Design a predictive analytics solution to identify patterns in customer retention and use them for improving the business.
- [Instructor] While analyzing the list of customers…who left you, you notice that Roger left…after being with you for two years,…while Jessica left you after just five months.…What prompted them to leave?…Is it the same reason, or were there different reasons?…Roger left when his contract expired,…but Jessica canceled the contract.…What prompted them to do so?…Is that a general pattern for those age groups…or specific to the experience they had with you?…You need to deep dive into the data you have…so that you can find causal relationships.…
Knowing root causes will help your business then adapt…to those and perform corrective and preventive actions.…The goal of this use case is to establish…causal relationships between various events…and attributes from the customers who left you.…The data for this exercise is again customer history records…for all customers who left your business.…The history needs to be summarized by customer.…Each feature variable must be a category…of two to five classes.…
The features might be age group, marital status,…
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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