This course uses an online computer store as a running use case. Understand how the business is organized and who its customers are.
- [Instructor] For the examples and exercises…in this course, we will use an imaginary…online computer store.…This use case best fits most of the real world cases…encountered in predictive customer analytics.…Imagine that this online store is analogous to Amazon.com.…Potential customers browse the website…for products of their choice.…The website monitors customer activity through sessions.…Customers are tagged using cookies on their browser.…
Individual customer activity is tracked within…and across sessions to establish models…that lead to customers ultimately buying the products.…Once the customer adds products to the shopping cart,…the store will also recommend products…that the customer might be interested in.…The store offers customer support and service…through phone, chat, and email.…Customers can file complaints…through any of the channels.…In addition, customers can provide reviews…for the products and service.…
The store analyzes review commends and complaints…to measure customer satisfaction levels.…
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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