Design a predictive analytics solution to predict customer lifetime value based on the initial events and activities of the customer.
- [Instructor] Customer Lifetime Value…is a monetary value that represents the amount…of revenue a customer will provide the business…over the lifetime of the relationship.…If you know ahead of time that Roger would bring in 10k…in revenue in the next five years,…as opposed to Jessica, who will only generate $500,…you can spend more marketing dollars on Roger…and harvest his full potential budget.…Tagging each customer with the Customer Lifetime Value…helps a business focus on those customers…who can bring in the most revenue in the future.…
Several numerical techniques exist…in computing Customer Lifetime Value,…but you can compute CLV reliably only for customers…who have a significant purchase history with the company.…But how can you compute this for a recent customer?…Well, predictive analytics is here to help.…The goal of this use case to build a regression model…that can predict the Customer Lifetime Value…for a new or recent customer,…based on his or her recent buying patterns.…
It will also help you to recompute CLV more accurately,…
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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