data story

From a marketing perspective, enabling the healthcare or prescription journey is a complex landscape of intertwined stakeholders, regulations, technologies and touchpoints. The interests and needs of healthcare providers (HCPs) and patients will converge and diverge at different points. Having the right technology infrastructure and targeting strategies is key to the successful completion of this journey.

As usual, my sketch over simplifies things but it does capture the key technology platforms, data models and touchpoints at each stage of the journey. For the most part, platforms and strategies stack on top of previous ones as we move along the journey–rather than being solely use in one stage. Given that healthcare is a highly regulated industry, a strong privacy and data governance strategy is of the upmost importance.

This is just a simplified reference architecture of today’s marketing technology stack. Excuse all the acronyms but I will be doing a post soon on the most used/overused acronyms in 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.

SEGMENTATION

Product Segmentation <> Collaborative Filtering

Journey Stage Segmentation <> Gaussian Mixture

Behavioral Segmentation <> K-Means

PREDICTION

Churn <> Logistic Regression

Conversion <> Decision Tree

Customer Lifetime Value <> Random Forest, XG Boost

PERSONALIZATION

A/B Testing <> Multi-Arm Bandit

Timing <> Sequence Analysis

Next Best Action <> Nearest Neighbor

LANGUAGE

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.

Contextual targeting is done through the use of keywords and categories. For example, we might serve an ad for Nike basketball shoes on the NBA website. Behavioral targeting relies on known user behaviors that have been tracked using cookies and is sometimes enhanced with demographic data. With third-party cookies phasing out, contextual targeting is making a comeback.