京东平台马甲消费偏好分析数据

通过收集和分析全国范围内有对马甲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：采集全国范围内马甲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（马甲）＝（马甲销售额/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，同品牌马甲产品单价在各区域、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 transaction data of vest products and relevant consumption data across provinces nationwide, this dataset provides in-depth insights into consumer preferences of JD.com platform users (including style, material, color, price, etc.), which can be applied to optimize and restructure the company's internal operations and strengthen overall collaboration within the apparel industry.
For the company itself, for high-preference product categories, we can lock in high-quality fabric suppliers in advance, optimize procurement costs, flexibly adjust production lines, reduce capital tied up in 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 provides data support for enterprises across the entire industry chain to formulate production and sales strategies, so as to better provide personalized products and services to customers.
1. Data Collection: Collect sales transaction data of vest products and consumption data of all other product categories nationwide.
2. Data Processing: Classify and sort the collected data to facilitate analysis and utilization.
3. Algorithm Processing: Analyze the processed data as follows:
Average Order Amount of All Product Categories = Total Sales of All Product Categories / Total Number of Orders of All Product Categories (rounded to two decimal places)
Preference Index L (Vest) = (Total Sales of Vests / Average Order Amount of All Product Categories) * (Total Number of Orders of All Product Categories / Total Sales of All Product Categories), which is used to calculate demand based on the average order amount of all product categories.
The data is in an aggregated and sorted state, mainly grouped by product category and not fully arranged in chronological order; Orders may involve bundling, group buying, or promotional offers, and the price difference of the same-brand vest products across regions and different times is ignored. Therefore, Total Sales of All Product Categories / Total Number of Orders of All Product Categories ≠ Total Sales of a Certain Category / Total Number of Orders of That Category. The all-category data is aggregated from the consumption preference data of multiple categories, and standardized algorithm processing is conducted using the average order amount of all product categories based on industry experience.
4. Data Classification, Grading and Reuse: According to the calculated preference index, categories with L>5.0 are classified as high-preference categories, 1.0<L≤5.0 as medium-preference categories, and L≤1.0 as low-preference categories. More precise production and marketing strategies are arranged according to the grades, for example, increasing the distribution volume of high-preference product categories.

搜集汇总

背景与挑战

背景概述

该数据集聚焦京东平台马甲消费偏好分析，包含587条记录，覆盖2024年全年数据，关键字段包括支付日期、订单来源、颜色、尺寸和地理位置等。通过偏好指数L（如L>5.0表示高偏好）算法，深度洞察用户消费行为，应用于优化企业内部运营和服装行业供应链策略，提升库存周转和市场响应速度。