Join Kumaran Ponnambalam for an in-depth discussion in this video Next steps, part of Predictive Customer Analytics.
- [Instructor] There is a lot you can learn and do…with predictive customer analytics beyond this course.…Here are some of my recommendations.…Try out the use cases in the course with data…from your own business or company.…That should give you a real life learning experience.…Try out the use cases in a big data machine learning…platform like Apache Mahout or Apache Spark.…That should give you experience in working with scale.…Check out the other courses in this platform…related to analytics and machine learning.…
Expand your knowledge to other tools and domains.…Explore the use of predictive analytics…in other fields like operations and finance.…Data always intrigues me.…I bet it intrigues you too.…So let's keep exploring it and find better ways…of extracting knowledge out of it.…
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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