Design a predictive analytics solution to create item groups based on common buyers and use them for recommendations.
- [Narrator] Generally, for business purposes,…customers are grouped based on various attributes.…For example, Roger might be grouped by a bank…as a middle age executive based on his age,…or as a gold customer based on his high account balance.…Jessica might be grouped as a young student…based on her occupation and age.…In the same way, products are also grouped…based on the type or use.…Like a laptop might be grouped as electronics…based on the type,…or a backpack might be grouped as college supplies…based on its use.…
Businesses try to build recommendation engines…based on these groups.…A new age of recommendation engines…have become very popular today and they are called…collaborative filtering recommendation engines.…Collaborative filtering works on just three pieces of data.…A user or a customer, an item,…and an affinity score between the user and the item.…For example, in Amazon, the user is the buyer,…the item is the product,…and the affinity is whether the user purchase this item.…
This affinity score would be a binary score…
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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