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An AI Guide to Marketing

Abundance of data and advances in technology–including the proliferation of low code and no code platforms–means that Artificial Intelligence and Machine Learning (AI/ML) Marketing is at the finger tips of most organizations. If we think in terms of Data, Technology, People, and Process, the first two are not the impediment to leveraging AI/ML to drive marketing effectiveness and sales. Rather, it is the shortage of skilled people and the lack of understanding of which models and processes solve different marketing challenges.

I put this basic framework together to give an idea of different AI/ML techniques and their applications in marketing. Do keep in mind that the combination of models and applications can number in the hundreds. This is why skilled data scientists and data engineers are so important to marketing organizations. To keep things simple I selected 12 tactics with their respective algorithms across four categories.


Product Segmentation <> Collaborative Filtering

Journey Stage Segmentation <> Gaussian Mixture

Behavioral Segmentation <> K-Means


Churn <> Logistic Regression

Conversion <> Decision Tree

Customer Lifetime Value <> Random Forest, XG Boost


A/B Testing <> Multi-Arm Bandit

Timing <> Sequence Analysis

Next Best Action <> Nearest Neighbor


Sentiment <> Convolutional Neural Network

Semantic Search <> OWL-Search

Content Extraction <> Support-Vector Machines

Again, any of these marketing tactics could use completely different AI/ML models or non at all depending on what we are trying to solve.

For more on this topic, see my other blog posts on understanding and applying machine learning.

Brand Nexus
Brand Nexus

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