河南地区产品洗护服务需求量分析预测数据

通过收集和分析河南地区用户对产品洗护服务的需求量数据以及近六个月洗护需求量较去年同期的变化，对河南地区用户对产品洗护的需求量作出的预测，店铺预测月洗护服务需求数为平台的商户调整产品材料采购、人员安排提供数据支持，区域大类预测月洗护服务需求数为店铺制定产品洗护服务销售策略提供数据支持，从而能更好地为用户提供更优质的服务体验。1.数据采集：采集河南地区，店铺、洗护物品、大类、下单时间、洗护服务数量（件）、店铺近6个月平均同比变化率、大类近6个月平均同比变化率等数据。2.数据处理：（1）单日店铺洗护服务总数（件），通过SUMIF函数对数据统计范围内同店铺当日所有洗护服务数量（件）求和，例如本条店铺为“店铺001”、下单时间为“2023-11-1”，单日店铺洗护服务总数为数据统计范围内为“店铺001”、下单时间为“2023-11-1”的洗护服务数量总和；（2）店铺月洗护服务总数（件）=数据统计范围内对应店铺“洗护服务数量（件）”和；（3）店铺日洗护服务平均数（件）=店铺月洗护服务总数（件）/当月天数；（4）店铺预测月洗护服务需求数（件）=店铺月洗护服务总数（件）*（1+店铺近6个月平均同比变化率）；（5）区域大类预测月洗护服务需求数（件）=（数据统计范围内对应大类“洗护服务数量（件）”和）*（1+大类近6个月平均同比变化率），就数据统计范围内对应大类“洗护服务数量（件）”和而言，例如本条数据大类为“服饰”，数据统计范围内对应大类“洗护服务数量（件）”和即为数据统计范围内所有大类为服饰的洗护服务数量总和；3.数据应用：通过这样的分析流程，商户能够更准确地把握市场动态，从而调整产品洗护服务销售策略、材料采购、人员安排。

This dataset is developed to forecast the demand for product cleaning and care services among users in Henan Province, by collecting and analyzing demand data and the year-on-year change in demand over the past six months relative to the same period last year. The forecasted monthly demand for cleaning services at individual stores provides data support for platform merchants to adjust product material procurement and staff scheduling, while the forecasted monthly demand for cleaning services by regional product categories offers data backing for stores to formulate sales strategies for their product cleaning and care services, thereby enabling better user service experiences.

1. Data Collection:
Collect data including store information, cleaned items, product categories, order time, number of cleaning service orders (units), average 6-month year-on-year change rate for individual stores, and average 6-month year-on-year change rate for product categories across Henan Province.

2. Data Processing:
(1) Daily total number of cleaning service orders per store (units): Calculate the sum of all cleaning service order quantities for the same store on the same day within the data statistics scope using the SUMIF function. For example, if the target store is "Store 001" and the order time is "2023-11-1", the daily total number of cleaning service orders for this store is the sum of all cleaning service order quantities where the store is "Store 001" and the order time is "2023-11-1" within the data statistics range.
(2) Monthly total number of cleaning service orders per store (units): Sum of the "number of cleaning service orders (units)" for the corresponding store within the data statistics scope.
(3) Daily average number of cleaning service orders per store (units) = Monthly total number of cleaning service orders per store (units) / Number of days in the current month.
(4) Forecasted monthly demand for cleaning services at individual stores (units) = Monthly total number of cleaning service orders per store (units) * (1 + average 6-month year-on-year change rate for the store).
(5) Forecasted monthly demand for cleaning services by regional product category (units) = Sum of the "number of cleaning service orders (units)" for the corresponding category within the data statistics scope * (1 + average 6-month year-on-year change rate for the product category). Regarding the sum of "number of cleaning service orders (units)" for the corresponding category, take the example of category "Apparel": the sum refers to the total number of cleaning service orders categorized as "Apparel" within the data statistics scope.

3. Data Application:
Through this analysis workflow, merchants can accurately grasp market dynamics, thereby adjusting their product cleaning and care service sales strategies, material procurement, and staff scheduling.

搜集汇总

数据集介绍

特点

该数据集包含河南地区产品洗护服务的需求量数据及预测信息，涵盖店铺、洗护物品、大类等字段，共998条记录，每月更新。通过分析近六个月的变化率，预测未来需求，为商户调整采购、人员安排及销售策略提供数据支持。