Learn Data Science in Marketing

Learn how to perform a tidy approach to classification problem with the new parsnip R package for machine learning.

I'm pleased to announce the introduction of correlationfunnel version 0.1.0, which officially hit CRAN yesterday. The correlationfunnel package is something I've been using for a while to efficiently explore data, understand relationships, and get to business insights as fast as possible.

Learn how going from Excel to R can speed up Exploratory Data Analysis, getting business insights 100X FASTER.

Kmeans clusering is an unsupervised machine learning algorithm that can be used to segment customers into similar groups for marketing.

Experience how to implement Machine Learning for A/B Testing step-by-step

Customer segmentation is the process of grouping customers by specific likeness. Creating customer segmentation enables a business to target specific groups of customers and personalize marketing for each group.

Student feedback led to a BRAND NEW CHEAT SHEET on customer segmentation and a major overhaul to our Week 6 Modeling Chapter in our Business Analysis with R Course.

The first article in a 3-part series on Excel to R, this article walks the reader through a Marketing Case Study exposing the 10X productivity boost from switching from Excel to R.

This article uses a Kaggle competition as an opportunity to show how data science can be used in digital marketing to answer a specific question, and take what is learned from the data and apply it to marketing strategies.

Predict customer churn using deep Learning Keras in R, with a 82% model accuracy.

This post is the third and final part in the customer segmentation analysis. The first post focused on K-Means Clustering to segment customers into distinct groups based on purchasing habits. The second post takes a different approach, using Pricipal Component Analysis (PCA) to visualize customer groups. The third and final post performs Network Visualization (Graph Drawing) using the igraph and networkD3 libraries as a method to visualize the customer connections and relationship strengths.

This post is the second part in the customer segmentation analysis. The first post focused on k-means clustering in R to segment customers into distinct groups based on purchasing habits. This post takes a different approach, using Pricipal Component Analysis (PCA) in R as a tool to view customer groups. Because PCA attacks the problem from a different angle than k-means, we can get different insights. We’ll compare both the k-means results with the PCA visualization. Let’s see what happens when we apply PCA.

In this machine learning with R tutorial, use k means clustering to segment customers into distinct groups based on purchasing habits.

Just because you’re a business professional does not mean you can’t or you shouldn’t pursue furthering yourself in analytics. Businesses view strategic decision making as a competitive advantage. You should too! Learning the basics behind data science not only adds value to your organization, it increases your value and thus your demand too.