Learn about the lifecycle of a customer from a business standpoint. Understand how the customer behaves and how it affects the business.
- [Instructor] Generally speaking, the relationship…between a customer and a business goes through a lifecycle.…Think about it this way.…Businesses provide products and services…that a customer wants,…and a customer looks at a business…to provide his wants and needs.…Let us first take a look at this…from the customer's perspective.…It all starts with a need.…Imagine that.…A customer needs a laptop, so he goes about looking…for options based on the technical specs,…deals, and customer service ratings.…
Then the customer selects a store to buy the laptop.…This is the purchasing process.…Once the customer has bought a product,…he may purchase additional items,…like cables, chargers, storage, et cetera.…The customer then continues to use…the product for a length of time.…He may face issues with repairs and upgrade parts.…After the laptop's lifetime, the customer might want…to buy from the same business or go…to a different business based on…his previous experience and current options.…
From a business perspective, the first stage…
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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