京东平台内衣消费偏好分析数据

通过收集和分析全国范围内有对内衣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：采集全国范围内内衣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（内衣）＝（内衣销售额/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，同品牌内衣产品单价在各区域、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。

This dataset is developed by collecting and analyzing provincial-level transaction and related consumption data of underwear products across the country, to gain in-depth insights into the consumption preferences of JD.com platform users (including product styles, materials, colors, prices, etc.). The findings can be applied to optimize and restructure the internal operations of the company, as well as enhance overall collaboration within the apparel industry.

For internal company operations, for high-preference product categories, the company 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 rate.

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 underwear products and consumption data of all other product categories across the country.

2. Data Processing: Classify and organize the collected data to facilitate subsequent analysis and utilization.

3. Algorithm Processing: Analyze the processed data using the following formulas:
- Average Order Value (AOV) across 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 (for underwear products) = (Sales revenue of underwear products / AOV of all product categories) * (Total number of orders of all product categories / Total sales revenue of all product categories)
This algorithm is developed to calculate demand based on the AOV of all product categories. The dataset is in an organized and consolidated state, primarily aggregated by product category rather than strictly chronological order. Orders may involve bundled purchases, combined group orders or promotional offers. Price discrepancies of same-brand underwear products across different 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 category. The full product category dataset is aggregated from the consumption preference data of multiple individual product categories, and standardized algorithm processing is conducted using the AOV of all product categories based on industry experience.

4. Data Classification, Grading and Reuse: Classify product categories based on the calculated preference index L: high-preference categories for L > 5.0, medium-preference categories for 1.0 < L ≤ 5.0, and low-preference categories for L ≤ 1.0. Formulate more precise production and marketing strategies according to the category grades, such as increasing inventory allocation for high-preference categories.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于京东平台内衣消费偏好分析，包含2024年全年1381条交易记录，涵盖颜色、尺寸、地理位置等关键字段，通过偏好指数L（如高偏好品类L>5.0）量化用户偏好。其特点在于结合全品类数据标准化算法，支持企业内部库存优化和行业协同策略制定，适用于服装行业的生产销售决策。