拼多多平台夹克消费偏好分析数据
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通过收集和分析全国范围内有对夹克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：采集全国范围内夹克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（夹克）＝（夹克销售额/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，同品牌夹克产品单价在各区域、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 transaction data and associated consumption data for jacket products across the nation, to gain in-depth insights into the consumer preferences of Pinduoduo platform users (including style, material, color, price, etc.). The insights can be utilized to optimize and restructure the company's internal operations, as well as enhance overall collaboration within the apparel industry. For the company itself: For high-preference product categories, the company can secure high-quality fabric suppliers in advance, optimize procurement costs, flexibly adjust production lines, reduce capital tied up in raw material and finished product inventory, and significantly improve inventory turnover rate. For the apparel industry: Industry collaboration can be carried out 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, so as to better provide personalized products and services for customers. 1. Data Collection: Collect jacket product sales and transaction data as well as consumption data of all other product categories across the country. (The GXG Pinduoduo Outlet Store in the "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 subsequent analytical usage. 3. Algorithm Processing: Analyze the processed data with the following formulas: - Average Order Value (AOV) 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 (Jacket) = (Jacket sales revenue / All-category AOV) * (Total number of all-category orders / Total all-category sales revenue) The algorithm is designed to calculate demand based on the all-category AOV. The dataset is in a consolidated state, aggregated primarily by product category rather than strictly chronological order. Orders may involve bundling, group buying, or promotional offers. Price differences of the same-brand jacket products across different regions and time periods are ignored. Therefore, Total all-category sales revenue / Total all-category order count ≠ Individual category sales revenue / Individual category order count. The all-category data is aggregated from consumption preference data of multiple product categories, and standardized algorithmic processing using the all-category AOV is adopted based on industry 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. More precise production and marketing strategies are formulated according to the classification grade, such as increasing the distribution volume of high-preference categories, etc.
