Join Kumaran Ponnambalam for an in-depth discussion in this video Expectations and course organization, part of Predictive Customer Analytics.
- [Instructor] The goal of this course is to discuss use cases in a customer lifecycle, why a predictive analytics can help a business, and show how to implement it. The course balances the business and technical sides of predictive customer analytics. I will zoom in on specific use cases, why businesses struggle to understand customer behavior. Based on these use cases, I will also show you how to devise a strategy to target customers. This course is generally intended for business analysts.
However, customer experience managers, data scientists, data architects, and managers can equally benefit from it. The prerequisites for this course are: an understanding of business and customers, familiarity with predictive analytics and machine learning algorithms, experience in doing machine learning with Python. I would like to specifically point out that I will not be showing you how to code in Python. Rather, I would like to use coding knowledge and apply it to common business problems.
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 37m 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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