OPEN LECTURE: MarTech & Artificial Intelligence

View the video recording of the Online MarTech Lecture:

Introduction

Hello everyone!

I’m really happy to welcome you to the first Online Open MarTech lecture.

My name is Megi, I have taught and studied MarTech for many years already, and with this series of Online Open Lectures, I would like to share with you what I know so far.

This series of lectures will contain all the topics related to MarTech, from broad approaches to certain examples and cases.

These lectures are for those who consider MarTech to be the topic, field, or direction of their career development. Also, these videos are for those who already have marketing experience but want to see a broader horizon and more examples of the application of technologies in marketing. Those who generally wish to learn something new can also benefit from these recordings of Online Open Lectures.

I really hope that this initiative of mine will be useful for you and helpful because I would love to share with you everything I know and invite you to this wonderful journey of finding something new, something interesting. If you ask me, this field of MarTech is incredibly fascinating and useful, and I’m happy to be able to guide you through it.

We all have different opinions on artificial intelligence (AI) and whether we need it or not in our society; however, it is difficult to underestimate its importance for the development of digital marketing nowadays.

This lecture is dedicated to introducing general definitions of AI and its applications in MarTech.

MarTech + AI

A couple of lectures ago we discussed a definition of MarTech. Let’s go through it once again:

“MarTech applies to major initiatives, efforts, and tools that harness technology to achieve marketing goals and objectives”. [1]

The definition covers both marketing goals and objectives (like brand awareness, sales increase, customer retention, etc.) and the technology used to meet these goals and objectives. This technology is different, not all of the tools use AI, however, as time passes, AI becomes more widespread and attainable.

AI – what do we know about it?

AI – “Artificial Intelligence” was first used as a term by John McCarthy in 1956​.

The key thing to consider is that AI is not easy to define – numerous applications and specificities​ are related to it.

How do we deal with such a situation?

1. We can find the definition suitable for the specific situation, industry, or tool; or ​

2. We can categorise the definitions into groups (for example, Russel and Norvig[2] – 8 definitions categorised into 4 dimensions (Thinking humanly, Thinking rationally, Acting humanly, Acting rationally))​.

Let’s consider this definition: ​

”AI is a technology that enables computers and machines to simulate human intelligence and problem-solving capabilities” [3]​

Does this definition make sense from a MarTech perspective?

I don’t think so because we don’t have a clear definition of what the following parts of the definition mean: “to simulate”, “human intelligence”, or “human problem-solving capabilities”.  

What we can do, however, is to break down AI as a field into tools most relevant to the marketing field and categorise them more broadly according to the AI feature they exhibit:

Examples of the AI-related terms and concepts[3], [4], [5]:​

• Digital assistants (Siri, Alexa, Google Assistant)​

• Generative AI tools (ex. Open AI's Chat GPT)​

• Natural language processing (NLP), Natural Language Generation (NLG), Large Language Model (LLM) ​

• Machine Learning (Supervised Learning (Regression, Classification) + Unsupervised Learning (Clustering) + Reinforcement (Decision Making)) + Deep Learning

Let’s define these terms:

• Digital assistant - also known as a predictive chatbot, is an advanced computer program that simulates a conversation with the people who use it, typically over the internet. [6]

• Generative AI - describes algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos. [7]

• Natural language processing (NLP) - a subfield of computer science and artificial intelligence (AI) that uses machine learning to enable computers to understand and communicate with human language. [8]

• Natural Language Generation (NLG) - a software process in which an artificial intelligence application produces an output with language that is understandable in English or another spoken or written language. [9]

• Large Language Models (LLM) - a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks. [10]

• Machine Learning - a branch of artificial intelligence and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. [11]

• Supervised Learning - subcategory of machine learning and artificial intelligence; it is defined by its use of labelled data sets to train algorithms that to classify data or predict outcomes accurately. [12]

• Unsupervised Learning - uses machine learning (ML) algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns or data groupings without the need for human intervention. [13]

• Deep Learning - a subset of machine learning that uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. [14]

We will dive deeper into these terms and their MarTech applications during other Open MarTech Lectures.

Concluding remarks

I hope it was useful! Do not hesitate to share this article with anyone you consider to be interested in this topic.

If you wish to learn more, you can follow the links:

Explore the MarTech field

• Learn about MarTech: Attend my Open Online MarTech Lectures