PurposeThis paper explores if and how Artificial Intelligence can contribute to marketing strategy formulation.Design/methodology/approachQualitative research based on exploratory in-depth interviews with industry experts currently working with artificial intelligence tools.FindingsKey themes include: (1) Importance of AI in strategic marketing decision management; (2) Presence of AI in strategic decision management; (3) Role of AI in strategic decision management; (4) Importance of business culture for the use of AI; (5) Impact of AI on the business’ organizational model. A key consideration is a “creative-possibility perspective,” highlighting the future potential to use AI not only for rational but also for creative thinking purposes.Research limitations/implicationsThis work is focused only on strategy creation as a deliberate process. For this, AI can be used as an effective response to the external contingencies of high volumes of data and uncertain environmental conditions, as well as being an effective response to the external contingencies of limited managerial cognition. A key future consideration is a “creative-possibility perspective.”Practical implicationsA practical extension of the Gartner Analytics Ascendancy Model (Maoz, 2013).Originality/valueThis paper aims to contribute knowledge relating to the role of AI in marketing strategy formulation and explores the potential avenues for future use of AI in the strategic marketing process. This is explored through the lens of contingency theory, and additionally, findings are expressed using the Gartner analytics ascendancy model.

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Across many industries, individuals are increasingly relying on customer ratings and reviews on social media. While customer reviews often provide detailed diagnostic information about experiences, customer ratings often reduce the experience down to a simple number. Moreover, there is evidence to support that customer rating inflation is occurring on social media sites over time, especially in the sharing economy, and especially with regard to travel and tourism experiences. This paper conceptualizes how customer experiences are reduced into customer reviews and further abridged into customer ratings in both the traditional and sharing economy contexts. We propose that customers observe how service providers present themselves as a professional (established chain hotel) or amateur (owner operated vacation rental apartment), and then form different service expectations and perceptions accordingly. We investigate 55,110 customer reviews and ratings of New York City's accommodation providers and indeed find evidence of rating inflation over the eight years studied.

In this paper, the authors demonstrate how IBM Watson, a super computer that uses artificial intelligence (AI), can be used in academic and managerial research. A brief description of how Watson originated and operates is followed by four examples of how Watson can be used in academic marketing research. The first two examples use Watson's personality and insight services to analyze qualitative depth interviews. The last two examples use Watson's emotion and sentiment analysis on textual data and qualitative depth interviews. These examples emphasize the value of Watson in the age of big data and its ability to analyze issues that would otherwise be too complex. The paper concludes with limitations, managerial implications, and future research.