河北省零售门店活跃状态生成营销策略数据

本数据通过河北省内零售门店用户年度内活跃状态，结合数据模型为多领域品牌针对不同活跃状态、不同区域开展营销策略提供数据支持。通过本单位自有的e店佳等系统，根据算法确定省及省内门店活跃状态，指导品牌制定更具针对性的个性化营销策略，优化成本效益。该数据方法可广泛应用于各种零售品牌，以及需要研究零售门店/实体经济营销模式的单位部门，有助于通过此类分析数据来制定个性化营销策略，节省成本、提升营销效果。1.数据处理：通过惠合科技合法自有的e店佳等店商平台，以统计时间为基准，以已在活动平台注册的河北省门店用户为对象，分析一年内，数据对象参与平台上投放的品牌营销活动的数据，利用IF函数进行活跃度的初步判断，IF【一年期内未参加】，则门店/用户活跃状态记为"/".IF【最近1个月内有登录/参与活动记录】，则门店/用户活跃状态记为“1个月内活跃”，以此类推划分成【/】、【1个月内活跃】、【1~3月内活跃】、【3~6月内活跃】、【6~12月内活跃】五个类型，分别用记为/区间、A区间、B区间、C区间、D区间。A、B区间，则门店评价为高活跃度门店；C、D区间则门店评价为一般活跃度门店；/区间则门店评价为低活跃度门店；2.数据计算：利用∑函数计算并直接输出不同活跃区间类型的门店总数，然后计算本省内活跃状态属于A区间的门店总数的占比：A区间门店占比＝活跃状态区间为A的门店总数/某省参与统计门店总数*100%，时间条件跟随数据时间，不另外进行强调。（例如：本年A区间门店占比＝本年活跃状态区间为A的门店总数/本年河北省参与统计门店总数*100%），IF【90%≤A区间门店占比＜100%】，则区域评价为“高活跃度省份”；IF【70%≤A区间门店占比＜90%】，则区域评价为“一般活跃度省份”；IF【0＜A区间门店占比＜70%】，则区域评价为“低活跃度省份”（根据实际情况，不存在占比0以及100%的情况）；3.数据复用：本数据的使用分为两步，首先通过算法计算对区域完成评价，加强对高活跃度省份的营销投入，减少对低活跃度省份的营销成本投入，投放产品优惠性营销稳定一般活跃度省份；第二步在省份评价的基础上对零售门店进行进一步的分析，结合地域差异（气候，经济发展情况，受众区别，客户偏好等）进行精准营销，根据零售门店活跃状态生成有针对性、可推广性的营销策略。

This dataset is derived from the annual activity status of users at retail stores within Hebei Province, and provides data support for multi-domain brands to develop marketing strategies targeting different activity statuses and regions via a data model. Through proprietary systems including E-Dianjia developed by our institution, the activity status of provincial and intra-provincial retail stores is determined via algorithms, to guide brands in formulating more targeted personalized marketing strategies and optimizing cost-effectiveness. This data methodology can be widely applied to various retail brands and units/departments that need to study retail store/real economy marketing models, and helps develop personalized marketing strategies using such analytical data to save costs and improve marketing outcomes.

1. Data Processing: Using legitimate in-house business platforms including E-Dianjia developed by Hehui Technology, taking the statistical time as the benchmark, and taking Hebei store users who have registered on the activity platform as the research objects, the data of participants in brand marketing activities launched on the platform within one year is analyzed. The IF function is used to preliminarily judge the activity level: if the user/store has not participated in any activities within one year, the activity status is recorded as "/"; if the user/store has login/activity participation records within the latest month, the activity status is recorded as "Active within 1 month"; similarly, five categories are divided: [/], [Active within 1 month], [Active within 1-3 months], [Active within 3-6 months], [Active within 6-12 months], which are respectively marked as / Interval, Interval A, Interval B, Interval C, Interval D. Stores in Interval A or B are evaluated as high-activity stores; those in Interval C or D are evaluated as medium-activity stores; stores in / Interval are evaluated as low-activity stores.

2. Data Calculation: The SUM function is used to calculate and directly output the total number of stores across each activity interval category. Then the proportion of Interval A stores in the province's active status is calculated: Proportion of Interval A Stores = (Total number of Interval A stores / Total number of statistically participating stores in a certain province) * 100%, with the time condition following the data timeline without additional emphasis. (For example: Annual proportion of Interval A Stores = (Annual total number of Interval A stores / Annual total number of statistically participating stores in Hebei Province) * 100%). The region is evaluated as follows: if 90% ≤ proportion of Interval A Stores < 100%, the region is rated as "High-activity Province"; if 70% ≤ proportion of Interval A Stores < 90%, the region is rated as "Medium-activity Province"; if 0 < proportion of Interval A Stores <70%, the region is rated as "Low-activity Province" (in practice, proportions of 0% and 100% do not exist).

3. Data Reuse: The use of this dataset is divided into two steps. First, evaluate regions via algorithmic calculations: increase marketing investment in high-activity provinces, reduce marketing cost input in low-activity provinces, and launch targeted promotional offers to stabilize marketing efforts in medium-activity provinces. Second, further analyze retail stores based on the provincial evaluation results, combine regional differences (including climate, economic development level, audience differences, customer preferences, etc.) to conduct precise marketing, and develop targeted and scalable marketing strategies based on the activity status of retail stores.

搜集汇总

背景与挑战

背景概述

该数据集包含河北省零售门店的活跃状态信息，用于生成营销策略。数据规模为501条，每年更新一次，适用于零售品牌和需要研究零售门店营销模式的单位部门。通过算法分析门店活跃状态和区域评价，帮助制定个性化营销策略，优化成本效益。