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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/applying-data-analytics-business-in-marketing
课程评论：没有评论
课程名称：市场营销中的数据分析应用 概述：本课程介绍通过多种分析方法来衡量客户满意度。我们将讨论客户满意度的组成部分、测量客户满意度的主要问题、影响客户满意度的统计分析方法、社交媒体数据的情感分析、社交媒体数据的影响分析以及社交媒体数据的文本摘要。课程旨在提供所需的基础，以通过分析与客户满意度相关的多种数据，做出更好的营销决策。 课程大纲： 1. **课程介绍与模块1：因果分析** 在模块初始部分，我们讨论市场营销中的分析及因果分析的重要性。首先，我们将概述分析对营销人员的重要性、各种类型的数据、在营销中应用分析的过程以及不同的分析方法。接着，我们将深入探讨因果分析。 2. **模块2：调查分析** 在第二个模块中，我们专注于使用回归分析调查数据。调查是组织衡量客户满意度等重要指标的关键工具。我们将对客户满意度的概念及其测量方式进行广泛理解，并讨论分析调查数据的工具，特别关注线性回归和逻辑回归两种回归方法。最后，通过使用航空公司客户满意度调查数据集的R语言操作，进行逻辑回归演示。 3. **模块3：文本分析** 本模块将学习各种文本分析方法。我们将首先介绍情感分析，这是利用文本数据分析客户满意度的最常见方法，并通过R Studio演示情感分析的步骤。接着，我们将关注文本摘要技术，包括将文本转换为可分析形式所需的预处理步骤，以及多词短语频率计数的分析。基于n-grams，我们将探讨更智能的方法自动检测优质短语，同时讨论LDA主题建模—检测文本主题的流行方法。最后，我们将强调监督机器学习及其应用示例。 4. **模块4：网络分析** 我们将介绍一种利用社交媒体数据分析客户满意度影响的方法。社交网络是理解人际互动和网络形成的理想数据集。识别社交媒体关系中的模式可以在营销决策中发挥作用。此外，我们将回顾影响者品牌个性分析，作为品牌寻找与自己个性相似的影响者的一种方法。
Name:Course Introduction and Module 1: Causal Analysis
Description:In the first module, we will discuss analytics in marketing and dive into causal analysis, an important tool for analytics. We will start with a broad overview of why analytics is important for marketers, what are the various types of data, the process of applying analytics in marketing, and the different types of analytics. We will then delve deeper into causal analysis.
Name:Module 2: Survey Analysis
Description:In the second module, we will focus on the analysis of survey data using regression. Surveys are one of the key tools used by organizations to measure important constructs like customer satisfaction. We will start with a broad understanding of the concept of customer satisfaction and various ways to measure it. Next, we will discuss the tools to analyze survey data. We will specifically focus on two regression methods—linear and logistic regressions. Finally, we will conclude the module with a hands-on logistic regression demonstration using an airline customer satisfaction survey dataset with R.
Name:Module 3: Text Analysis
Description:We will learn about the various methods of text analysis. We will first introduce you to sentiment analysis—the most prevalent means of analyzing customer satisfaction with textual data. We will demonstrate the sentiment analysis steps via R Studio. Then, we will shift our focus to text summarization techniques. We begin by listing the pre-processing steps required to bring the text to an analyzable form. Next, we look at how the frequency counts of multi-word phrases of pre-processed text can reveal the common terms being discussed. Building on top of the n-grams, we move onto a more intelligent method to automatically detect quality phrases. We will also discuss the LDA Topic Modeling - a very popular way to detect topics in a body of texts. We will wrap up this module with a highlight on supervised machine learning and an example of its application.
Name:Module 4: Network Analysis
Description:We will introduce a method to analyze customer satisfaction influence using social media data. Social networks are the perfect dataset to utilize network analysis to understand how people are interacting with other people and forming networks. Identifying a pattern in social media relationships can be useful when making marketing decisions. We will also review influencer brand personality analysis that can be used as a method for brands to find influencers similar in personality to themselves.
This course introduces students to customer satisfaction measurement through a wide range of analytical approaches. We will discuss the components of customer satisfaction, major issues in measuring customer satisfaction, statistical methods in analyzing customer satisfaction influence, sentiment analysis with social media data, influence analysis with social media data, and text summarization with social media data. This course aims to provide the foundation required to make better marketing decisions by analyzing multiple types of data related to customer satisfaction.
