护眼款喷雾在京东平台上的销量预测数据

本数据聚焦于预测护眼款喷雾在京东平台上的销量，为商家及外部相关方提供了重要的决策依据，具有显著的应用价值。具体体现在以下方面：
1.优化库存管理：对商家而言，通过预测护眼款喷雾在京东平台上的销量，可以科学制定采购计划，合理调配库存，避免库存积压或供应不足，提高运营效率和市场响应速度。同时，也有助于提前布局市场，制定针对性的促销策略，抢占市场份额，提升销售业绩。
2.支持平台运营决策：对京东平台而言，基于销量预测数据，可以更精准地规划流量分配和资源位投放，降低运营风险，优化平台效率，提升商家和消费者满意度。
3.辅助产品研发与市场调研：对于同行而言，本数据可帮助分析市场需求趋势，优化产品功能或调整包装设计，以更贴合消费者偏好。同时，本数据可用于竞品分析，对比同类产品的销售表现，从而制定差异化的市场策略。1.数据采集：
采集护眼款喷雾在京东平台的销售数据，包括商品编号、店铺编号、商品名称、销售日期、销售数量、销售金额。
2.数据预处理：
对采集的数据进行清洗，去除重复记录，处理缺失值。
3.数据加工与分析：
（1）计算历史销量：对于护眼款喷雾，使用SUMIFS函数对销售数量进行累加，分别计算出其过去365天、90天和30天的总销量。（2）建立销量预测模型：护眼款喷雾的未来30天销量预测值=[(过去365天总销量÷365×a)+(过去90天的总销量÷90×b)+(过去30天的总销量÷30×c)]30×k；其中，系数a=0.5，b=0.3，c=0.2，调整因子k=1.05。系数a、b、c反映数值对未来30天销量预测的影响程度，由于算法更注重长期销售趋势的影响，因此a被赋予了最高的权重。k是基于护眼款喷雾在京东平台的市场增长预期给出的修正值。

This dataset focuses on predicting the sales volume of eye-protection spray on the JD.com platform, providing important decision-making basis for merchants and relevant external parties, with significant application value, which is reflected in the following aspects:
1. Optimize inventory management: For merchants, predicting the sales volume of eye-protection spray on JD.com can help formulate scientific procurement plans, reasonably allocate inventory, avoid overstock or stockout, improve operational efficiency and market response speed. Meanwhile, it also helps to lay out the market in advance, formulate targeted promotion strategies, seize market share and improve sales performance.
2. Support platform operation decision-making: For JD.com, based on sales volume prediction data, it can more accurately plan traffic allocation and resource position placement, reduce operational risks, optimize platform efficiency, and improve the satisfaction of merchants and consumers.
3. Assist product R&D and market research: For peers, this dataset can help analyze market demand trends, optimize product functions or adjust packaging design to better align with consumer preferences. Meanwhile, it can be used for competitive product analysis, comparing the sales performance of similar products to formulate differentiated market strategies.

1. Data collection:
Collect sales data of eye-protection spray on JD.com, including product ID, shop ID, product name, sales date, sales quantity and sales amount.
2. Data preprocessing:
Clean the collected data, remove duplicate records and handle missing values.
3. Data processing and analysis:
(1) Calculate historical sales volume: For eye-protection spray, use the SUMIFS function to accumulate sales quantity, and calculate the total sales volume of the past 365 days, 90 days and 30 days respectively.
(2) Establish sales volume prediction model: The 30-day future sales volume prediction value of eye-protection spray = [(Total sales volume in the past 365 days ÷ 365 × a) + (Total sales volume in the past 90 days ÷ 90 × b) + (Total sales volume in the past 30 days ÷ 30 × c)] × 30 × k; Among them, the coefficients a=0.5, b=0.3, c=0.2, and the adjustment factor k=1.05. The coefficients a, b and c reflect the impact degree of the values on the 30-day future sales volume prediction. Since the algorithm pays more attention to the impact of long-term sales trends, a is given the highest weight. k is the correction value based on the market growth expectation of eye-protection spray on the JD.com platform.

搜集汇总

背景与挑战

背景概述

该数据集记录了护眼款喷雾在京东平台的销售数据，包含591条CSV格式记录，每日更新，涵盖商品编号、销售日期、销售数量等字段，并基于历史销量计算未来30天预测值，用于优化库存管理和市场策略。算法采用加权平均模型，强调长期销售趋势，系数a、b、c分别赋予0.5、0.3、0.2权重，调整因子k为1.05，以支持商家决策和平台运营。