天猫平台卫衣消费偏好分析数据

通过收集和分析全国范围内有对卫衣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：采集全国范围内卫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（卫衣）＝（卫衣销售额/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，同品牌卫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 constructed by collecting and analyzing provincial-level transaction data of sweatshirt products and relevant consumption data across the country, aiming to gain in-depth insights into the consumption preferences of Tmall platform users (including styles, materials, colors, prices, etc.). It can be applied to optimize and reshape the company's internal operations, as well as strengthen overall collaboration within the apparel industry.

For the company internally: For high-preference product categories, the company can lock in high-quality fabric suppliers in advance, 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: The dataset enables cross-enterprise collaboration to jointly develop new products that better align with market demand, optimize product design from the source, and enhance the responsiveness and competitiveness of the supply chain. It thus provides 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 sweatshirt product sales transaction data and consumption data of all other product categories nationwide.
2. Data Processing: Classify and sort the collected data to facilitate subsequent analysis and application.
3. Algorithm Processing: Analyze the processed data with the following metrics:
- Average Order Amount 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 (Sweatshirts) = (Sales Revenue of Sweatshirts / Average Order Amount 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 amount of all product categories.
The dataset is in a consolidated and organized state, mainly aggregated by product categories rather than strictly following chronological order. Orders may involve bundling, group purchases, or promotional discounts. Price differences of sweatshirts under the same brand across different regions and time periods are ignored. Therefore, the average order amount of all product categories is not equivalent to the average order amount of a single product category. The all-category data is aggregated from consumption preference data of multiple product 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: Classify product categories based on the calculated preference index L:
- High-preference category: L > 5.0
- Medium-preference category: 1.0 < L ≤ 5.0
- Low-preference category: L ≤ 1.0
Develop more precise production and marketing strategies according to these categories, such as increasing the inventory allocation and distribution volume of high-preference product categories.

搜集汇总

背景与挑战

背景概述

该数据集包含25232条企业数据，通过分析天猫平台卫衣消费偏好（如款式、材质、颜色、价格），帮助优化公司内部运营和服装行业协同。数据采用偏好指数算法进行分类，支持高、中、低偏好等级划分，以指导精准生产营销策略，提升库存周转率和供应链竞争力。