河北省用户活跃度分级数据

RFE模型是根据用户最近一次访问时间R（ Recency）、访问频率 F（Frequency）和页面互动度 E（Engagements）计算得出的RFE得分。 评估用户的活跃度，将活跃度分为多个等级，以根据不同的活跃等级开展不同的营销活动。例如通过活动邀请、 精准广告投放、会员活动推荐等提升用户的活跃度。RFE模型可以为所有需要对会员或用户进行活跃度分析管理的企业提供数据支持。1、 数据采集自方太幸福家App。对采集到的河北省数据进行清洗、降噪、脱敏、聚集、分析，得到R（最近访问时间）、F（访问频次）、E（页面互动度）的值；
2、构建顾客画像：
（1）打分：
-R得分：R<31，得5分；30<R<91，得4分；90<R<181，得3分；180<R<241，得2分；R>240，得1分；
-F得分：F<11，得1分；10<F<16，得2分；15<F<21，得3分；20<F<26，得4分；F>25，得5分；
-E得分：E<4，得1分；3<E<9，得2分；8<E<16，得3分；15<E<21，得4分；E>20，得5分；
（2）计算RFE得分：RFE得分=R得分*0.3+F得分*0.3+E得分*0.4；
3、数据应用：
根据RFE得分对顾客进行分级：RFE得分≤1，为E级；1<RFE得分≤2，为D级；2<RFE得分≤3，为C级；3<RFE得分≤4，为B级；RFE得分>4，为A级；进而根据客户等级，对用户的活跃度做分析，如顾客等级为C、D、E，但每次访问时的交互数据良好，则针对这部分用户重点通过活动邀请、精准广告投放、会员活动推荐等提升用户回访频率。

The RFE model computes the RFE score based on three metrics: Recency (R, the user's most recent visit time), Frequency (F, the visit frequency), and Engagements (E, the page interaction level). It is used to assess user activity, categorize users into multiple activity tiers, and implement targeted marketing strategies based on different activity levels, such as event invitations, precision advertising, and member activity recommendations, to enhance user activity. The RFE model can offer data support for all enterprises that require activity analysis and management of their members or users.
1. Data collection and preprocessing: Data is collected from the Fangtai Happiness Home App. The collected data from Hebei Province is cleaned, denoised, desensitized, aggregated and analyzed to obtain the values of R (most recent visit time), F (visit frequency) and E (page interaction level);
2. Customer profiling construction:
(1) Scoring rules:
- R score: 5 points when R < 31; 4 points when 30 < R < 91; 3 points when 90 < R < 181; 2 points when 180 < R < 241; 1 point when R > 240;
- F score: 1 point when F < 11; 2 points when 10 < F < 16; 3 points when 15 < F < 21; 4 points when 20 < F < 26; 5 points when F > 25;
- E score: 1 point when E < 4; 2 points when 3 < E < 9; 3 points when 8 < E < 16; 4 points when 15 < E < 21; 5 points when E > 20;
(2) RFE score calculation: RFE score = R score * 0.3 + F score * 0.3 + E score * 0.4;
3. Data application:
Classify customers based on their RFE scores: Grade E if RFE score ≤ 1; Grade D if 1 < RFE score ≤ 2; Grade C if 2 < RFE score ≤ 3; Grade B if 3 < RFE score ≤ 4; Grade A if RFE score > 4. Further analyze user activity based on the customer tiers: for users who fall into tiers C, D or E but have good interaction data during each visit, focus on improving their revisit frequency through measures such as event invitations, precision advertising, and member activity recommendations.

搜集汇总

数据集介绍

特点

河北省用户活跃度分级数据是一个包含1717条记录的数据集，每日更新，采用RFE模型评估用户活跃度并分级，适用于精准营销和用户活跃度管理。