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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/foundations-marketing-analytics
课程评论：没有评论
课程名称：市场分析基础 概述： 本课程专为希望将统计知识和技术应用于商业环境的学生、商业分析师和数据科学家设计，尤其适合有一定统计学基础并熟悉R语言或其他编程语言的学员。参与者需具备数据库和数据分析技术（如回归、分类和聚类）的知识。课程将在R Studio环境中进行，通过讲座和教程帮助学员巩固能力，并自由探索数据及统计函数。 在当前大数据和商业分析的背景下，有效的数据分析能力对公司具有明显的竞争优势，尤其在市场营销领域。课程将探讨如何解答客户的基本问题，包括：客户是谁、应针对哪些客户进行营销以及客户的未来价值。 课程内容： 1. **模块0：市场分析基础介绍** 介绍市场分析领域及课程结构，同时探索将用于整个课程的零售数据集，并在R中设置环境。 2. **模块1：统计细分** 学习统计细分的原理，计算客户的统计指标，并识别数据库中的同质客户群体，通过讲座和R教程相结合，确保学员能够应用所学概念。 3. **模块2：管理细分** 学习管理细分这一工具，了解其如何超越统计技术，并掌握如何在当前及以往时间对数据库进行细分，帮助管理者做出更有效的决策。 4. **模块3：目标市场及评分模型** 了解如何构建评分模型以预测客户行为并进行精准营销，通过两个预测结合的方式，确定客户的购买意愿及消费金额。 5. **模块4：客户终身价值** 学习如何使用R进行终身价值分析，包括估算转移矩阵，以预测客户群体未来几年的演变及其潜在价值。 通过本课程，学员将掌握市场分析的基本基础，能够更有效地解析数据，提升营销决策的精准度和效果。
Part: 1
Title:Module 0 : Introduction to Foundation of Marketing Analytics
Description:In this short module, we will introduce the field of marketing analytics, and layout the structure of this course. We will also take that opportunity to explore a retailing data set that we’ll be using throughout this course. We will setup the environment, load the data in R (we’ll be using the RStudio environment), and explore it using simple SQL statements.
Part: 2
Title:Module 1 : Statistical segmentation
Description:In this module, you will learn the inner workings of statistical segmentation, how to compute statistical indicators about customers such as recency or frequency, and how to identify homogeneous groups of customers within a database. We will alternate lectures and R tutorials, making sure that, by the end of this module, you will be able to apply every concept we will cover.
Part: 3
Title:Module 2 : Managerial segmentation
Description:Statistical segmentation is an invaluable tool, especially to explore, summarize, or make a snapshot of an existing database of customers. But what most academics will fail to tell you is that this kind of segmentation is not the method of choice for many companies, and for good reasons. In this module, you will learn to perform managerial segmentations, which are not built upon statistical techniques, but are an essential addition to your toolbox of marketing analyst. You will also learn how to segment a database now, but also at any point in time in the past, and why it is useful to managers to do so.
Part: 4
Title:Module 3 : Targeting and scoring models
Description:How can Target predict which of its customers are pregnant? How can a bank predict the likelihood you will default on their loan, or crash your car within the next five years, and price accordingly? And if your firm only has the budget to reach a few customers during a marketing campaign, who should it target to maximize profit? The answer to all these questions is… by building a scoring model, and targeting your customers accordingly. In this module, you will learn how to build a customer score, which in marketing usually combines two predictions in one : what is the likelihood that a customer will buy something, and if he does, how much will he buy for?
Part: 5
Title:Module 4 : Customer lifetime value
Description:In this module, you will learn how to use R to execute lifetime value analyses. You will learn to estimate what is called a transition matrix -which measures how customers transition from one segment to another- and use that information to make invaluable predictions about how a customer database is likely to evolve over the next few years, and how much money it should be worth.
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R. Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage. It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on. But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future. That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
