湖南省零售门店会员价值分布数据

本数据通过分析湖南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南省门店会员为对象，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-domain brands to develop marketing strategies targeting members of different value tiers and different regions, by analyzing the consumption status and activity state of retail store members in Hunan Province over a specific time period, combined with data models. Using the self-owned GIC system of our organization, the value distribution of provincial and in-store members is determined via algorithms, to guide brands to adopt differentiated marketing strategies for different member groups and optimize marketing plans. This data methodology can be widely applied to various retail brands and institutional departments that need to study retail store/real economy marketing models, and helps formulate more refined marketing strategies, improve marketing effectiveness and member marketing performance through such analytical data.

Through the legally owned GIC platform of Damo Network Technology Co., Ltd., taking the past 1 year as the statistical cycle, targeting Hunan Province store members who have registered on the platform, and with daily update frequency, the consumption data of the data subjects within the most recent 1-year period as of the current time is analyzed.
1. Calculate the overall average customer unit price of these data subjects according to the formula "∑(Actual Payment Amount / Number of Consumption Times) / 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 times C of each customer according to the formula "Annual Average Consumption Times = Valid Consumption Times / Consumption Cycle", where "Valid Consumption Times" refers to the number of consumption orders of each customer (fully refunded orders are not counted); "Consumption Cycle" = (Latest Consumption Date - First Consumption Date + 1) / 365, and the result is calculated as 1 if it is less than 1;
3. Divide into four member value tiers based on the values of M and C: Core Member [M ≥ 3*K and C ≥ 3], Potential Member [members other than Core, Regular and Non-consuming Members], Regular Member [C = 1], Non-consuming Member [M = 0 or C = 0];
4. Data calculation: Use the IF function to judge the value of each member and classify them into different member value groups according to their consumption level. For example, if a member has 2 annual average consumption times and their total consumption amount does not reach 3 times the average customer 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 for key maintenance of "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 Pareto principle, if the proportion of Core Members is too low, it indicates that the member pool has low health, and merchant stores need to diagnose and optimize their current member operation strategies, etc.

搜集汇总

背景与挑战

背景概述

该数据集记录了湖南省零售门店会员的价值分布情况，包含501条数据，每日更新，通过分析会员消费行为将客户划分为核心、潜力、普通和未消费四类。它主要用于支持零售品牌优化营销策略，提升会员管理效率，基于算法模型评估会员价值并指导精细化运营。