Customer Service Value in Marketing Analytics
Learn how to use customer service value in marketing analytics as well as customer churn from this free online course.
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Customer Service Value in Marketing Analytics is a free online course that offers a comprehensive guide to understanding the importance of the customer churn approach in marketing. This course will teach you how various techniques can be used to predict customer-churning behaviour in marketing analytics. It begins with an analysis of customer service control, and how it can be used to solve real-life problems.
Next, you will learn about text mining, as well as the impact of Internet availability on the analytics process. You will also study the features and application of natural language processing in analytics, as well as the roles of information and digital technologies. Then you will discuss the features of tokenization, as well as how to use the mining natural language processing approach in marketing. Analyze the customer lifetime value approach, as well as studying how to maintain a relationship with customers. Finally, you will grapple with customer relationship management, as well as the other features of the economics of CRM in marketing analytics.
This free online course provides practical knowledge and numerous applications like spam detection in text mining and sentiment analytics, as well as how to use the naive Bayes classifier algorithm for the spam detection process. This course will be of significant interest to business organizations, programmers and anyone interested in using marketing analytics for private or professional e-commerce.Start Course Now
Customers Churn and Customer Service Value
Customers Churn and Customer Service Value - Learning Outcomes
Customer Churn Prediction
Customer Churn Control
Customer Switching Behavior
Customer Lifetime Value
Customer Lifetime Value in Specific Cases
Survival Analysis in Customer Churn
Customers Churn and Customer Service Value - Lesson Summary
Text Mining and Sentiment Analytics
Text Mining and Sentiment Analytics - Learning Outcomes
Text Mining Application
Text Mining in Marketing
R Programming in Naive Bayes Algorithm
Text Mining and Sentiment Analytics - Lesson Summary
Upon completion of this course, you should be able to:
Explains the concept of customer service control in customer lifetime value
Describe how to check the ROC curve and optimal curve in the customer churn prediction
Discuss the things marketers can focus on in customer sifting situations
List various nuances that can be used to predict customer churning behavior
Explain the concept of text mining and the impact of Internet availability
List the features of tokenization and mining natural language processing approach
Outline the features of text mining and sentiment analysis
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