Design a predictive analytics solution to identify prospects who have a high propensity for buying your products.
- [Narrator] You have a product, high-end laptops.…Who are the potential customers…who have a higher likelihood to buy from you?…Is it Roger, the middle-aged guy…with the family and the decent income?…Or is it Jessica, the college student…with a low-paying job?…Roger, in this case, has a higher propensity…to buy from you.…Jessica might like cheaper student laptops.…This inference is based on its demographics.…The first big challenge any marketing department has…is to identify prospective customers…who have a higher likelihood to buy from you.…
The goal of this use case is to generate…a propensity score for each of your prospects…identified by the marketing department.…The propensity score can be a binary, zero, or one.…Or better yet, it can be a continuous score…all the way from zero to one.…What data would you use?…At this stage, the only set of data…that is available to you is prospect demographic attributes,…attributes such as age, salary, family, etc.…
Regarding events, these prospects may or may not…have been involved in any events with your business,…
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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