Complete a predictive analytics exercise in Python to group problem types and analyze them in order to optimize their resolution.
- (voiceover) In this lecture,…we're going to see how we can group…different problem types into similar items…and then analyze them as a group…to see if you see any patterns…and then make some actions to…solve them efficiently and effectively.…The exercise files for this one…are available under the folder 05_05.…The data file is the issues.csv,…and the notebook is the grouping file.…Lets take a look at the data that we have.…
We have in column A, that different problem types…and then metrics about each of this problem type,…the number of times they happen,…average calls to resolve,…average resolution time, recurrence rate,…and replacement rate.…So this is the data we are going to use…to group the different problem types.…Going to the notebook,…this notebook is called grouping…we start out by importing a number of python libraries…and then we load up the issues.csv file…into the beta frame.…
We take a look at that data frame to make sure that…they are noted correctly as intent floats.…We also do a head to make sure that the data is looking good…
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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