Machine learning, neural networks, and deep learning (neural networks with additional layers) are some of the current methods by which thinking AI processes data. Thinking AI is good at recognizing patterns and regularities in data, for example, text mining, speech recognition, and facial recognition. Thinking AI is designed for processing data to arrive at new conclusions or decisions. For example, remote sensing, machine translation, classification algorithms, clustering algorithms, and dimensionality reduction are some current technologies that can be considered mechanical AI. Mechanical AI is designed for automating repetitive and routine tasks. Ordered by the difficulty with which AI can address them, there are mechanical, thinking, and feeling AI intelligences (Huang and Rust 2018 Huang et al. We conceptualize AI as the use of computational machinery to emulate capabilities inherent in humans, such as doing physical or mechanical tasks, thinking, and feeling the multiple AI intelligence view considers that, rather than treating AI as a thinking machine, AI can be designed to have multiple intelligences, as humans have, for different tasks. It shows that there are multiple AI intelligences that a marketer can leverage: mechanical, thinking, and feeling. However, academic marketing research to date provides insufficient guidance about how best to leverage the benefits of AI for marketing impact.įull size image Multiple AI intelligencesįigure 1 illustrates that AI can play critical roles in all three strategic marketing stages. It has even been asserted that AI will change the future of marketing substantially (Davenport et al. Replika, a machine learning-based chatbot, provides emotional comfort to consumers by mimicking their styles of communication.
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Lexus uses IBM Watson to write its TV commercial scripts, “Driven by Intuition.” Affectiva, based on affective analytics, recognizes consumers’ emotions while they are watching commercials. Macy’s On Call uses natural language processing to provide an in-store personal assistant to customers.
RedBalloon uses Albert’s AI marketing platform to discover and reach new customers. Domino’s pizza is experimenting with autonomous cars and delivery robots to deliver pizza to the customer’s door. For example, ’s Prime Air uses drones to automate shipping and delivery. We see wide applications of AI in various areas of marketing. We apply this framework to various areas of marketing, organized by marketing 4Ps/4Cs, to illustrate the strategic use of AI.Īrtificial intelligence (AI) in marketing is currently gaining importance, due to increasing computing power, lower computing costs, the availability of big data, and the advance of machine learning algorithms and models. At the marketing action stage, mechanical AI can be used for standardization, thinking AI for personalization, and feeling AI for relationalization. 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 research stage, mechanical AI can be used for data collection, thinking AI for market analysis, and feeling AI for customer understanding. This framework lays out the ways that AI can be used for marketing research, strategy (segmentation, targeting, and positioning, STP), and actions. 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.
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