Design a predictive analytics solution to group customers based on the value they bring to the business.
- [Instructor] One of the most early and basic…customer management processes is to classify your customers…into various buckets, like bronze and gold,…based on their past business and perceived future business.…It then helps the business to target them differentially.…When a new or potential customer is identified,…classifying him or her as early as possible…in the business cycle helps a business to focus more…resources on those customers with significant future value.…
Given Roger's demographics, he might qualify…as a gold customer since he would buy premium electronics…for him and his growing family.…Jessica, on the other hand, might be budget focused…and might not spend a lot on electronics…in the next five years.…She would be a bronze customer.…Given that, you want to focus your marketing resources…on Roger to generate most bang for the buck.…The goal of this use case is to build a prediction model…that can classify new customers as silver, gold,…or platinum, or any other classification as you like.…
Given that we are focused on new customers,…
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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