Design a real-time solution to decide if a chat window needs to be offered to a website visitor based on their propensity to buy.
- [Instructor] Imagine this. Roger is on your website.…He starts browsing for laptops.…You have online sales rep ready to engage…your website visitors, and entice them to buy your products.…But you typically have so many visitors,…and most of them are window shoppers.…You want to offer your sales rep through chat,…only to visitors who are serious…about buying on that given day.…You don't want to waste your sales rep time…on Roger, if he is just window shopping.…On the other hand, if he is serious,…you want to engage him…before he goes to a competitor website.…
So Roger starts comparing laptops.…Does that mean he's making a decision?…He starts reading reviews.…Is he serious about buying?…He's checking out warranties.…Has he made a decision?…How do you decide?…The goal of this use case is to iteratively predict…a customer's propensity to buy,…based on real time actions…the customer does on your website.…As the customer does activities on your website,…you want to keep computing…and revising the propensity score.…
The source data for this use case are prospect attributes…
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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