拼多多平台皮衣消费偏好分析数据

通过收集和分析全国范围内有对皮衣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：采集全国范围内皮衣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（皮衣）＝（皮衣销售额/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，同品牌皮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。By collecting and analyzing transaction data of leather garment products and relevant consumption data across all provinces in China, this dataset provides in-depth insights into consumer preferences (including style, material, color, price, etc.) of Pinduoduo platform users. It can be applied to optimize and restructure the company's internal operations, as well as strengthen 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 tied up in raw material and finished product inventories, and significantly improve inventory turnover ratio. For the apparel industry: Industry-wide collaboration can be facilitated to co-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 dataset provides data support for enterprises across the entire industry chain to formulate production and sales strategies, so as to better deliver personalized products and services to customers. 1. Data Collection: Collect sales transaction data of leather garment products and consumption data of all other product categories nationwide. (Note: The "GXG Pinduoduo Outlet Store" listed in the "Order Store Source" field 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 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 (Leather Garments) = (Total Sales of Leather Garments / Average Order Value of All Product Categories) * (Total Number of Orders of All Product Categories / Total Sales of Leather Garments), which is calculated based on the demand standardized by the average order value of all product categories. The dataset is in a post-processed state, mainly aggregated by product category, and not fully ordered chronologically. Orders may involve bundling, group purchases, or promotional discounts. Price differences of the same brand's leather garment products across different regions and time periods are not taken into account. Therefore, Total Sales of All Product Categories / Total Number of Orders of All Product Categories ≠ Total Sales of a Single Product Category / Total Number of Orders of That Product Category. The full-category data is aggregated from consumption preference data of multiple product categories, and standardized algorithm processing using the average order value of all product categories is adopted 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 Formulate more precise production and marketing strategies based on the classification, such as increasing the distribution volume of high-preference product categories.