Design a predictive analytics solution to identify customers who might potentially leave the business for a competitor.
- [Narrator] When it comes to customer attrition,…the best approach a business can take…is to correctly identify customers who might leave,…and take preventative action to keep them.…You might have noticed that Roger has been calling of late…to complain about the quality of service.…Jessica has called you last week, quoting…a competitive price and whether you can match it.…Do these actions mean that they are going to leave you?…They may, or they may not.…It would help for you to really understand…customer behavior so that you can focus…your attention efforts on the right customer.…
The goal for this use case is to identify customers…who are at risk of leaving you.…You can then either classify them as at risk,…not at risk, or actually give them a risk score…as shown in the slide.…The data for this use case will be…customer demographics and customer history records.…For each customer, there will be one history record…with a summary of different types of information:…length of tenure, last purchase date,…total issues reported, competitor prices cited, et cetera.…
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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