中俄跨境商品家具消费偏好数据

通过收集和分析中俄跨境商品销售对家具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家具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，存在家具品类产品消费行为，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。

This dataset is constructed by collecting and analyzing relevant consumption data on transaction behaviors of furniture category in Sino-Russian cross-border commodity sales, to figure out the demand preferences for furniture category products in Sino-Russian cross-border commodity sales. Measures like increasing inventory stocking for high-preference categories and reducing inventory for low-preference categories are adopted, so as to provide data support for full-chain enterprises in this industry to formulate production and marketing strategies, and better deliver personalized products and services for customers.
1. Data Collection: Collect relevant sales transaction data with consumption behaviors of furniture category products 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 formulas:
- Average sales amount of all categories = Total sales amount of all categories / Total sales volume of all categories
- Preference index L of a given category = (Total sales amount of the category / Average sales amount of all categories) × (Total sales volume of all categories / Total sales amount of the category)
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 based on the demand calculated by the average sales amount of all categories.
The data is in a post-processed 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 of all categories / Total sales volume of all categories ≠ Total sales amount of a single category / Sales volume of that 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 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 tiers, such as increasing the stocking volume of high-preference categories.