Design a predictive analytics solution to identify the intent when a customer contacts a business.
- [Instructor] Remember that Roger had some issues…with his laptop setup?…Upon calling the vendor, he was faced with some frustration…that caused him to give up.…Let's think about what went wrong.…Given that Roger has already given his phone number…during the sales process, shouldn't customer support…automatically identify him with this caller ID?…He is calling again within two days…after the delivery of the laptop.…Wouldn't that mean that his call is most likely…related to that purchase?…During the first call, customer support…created a ticket for his laptop keyboard troubles…and gave him some advice.…
However, he is calling again after two hours.…Can't customer support guess that his second call…is most likely related to this first ticket?…The goal of this use case is to build a prediction model…that can predict the possible reason the customer…is contacting the business through customer support.…Knowing the reason will help the business…to quickly direct the contact to the right skilled person…and get it resolved on the first interaction.…
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 34m 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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