Presenting recent books: “Artificial intelligence for sales, marketing and service” by Peter Gentsch
How can companies use artificial intelligence-based systems, and what advantages do they entail? In his latest book, Peter Gentsch suggests the bridge from the technology and methods of artificial intelligence to business scenarios and added value.
A small insight into what systems based on artificial intelligence (AI) can do today is already provided by the foreword to the new book by Peter Gentsch. Because the first sentences are not by the author, but by an AI. But, of course, texts written by computers are not the sum total of what computers can do today. AI is also increasingly behind administrative, dispositive and planning processes in marketing, sales and management, and thereby plays a central role in corporate practice.
In the first three chapters – “Introduction,” “Big Data” and “Algorithmics and Artificial Intelligence” – Gentsch lays the foundation for a better understanding of artificial intelligence as well as its technologies and methods. Anyone who would like to know more about the contexts, developments and the current state of research has made a great find here. In these chapters, Gentsch also explains the individual layers of his AI Business Framework, which becomes relevant in further course. Within this framework, all important topics and concepts are systematized, classified and related to one another. It thereby serves as a “transmission belt from the success factors and drivers of AI in companies to the business applications.”
The basis of this framework, the Enabler Layer, is formed from the success factors and drivers behind the development of AI: the internet, quantum computers, the permanently growing performance of processors as well as the development of electronic circuits that are inspired by nerve cell networks. The next layer is the Big Data Layer, which also includes the Internet of Things and its innumerous networked smart devices. Big Data refers to datasets whose scope exceeds the performance of typical database tools for capturing, storage, administration and analysis. The third layer, Artificial Intelligence Methods and Technology, is comprised of the current and substantial technologies and approaches in AI research; for example, natural language processing, machine learning and robotics.
Use Cases of Artificial Intelligence
In the fourth chapter, “Algorithmics Business,” Gentsch suggests the bridge to corporate practice on the basis of various use cases – the fourth layer of the AI framework. In doing so, he presents eleven use cases.
Automated Customer Service: Progress in computer linguistics is shaping customer service much more efficiently, because systems can understand and clarify simple issues, in natural language as well.
Content Creation: Digital data offers great potential for automated content creation. For example, algorithms can create texts automatically and in real time based on facts and figures. It is difficult to distinguish these texts from those written by humans.
Conversational Commerce, Chatbots, Personal Assistants: Customers can communicate with systems by means of spoken or written language. This enables even people who are less technologically oriented to handle new technologies. Anyone who continues with their solution here will have, in the medium term, a type of portal for other companies, through which they can offer their products. The author dedicates an entire chapter to conversational commerce.
Customer Insights: On the internet, for example, thousands of product reviews can be analyzed automatically at any time. Intelligent bots capture and integrate these reviews from various platforms. In doing so, customers’ central statements are automatically extracted from the free texts.
Fake and Fraud Detection: In the same way that bots can be used for targeted advertising, disinformation or manipulation, they can also be used for the automatic recognition of manipulations. They recognize patterns in posting frequency and times, in the follower network as well as in content and tonalities.
Lead Prediction and Profiling: Potential customers can be recognized automatically. For example, new customers and markets can be identified on the basis of specified customer profiles through statistical twins – including suitable communications and sales triggers.
Media Planning: AI and algorithms enable transparent and efficient media planning, in which a multitude of relevant media data points are captured and evaluated.
Pricing: The use of AI software to set retail prices is growing. Algorithms analyze thousands of data points and calculate the optimal price – which is to say, the price that consumers are prepared to pay.
Process Automation: Robotic process automation automates routine tasks such as data extraction and preparation. This relieves customers of having to complete these routine tasks and improves service and the customer experience.
Product/Content Recommendation: Recommendation machines are now an integral part of a modern online store. AI procedures that take account of a large number of data points are replacing the old algorithms like “Anyone who has bought Product A has also bought Product B.”
Sales Volume Prediction: Statistical methods are usually used to predict sales. However, these methods are based on only a small amount of data. With AI, numerous further data points can be taken account of in real time.
Perspectives for Companies
Finally, the uppermost layer of the AI framework, the Business Layer, comes into focus. This includes the processes that directly affect the consumers, as well as the internal interface functions such as controlling, innovation management, etc. This is because companies can, on the one hand, better approach customers through the use of artificial intelligence and thereby create added value, which is still, in Gentsch’s view, frequently underestimated. Thus, with the help of artificial intelligence, personalized product and price combinations can be implemented for every customer. On the other hand, AI-based bots are excellently suited for use within companies. They can plan business trips, process expense claims, answer questions that are posed to the personnel department, organize meetings and prepare reports.
The ways in which companies benefit from the opportunities provided by artificial intelligence today is shown on the basis of various practical examples The Otto Group, for example, uses Big Data and AI for marketing and media controlling. A customer’s activities are measured systematically via various touchpoints. On this basis, the optimal channel mix for communication is calculated by automatically determining the value contribution of every point of contact. Through this, the points of contact that have a direct conversion function, and those that are more supportive, can be stated. UPS allows the most efficient transport route to be determined through an algorithm; Netflix uses algorithmic marketing to personalize content for users and to recommend titles. But there are also negative examples that demonstrate the limits and risks of algorithmic marketing. That is why, according to the author, it is essential that algorithms are taken account of and checked. There is also the risk of overkill targeting: If a consumer sees too much personalized advertising that might also be based on particularly deep insights into private information, this can be perceived as sinister and negative.
Conversational Commerce – the Successor of E-Commerce
The sixth chapter is about scenarios in which conversational commerce could provide benefits and use. Although every form of retail traditionally begins with a conversation, in times of online shopping this conversation is pushed into the background. Digital language assistants are changing that. At the same time, companies must not only react to the technological developments, but also meet the new requirements of the customers: Networked and informed consumers expect a company that (re)acts quickly – preferably in real time – and competently. However, this also provides opportunities, because the entire customer journey, from the product evaluation, to the purchase, to service, can be mapped and optimized through language and text-based interfaces.
Probably the first implementation of conversational commerce took place through WeChat in China. WeChat combines messaging with consumption, and is one of the largest standalone messaging apps in terms of active users. In 2016, Facebook opened up its Messenger to companies. Amazon’s Echo is a further example of conversational commerce, as it offers access to Amazon’s entire product catalog. However, the chatbots and personal assistants are still at a very early stage of development. The potential that is dormant here is shown by the success of WeChat. And in the same way the assistants are continually growing, in the long term, an optimally individual and automated interaction between customers and companies can be expected.
In his book, Peter Gentsch also gives various guest authors the chance to speak. Thus, among others, Andreas Kulpa (DATAlovers) shows how Deep Learning enables new paths for winning customers and markets. Dr. Nils Hafner (Lucerne University of Applied Sciences and Arts) subjects the use of AI and Big Data in customer service to a reality check. And Dr. Darko Obradovic (Insiders Technologies) uses the example of private health insurance to show how timely and efficiently customer communication can take place via mobile end devices.
Peter Gentsch: “Artificial Intelligence for Sales, Marketing and Service.” With AI and Bots toward an Algorithmic Business – Concepts, Technologies and Best Practices, Gabler Verlag, 2018, EUR 49.99
Peter Gentsch is an entrepreneur and expert in the field of digital management, AI and Big Data, as well as the holder of the chair for International Business Administration at the University of Aalen, with an emphasis on CRM, E-business and digital intelligence. He has worked with AI and algorithmics in theory and practice since the 1990s, and is therefore one of the pioneers in Germany.
Author: Editorial team at “Future. Customer”
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