商业空间租赁小程序用户分层数据
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通过对用户小程序使用行为数据进行分析，对用户进行标签制定，定位用户级别，帮助企业更好地理解和服务用户，提升用户体验和业务成效。通过用户分层，可以识别出可能流失的高风险用户群体，企业可采取预防措施，如提供个性化关怀或优惠，以减少用户流失。对于低活跃用户，企业可采取相应的激活措施，如发送个性化的优惠信息或提醒，以提高用户的活跃度和留存率。小程序用户分层助力丰富商业空间租赁行业用户画像，帮助行业更好地了解用户，为用户提供个性化的商品和服务。1、数据收集清洗：从企业内部数据库里筛选出商业空间租赁小程序用户的相关数据，对原始数据进行处理，去除异常数据。 2、数据处理：用IF函数结合赋分法，对用户的行为评分。AVERAGE函数计算2024年1月至3月注册用户平均访问次数、2024年1月至3月注册用户平均访问天数，累计访问次数分A=IF(单用户累计访问次数>2024年1月至3月注册用户平均访问次数,2,0），累计访问天数分B=IF(单用户累计访问天数>2024年1月至3月注册用户平均访问天数,2,0），近7天访问次数分C=(数据获取时间近7天访问次数>0,2,0），认证分D=IF(单用户已认证,3,0)，vip分E=IF(单用户升级vip,5,0)，以上均数都用AVERAGE函数计算得出。 3、用户分层：用户综合得分K=A+B+C+D+E，K取值范围[0,14]。K∈(10,14]为A类用户，K∈(5,10]为B类用户，K∈(3,5]为C类用户，K∈[0,3]为D类用户。
By analyzing the usage behavior data of mini-program users, we establish user tags and classify user tiers, helping enterprises better understand and serve users, thereby improving user experience and business outcomes. User stratification enables enterprises to identify high-risk user groups prone to churn, and take preventive measures such as delivering personalized care or preferential offers to reduce user churn. For low-activity users, enterprises can implement corresponding activation measures, such as sending personalized promotional messages or reminders, to boost user activity and retention rate. Mini-program-based user stratification helps enrich the user portraits of the commercial space leasing industry, enabling the industry to better understand users and provide personalized products and services. 1. Data Collection and Cleaning: Screen out relevant data of commercial space leasing mini-program users from the enterprise's internal database, and preprocess the raw data to remove abnormal entries. 2. Data Processing: Adopt the IF function combined with a scoring framework to score user behaviors. First, calculate the average number of visits and average number of visit days of registered users from January to March 2024 using the AVERAGE function. The cumulative visit score A is defined as IF(the user's cumulative visit times > the average visit times of registered users from Jan to Mar 2024, 2, 0); the cumulative visit days score B is IF(the user's cumulative visit days > the average visit days of registered users from Jan to Mar 2024, 2, 0); the recent 7-day visit score C is IF(the user's visit times in the last 7 days > 0, 2, 0); the certification score D is IF(the user has completed certification, 3, 0); the VIP score E is IF(the user has upgraded to VIP, 5, 0). All the above average values are calculated using the AVERAGE function. 3. User Stratification: The comprehensive user score K = A + B + C + D + E, with a value range of [0, 14]. Users are classified as follows: Type A users with K ∈ (10, 14], Type B users with K ∈ (5, 10], Type C users with K ∈ (3, 5], and Type D users with K ∈ [0, 3].
