Raise customer satisfaction while optimizing your business

Image of woman shopping online leaving a 1-star negative customer review

If you could confidently predict that a customer was going to leave a negative review, what would your business do differently?

  • Proactively intervene to improve customer experience and hopefully divert them from leaving bad public review scores?
  • Learn from important features in your predictive model to fix the root causes…

Balancing precision and recall for actionable retention tactics

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…

Getting Started

Step-by-step guide for data preparation and predictive modeling

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.

Conceptual Workflow for Linear Regression

The CRoss Industry Standard Process for Data Mining

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.

Chuck Utterback

Data Obsessed. Analytically Inclined. Continuous Learning. www.insightconsultinggrp.com

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