If you could confidently predict that a customer was going to leave a negative review, what would your business do differently?
The answer is “yes” to both, with careful consideration of the cost-benefit tradeoff and the company’s ability to implement corrective actions.
“Negative reviews have convinced 94 percent of consumers to avoid a business” — Review Trackers
Brazil’s leading e-commerce marketplace for small businesses is Olist. Olist…
Accuracy, please take a back seat. We’ll be promoting Precision and Recall today.
“Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers” — Wikipedia
For this post, let’s agree on the universal assumption that “customer churn is bad”, and “customer retention is good”.
Abstract: This article takes a different slant on a well-traveled churn dataset that does not have the right features to blindly deploy even the best predictive model we can generate. Below I’ll share the problem statement, data preparation steps, feature analysis, visualizations and select Python code from…
Starting out building your first multiple linear regression predictive model using Python can feel daunting! This post offers a practical workflow, guide, and example code of one approach that builds on CRISP-DM. I hope you’ll find it useful and welcome your comments.
The CRoss Industry Standard Process for Data Mining is a leading process model that describes the data science life cycle. This project follows the below tactical workflow in building a linear regression model. The process diagram sequences sub-tasks for four CRISP-DM processes spanning Data Understanding, Data Preparation, Modeling and Evaluation.
On of the biggest benefits I've seen companies obtain using Tableau is shifting advanced data transformation and viz control to business versus IT. Business can create small transformations, semantic views, on-demand hierarchies without looping through IT. Speed to insight increases greatly, especially given the easy viz best practices. I'm glad Tableau is a leader in the "citizen data scientist" movement.
The Tableau data visualizations in this story use my personal fitness data from iPhone apps Apple Health, Peloton, and Symple. I’m a data geek and a big fan of the quantitative health movement. For over five years I have been tracking my workouts, steps, meditations, sleep, weight, net carbs, heart rate, blood pressure, symptoms, supplements, journal notes and how I spend my time daily. I’ve used this data as a continuous monitoring tool to balance my health and build awareness of these connected factors.
I only have 75% of my meniscus left in each knee. My left knee meniscectomy in…