中俄跨境商品滑雪和滑雪服消费偏好数据

通过收集和分析中俄跨境商品销售对滑雪和滑雪服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滑雪和滑雪服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，存在滑雪和滑雪服品类产品消费行为，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：全品类平均销售金额＝全品类销售总额/全品类销售总数量，某品类产品偏好指数L＝（某品类产品销售总额/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 the skiing and skiwear product categories in China-Russia cross-border commodity sales, this dataset aims to understand the demand preferences for these product categories in such cross-border sales. Measures including increasing inventory for high-preference products and reducing inventory for low-preference ones are adopted, providing 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 relevant sales transaction data of consumers who have purchased skiing and skiwear products within the scope of China-Russia 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 using the following formulas:
- Total average sales amount across all categories = Total sales amount of all categories / Total sales volume of all categories
- Preference index L of a certain product category = (Total sales amount of the category / Total average sales amount across all categories) * (Total sales volume of all categories / Total sales amount of all categories)
Note: The term "Total sales volume of all categories / Total sales amount of all categories" is a constant, which is used to standardize the algorithm for calculating demand based on the total average sales amount across all categories.
The dataset is in an organized state, mainly aggregated by region, and not fully ordered chronologically. Orders may involve bundling, group purchases or promotional offers. Price differences of the same product category across different regions and time periods are ignored. Therefore, Total sales amount of all categories / Total sales volume of all categories ≠ Total sales amount of a single category / Total sales volume of a single category. Standardized algorithm processing using the total average sales amount across all 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 according to 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条记录，聚焦于中俄跨境商品中滑雪和滑雪服品类的消费偏好分析，通过计算偏好指数L（如高偏好品类L>5.0）来识别用户需求；数据每季度更新，应用场景包括优化库存和制定销售策略，为企业提供数据支撑。