Discuss why change is here to stay, and how to continuously adapt to it.
- [Instructor] Things change as you go.…Your company's direction and product mix change.…Your customers' wants and needs change.…Channels of communication change.…Old competitors die; new competitors abound.…Current competitors also change.…The business, political, and social environments change.…Change is the only thing that does not change.…Predictive analytics is not a project.…It is a never-ending journey that constantly listen to…and adapts to the changes that are happening.…Predictive analytics will only stay successful as long as…it adapts to the changing world in which it works.…
Plan for a continuous improvement…process within the business.…Absorb and analyze external changes periodically…and evaluate the need for adaption.…Plan for adaption.…It needs a goal, resources, and owners.…Provide a budget for periodic evaluation and changes.…
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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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