湖北省零售门店会员价值分布数据
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本数据通过分析湖北省内零售门店会员在一定时间周期内的消费情况、活跃状态，结合数据模型为多领域品牌针对不同价值的会员、不同区域开展营销策略提供数据支持。通过本单位自有的GIC系统，根据算法确定省以及省内门店会员的价值分布情况，指导品牌针对不同类的会员采取不同的营销策略，优化营销方案。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更精细化的营销策略、提升营销效果和会员营销水平通过达摩网络科技有限公司合法自有的GIC平台，以近1年为统计周期，以已在平台注册的湖北省门店会员为对象，按每天的更新频次分析当下近1年内、数据对象在这段时间内的消费数据， 1、根据公式“∑（实付金额/消费次数）/N”计算出这些数据对象整体的平均客单价、记作K； 2、统计出时间周期内每个客户的消费总额、记作M；再根据公式“年平均消费次数=有效消费次数 / 消费周期”计算出每个客户的年平均消费次数C，其中“有效消费次数”是每个客户消费订单笔数（全部退款的不计算在内）；“消费周期”=(最近消费日期 - 首次消费日期 + 1) / 365，若结果小于1时按1计算； 3、根据M与C的值划分成四个会员价值区间：核心会员【M≥3*K且C≥3】、潜力会员【除核心会员、普通会员、未消费会员的其他会员】、普通会员【C=1】、未消费会员【M=0或C=0】； 4、数据计算：利用IF函数对每个会员进行价值判断、根据其消费水平归类到不同会员价值客群。例如某会员的年平均消费次数是2次，消费总额未达到3倍平均客单，则这个会员为“潜力会员” 5、根据会员价值分布的结果，可以针对性的开展营销和维护，例如针对“核心客户”投入更多资源进行重点维护，针对“潜力会员”可以采取更优惠的策略将其提升为核心客户；还可根据会员价值分布情况分析会员池的健康度，根据二八法则，若核心会员占比太少，说明会员池健康度较低，商家门店需要诊断优化目前的会员运营策略等。
This dataset provides data support for multi-sector brands to develop marketing strategies targeting members of different value tiers and across different regions, by analyzing the consumption status and activity levels of members at retail stores within Hubei Province over a specified time period combined with data models. Using the proprietary GIC system developed by the dataset’s originating institution, this work determines the value distribution of provincial and in-store members via algorithms, to guide brands in adopting differentiated marketing strategies for different member groups and optimizing their marketing plans. This data analysis method can be widely applied to various retail brands and institutional departments studying the marketing models of retail stores/real economy, helping formulate more refined marketing strategies, improve marketing effectiveness and member marketing performance by leveraging such analytical data. Via the legitimately proprietary GIC platform owned by Damo Network Technology Co., Ltd., this analysis takes the past 12 months as the statistical cycle, targets members of Hubei retail stores who have registered on the platform, and analyzes the consumption data of these data subjects within this 12-month period with a daily update frequency: 1. Calculate the overall average customer unit price of these data subjects using the formula "∑(Actual Payment Amount / Number of Consumption Orders)/N", denoted as K; 2. Count the total consumption amount of each customer within the statistical cycle, denoted as M; then calculate the annual average consumption frequency C for each customer using the formula "Annual Average Consumption Frequency = Valid Consumption Count / Consumption Cycle", where "Valid Consumption Count" refers to the number of consumption orders placed by each customer (fully refunded orders are excluded); "Consumption Cycle" = (Latest Consumption Date - First Consumption Date + 1)/365, and the result is set to 1 if it is less than 1; 3. Divide members into four value segments based on the values of M and C: - Core Members: M ≥ 3*K and C ≥ 3 - Potential Members: All members excluding core, regular and non-consuming members - Regular Members: C = 1 - Non-consuming Members: M = 0 or C = 0; 4. Data Calculation: Use the IF function to conduct value judgment for each member and classify them into different member value groups based on their consumption level. For example, if a member has an annual average consumption frequency of 2 times and their total consumption amount does not reach 3 times the average unit price, this member is classified as a "Potential Member"; 5. Targeted marketing and maintenance can be carried out based on the results of member value distribution. For example, invest more resources to focus on maintaining "Core Members", adopt more preferential strategies for "Potential Members" to upgrade them to core members; the health of the member pool can also be analyzed based on the member value distribution. According to the 80/20 Pareto principle, if the proportion of core members is too low, it indicates that the health of the member pool is poor, and merchant stores need to diagnose and optimize their current member operation strategies, etc.
