中俄跨境商品裙子消费偏好数据

通过收集和分析中俄跨境商品销售对裙子品类交易行为的相关消费数据，了解中俄跨境商品销售中对裙子品类产品的需求偏好，针对高偏好分类产品采用多备货，低偏好分类产品减少库存等措施，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：采集中俄跨境电商销售范围内，存在裙子品类产品消费行为，相关销售交易数据。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：本品类商品销售金额（元）=折扣价（元）*本品类商品销量（件），本品类销售总金额（元）=所有本品类商品销售金额（元）的总和，全品类平均销售金额＝全品类销售总额/全品类销售总数量，本品类产品偏好指数L＝（本品类产品销售总额/全品类平均销售金额）*（全品类销售总数量/全品类销售总额），“全品类销售总数量/全品类销售总额”是常数，用于将算法确定为基于全品类平均销售金额的需求量进行计算。数据为整理后状态，主要根据地区汇集，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，同品类产品单价在各区域、不同时间的差价忽略不计，因此全品类销售总额/全品类销售总数量≠某品类销售总额/某品类销售数量，依据行业经验采用全品类平均销售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，根据地区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 relevant consumption data on transaction behaviors of skirt products in Sino-Russian cross-border commodity sales, this work aims to understand the demand preferences for skirt products in Sino-Russian cross-border sales. Measures including increasing inventory for high-preference product categories and reducing inventory for low-preference ones are adopted, so as to provide data support for full-chain enterprises in this industry to formulate production and sales strategies, and better provide personalized products and services for customers.
1. Data Collection: Collect relevant sales transaction data with skirt product consumption behaviors within the scope of Sino-Russian cross-border e-commerce sales.
2. Data Processing: Classify and organize the collected data to facilitate subsequent analysis and application.
3. Algorithm Processing: Analyze the processed data with the following rules and formulas:
- Sales amount of a single skirt product ("CNY") = Discount price ("CNY") * Sales volume of the product (units)
- Total sales amount of the skirt category ("CNY") = Sum of sales amounts of all skirt products ("CNY")
- Average sales amount across all categories = Total sales amount across all categories / Total sales volume across all categories
- Preference index L of the skirt category = (Total sales amount of the skirt category / Average sales amount across all categories) * (Total sales volume across all categories / Total sales amount across all categories)
Note: The term "Total sales volume across all categories / Total sales amount across all categories" is a constant, which is used to standardize the algorithm to calculate demand based on the average sales amount of all categories.
The data is in a post-processing consolidated state, mainly aggregated by region, and not fully ordered chronologically. Orders may involve bundling, group buying, or promotional offers. Price differences of the same category products across different regions and time periods are ignored. Therefore, Total sales amount across all categories / Total sales volume across all categories ≠ Total sales amount of a certain category / Total sales volume of the certain category. Based on industry experience, the average sales amount of all categories is adopted for standardized algorithm processing.
4. Data Classification, Grading and Reuse: Classify the skirt product categories according to 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 regional levels, such as increasing the stocking volume for high-preference product categories.

搜集汇总

背景与挑战

背景概述

该数据集包含601条中俄跨境电商裙子品类的销售记录，统计时间为2022年1月至3月，每季度更新。数据通过计算偏好指数L（如示例L=4.98对应'中偏好品类'）量化消费偏好，用于分析裙子需求特征，支持企业优化库存和营销策略。