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How Artificial Intelligence Will Change the Future of Marketing Journal of the Academy of Marketing Science https://doi.org/10.1007/s11747-019-00696-0 CONCEPTUAL/THEORETICAL PAPER How artificial intelligence will change the future of marketing Thomas Davenport1 & Abhijit Guha2 & Dhruv Grewal3 & Timna Bressgott4 # The Author(s) 2019 Abstract In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers. Keywords Artificial intelligence . Marketing strategy . Robots . Privacy . Bias . Ethics AI is going to make our lives better in the future. ride-sharing businesses must evolve to avoid being marginal- —Mark Zuckerberg, CEO, Facebook ized by AI-enabled transportation models; demand for auto- mobile insurance (from individual customers) and breathaly- zers (fewer people will drive, especially after drinking) will likely diminish, whereas demand for security systems that Introduction protect cars from being hacked will increase (Hayes 2015). Driverless vehicles could also impact the attractiveness of real In the future, artificial intelligence (AI) appears likely to in- estate, because (1) driverless cars can move at faster speeds, fluence marketing strategies, including business models, sales and so commute times will reduce, and (2) commute times processes, and customer service options, as well as customer will be more productive for passengers, who can safely work behaviors. These impending transformations might be best while being driven to their destination. As such, far flung understood using three illustrative cases from diverse indus- suburbs may become more attractive, vis-à-vis the case today. tries (see Table 1). First, in the transportation industry, driver- Second, AI will affect sales processes in various industries. less, AI-enabled cars may be just around the corner, promising Most salespeople still rely on a telephone call (or equivalent) to alter both business models and customer behavior. Taxi and as a critical part of the sales process. In the future, salespeople Thomas Davenport, Abhijit Guha, Dhruv Grewal and Timna Bressgott contributed to the writing of the paper. Mark Houston served as accepting Editor for this article. * Timna Bressgott 1 Department of Technology, Operations, and Information [email protected] Management, Babson College, Babson Park, MA 02457, USA 2 Thomas Davenport Department of Marketing, Darla Moore School of Business, [email protected] University of South Carolina, Columbia, SC 29208, USA 3 Department of Marketing, Babson College, Babson Abhijit Guha Park, MA 02457, USA [email protected] 4 Department of Marketing and Supply Chain Management, Dhruv Grewal Maastricht University, Tongersestraat 53, 6211, LM [email protected] Maastricht, The Netherlands J. of the Acad. Mark. Sci. will be assisted by an AI agent that monitors tele- insights about not only the ultimate promise of AI, but also conversations in real time. For example, using advanced voice the pathway and timelines along which AI is likely to develop. analysis capabilities, an AI agent might be able to infer from a This paper addresses the issues above, building not only from customer’s tone that an unmentioned issue remains a problem a review of literature across marketing (and more generally, and provide real-time feedback to guide the (human) business), psychology, sociology, computer science, and ro- salesperson’s next approach. In this sense, AI could augment botics, but also from extensive interactions with practitioners. salespersons’ capabilities, but it also might trigger unintended Second, the preceding examples highlight mostly positive negative consequences, especially if customers feel uncom- consequences of AI, without detailing the widespread, justifi- fortable about AI monitoring conversations. Also, in the fu- able concerns associated with their use. Technologists such as ture, firms may primarily use AI bots,1 which—in some Elon Musk believe that AI is “dangerous” (Metz 2018). AI cases—function as well as human salespeople, to make initial might not deliver on all its promises, due to the challenges it contact with sales prospects. But the danger remains that if introduces related to data privacy, algorithmic biases, and customers discover that they are interacting with a bot, they ethics (Larson 2019). may become uncomfortable, triggering negative We argue that the marketing discipline should take a lead consequences. role in addressing these questions, because arguably it has the Third, the business model currently used by online retailers most to gain from AI. In an analysis of more than 400 AI use generally requires customers to place orders, after which the cases, across 19 industries and 9 business functions, online retailer ships the products (the shopping-then-shipping McKinsey & Co. indicates that the greatest potential value model—Agrawal et al. 2018;Gansetal.2017). With AI, of AI pertains to domains related to marketing and sales online retailers may be able to predict what customers will (Chui et al. 2018), through impacts on marketing activities want; assuming that these predictions achieve high accuracy, such as next-best offers to customers (Davenport et al. retailers might transition to a shipping-then-shopping business 2011), programmatic buying of digital ads (Parekh 2018), model. That is, retailers will use AI to identify customers’ and predictive lead scoring (Harding 2017). The impact of preferences and ship items to customers without a formal or- AI varies by industry; the impact of AI on marketing is highest der, with customers having the option to return what they do in industries such as consumer packaged goods, retail, bank- notneed(Agrawaletal.2018;Gansetal.2017). This shift ing, and travel. These industries inherently involve frequent would transform retailers’ marketing strategies, business contact with large numbers of customers, and produce vast models, and customer behaviors (e.g., information search). amounts of customer transaction data and customer attribute Businesses like Birchbox, Stitch Fix and Trendy Butler al- data. Further, information from external sources, such as so- ready use AI to try to predict what their customers want, with cial media or reports by data brokers, can augment these data. varying levels of success. Thereafter, AI can be leveraged to analyze such data and de- The three use cases (above) illustrate why so many aca- liver personalized recommendations (relating to next product demics and practitioners anticipate that AI will change the to buy, optimal price etc.) in real time (Mehta et al. 2018). face of marketing strategies and customers’ behaviors. In fact, Yet marketing literature related to AI is relatively sparse, a survey by Salesforce shows that AI will be the technology prompting this effort to propose a framework that describes most adopted by marketers in the coming years (Columbus both where AI stands today and how it is likely to evolve. 2019). The necessary factors to allow AI to deliver on its Marketers plan to use AI in areas like segmentation and ana- promises may be in place already; it has been stated that “this lytics (related to marketing strategy) and messaging, person- very moment is the great inflection point of history” (Reese alization and predictive behaviors (linked to customer behav- 2018, p. 38). Yet this argument can be challenged. First, the iors) (Columbus 2019). Thus, we also propose an agenda for technological capability required to execute the preceding ex- future research, in which we delineate how AI may affect amples remains inadequate. By way of an exemplar, self- marketing strategies and customer behaviors. In so doing, driving cars are not ready for deployment (Lowy 2016), we respond to mounting calls that AI be studied not only by as—amongst other things—currently self-driving cars cannot those in computer science, but also studied by those who can handle bad weather conditions. Predictive analytics also need integrate and incorporate insights from psychology, econom- to improve substantially before retailers can adopt shipping- ics and other social sciences (Rahwan et al. 2019;alsosee then-shopping practices that avoid substantial product returns Burrows 2019). and the associated negative affect. Putting all this together, it appears that marketing managers and researchers need Introduction to artificial intelligence 1 Miller (2016) outlines the difference between an AI bot and a chatbot. In “ brief, chatbots rely on (relatively) simple algorithms, whereas AI bots have Researchers propose that AI refers to programs, algorithms, greater capabilities, incorporating complex algorithms and NLP. systems and machines that demonstrate intelligence” (Shankar J. of the Acad. Mark. Sci. Table 1 Select use cases (in the order in which they appear in the paper) Industry or Usage Context
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