In this session, Jim Harmon will host a discussion of the original Net Promoter Score paper as seen through a machine learning lens.
Would you recommend [fill in business name here] to friends and colleagues? The answer to this question yields the “one number you need to grow” your business, according to consultant Frederick Reichheld. But is it really “the one”? In this session, we’ll explore some of the issues with Net Promoter Score or NPS from a machine learning point of view. Specifically, we’ll touch on the black box nature of the output, data leakage and multiple comparisons in the paper’s analysis, information loss in the metric computation, and more!
Here’s the original paper (it’s the easiest read you’ll ever have for ATOM!!!):
Advanced Topics on Machine learning ( ATOM ) is a learning and discussion group for cutting-edge machine learning techniques in the real world. We work through winning Kaggle competition entries or real-world ML projects, learning from those who have successfully applied sophisticated data science pipelines to complex problems.
As a discussion group, we strongly encourage participation, so be sure to read up about the topic of conversation beforehand!
ATOM can be found on PuPPy’s Slack under the channel #atom, and on PuPPy’s Meetup.com events. Info for the slack channel and PuPPy in general can be found at https://www.pspython.com/.
Slides from previous discussions can be found at https://gitlab.com/users/puppy-atom/projects.
We're kindly hosted by Galvanize (https://www.galvanize.com). Thank you!