Discuss the challenges a company faces in ensuring that their customers do not move that company's competitors.
- [Voiceover] Roger has been buying computer accessories…from you for quite some time.…However, your business has some competition.…He keeps seeing ads from computers…while browsing the internet.…His friends talk about new websites which offer much better…deals and supposedly higher quality products.…On top of all that, Roger has been frustrated with some…clients actions with you.…He is tempted to try out other websites,…so how can you stop Roger from leaving you?…Let's explore a couple of scenarios…to put this problem in context.…
Different customers have different loyalty behaviors.…Roger might choose one vendor and will stick to them…until something very serious happens in the relationship.…Jessica on the other hand,…is constantly exploring new options.…She might be buying from more than one vendor at any time…and keeps her options open.…Sometimes you know the customer has moved on…because they canceled your service.…Sometimes, you have to inference from them not buying from…you for a very long time.…Because of vendor availability on the internet,…
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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