Predictive Customer Analytics

This is the repository for the LinkedIn Learning course Predictive Customer Analytics. The full course is available from LinkedIn Learning.

Use big data to tell your customer's story, with predictive analytics. In this course, instructor Kumaran Ponnambalam teaches you about the customer life cycle and how predictive analytics can help improve every step of the customer journey.

Start off by learning about the various phases in a customer's life cycle. Explore the data generated inside and outside your business, and ways the data can be collected and aggregated within your organization. Then review multiple use cases for predictive analytics in each phase of the customer's life cycle, including acquisition, upsell, service, and retention. For each phase, you also build one predictive analytics solution in Python. In the final videos, Kumaran introduces best practices for creating a customer analytics process from the ground up.

Instructions

This repository contains the exercise files in a folder called "Exercise Files". This folder contains both the Data (.csv) files, as well as the Jupyter Notebook (.ipynb) files used in the course.

Follow the prompts in the video to load the correct exercise file.

Installing

1. To use these exercise files, you must have the following installed:
   • Python 3.8
   • Anaconda
2. Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.
3. Follow along with video 00_03 "Using the exercise files" for setup instructions.

Instructor

Kumaran Ponnambalam

Check out my other courses on LinkedIn Learning.

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