天猫平台短款羽绒服消费偏好分析数据

通过收集和分析全国范围内有对短款羽绒服产品交易行为的省份以及相关消费数据，深度洞察天猫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：采集全国范围内短款羽绒服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（短款羽绒服）＝（短款羽绒服销售额/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，同品牌短款羽绒服产品单价在各区域、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 short-length down jacket products and relevant consumption data across provinces nationwide, this work gains in-depth insights into the consumer preferences (including style, material, color, price, etc.) of Tmall platform users. The findings can be applied to optimize and restructure the company's internal operations, as well as enhance overall collaboration within the apparel industry.
For the company internally, for high-preference product categories, we can lock in high-quality fabric suppliers in advance, optimize procurement costs, flexibly adjust the production line, reduce capital occupation of raw material and finished product inventory, and significantly improve the inventory turnover rate. For the apparel industry, we can jointly develop new products that better meet market demand through industry collaboration, optimize product design from the source, and enhance the response speed and competitiveness of the supply chain. Thus, this dataset 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 for customers.

1. Data Collection: Collect sales transaction data of short-length down jacket products and consumption data of all other product categories nationwide.
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 of all product categories / Total number of orders of all product categories (rounded to two decimal places)
- Preference Index L (short-length down jackets) = (Sales of short-length down jackets / Average order value 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 value of all product categories as determined by the algorithm.
The data is in a post-processed state, mainly aggregated by product category, and not fully sorted in chronological order. Orders may involve bundling, group purchases, or promotional discounts. Price differences of the same-brand short-length down jacket products across different regions and time periods are ignored. Therefore, Total sales of all product categories / Total number of orders of all product categories ≠ Sales of a single product category / Total number of orders of that category. The all-category data is aggregated from the consumption preference data of multiple product categories, and standardized algorithm processing is conducted using the average order value of all product categories based on industry experience.
4. Data Classification, Grading and Reuse: According to the calculated preference index, product 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 based on the classification grades, such as increasing the inventory allocation of high-preference categories.

搜集汇总

背景与挑战

背景概述

该数据集包含14903条企业数据，聚焦天猫平台短款羽绒服的消费偏好分析，通过采集全国交易数据并计算偏好指数，识别高、中、低偏好品类。其特点在于基于实际消费行为，采用标准化算法处理，旨在优化企业内部运营和服装行业供应链决策，提升市场响应效率。