Design a predictive analytics solution to suggest the best retention incentives for different types of customers.
- [Instructor] Roger has complained about quality issues…with his laptop to his vendor.…It took multiple iteration for the vendor to fix them.…Roger expressed his unhappiness.…The vendor offered him a discount coupon…for the next purchase.…Roger still went to a competitor.…Why?…A discount coupon kept Jessica…from moving over to another vendor.…So why didn't it work for Roger?…Different customers react differently…to different offers to keep them.…It is important to identify what works for each customer…and extend them the appropriate offer.…
This use case can help you do that.…Suppose your business has a list of offers…which can be used to entice your customers,…say, discounts, extended warranty,…gifts, movie tickets, et cetera.…The goal of this exercise…is to identify the best offer for each customer…that would entice him to stay with you.…There will be two sets of data used for this use case.…First, it will be the demographics data for the customers.…Second, it will be the affinity tables…between the customers and offers.…
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 34m 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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