AI for Marketing and Product Innovation

2 Introductory Concepts for Artificial Intelligence and Machine Learning for Marketing

To make robots practical, flaws must be removed.

To make robots endearing, flaws must be added.

– Khang Kijarro Nguyen

In this chapter we offer nine introductory concepts used in the fields of AI and ML. We also discuss the successful application of this knowledge in four core areas. You will see that all of these concepts are minor variations of each other. We present them for the sake of completeness and creating familiarity with the jargon, thinking, and philosophy behind Machine Learning and Artificial Intelligence.

Concept 1: Rule-based Systems

The two chief methods of making inferences from data are rule-based systems and ML. It is useful for marketing professionals to have some knowledge of both methods. ML did not replace rule-based systems, but rather became another tool in the marketer’s toolbox. Rule-based systems still have their place as a simpler form of AI, so marketing professionals can reasonably consider using one or the other, or even both.

Rule-based systems can store and manipulate information for various useful purposes. Many AI applications use rule-based systems. The term generally applies to systems that involve manmade rules, featuring a series of IF–THEN statements, such as IF “A,” THEN “B” else IF “C,” THEN “D” and so forth. In terms of real-world applications, a rule-based program might tell a banker, “IF the loan applicant has a credit score below 500, THEN ...