Design a predictive analytics solution to analyze how customers respond to various offers from business for additional sales.
- [Instructor] Let's face it,…when the customer buys something from your business…and then randomly switches to another business…for the same service, it hurts.…You want to keep your customers interested in buying…more products and services from you.…So, you reach out to your customers with coupons,…offers, deals, packages, discounts, et cetera,…however you can.…Do all customers respond to these offers in the same way?…No.…There is a good chance that Roger…would be more interested in coupons…that he gets during Christmas…so that he can buy gifts for his family.…
Identifying how customers respond to your offers…helps you target your marketing dollars…on customers who are most likely to respond.…The goal of this use case is to identify distinct patterns…in which your customers respond to offers.…You group your customers into clusters.…Then, identify patterns common to these clusters or groups,…and then devise marketing schemes…that generate better revenues for each of these…individual clusters or groups.…The data that will be used for this use case…
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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