Design a predictive analytics solution to identify items that are typically bought together in a transaction, and use that affinity for recommendations.
- [Instructor] Let's put our knowledge of upselling…and cross selling into play.…Imagine the following scenario.…Roger walks into his favorite departmental store…and is shocked to find…that the shelves have been rearranged.…The products are not where they usually are.…Roger asked, why did they do that?…The answer is something we're going to see in this use case.…The departmental store has analyzed shopper buying patterns…and arranged the products such that the products…bought together are stocked and displayed near each other.…
This is the reason why you see milk and bread together,…or beer and chips together, even though one requires…refrigeration and the other one doesn't.…The goal of this use case is to find items…frequently bought together.…We are focused here on those that are bought…in a single transaction, not items bought…by the same customer across time.…The customer is immaterial here.…By finding such items it is possible for you…to stock them together, create product bundles,…or offer them as this item is usually bought…
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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