Learn about the customer life cycle and how predictive analytics can help improve every step of the customer journey. Use predictive analytics to identify, attract, and retain the best customers for your business.
- [Instructor] My name is Kumaran Ponnambalam; welcome to this Predictive Customer Analytics course with Python. Your business is built around customers. You want to understand and predict your customer behavior, and this course will help you do exactly that. I will start off by discussing the customer lifecycle and how predictive analytics can play a major role in all its stages. Then I will look at individual stages in the cycle and discuss three use cases for each stage for predictive analytics.
I will explore data and different algorithms that can be used in these cases. I will also show you how to execute one of those use cases in Python. Finally, I will provide you tips and best practices for implementing predictive customer analytics. I hope this course helps you to apply the use cases learned in your business to acquire and keep your customers. So, let's get started.
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
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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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