京东平台袜子消费偏好分析数据
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通过收集和分析全国范围内有对袜子产品交易行为的省份以及相关消费数据，深度洞察京东平台用户的消费偏好（如款式、材质、颜色、价格等），可应用于对公司内部运营的优化与重塑以及服装行业整体协同增强。对公司内部而言，对于高偏好品类，可以提前锁定优质面料供应商，优化采购成本，可灵活调整生产线，降低原材料和成品库存的资金占用，显著提升库存周转率。对于服装行业而言，可以行业协同共同开发更符合市场需求的新品，从源头优化产品设计，增强供应链条的响应速度与竞争力。从而为本行业的全链条企业制定生产销售策略提供数据支撑，更好地为客户提供个性化的商品和服务。1、数据采集：采集全国范围内袜子产品销售交易数据以及其他所有品类产品消费数据。2、数据处理，对采集到的数据进行分类、梳理，便于分析使用。3、算法加工：将处理后的数据进行分析：全品类产品平均订单金额＝全品类产品销售额/全品类产品订单总数量（保留两位小数），偏好指数L（袜子）＝（袜子销售额/全品类产品平均订单金额）*（全品类产品订单总数量/全品类产品销售额），用于将算法确定为基于全品类产品平均订单金额的需求量进行计算。数据为整理后状态，主要根据产品种类汇集，不完全按照时间先后顺序；订单可能存在捆绑/拼单/活动优惠，同品牌袜子产品单价在各区域、不同时间的差价忽略不计，因此全品类产品销售额/全品类产品订单总数量≠某品类产品销售额/某品类产品订单总数量，全品类数据由多个品类消费偏好数据集合汇总得出，依据行业经验采用全品类产品平均订单金额进行标准化算法处理。4、数据分类分级复用：根据计算出的偏好指数，L>5.0记为高偏好品类，1.0＜L≤5.0记为中偏好品类，1.0≥L记为低偏好品类，根据等级安排更精准的生产营销策略，例如：加大高偏好品类的铺货量等。
By collecting and analyzing sock product transaction data and related consumption data across all provinces in China, this dataset provides in-depth insights into JD.com platform users' consumer preferences (e.g., style, material, color, price, etc.), which can be applied to optimize and restructure internal company operations and enhance overall collaboration within the apparel industry. For internal company operations, for high-preference product categories, enterprises can lock in high-quality fabric suppliers in advance to optimize procurement costs, flexibly adjust production lines, reduce capital occupation of raw material and finished product inventories, and significantly improve inventory turnover rates. For the apparel industry, industry collaboration can be carried out to jointly develop new products that better meet market demand, optimize product design from the source, and enhance the response speed and competitiveness of the supply chain. This will provide data support for enterprises across the entire industry chain to formulate production and sales strategies, and better deliver personalized products and services to customers. 1. Data Collection: Collect sales transaction data of sock products and consumption data of all other product categories across China. 2. Data Processing: Classify and organize the collected data to facilitate subsequent analysis and usage. 3. Algorithm Processing: Analyze the processed data with the following formulas: - Average order value of all product categories = Total sales revenue of all product categories / Total number of orders of all product categories (rounded to two decimal places) - Preference Index L (socks) = (Sock sales revenue / Average order value of all product categories) * (Total number of orders of all product categories / Total sales revenue of all product categories) This algorithm is designed to calculate demand based on the average order value of all product categories. The dataset is in an organized state, mainly aggregated by product category, and not fully arranged in chronological order. Orders may involve bundling, group purchasing, or promotional discounts, and price differences of socks under the same brand across regions and time periods are ignored. Therefore, Total sales revenue of all product categories / Total number of orders of all product categories ≠ Sales revenue of a single product category / Total number of orders of that product category. The all-category data is aggregated from consumption preference data of multiple product categories, and standardized algorithm processing using the average order value of all product categories is adopted based on industry practice. 4. Data Classification, Grading and Reuse: Based on the calculated preference index, product categories are classified as follows: high-preference category if L>5.0, medium-preference category if 1.0<L≤5.0, and low-preference category if L≤1.0. More precise production and marketing strategies are formulated according to the classification, such as increasing the distribution volume of high-preference product categories, etc.
