable arrangement of production orders and targeted regional pre-stocking. Data Collection: Collect sales-related data including order source store, color, size, season, province, city, district, product category and other related fields. Data Processing: Clean the collected data, delete duplicate entries, handle missing values, identify and process outliers to ensure data accuracy and consistency. Data Analysis: Conduct statistical analysis on the processed data. First, count the top 6 product categories by sales volume in each season within Guizhou region based on seasonal changes. Then perform targeted analysis using the "color" and "size" fields respectively: calculate the color preference degree and size proportion of customers for the top 6 product categories, where color preference degree = the order volume of a specific color for a category / the total order volume of that category, and size proportion = the order volume of a specific size for a category / the total order volume of that category. Through the above calculations, we can figure out the product categories preferred by local customers in each season, as well as their color preferences and size selection proportions for these categories. Finally, use a self-built model combined with Business Intelligence (BI) tools to visualize the analysis results. Data Application: Apply the analysis results to operational scenarios, helping brand stores better understand customer preferences and sales size choices across seasons in this region, so as to formulate more precise marketing strategies and guide enterprises to reasonably arrange production and pre-stocking.
