AI for Business Practice

Literature on the subject of big data and AI is frequently very technical and

informatics-focused. This book sees itself as a transmission belt that trans-

lates the language of business in the spirit of potentials and limitations. At

the same time, the technologies and methods do not remain to be a black

box. They are explained in the scope of the chapters on the basics in such a

way that they are accessible even without having studied informatics.

In addition, the frequently existing lack of imagination between the

potentials of big data, business intelligence and AI and the successful

application thereof in business practice is closed by various best practice

examples. The relevance and pressure to act in this area do happen to be

repeatedly postulated, yet there is a lack of a systematic reference frame and

a contextualisation and process model on algorithmic business. This book

would like to close that roadmap and implementation gap.

The discussion on the subjects is very industry-oriented, especially in

Germany. Industry 4.0, robotics and the IoT are the dominating topics. The

so-called customer facing functions and processes in the fields of marketing,

sales and service play a subordinate role in this. As the lever for achieving

competitive advantages and increasing profitability is particularly high in

these functions, this book has made it its business to highlight these areas

in more detail and to illustrate the outstanding potential by numerous best

practices:

• How can customer and market potentials be automatically identified and

profiled?

• How can media planning be automated and optimised on the basis

of AI?

• How can product recommendations and pricing be automatically derived

and controlled?

• How can processes be controlled and coordinated smartly by AI?

• How can the right content be automatically generated on the basis of AI?

• How can customer communication in service and marketing be opti-

mised and automated to increase customer satisfaction?

• How can bots and digital assistants make the communication between

companies and consumers more efficient and more smart?

• How can the customer journey optimisation be optimised and automated

on the basis of algorithmics and AI?

• What significance do algorithmics and AI have for Conversational

Commerce?

• How can modern market research by optimised intelligently?

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Why Use Machine Learning?

Consider how you would write a spam filter using traditional programming techniques (Figure 1-1): 1. First you would look at what spam typically looks like. You might notice that some words or phrases (such as “4U,” “credit card,” “free,” and “amazing”) tend to come up a lot in the subject. Perhaps you would also notice a few other patterns in the sender’s name, the email’s body, and so on. 2. You would write a detection algorithm for each of the patterns that you noticed, and your program would flag emails as spam if a number of these patterns are detected. 3. You would test your program, and repeat steps 1 and 2 until it is good enough. Since the problem is not trivial, your program will likely become a long list of com‐ plex rules—pretty hard to maintain. In contrast, a spam filter based on Machine Learning techniques automatically learns which words and phrases are good predictors of spam by detecting unusually fre‐ quent patterns of words in the spam examples compared to the ham e...

What is Machine Learning

The term machine learning (ML) as a part of artificial intelligence is ubiq- uitous nowadays. The term is used for a wide number of various appli- cations and methods that deal with the “generation of knowledge from experience”. The well-known US computer scientist Tom Mitchell defines machine learning as follows: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E (Mitchell 1997). An illustrative example of this would be a chess computer program that improves its performance (P) in playing chess (the task T) by experience (E), by playing as many games as possible (even against itself ) and analysing them (Mitchell 1997). Machine learning is not a fundamentally new approach for machines to generate “knowledge” from experience. Machine learning technology was used to filter out junk e-mails a long time ago. Whilst spam filters that tack- ...

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