How to Optimize Marketing Campaigns

Using AI to predict which consumers are most likely to accept a marketing offer.

Overview

We’ll show how to optimize a marketing campaign using 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 significantly improve your targeting for future 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. We’ll prompt Julius to visualize this relationship:

“Let’s plot income vs. total spend with a regression line”

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.

“Create a linear matrix heatmap of income vs. the different spending categories”

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 targeting.

The prompt we used in Julius was:

“Perform a complex, accurate analysis aiming to improve future campaign performance”

You can also explicitly ask Julius to perform a logistic regression, which is the type of analysis best suited for developing a predictive model for this type of dataset.

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 can help tailor future campaigns to target customers most likely to respond, removing guesswork and increasing effectiveness.