How to use AI to Optimize Marketing Campaigns

Overview

Today, we're exploring a mock dataset featuring customer demographics and direct marketing campaign interactions for a specialty food retailer. Our goal is to demonstrate a quick, clear-cut method of data analysis with Julius that can remove guesswork and help optimize your marketing campaigns.

High-level purchasing behavior analysis

Let's start out with a high-level analysis of customer demographics. To get an understanding of purchasing behavior, let's see if declared income affects total spend in our app.

As we can see, as predicted, there is a clear correlation between income and total spend. Let's dig into this a bit further.

Further exploration of customer spend

To understand the correlation of income with particular spending categories, let's create a regression matrix.

Some insights we can derive:

• Income is mostly highly correlated with spend on wine and meat products.
• Gold-level products are less correlated with income than regular products.
• Those who purchase sweet products aren't as likely to make purchases in the wine category relative to other categories.

Now that we've got an understanding of general buyer behavior, let's see if we can get a better understand of consumer behavior related to our campaign.

Optimizing future campaigns

Our next objective is to develop a model which uses the past campaign data to predict which customers are most likely to accept the offer, ensuring the next campaign is more profitable via improved targetting.

The prompt used to generate this model was:

"Perform a complex, accurate analysis aiming to improve future campaign performance."

By running a logistic regression, we have identified the most relevant variables for predicting which consumers accept our direct marketing campaign offer. With 89.1% accuracy, our model will help tailor future campaigns to target customers most likely to respond, removing guesswork and increasing effectiveness.