文具行业基于触达方式的用户评级数据
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此数据的核心是通过营销活动的触达方式，结合文具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文具行业、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 touchpoints with user feedback behaviors toward various touchpoints among stationery industry users, analyze user purchase intentions to predict sales conversion rates, and assign user loyalty ratings based on user purchase history data. This assists enterprises in evaluating campaign completion status, analyzing marketing effectiveness, and providing data support for the planning of diverse enterprise activities and the formulation of marketing promotion strategies. This data methodology can be widely applied to sectors including the stationery industry, retail enterprises, telecommunications operators, healthcare, and public service institutions. It enables enterprises to develop operational strategies and touchpoint approaches based on such analytical data, thereby reducing costs, improving marketing outcomes, and enhancing user loyalty. Data Collection: Data is generated and analyzed via Shuyun's self-developed Kirin CRM marketing system, with the dataset updated daily. Data Processing: Specific user IDs are adopted 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 through user purchase intentions, and user loyalty ratings are determined in combination with user purchase history data. Data Enrichment: 1. Touchpoint types: SMS, Email, Super SMS, WeChat template message, AI outbound call, Taobao SMS, Mini Program subscription message, Douyin SMS, WeChat Work message, Shopping guide 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 intent tiers: P1 (No intent), P2 (Preliminary understanding), P3 (Product comparison), P4 (Ready to purchase) 5. User intent coefficients: P1 (0.1), P2 (0.3), P3 (0.5), P4 (0.8) 6. Predicted user sales conversion rate formula: Predicted User Sales Conversion Rate = (User Behavior Coefficient * 0.2 + User Purchase Intent Score * 0.4 + (User Purchase History Count * 0.4)/12) * 100% 7. The predicted sales conversion rate facilitates actual user sales conversion 8. User loyalty ratings: 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 grouped management of this dataset, enterprises can predict user sales conversion rates based on users' response behaviors to different touchpoints, then determine users' loyalty to the enterprise. This allows for better implementation of differentiated marketing strategies, promoting the improvement of user loyalty and the selection of optimal touchpoints.
