北京市品牌营销活动用户活跃指数分析数据

研究北京市内，品牌在店铺投放各类营销活动时，已注册的门店用户的参与情况（一个门店主体即为一个用户）。包括合作品牌数量，参与活动类型，通过ST模型，计算活跃指数。基于这一部分数据，品牌可以了解门店营销状况以及未来可能的变化趋势，有助于指导品牌营销模式的优化和迭代，有助于在投入产出规划上提供帮助，为对店商活跃度与品牌营销策略分析等场景有需求的企业、机构提供数据支持，同时辅助当地政府进行市场监管。1.数据采集：数据采集来自惠合科技合法自有的e店佳等店商平台（平台致力于在品牌-商户-消费者之间构建业务服务桥梁）。研究2024年北京市内，品牌在店铺投放各类营销活动时，已注册的门店用户的参与情况，包括合作品牌数量（SUM），参与活动类型（TYPE）。进行分析加工，对数据进行清洗、去重，剔除无效数据。；2.构建用户/门店画像：（1）打分：北京市内已注册的门店，每参与一个品牌的营销活动， SUM得分记1，累加计算；每参与一种活动，TYPE得分记1，累计计算；（2）计算ST得分：ST得分=S得分*0.3+T得分*0.4（0.3和0.4为加权计算系数）； 3、数据应用： 根据ST得分对北京市门店用户活跃度进行分级：0≤ST得分≤0.7，为D级活跃度；0.7<ST得分≤1.0，为C级活跃度；1.0<ST得分≤1.4，为B级活跃度；1.4<ST得分，为A级活跃度。对用户的活跃度做分析，如门店活跃度等级为D、C，针对这部分用户通过活动邀请提升参与活动的意愿;如门店活跃度等级为B，通过优惠方案提高活跃度；如门店活跃度等级为A，针对这部分用户通过重点活动邀请、专享优惠投放等稳定活动活跃度。

This study investigates the participation status of registered store users (one store entity is treated as one user) when brands launch various marketing campaigns in stores across Beijing, including the count of cooperating brands and types of participated campaigns, with the activity index calculated via the ST model. Based on this dataset, brands can obtain insights into store marketing conditions and future potential trends, which helps guide the optimization and iteration of brand marketing models, support input-output planning, provide data support for enterprises and institutions requiring scenarios such as store business activity and brand marketing strategy analysis, and also assist local governments in market supervision.

1. Data Collection
Data is collected from legitimate in-house store platforms including e-Dianjia under Huihe Technology, which is committed to building a business service bridge among brands, merchants and consumers. This study focuses on the participation of registered store users when brands launch various marketing campaigns in stores within Beijing in 2024, covering two metrics: the count of cooperating brands (denoted as SUM) and the types of participated campaigns (denoted as TYPE). Data preprocessing including cleaning, deduplication and invalid data elimination is performed.

2. User/Store Profile Construction
(1) Scoring: For each registered store in Beijing, the total SUM score is incremented by 1 for each brand's marketing campaign it participates in, and the cumulative total is calculated; the total TYPE score is incremented by 1 for each type of campaign it participates in, and the cumulative total is calculated.
(2) ST Score Calculation: ST score = 0.3 × total SUM score + 0.4 × total TYPE score, where 0.3 and 0.4 are the weighted coefficients.

3. Data Application
Classify the activity levels of Beijing store users based on their ST scores:
- Level D: 0 ≤ ST score ≤ 0.7
- Level C: 0.7 < ST score ≤ 1.0
- Level B: 1.0 < ST score ≤ 1.4
- Level A: ST score > 1.4
Analyze user activity levels and take targeted measures: for users with activity levels D or C, improve their willingness to participate in campaigns via activity invitations; for users with Level B, enhance their activity through preferential schemes; for users with Level A, stabilize their activity via key activity invitations and exclusive preferential offers.