儿童雨衣产品重庆消费偏好数据

通过收集和分析全国范围内对儿童雨衣产品消费数据，构建基于数据驱动的智能决策体系，围绕“儿童审美偏好”与“功能性需求”两大消费热点，深入解析各省需求偏好与区域消费特征，建立动态产能规划模型与区域化备货策略，同时赋能零售终端实现精准选品与场景化陈列，推动行业向数据驱动的敏捷供应链转型，实现供需精准匹配与市场增长潜力的高效释放。1、数据采集：采集全国范围内，存在儿童雨衣产品消费行为的省份，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：全国平均销售金额＝全国销售总额/全国销售总数量，某省偏好指数L＝（某省销售总额/全国平均销售金额）*（全国销售总数量/全国销售总额），“全国销售总数量/全国销售总额”是常数，用于将算法确定为基于全国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，同品牌儿童雨衣产品单价在各区域、不同时间的差价忽略不计，因此全国销售总额/全国销售总数量≠某省销售总额/某省销售数量，依据行业经验采用全国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>200记为高偏好省份，50＜L≤200记为中偏好省份，50≥L记为低偏好省份，根据地区等级安排更精准的生产营销策略，例如：加大高偏好省份的铺货量等。

Through collecting and analyzing national consumer expenditure data on children's raincoats, this dataset constructs a data-driven intelligent decision-making system. Focusing on two core consumer hotspots: "children's aesthetic preferences" and "functional requirements", it conducts in-depth analysis of provincial demand preferences and regional consumption characteristics, establishes a dynamic production capacity planning model and regional inventory allocation strategies, empowers retail terminals to achieve accurate product selection and scenario-based product display, promotes the industry's transformation toward a data-driven agile supply chain, and realizes precise matching of supply and demand as well as efficient unlocking of market growth potential.

1. Data Collection: Collect relevant sales transaction data from all provinces across the country where there is consumer behavior for children's raincoats.

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 and notes:
National Average Sales Amount = Total National Sales Revenue / Total National Sales Volume
Provincial Preference Index L = (Provincial Sales Revenue / National Average Sales Amount) * (Total National Sales Volume / Total National Sales Revenue)
"Total National Sales Volume / Total National Sales Revenue" is a constant, which is used to standardize the algorithm for demand calculation based on the national average sales amount. The data is in a consolidated state, primarily aggregated by region and not strictly ordered chronologically. Orders may involve bundling, group purchases, or promotional discounts; price differences of the same-brand children's raincoats across different regions and time periods are ignored. Therefore, Total National Sales Revenue / Total National Sales Volume ≠ Provincial Sales Revenue / Provincial Sales Volume. The national average sales amount based on industry experience is adopted for standardized algorithm processing.

4. Data Classification, Grading and Reuse: Based on the calculated preference index, provinces are categorized into three groups: high-preference provinces (L>200), medium-preference provinces (50<L≤200) and low-preference provinces (L≤50). More precise production and marketing strategies are formulated according to the regional classification, such as increasing product distribution in high-preference provinces.