Design a predictive analytics solution to group problem types and analyze them in order to optimize their resolution.
- [Instructor] Technical support teams handle…various types of problems every day.…Some problems are frequent and straightforward…that the technician on call can spell out the solution…even before the customer has completed the explanation.…Others are infrequent and complex that would require…multiple phone calls and onsite visit to fix.…An issue like internet connectivity…is easier to troubleshoot,…but a blue screen on a laptop is much harder.…Real people resources are expensive,…so businesses need to find ways to optimize their time.…
This includes providing online help for some problems.…It includes more rigorous training…for the tech support team, or creating new…specialist positions for complex issues.…To help businesses make these decisions they want to group…problems based on similar attributes.…Here is the goal for this use case.…From a long list of problem types…the business should identify logical groups…that can then be used to develop…an optimal resolution plan with least human effort.…
The data used for this use case would be problem statistics…
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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