汽车用品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，结合汽车用品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汽车用品行业、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 touchpoints of marketing campaigns with user feedback behaviors on various touchpoints in the automotive supplies industry, analyze user purchase intentions to predict sales conversion rates, and rate user loyalty based on user purchase history data. This helps enterprises assess campaign completion, analyze marketing effectiveness, and provide data support for the planning of various types of corporate activities and the formulation of marketing promotion strategies.

This data methodology can be widely applied to enterprises in industries such as automotive supplies, retail, telecommunications operators, healthcare, and people's livelihood services. It helps enterprises formulate operation strategies and touchpoint strategies through such analytical data, thereby reducing costs, improving marketing effectiveness, and enhancing user loyalty.

### Data Collection
Data is generated and analyzed through Shuyun's self-developed Qilin CRM marketing system, and updated daily.

### Data Processing
Specific user IDs are used as unique identifiers. Based on the data source model, raw data is cleaned and deduplicated. User behavior coefficients are defined according to user behaviors, user sales conversion rates are predicted based on user purchase intentions, and user loyalty ratings are determined in combination with user purchase history data.

### Data Processing Details
1. Touchpoint Types: SMS, email, Super SMS, WeChat template message, AI outbound call, Taobao SMS, Mini Program subscription message, Douyin SMS, WeChat Work message, sales assistant 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 Intention Levels: P1 (no intention), P2 (preliminary 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 coefficient * 0.4 + user purchase count * 0.4 / 12) * 100%
7. User sales conversion rate promotes user sales conversion.
8. User Loyalty Rating: A (more than 10 purchases), B (6-9 purchases), C (2-5 purchases), D (1 purchase), E (no purchases)

### Data Application
Through comprehensive analysis and grouping management of this dataset, enterprises can predict user sales conversion rates based on users' reactive behaviors to touchpoints, thereby determining users' loyalty to the enterprise. This enables better implementation of differentiated marketing strategies, promoting the improvement of user loyalty and the selection of effective touchpoints.

搜集汇总

背景与挑战

背景概述

该数据集包含1501条汽车用品行业用户数据，每日更新，记录了用户对不同触达方式的反馈行为、购买意向和忠诚度评级。通过分析这些数据，企业可以优化营销策略，提升销售转化率和用户忠诚度。