Complete a predictive analytics exercise in Python to predict customer lifetime value based on the initial events and activities of the customer.
- In this lecture, we're going to see…how we can predict the customer lifetime value…for new customers based on models…that we will base on existing customer data.…The exercise files are available under the folder 0405.…The data in this file is history.csv.…And the code is in the CLV notebook.…Let's take a look at the data that we have.…So we have one line per customer…for existing, mature customers,…customers who have been with you maybe…for two to three years.…
And you have the first six months of revenue…generated by these customers,…month one to month six,…how much revenue they generated,…and the customer's lifetime value.…Which may be possibly, is like a 3 year…overall revenue that they gave.…That is something you can decide…based on the length to which your customers…stay with your business.…So this is the data that we're going to use,…and we're going to use this to build…a linear regression model…that can then be used to predict…the customer lifetime value.…
So moving on to the core,…we start out by importing…
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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