Complete a predictive analytics exercise in Python to identify patterns in customer retention and use them for improving the business.
- [Instructor] In this lecture, we're going to see…how we can find out patterns in customer attrition data…and then use those patterns to make business decisions.…For this one, the exercise files are available in the…directory 0605…and the data is available in the…attrition.csv file and the patterns is the notebook…that we're going to use.…The warranty_basket is a temporary file that will be…created as a part of your exercise…and that we would see later.…
Let's start off by looking at the attrition.csv file.…This contains data about customers…who have left the company.…It tells you like how long they have been with the company,…one to three months, one year to two years, how did they…leave the company, is it because they canceled…or the service expired and the reason they left,…better deals, not happy, what age group they are,…what is their employment status, marital status,…how many times have they renewed, how many problems…they had with you and if they had any offers…from outside parties.…
This is the customer attrition data that you have…
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 three 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, author Kumaran Ponnambalam introduces best practices for creating a customer analytics process from the ground up.
- Understanding the customer life cycle
- Acquiring customer data
- Applying big data concepts to your customer relationships
- Finding high propensity prospects
- Upselling by identifying related products and interests
- Generating customer loyalty by discovering response patterns
- Predicting customer lifetime value (CLV)
- Identifying dissatisfied customers
- Uncovering attrition patterns
- Applying predictive analytics in multiple use cases
- Designing data processing pipelines
- Implementing continuous improvement
Skill Level Intermediate
Business Analytics: Prescriptive Analyticswith Alan Simon2h 40m Intermediate
Data Science Foundations: Python Scientific Stackwith Miki Tebeka3h 34m Intermediate
1. Customer Analytics Overview
2. Will You Become My Customer?
3. What Else Are You Interested In?
4. How Much Is Your Future Business Worth?
5. Are You Happy With Me?
6. Will You Leave Me?
7. Best Practices
Choose the right data1m 19s
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