中俄跨境商品滑雪板的靴子消费偏好数据

通过收集和分析中俄跨境商品销售对滑雪板的靴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滑雪板的靴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，存在滑雪板的靴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：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 developed by collecting and analyzing relevant consumption data on transaction behaviors of ski boots category products in Sino-Russian cross-border commodity sales, to understand the demand preferences for ski boots category products in Sino-Russian cross-border sales. Measures including increasing inventory stocking for high-preference category products and reducing inventory for low-preference category products are adopted, so as to provide data support for full-chain enterprises in the industry to formulate production and sales strategies, and better deliver personalized products and services to customers.

1. Data Collection: Collect relevant sales transaction data of ski boots category products with consumption behaviors within the scope of Sino-Russian cross-border e-commerce sales.
2. Data Processing: Classify and sort out the collected data to facilitate subsequent analysis and utilization.
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 certain category = (Total sales amount of the category / Average sales amount of 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 standardizes the algorithm to calculate demand based on the average sales amount of all categories.
The dataset is in organized status, mainly aggregated by region, and not fully sorted in chronological order. Orders may involve bundling, group purchases or promotional offers, and 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 certain 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 products based on the calculated preference index: high-preference category for L > 5.0, medium-preference category for 1.0 < L ≤ 5.0, and low-preference category for L ≤ 1.0. Formulate more precise production and marketing strategies according to regional tiers, such as increasing the stocking volume for high-preference categories.

搜集汇总

背景与挑战

背景概述

该数据集聚焦中俄跨境商品中滑雪板靴子的消费偏好，包含601条记录，每季度更新，涵盖2022年1月至3月的销售数据，如折扣价、销量和偏好指数。通过算法计算偏好指数（高偏好为L>5.0），帮助企业识别高需求产品，优化库存和营销策略，提升跨境销售效率。