ASQ 0704 & AQA Phoenix Joint Monthly Meeting

Topic Summary

Building a Profitable Customer Base: A Case Study with Data Support

How do we determine how to keep customers happy and coming back for more while still maximizing profit that keeps our shareholders happy?

In this presentation, Jami Hampton will demonstrate techniques for quality analysis and improvement that can be used by statisticians and non-statisticians, content experts and business experts, individual contributors and managers. Using JMP statistical discovery software’s unique show me paradigm, Jami demonstrates how to interact with, explore, and analyze your data to and discover causes and possible solutions to quality conundrums facing most organizations.

In this case study, we use data mining and visual data analysis techniques to examine over 200 thousand mobile phone customer information to determine what is most likely to make a customer leave the service provider. We also determine what customer cohorts are most likely to be profitable so that we can develop the appropriate programs to keep them. Sound complicated? It's not. The data and techniques will be easy to understand by anyone, including a Six Sigma practitioner, who handles customer data. The techniques shown are also applicable to mining manufacturing data.