AI in Marketing, Sales and Service_ How Marketers without a Data Science Degree can use AI, Big Data and Bots

 Artifcial intelligence (AI) has catered for an immense leap in development

in business practice. AI is also increasingly addressing administrative, dispositive and planning processes in marketing, sales and management on the way

to the holistic algorithmic enterprise. Tis introductory chapter deals with

the motivation for and background behind the book: It is meant to build a

bridge from AI technology and methodology to clear business scenarios and

added values. It is to be considered as a transmission belt that translates the

informatics into business language in the spirit of potentials and limitations.

At the same time, technologies and methods in the scope of the chapters

on the basics are explained in such a way that they are accessible even without having studied informatics—the book is regarded as a book for business

practice.

1 AI Eats the World 3

1.1 AI and the Fourth Industrial Revolution 3

1.2 AI Development: Hyper, Hyper… 5

1.3 AI as a Game Changer 6

1.4 AI for Business Practice 8

Reference 9

2 A Blufer’s Guide to AI, Algorithmics and Big Data 11

2.1 Big Data—More Tan “Big” 11

2.1.1 Big Data—What Is Not New 12

2.1.2 Big Data—What Is New 12

2.1.3 Defnition of Big Data 12

2.2 Algorithms—Te New Marketers? 14

2.3 Te Power of Algorithms 15

2.4 AI the Eternal Talent Is Growing Up 17

2.4.1 AI—An Attempt at a Defnition 17

2.4.2 Historical Development of AI 18

2.4.3 Why AI Is Not Really Intelligent—And Why

Tat Does Not Matter Either 22

References 24

Contents

Part II AI Business: Framework and Maturity Model

3 AI Business: Framework and Maturity Model 27

3.1 Methods and Technologies 27

3.1.1 Symbolic AI 27

3.1.2 Natural Language Processing (NLP) 28

3.1.3 Rule-Based Expert Systems 28

3.1.4 Sub-symbolic AI 29

3.1.5 Machine Learning 31

3.1.6 Computer Vision and Machine Vision 33

3.1.7 Robotics 34

3.2 Framework and Maturity Model 34

3.3 AI Framework—Te 360° Perspective 34

3.3.1 Motivation and Beneft 34

3.3.2 Te Layers of the AI Framework 35

3.3.3 AI Use Cases 36

3.3.4 Automated Customer Service 36

3.3.5 Content Creation 36

3.3.6 Conversational Commerce, Chatbots

and Personal Assistants 37

3.3.7 Customer Insights 37

3.3.8 Fake and Fraud Detection 38

3.3.9 Lead Prediction and Profling 38

3.3.10 Media Planning 39

3.3.11 Pricing 39

3.3.12 Process Automation 40

3.3.13 Product/Content Recommendation 40

3.3.14 Sales Volume Prediction 41

3.4 AI Maturity Model: Process Model with Roadmap 41

3.4.1 Degrees of Maturity and Phases 41

3.4.2 Beneft and Purpose 48

3.5 Algorithmic Business—On the Way Towards Self-Driven

Companies 49

3.5.1 Classical Company Areas 50

3.5.2 Inbound Logistics 50

3.5.3 Production 53

3.5.4 Controlling 53

3.5.5 Fulflment 53

3.5.6 Management 54

3.5.7 Sales/CRM and Marketing 54

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