Editorial

<abstract xmlns="http://www.w3.org/1999/xhtml">

Whatever your perception of AI is, the machine age of marketing has arrived. To fully grasp how AI is changing every fabric of both our professional and private lives, we need to abstract beyond the presence of autonomous cars, digital voice assistants, or using machines to translate some text for us. AI is creating new forms of competition, value chains, and novel ways of orchestrating economies around the world. AI is more than just technology, it’s creating a new economy. The fuel that runs this economy is the combination of computational processing power, data, and the algorithms that process this data.
AI has the potential to make our life easier, but this convenience might come at a price which we have to pay such as biases directly built-in to the algorithms we use, data privacy issues or failed AI projects in business practice. But without testing, failing, and learning from our failures, there
</abstract>

The Machine Age of Marketing: How Artificial Intelligence Changes the Way People Think, Act, and Decide

<abstract xmlns="http://www.w3.org/1999/xhtml">

Powered by better hardware and software, and fueled by the emergence of computational social science, digital traces of human activity can be used to make highly personal inferences about their owner’s preferences, habits and psychological characteristics. The gained insights allow the application of psychological targeting and make it possible to influence the behavior of large groups of people by tailoring persuasive appeals to the psychological needs of the target audiences. On the one hand, this method holds potential benefits for helping individuals make better decisions and lead healthier and happier lives. On the other hand, there are also several potential pitfalls related to manipulation, data protection and privacy violations. Even the most progressive data protection regulations of today might not adequately address the potential abuse of online information in the context of psychological targeting, highlighting the need for further policy interventions and regulations.
</abstract>

Using Consumers’ Digital Footprints for More Persuasive Mass Communication

<abstract xmlns="http://www.w3.org/1999/xhtml">

Thanks to the rapid progress in the field of artificial intelligence algorithms are able to accomplish an increasingly comprehensive list of tasks, and often they achieve better results than human experts. Nevertheless, many consumers have ambivalent feelings towards algorithms and tend to trust humans more than they trust machines. Especially when tasks are perceived as subjective, consumers often assume that algorithms will be less effective, even if this belief is getting more and more inaccurate.
To encourage algorithm adoption, managers should provide empirical evidence of the algorithm’s superior performance relative to humans. Given that consumers trust in the cognitive capabilities of algorithms, another way to increase trust is to demonstrate that these capabilities are relevant for the task in question. Further, explaining that algorithms can detect and understand human emotions can enhance adoption of algorithms for subjective tasks.
</abstract>

Let the Machine Decide: When Consumers Trust or Distrust Algorithms

<abstract xmlns="http://www.w3.org/1999/xhtml">

More and more companies are using chatbots in customer service. Instead of with a human employee, customers interact with a machine. Many companies give these chatbots human traits through names, human-like appearances, a human voice or even character descriptions. Intuitively such a humanization strategy seems to be a good idea.
Studies show, however, that the humanization of chatbots is perceived in a nuanced way and can also backfire. Especially in the context of customer complaints, human-like chatbots can intensify negative reactions of angry customers, because their performance is judged more critically compared to non-humanized chatbot variants. Service managers should therefore consider very carefully whether and in which situations they should use humanized service chatbots.
</abstract>

When Humanizing Customer Service Chatbots Might Backfire

<abstract xmlns="http://www.w3.org/1999/xhtml">

The implementation of bot interfaces varies tremendously in current industry practice. They range from the human-like to those that merely present a brand logo or a digital avatar. Some applications provide a maximum amount of information with limited turn-taking between the user and the interface; others offer only short pieces of information and require more turn-taking. Instead of simply implementing the default option provided by chatbot providers and platforms, companies should consider very carefully how the specifics of the chatbot interface might affect the user experience. Simple mechanics such as increasing the frequency of interactions leads to greater trust and a more enjoyable user experience. Also, personalizing chatbots with basic consumer characteristics such as gender increases trust and improves the perceived closeness between the customer and the chatbot – and ultimately the brand. Brand managers should therefore consider chatbots not as merely another digital marketing fad or a way to save costs through service automation. When implemented wisely, they are even able to increase a company’s upselling potential.
</abstract>

AI-Driven Sales Automation: Using Chatbots to Boost Sales

<abstract xmlns="http://www.w3.org/1999/xhtml">

The algorithms that control AVs will need to embed moral principles guiding their decisions in situations of unavoidable harm. Manufacturers and regulators are confronted with three potentially incompatible objectives: being consistent, not causing public outrage, and not discouraging buyers. The presented moral machine study is a step towards solving this problem as it tries to learn how people all over the world feel about the alternative decisions the AI of self-driving vehicles might have to make. The global study displayed broad agreement across regions regarding how to handle unavoidable accidents. To master the moral challenges, all stakeholders should embrace the topic of machine ethics: this is a unique opportunity to decide as a community what we believe to be right or wrong, and to make sure that machines, unlike humans, unerringly follow the agreed-upon moral preferences. The integration of autonomous cars will require a new social contract that provides clear guidelines about who is responsible for different kinds of accidents, how monitoring and enforcement will be performed, and how trust among all stakeholders can be engendered.
</abstract>

The Media Lab, Institute for Data, Systems and Society, Massachusetts Institute of Technology

Center for Humans and Machines, Max-Planck Institute for Human Development

The Thorny Challenge of Making Moral Machines: Ethical Dilemmas with Self-Driving Cars

<abstract xmlns="http://www.w3.org/1999/xhtml">

Consumers produce enormous amounts of textual data of product reviews online. Artificial intelligence (AI) can help analyze this data and generate insights about consumer preferences and decision-making. A GfK research project tested how we can use AI to learn consumer preferences and predict choices from publicly available social media and review data. The common AI tool “Word Embeddings” was used and has shown to be a powerful way to analyze the words people use. It helped reveal consumers’ preferred brands, favorite features and main benefits. Language biases uncovered by the analysis can indicate preferences. Compared to actual sales data from GfK panels, they fit reasonably within various categories. Especially when data volumes were large, the method produced very accurate results. By using free, widespread online data it is completely passive, without affecting respondents or leading them into ranking or answering questions they would otherwise not even have thought of. The analysis is fast to run and no fancy processing power is needed.
</abstract>

Understanding Consumer Preferences from Social Media Data

<abstract xmlns="http://www.w3.org/1999/xhtml">

While many customers are still reluctant to entrust themselves to Alexa, Cortona or Siri in their homes, they seem to be less worried about controlling their TV sets via voice control. Comcast started offering a voice-based remote control in 2015 and has extended this service continuously. In the vast world of home entertainment, it seems that voice has come just in time to help consumers navigate and control their ever-increasing home entertainment options. Jan Neumann explains how Comcast enables its customers to comfortably boil down a huge entertainment portfolio to personally relevant content on the TV screen, and how the company remains successful in the highly competitive home entertainment market.
</abstract>

Talking Versus Typing: The Power of Voice-Based Remote Controls

AHEAD OF PRINT

Volume 16 (2024): Issue 1 (May 2024) Generative AI – Reshaping the Marketing Landscape

Volume 15 (2023): Issue 2 (November 2023) Virtual Consumer Experiences

Volume 15 (2023): Issue 1 (May 2023) Marketing Dashboards – The Next Generation

Volume 14 (2022): Issue 2 (November 2022) MarTech and SalesTech

Volume 14 (2022): Issue 1 (May 2022) Conscious Consumption

Volume 13 (2021): Issue 2 (November 2021) Brand Activism

Volume 13 (2021): Issue 1 (May 2021) The Dark Sides of Digital Marketing

Volume 12 (2020): Issue 2 (November 2020) The Reputation Economy

Volume 12 (2020): Issue 1 (May 2020) Crowd Innovation: Hype or Help

Volume 11 (2019): Issue 2 (November 2019) AI and the Machine Age of Marketing

Volume 11 (2019): Issue 1 (May 2019) The Future of Retailing

Volume 10 (2018): Issue 2 (October 2018) IoT - Consumers and the Internet of Things

Volume 10 (2018): Issue 1 (May 2018) Brand Risk Matters

Volume 9 (2017): Issue 2 (November 2017) The Connected Consumer

Volume 9 (2017): Issue 1 (May 2017) Digital Transformation

Volume 8 (2016): Issue 2 (November 2016) Marketing and Data Science

Volume 8 (2016): Issue 1 (May 2016) Responsible Marketing

Volume 7 (2015): Issue 2 (November 2015) Marketing Meets Product Design

Volume 7 (2015): Issue 1 (May 2015) Truly Accountable Marketing

Volume 6 (2014): Issue 2 (November 2014) Social Brand Engagement

Volume 6 (2014): Issue 1 (May 2014) Emotions in Marketing

Volume 5 (2013): Issue 2 (November 2013)

Volume 5 (2013): Issue 1 (May 2013)

Volume 4 (2012): Issue 2 (November 2012)

Volume 4 (2012): Issue 1 (May 2012)

Volume 3 (2011): Issue 2 (November 2011)

Volume 3 (2011): Issue 1 (May 2011)

Volume 2 (2010): Issue 2 (November 2010)

Volume 2 (2010): Issue 1 (May 2010)

Volume 1 (2009): Issue 2 (November 2009)

Volume 1 (2009): Issue 1 (May 2009)

NIM Marketing Intelligence Review

 The NIM Marketing Intelligence Review (NIM MIR) is a leading global journal for marketing decision makers. It offers marketing knowledge from well-known academic think tanks all over the world, and focuses on all aspects of marketing intelligence, e.g. new strategies based on academic and empirical market analyses, new models and methods enabling more effective marketing decision making, and state-of-the-art articles on current fields of marketing research. Readers will be informed about important developments in marketing theory, marketing methodology and marketing strategies.  Volumes 1 to 10 were published under the name GfK Marketing Intelligence Review (GFK MIR). The first issue was published in June 2009.  The NIM MIR delivers relevant insights from academic research, using practical and accessible rather than academic language, supported by professional journalists who "translate" academic papers. We publish papers that contribute new and often fascinating knowledge, for instance about customer behavior, brand or pricing strategies, data analysis or decision making techniques. All papers are intensively reviewed from an academic as well as a practical perspective. Our mission is to present high-quality articles by top authors whose work has appeared in leading academic marketing journals, thereby allowing readers to follow the progress of marketing science.  The NIM MIR is aiming to achieve a high level of acceptance and an excellent reputation among upscale target groups within marketing management, and among marketing instructors looking for interesting applications of the latest marketing concepts and methods. Readers of the NIM MIR have their fingers right on the pulse of marketing science  The NIM Marketing Intelligence Review is published by the Nuremberg Institute for Market Decisions (Nürnberg Institut für Marktentscheidungen e.V., formerly the GfK Verein e.V.), which has long-held academic ties with the University of Nuremberg, Germany.The NIM has repositioned itself as an interdisciplinary, non-commercial institute for researching market decision. The NIM remains majority shareholder of the Nuremberg based market research company GfK.  The MIR is managed by a team of managers and marketing scholars from universities all over the world (See editorial board).Each issue has a special topic and is edited by a leading researcher in the respective field. It is published biannually in May and October of each year.  Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 

Volume 11 (2019): Issue 2 (November 2019) - AI and the Machine Age of Marketing