Understand the role that analytics plays in each phase of the customer life cycle.
- [Instructor] We briefly discussed…the customer life cycle earlier.…Now, you may be wondering…where can predictive customer analytics help in that cycle…for better outcomes for your business.…Well, it starts with customer acquisition.…Customer analytics can identify customers…who are most likely to buy your products…and services through attribute and behavioral analytics.…It can identify the right channels…like e-mail, phone or social media…to contact these prospects.…
It can also be used to determine price points and discounts…that will most likely attract prospects.…The next case where customer predictive analytics can help…is in upselling.…When a customer buys a product…they most likely want complimentary products.…For instance, a laptop buyer would need cases,…cables and warranty.…Predictive customer analytics can help…identify the products and brands…that a buyer of a given product brand would buy…and help the business to recommend those.…
If you have used Amazon, you have already experienced this.…At the bottom of each product page…
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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