奢侈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，结合奢侈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奢侈品行业、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 the touchpoint methods of marketing campaigns with the feedback behaviors of luxury industry users towards various touchpoint methods, analyze user purchase intentions to predict user sales conversion rates, and rate user loyalty based on user purchase history data, so as to help enterprises assess the completion status of campaigns, analyze the effectiveness of marketing activities, and provide data support for the planning of different types of enterprise activities and the formulation of marketing promotion strategies. This dataset method can be widely applied to luxury industry, retail enterprises, telecommunications operators, medical and health care, people's livelihood services and other institutions, helping enterprises formulate operational strategies and touchpoint methods through such analytical data, save costs, improve marketing effects and user loyalty.

Data Collection: Data is generated and analyzed through Shuyun's self-developed Kylin CRM marketing system, and updated daily.
Data Preprocessing: Take specific user IDs as unique identifiers, clean and deduplicate the original data according to the data source model, define user behavior coefficients, predict user sales conversion rates through user purchase intentions, and determine user loyalty ratings combined with user purchase history data.
Detailed Data Processing: The "touchpoint methods" in this dataset include: SMS, Email, Super SMS (Rich Communication Services, RCS), WeChat template messages, AI outbound calls, Taobao SMS, Mini Program subscription messages, Douyin SMS, Enterprise WeChat messages, and sales assistant tasks; "User behaviors" include: Click, Reply, Unsubscribe, Open, Submit, Share; "User behavior coefficients" are: Click(0.3), Reply(0.6), Unsubscribe(0.1), Open(0.5), Submit(0.7), Share(0.8); "User purchase intentions" are: P1(No intention), P2(Preliminary understanding), P3(Product comparison), P4(Ready to purchase); "User intent coefficients" are: P1(0.1), P2(0.3), P3(0.5), P4(0.8); The formula for user sales conversion rate prediction is: (User behavior coefficient * 0.2 + User purchase intent * 0.4 + User purchase history * 0.4 / 12) * 100%; The predicted user sales conversion rate promotes user sales conversion; "User loyalty ratings" are: 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 group management of this dataset, enterprises can predict user sales conversion rates based on users' reactive behaviors towards touchpoint methods, thereby determining users' loyalty to the enterprise, better implementing differentiated marketing strategies, and thus promoting the improvement of user loyalty and the selection of effective touchpoint methods.

搜集汇总

背景与挑战

背景概述

该数据集包含1501条奢侈品行业用户触达方式及行为数据，每日更新，通过用户行为系数、购买意向等指标预测销售转化率并评级用户忠诚度，助力企业优化营销策略。数据涵盖15个字段，包括触达方式、用户行为、购买历史等关键维度，适用于奢侈品行业及零售企业的精准营销分析。