Design a predictive analytics solution to group customers based on their common buying patterns and preferences.
- [Narrator] Let us take a look at the scenario…in which you can group similar customers…into advertising buckets.…Imagine that our old friend Roger…has just signed up as a new customer with your business…and has bought a laptop.…Great, but what other general product categories…will he be interested in?…Can we sell him books?…Will he be interested in music CD's?…There are a couple of ways to attack the issue.…I would like to suggest the possibility…of identifying other customers who are like Roger.…
We can then use data from these customers…to recommend products to Roger.…Think about it.…Roger is a new customer.…He really has no history with your business…and you don't know much about him.…How about, you can try to fit him…into a sort of pre-created customer groups…based on certain personal attributes.…Then, based on common buying preferences for that group,…you can recommend different products to Roger.…
There are two sets of data required for this use case.…First is the demographics of the customer.…Then, you need the sale transactions…
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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