A strategic framework for artificial intelligence in marketing

Abstract

The authors develop a three-stage framework for strategic marketing planning, incorporating multiple artificial intelligence (AI) benefits: mechanical AI for automating repetitive marketing functions and activities, thinking AI for processing data to arrive at decisions, and feeling AI for analyzing interactions and human emotions. This framework lays out the ways that AI can be used for marketing research, strategy (segmentation, targeting, and positioning, STP), and actions. At the marketing research stage, mechanical AI can be used for data collection, thinking AI for market analysis, and feeling AI for customer understanding. At the marketing strategy (STP) stage, mechanical AI can be used for segmentation (segment recognition), thinking AI for targeting (segment recommendation), and feeling AI for positioning (segment resonance). At the marketing action stage, mechanical AI can be used for standardization, thinking AI for personalization, and feeling AI for relationalization. We apply this framework to various areas of marketing, organized by marketing 4Ps/4Cs, to illustrate the strategic use of AI.

Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda

“…Ref. [71] suggested an approach utilizing traditional AI to help executives in making decisions about the product pricing, the investments in promoting and other marketing strategies, additionally the production and distribution planning. [8] examined the significant role of intelligent analytical techniques in the formulation and implementation of marketing strategies, yet the paper outcomes concluded the low degree of adoption of the analytical tools dependent on machine learning to marketing management.…”

Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective

“…To identify relevant scholarly work on AI ethics and AI in marketing, we conducted a systematic search of published papers. First, we performed a keyword search of electronic databases (Web of Science, EBSCO, and Google Scholar) using the following keywords: “ethic*,” “guidelines,” “principles,” “framework,” (for AI ethics) and “marketing,” “service,” “retailing,” “consumer,” “customer,” (for AI in marketing) each in combination with “artificial intelligence,” “AI,” “artificial,” “machine learning,” “algorithm*,” “robot*.” Second, we searched review and seminal articles in both fields (e.g., Davenport et al, 2020 ; Huang & Rust, 2021b ; Kaplan & Haenlein, 2019 ; and Floridi et al, 2018 ; Jobin et al, 2019 ; Mittelstadt, 2019 , respectively), examining their references and applying an ancestry tree search by screening all papers citing these articles. Third, we performed manual search of journal outlets that turned out to be major sources for journal articles dealing with AI in marketing and business (e.g., Journal of the Academy of Marketing Science , Journal of Business Research , Journal of Interactive Marketing , Journal of Service Research , Journal of Marketing ; and particularly special issues on AI or robots in these journals) and AI ethics (i.e., Ethics and Information Technology , Minds and Machin es, Nature Machine Intelligence , Science and Engineering Ethics ).…”

“…AI can be conceptualized as “the use of computational machinery to emulate capabilities inherent in humans” (Huang & Rust, 2021b , p. 31) and refers to “programs, algorithms, systems or machines that demonstrate intelligence” in its simplest sense (Shankar, 2018 , p. vi). In more methodological terms, AI can be defined as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” (Kaplan & Haenlein, 2019 , p. 17).…”

“…Artificial intelligence (AI) is not just a technology but a powerful force reshaping and benefiting societies by reducing costs and risks, increasing consistency and reliability, and providing new solutions to complex problems (Taddeo & Floridi, 2018 ). AI systems and applications have become pervasive across industries and sectors (Campbell et al, 2020 ) and also bring forth diverse opportunities for marketing strategy and actions (e.g., Huang & Rust, 2021b ) as well as customer experience (e.g., Puntoni et al, 2021 ), relationships (e.g., Libai et al, 2020 ) and engagement (Kumar et al, 2019 ). The increasing computational power, data availability and intensity, context awareness, and emotional-sensing capabilities of AI allow to tailor customized and personalized offerings, and establish and maintain responsive customer interactions and relationships with experiential value (e.g., Grewal, Hulland, et al, 2020 ; Huang & Rust, 2021b ; Ma & Sun, 2020 ; Puntoni et al, 2021 ).…”

“…Thus, ethical challenges related to AI in marketing can emerge on the customer, company, and societal levels. However, the discussion of ethical issues of AI in marketing is partly anecdotal and has hitherto focused on specific AI applications or aspects thereof and/or single ethical principles, for example, explainability (e.g., De Bruyn et al, 2020 ; Huang & Rust, 2021b ; Rai, 2020 ) or privacy (e.g., Davenport et al, 2020 ; Kumar et al, 2019 ; Puntoni et al, 2021 ).…”