Marketing expertise is the decisive design element of data analysis for marketing. The equation of correlation and causality is the cardinal error that still unites management and data science today. The dominant hypothesis-driven approach to data analysis has its limits. At best, it produces racing cars that are designed to drive over open, hilly terrain and too often fail.

What is AI? Statistical modeling finds the parameters of a fixed, predetermined formula. Artificial intelligence finds a new, optimized formula, not just its parameters. However, AI suffers from model drift, discrimination and the risk of performing poorly in live operation. Explainable AI does not solve the problem and offers a dangerous false transparency. The solution is called Causal AI. This is for AI like the filter system of a distillery. Ultimately, it is an absolute must.

And this is how you proceed with Causal AI: Start with a “blank sheet of paper” and write down what could influence your target variables. Then obtain the data for this. In addition to internal and external data sources, also consider market research. It offers unique opportunities to understand the inner life of customers.

Model the data with an AI algorithm that ensures in the learning process that predictions are only based on causal causes. Model a causal network, not a pure input-output relationship. Open the AI black box with suitable simulation algorithms. Algorithmically check the causal directions and search for confounders. Optimize your model and recalculate until it is meaningful and useful enough. Finally, standardize everything in one process – from the data, the preparation, the method to the preparation of the results.