Customer Engagement with Chatbots and Collaboration Bots: Methods, Chances and Risks of the Use of Bots in Service and Marketing

Relevance and Potential of Bots for Customer

Obtaining information, flight check-ins or keeping a diary of one’s own

diet—all of this is possible in dialogue today. Customers can ask questions

via Messenger or WhatsApp or initiate processes. This service is comfortable

for the customer, available at all times via mobile and promises fast answers

or smooth problem-solving. A meanwhile strongly increasing number of companies is already relying on this means of contact and the figures on chat

usage speak in favour of this means supplementing or even replacing many

apps and web offers in the future. The reasons for this are manifold.

Figures of the online magazine Business Insider 1 reveal a clear develop-

ment away from the public post to the use of private messaging services such

as Facebook Messenger or WhatsApp. Facebook meanwhile has a user base

of around 1.7 billion people worldwide; 1.1 billion people use WhatsApp,

and Twitter can nevertheless still record 310 million users around the globe.

The platforms are growing fast, customers are accepting these platforms and

are using them exceedingly intensively. And even technology has long grown

out of the prototypes: IBM Watson won against a human being in the US

game show “Jeopardy” in as early as 2011—the handling of customer dialogues in contrast seems to be downright simple

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Possible Limitations of AI-Based Bots

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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- ...

AI the Eternal Talent Is Growing Up

The subject of AI is nothing new—it has been discussed since the 1960s. The great breakthrough in the business world has failed to appear, but for a few exceptions. Thanks to the immensely increased computing power, the methods can now be massively parallelised and intensified. Innovative deep learning and predictive analytics methods paired with big data technology facilitate a quantum leap of AI potential benefits for business applications and problems. In the last ten years, the breakthrough with regard to the applicability in business practice has succeeded due to this further devel- opment. At present, the discussion is, on the one hand, shaped by hardly realistic science fiction scenarios that postulate computers taking over man- kind. On the other hand, there is a strongly informatics-/technology-laden discourse. In addition to that, there are singular popular science publications as well as articles in the daily press. The latter adhere to the exemplary level without holis...

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