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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/wharton-customer-analytics
课程评论：没有评论
课程名称：客户分析 课程概述：在当今的信息时代，关于我们的浏览和购买模式的数据无处不在。从信用卡交易、在线购物车，到客户忠诚度程序和用户生成的评分/评论，有大量的数据可以用来描述过去的购买行为、预测未来的行为，并提供影响未来购买决策的新方法。本课程由沃顿商学院四位顶尖的营销教授主讲，涵盖客户分析的关键领域：描述性分析、预测性分析、处方性分析，以及它们在包括亚马逊、谷歌和星巴克等实际商业实践中的应用。此课程旨在为学员提供分析领域的概述，使其能够做出更明智的商业决策，并不旨在使学员具备执行客户分析的能力。 学习成果： 完成课程后，学员能够： 1. 描述公司用于客户数据收集的主要方法，并理解这些数据如何影响商业决策。 2. 描述预测客户行为的主要工具，并识别每种工具的适用场景。 3. 交流客户分析的关键理念及其对商业决策的影响。 4. 讲述客户分析的历史及顶尖企业的最新最佳实践。 课程大纲： 1. **客户分析简介**：了解什么是客户分析，课程结构及学习内容概述。 2. **描述性分析**：学习客户行为的数据能和不能描述的内容，以及最有效的数据收集和解释方法。探索数据与决策之间的协同作用，了解如何通过数据为产品、营销活动等策略提供支持。 3. **预测性分析**：在收集和解释数据后，学习如何利用过去的行动数据预测未来行为，了解各种预测工具的使用场景及其相关语言和框架。 4. **处方性分析**：学习如何将数据转化为可行动的建议，包括如何提出正确的问题、定义目标及优化成功的策略，以支持企业的业务目标。 5. **应用案例研究**：探索顶尖企业如何将数据应用于客户导向的营销实践，以了解客户分析的五个关键步骤，并学习如何在自己的公司中有效地应用这些创新和有效的数据驱动实践。 通过本课程，学员将具备运用客户分析做出数据驱动决策的基础知识，为未来的商业成功打下良好基础。
Name:Introduction to Customer Analytics
Description:What is Customer Analytics? How is this course structured? What will I learn in this course? What will I learn in the Business Analytics Specialization? These short videos will give you an overview of this course and the specialization; the substantive lectures begin in Week 2.
Name:Descriptive Analytics
Description: In this module, you’ll learn what data can and can’t describe about customer behavior as well as the most effective methods for collecting data and deciding what it means. You’ll understand the critical difference between data which describes a causal relationship and data which describes a correlative one as you explore the synergy between data and decisions, including the principles for systematically collecting and interpreting data to make better business decisions. You’ll also learn how data is used to explore a problem or question, and how to use that data to create products, marketing campaigns, and other strategies. By the end of this module, you’ll have a solid understanding of effective data collection and interpretation so that you can use the right data to make the right decision for your company or business.
Name:Predictive Analytics
Description:Once you’ve collected and interpreted data, what do you do with it? In this module, you’ll learn how to take the next step: how to use data about actions in the past to make to make predictions about actions in the future. You’ll examine the main tools used to predict behavior, and learn how to determine which tool is right for which decision purposes. Additionally, you’ll learn the language and the frameworks for making predictions of future behavior. At the end of this module, you’ll be able to determine what kinds of predictions you can make to create future strategies, understand the most powerful techniques for predictive models including regression analysis, and be prepared to take full advantage of analytics to create effective data-driven business decisions.
Name:Prescriptive Analytics
Description:How do you turn data into action? In this module, you’ll learn how prescriptive analytics provide recommendations for actions you can take to achieve your business goals. First, you’ll explore how to ask the right questions, how to define your objectives, and how to optimize for success. You’ll also examine critical examples of prescriptive models, including how quantity is impacted by price, how to maximize revenue, how to maximize profits, and how to best use online advertising. By the end of this module, you’ll be able to define a problem, define a good objective, and explore models for optimization which take competition into account, so that you can write prescriptions for data-driven actions that create success for your company or business.
Name:Application/Case Studies
Description:How do top firms put data to work? In this module, you’ll learn how successful businesses use data to create cutting-edge, customer-focused marketing practices. You’ll explore real-world examples of the five-pronged attack to apply customer analytics to marketing, starting with data collection and data exploration, moving toward building predictive models and optimization, and continuing all the way to data-driven decisions. At the end of this module, you’ll know the best way to put data to work in your own company or business, based on the most innovative and effective data-driven practices of today’s top firms.
Data about our browsing and buying patterns are everywhere. From credit card transactions and online shopping carts, to customer loyalty programs and user-generated ratings/reviews, there is a staggering amount of data that can be used to describe our past buying behaviors, predict future ones, and prescribe new ways to influence future purchasing decisions. In this course, four of Wharton’s top marketing professors will provide an overview of key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices including Amazon, Google, and Starbucks to name a few. This course provides an overview of the field of analytics so that you can make informed business decisions. It is an introduction to the theory of customer analytics, and is not intended to prepare learners to perform customer analytics. Course Learning Outcomes: After completing the course learners will be able to... Describe the major methods of customer data collection used by companies and understand how this data can inform business decisions Describe the main tools used to predict customer behavior and identify the appropriate uses for each tool Communicate key ideas about customer analytics and how the field informs business decisions Communicate the history of customer analytics and latest best practices at top firms
