计算设备行业基于触达方式的用户评级数据

此数据的核心是通过营销活动的触达方式，结合计算设备行业用户对各类触达方式的反馈行为，定义用户行为系数，分析用户购买意向来预测用户销售转化率，并根据用户购买历史数据，对用户忠诚度进行评级，帮助企业定位活动完成情况，分析营销活动的有效性，为企业不同类型活动的策划以及活动推广营销策略提供数据支持。该数据方法可广泛应用于计算设备行业、零售企业、电信运营公司、医疗健康、民生服务等单位，有助于企业通过此类分析数据来制定运营策略和触达方式，节省成本、提升营销效果和用户忠诚度。

数据采集：通过数云自研的麒麟CRM营销系统生成数据进行分析加工，每日更新。 数据处理：取特定用户ID为唯一标识，根据数据来源模型，对原始数据经过清洗和去重，根据用户行为定义用户行为系数，通过用户购买意向预测用户销售转化率，结合用户购买历史数据确定用户忠诚度评级。 数据加工：该数据集中“触达方式”：短信、邮件、超级短信、微信模板消息、AI外呼、淘宝短信、小程序订阅消息、抖音短信、企微消息、导购任务；“用户行为”：点击、回复、退订、打开、提交、分享；“用户行为系数”：点击(0.3)、回复(0.6)、退订(0.1)、打开(0.5)、提交(0.7)、分享(0.8)；“用户购买意向”：P1(无意向)、P2(初步了解)、P3(比较产品)、P4(准备购买)；“用户意向系数”：P1(0.1)、P2(0.3)、P3(0.5)、P4(0.8)；用户销售转化率预测 = （用户行为系数*0.2+用户购买意向*0.4+用户购买历史*0.4/12）*100%；用户销售转化率推进用户销售转化；“用户忠诚度评级”：A(10次以上)、B(6-9次)、C(2-5次)、D(1次)、E(无购买)。 数据应用：通过此数据的全面分析和分组管理，企业能够通过用户以触达方式的反应行为，预测用户的销售转化率，从而确定用户以于企业的忠诚度，能更好的实现差异化营销策略，从而推动用户忠诚度的提升和有效触达方式的选择。

The core of this dataset is to define user behavior coefficients by combining marketing campaign touchpoint channels with the feedback behaviors of computing device industry users towards various touchpoints, analyze users' purchase intentions to predict their sales conversion rates, and rate user loyalty based on their purchase history data. This helps enterprises assess campaign completion status, analyze marketing effectiveness, and provide data support for the planning of various corporate campaigns and the development of marketing promotion strategies.

This data methodology can be widely applied to computing device industry, retail enterprises, telecommunications operators, healthcare, people's livelihood service institutions and other organizations, assisting enterprises in formulating operational strategies and touchpoint methods based on such analytical data to reduce costs, improve marketing effectiveness and enhance user loyalty.

### Data Collection
The data is generated and processed for analysis via Shuyun's self-developed Kirin CRM Marketing System, and is updated daily.

### Data Processing
A specific user ID is adopted as the unique identifier. Based on the data source model, raw data is cleaned and deduplicated. User behavior coefficients are defined according to users' behavioral data, user sales conversion rates are predicted based on their purchase intentions, and user loyalty ratings are determined by combining their purchase history data.

### Data Processing Details
1. Touchpoint Channels: SMS, email, Rich Communication Services (RCS, super SMS), WeChat template message, AI outbound call, Taobao SMS, miniprogram subscription message, Douyin SMS, WeChat Work message, sales guidance task
2. User Behaviors: Click, reply, unsubscribe, open, submit, share
3. User Behavior Coefficients: Click (0.3), Reply (0.6), Unsubscribe (0.1), Open (0.5), Submit (0.7), Share (0.8)
4. User Purchase Intentions: P1 (No purchase intention), P2 (Initial understanding), P3 (Product comparison), P4 (Ready to purchase)
5. User Intention Coefficients: P1 (0.1), P2 (0.3), P3 (0.5), P4 (0.8)
6. Predicted User Sales Conversion Rate: (User Behavior Coefficient * 0.2 + User Purchase Intention * 0.4 + User Purchase History * 0.4 / 12) * 100%; This predicted sales conversion rate facilitates actual user sales conversion.
7. User Loyalty Ratings: A (10+ purchases), B (6-9 purchases), C (2-5 purchases), D (1 purchase), E (No purchases)

### Data Application
Through comprehensive analysis and group management of this dataset, enterprises can predict users' sales conversion rates based on their reaction behaviors to different touchpoint channels, thereby determining users' loyalty to the enterprise. This enables better implementation of differentiated marketing strategies, promoting the enhancement of user loyalty and the selection of effective touchpoint channels.

搜集汇总

背景与挑战

背景概述

该数据集包含计算设备行业用户对各类触达方式的反馈行为数据，用于预测用户销售转化率和评估用户忠诚度，帮助企业优化营销策略。数据集包含1501条记录，每日更新，适用于计算设备行业、零售企业、电信运营公司等多个行业。