Corporate

An Introduction to Artificial Intelligence Applied to Marketing

Corporate

50 hours

Introduction

Training in An Introduction to Artificial Intelligence Applied to Marketing provides a comprehensive overview of AI and how it is applied in effective marketing strategies. Throughout this programme, participants will explore key concepts in AI, its historical development and the categories of techniques used in this discipline. The training focuses on the application of AI processes in marketing strategies, including market research, product design, advertising strategies and digital marketing.

The following are addressed ethical and legal issues related to AI in marketing, including the intellectual property and the ethical implications of AI for consciousness and emotions.

Objectives

Understanding the basic concepts Artificial Intelligence (AI) and its application in marketing.
Explore the historical development AI and the main schools of thought.
Identify the different AI techniques and categories, including Machine Learning and Deep Learning.
Addressing the ethical and legal considerations related to AI in marketing.

MODULE 1: INTRODUCTION TO ARTIFICIAL INTELLIGENCE

TEACHING UNIT 1. AN INTRODUCTION TO THE BASIC CONCEPTS OF ARTIFICIAL INTELLIGENCE.

Characterisation of artificial intelligence
Applications of the nomenclature and concepts associated with AI.
Resources required for the use of AI.
The current generation of AI applications.

TEACHING UNIT 2. THE EVOLUTION OF ARTIFICIAL INTELLIGENCE

Timeline and key milestones
Schools of thought on which conventional, computational AI is based

TEACHING UNIT 3. IDENTIFYING THE VARIOUS TECHNIQUES FOR THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE.

Categories of artificial intelligence
Machine Learning Techniques
Differences between machine learning and deep learning
Assistive technologies. User interfaces. Computer vision

TEACHING UNIT 4. AREAS OF APPLICATION OF ARTIFICIAL INTELLIGENCE.

Current AI-based applications. Practical applications.
Problem-solving using AI applications.
Context for the use of AI tools.
Requirements and limitations of AI-based applications.

TEACHING UNIT 5. THE ETHICAL AND LEGAL CONTEXT OF ARTIFICIAL INTELLIGENCE

Artificial intelligence, consciousness and emotions
Critical schools of thought
Intellectual property in AI.

MODULE 2: ARTIFICIAL INTELLIGENCE PROCESSES APPLIED TO MARKETING STRATEGIES

TEACHING UNIT 1. APPLICATION OF ARTIFICIAL INTELLIGENCE PROCESSES TO THE FIELD OF MARKET RESEARCH

Characterisation of AI-based applications for market analysis
Ethical and legal implications for the sector regarding the scope of AI
Use of AI-based market research techniques and tools

TEACHING UNIT 2. THE DEVELOPMENT OF AI IN THE FIELD OF PRODUCT OR SERVICE DESIGN

Application of AI techniques and tools for decision-making
Integration of AI design and development methodologies

TEACHING UNIT 3. IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN THE FIELD OF ADVERTISING STRATEGY

Characterisation of AI-based advertising applications
The concept of programmatic advertising
Using tools and techniques to optimise the advertising strategy
Brand image management
Applying techniques and strategies from success stories

TEACHING UNIT 4. APPLYING THE LATEST ADVANCES IN ARTIFICIAL INTELLIGENCE TO DIGITAL MARKETING

Most commonly used application ecosystems and techniques
Using the main social media marketing tools
Setting up and managing a web analytics account
Design and management of a web advertising campaign
Design and management of a social media advertising campaign

MODULE 3: DEVELOPMENT OF TAILORED ARTIFICIAL INTELLIGENCE SOLUTIONS FOR THE MARKETING SECTOR

TEACHING UNIT 1. CREATING A PREDICTIVE MODEL USING A “NO-CODE” TOOL”

BigML features and sections
Monitoring the process of developing a predictive model
Integrating the model created in BigML into a marketing application

TEACHING UNIT 2. APPLICATION OF GCP (GOOGLE CLOUD PLATFORM) TOOLS FOR AI.

Data management with BigQuery
Creating a predictive model with BigQuery
Creating a dashboard (KPI) with DataStudio
Creating an intelligent agent with DialogFlow

TEACHING UNIT 3. INTRODUCTION TO AI DEVELOPMENT WITH PYTHON

Proposal for a machine learning algorithm
Running the code to generate an AI model
Characterisation of an autoencoder and a convolutional neural network
The process of designing and programming an AI solution