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所在平台: Udemy |
课程主页: https://www.udemy.com/course/customer-lifetime-value-predictive-model-with-python/
课程评论：没有评论
课程名称：使用Python构建客户生命周期价值预测模型 课程概述：如果你希望开始数据分析的职业生涯，或者在商业中应用机器学习的专业知识，这门课程是你必须选择的！课程将讲授一系列关于实用营销AI模型的讲座——“客户生命周期价值模型”（CLV模型），有时也称为“复购建模”。你将学习到一种非常有用的AI预测模型，用于营销活动和促销。这些CLV模型在零售银行、保险和其他销售相关行业中被广泛使用，因为它帮助商业拥有者挑选出最有价值的客户，从而提高业务表现。 本课程的主要价值体现在以下几个方面： 1. CLV模型的创建过程简洁高效，易于快速构建。 2. 可以利用模型预测客户在特定未来时间段内的购买行为或偏好。 3. CLV模型用于预测客户的复购概率。 4. 分析不同客户的活动及忠诚度，帮助解决客户留存问题。 5. 根据CLV模型的输出，商业拥有者能够计算及排名客户生命周期价值。 课程目标是让你掌握如何有效利用大数据和AI算法进行智能营销。例如，成功预测出谁会在下个月购买商品，就能有效地针对这些客户实施市场策略，比如发起广告、应用推荐系统进行“交叉销售”或“交叉推荐”。同时，商业拥有者也能通过模型预测得知哪些客户对提供的商品或服务不感兴趣，从而采取其他营销策略或促销活动，使这些沉默的客户变得更为“活跃”。 除了商业价值，课程还将教授一些实用的统计学、机器学习和AI算法知识与技能，并结合Python编程。主要内容包括： 1. 在CLV模型中使用的各种统计分布函数，如几何分布和负二项分布的理解。 2. 使用Python中的Lifetime包创建BG/NBD CLV模型。 3. Lifetime包中的不同分析和图形工具，包括实施方法和解释。 4. 使用Python编程进行CLV模型的数据探索、清洗和特征生成。 5. 模型特征选择、特征工程、交叉验证和性能跟踪。 6. 如何将第三方数据应用到CLV建模中的讲解。 7. 梯度提升树算法框架及实施，包括Xgboost和Lightgbm算法在CLV建模中的应用介绍。 通过这门课程，你将能够有效运用统计和机器学习知识，提升在数据分析和AI应用方面的技能。
If you wish to start the data analytics career or apply machine learning expertise into business, this is the right course you must choose!Here I will provide a series of lectures on a practical marketing AI model - 'Customer Life Value Model', or CLV model. The method is also sometimes called the 'repurchase modeling'. I would say what you will learn is a very useful AI forecasting model for marketing campaign and promotion. Because the CLV models I am teaching in this course are currently widely used in retail banking, insurance, and other sales-related industries. Why? Since it helps business owners select the most valuable customers to get their business better and better!The value of my course is mainly reflected in the following aspects:1. The CLV models can be quickly created because the process and features for building models are very concise and efficient.2. One can utilize the model to predict the customer's purchase behavior or purchase preference for a specific merchandise in a given future time period.3. The CLV model can be used to predict the probability of customers' repurchase behavior.4. The CLV model can be used to analyze the activity and loyalty of different customers - help you solve customer retention problems.5. Based on the output of the CLV model, business owners can calculate and rank the customer lifetime value.The objective of the course is to let you master how to effectively use the big data and AI algorithms for intelligent marketing. For example, if you can successfully predict who will buy the commodities in the next month based on historical transaction data, then you would effectively apply some market strategies into these customers, like by launching advertisements, applying recommender systems for ‘cross sales' or ‘cross recommendation' At the same time, The business owners will also realize from the model's prediction who are not interested in the goods or services they are providing, perhaps they can adopt some other marketing strategies or promotion to make these silent customers become more 'active' or 'waked up'.In addition to the business value you can absorb from the course, I also teach you some practical statistical, machine learning and AI algorithm knowledge and skills, combined with the Python programming coding. This mainly covers:1. Various statistical distribution functions such Geometric / Negative Binomial used in the CLV models and interpretations.2. Lifetime package in Python to create BG/NBD CLV model.3. Different analytical and graphics tools in Lifetime package including implementation methods and interpretation.4. Data exploration, cleaning and feature generation for CLV models with Python programming.5. Model feature selection, feature engineering, cross validation and performance tracking.6. Lecture on how to apply the third party data into CLV modeling.7. Introduction of gradient boost tree algorithms' framework and implementation including Xgboost and Lightgbm algorithm into CLV modeling.
