拼多多平台内裤消费偏好分析数据

通过收集和分析全国范围内有对内裤产品交易行为的省份以及相关消费数据，深度洞察拼多多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：采集全国范围内内裤产品销售交易数据以及其他所有品类产品消费数据。（“订单店铺来源”中的GXG拼多多奥莱店即为拼多多平台GXG男装官方奥莱店）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（内裤）＝（内裤销售额/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，同品牌内裤产品单价在各区域、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 provincial transaction data and related consumption data for underwear products across the nation, this dataset delivers in-depth insights into consumer preferences of Pinduoduo platform users (including style, material, color, price, etc.), which can be utilized to optimize and restructure internal corporate operations and strengthen overall collaboration within the apparel industry.

For internal corporate operations: For high-preference product categories, the enterprise can secure high-quality fabric suppliers in advance to optimize procurement costs, flexibly adjust production lines, reduce capital tied up in raw material and finished product inventory, and markedly improve inventory turnover rate.

For the apparel industry, industry collaboration can be leveraged to jointly develop new products that better align with market demand, optimize product design from the source, and enhance supply chain responsiveness and competitiveness. This will provide data support for full-chain enterprises in the industry to formulate production and sales strategies, ultimately delivering more personalized products and services to customers.

1. Data Collection: Collect nationwide sales transaction data of underwear products and consumption data of all other product categories. (The GXG Pinduoduo Outlet Store listed under "Order Store Source" refers to the official outlet store of GXG Men's Wear on the Pinduoduo platform.)

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

3. Algorithm Processing: Analyze the processed data with the following formulations:
Average order amount of all product categories = Total sales revenue of all product categories / Total number of orders for all product categories (rounded to two decimal places)
Preference Index L (underwear) = (Underwear sales revenue / Average order amount of all product categories) * (Total number of orders for all product categories / Total sales revenue of all product categories)
This index is designed to quantify demand based on the average order amount of all product categories. The dataset is in a consolidated state, primarily aggregated by product category rather than strict chronological order. Orders may involve bundling, group purchases, or promotional offers. Price fluctuations of the same brand's underwear products across different regions and time periods are excluded from consideration. Therefore, Total sales revenue of all product categories / Total number of orders for all product categories ≠ Total sales revenue of a single product category / Total number of orders for that product category. The all-category data is compiled from consumption preference data across multiple product categories, and standardized algorithm processing using the average order amount of all product categories is applied based on industry best practices.

4. Data Classification, Grading and Reuse: Based on 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. Develop more precise production and marketing strategies according to the classification, such as increasing the distribution volume of high-preference products, etc.

搜集汇总

背景与挑战

背景概述

该数据集记录了2024年拼多多平台内裤消费数据，包含2083条记录，涵盖支付日期、地区、颜色、尺寸等字段。通过偏好指数分析显示内裤为高偏好品类，可用于优化企业采购、库存管理和行业新品开发，提升供应链响应速度。