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.