Learn about the importance of customer loyalty in running a business.
- [Man] In business there are some golden sayings…that everyone needs to appreciate and adopt.…Eighty percent of your business comes from 20%…of your customers.…It costs 10 times less to sell to an existing customer…than to find a new customer.…Different customers generate different levels…of sales for a business.…You need to identify and nurture these top customers…to ensure a steady stream of revenue.…But the main question is, how indicative of future revenue…is the past revenue?…Is your best customer in the past going…to be your best customer in the future?…Is this outcome in your hands?…You want to identify these customers who bring you…significant future business and nurture them,…and ensure that they are happy and satisfied with you.…
How do you do that?…You need a way to compute a customer's lifetime value.…So, let's get back to our buddy, Roger.…Roger just bought a laptop from you.…So, what kind of future revenues can you expect from him?…Well, he just bought a laptop just now,…so he might not need a laptop in the next three years,…
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Use big data to tell your customer's story, with predictive analytics. In this course, you can learn about the customer life cycle and how predictive analytics can help improve every step of the customer journey.
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 34m 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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