Role of artificial intelligence in marketing strategies and performance

Abstract

This study examines the role of artificial intelligence (AI) as a marketing strategy and explains its contribution to firms and the factors influencing their development. The study tests an empirical model based on the research variables and constructs identified using structural equation modeling and the fuzzy set qualitative comparative analysis (FsQCA) approach. Data were collected from 278 food firms. The findings indicate that the implementation of an AI marketing strategy affects performance. Additionally, this study shows that marketing capabilities, customer value co‐creation, and market orientation are positively related to performance. Finally, the results highlight that marketing capabilities, customer value co‐creation, and market orientation affect the development of AI marketing strategies. The research results of FsQCA find that causal conditions of marketing capabilities, customer value co‐creation, market orientation, and AI marketing strategy are necessary and sufficient recipes for higher firm performance.

The Influence of Digital Technology on Marketing Strategy

“…Research by highlights the strategic application of AI in marketing, where different types of AI can be utilized at various stages of the marketing strategy process, such as segmentation, targeting, and positioning. Wu & Monfort (2022) emphasize that factors like marketing capabilities and customer co-creation positively influence AI marketing strategy and performance. Moreover, Ho & Chow (2023) point out that in AI marketing efforts, factors like information, accessibility, and customization can significantly impact brand experience.…”

Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research

“…Prior research has examined consumers' technology‐facilitated brand engagement in the context of technologies, including social media, virtual‐, augmented‐, or mixed reality applications, and automated or artificially intelligent technologies, among others (e.g., Puntoni et al, 2021), which have been demonstrated to boost corporate performance (Huang & Rust, 2021). Here, consumers’ engagement with artificially intelligent technologies—in particular, those able to perform tasks without human intervention (e.g., generative or predictive artificial intelligence [AI]; Dwivedi et al, 2023)—is receiving widespread attention in the consumer psychology and marketing literatures (Sampson, 2021; Wu & Monfort, 2023). Among these, the AI subclasses of machine learning (i.e., an AI subset, which uses algorithms that flexibly adapt to data constellations to improve task performance) and deep learning (i.e., a machine learning subset that involves computing multilayer neural networks) technologies are able to offer improved decisions or predictions over time based on the deployed (e.g., big) data (Hollebeek et al, 2021; Pradeep et al, 2019), thus offering consumers increasingly accurate (e.g., product) solutions (Leung et al, 2018), provided high‐quality training data and state‐of‐the‐art algorithms are used.…”

Pricing Powered by Artificial Intelligence: An Assessment Model for the Sustainable Implementation of AI Supported Price Functions

“…In this context, Artificial Intelligence (AI) finds increasing application for sales forecast and price or revenue prediction problems and optimization. To date, a wave of AI point solutions has been proposed (Agrawal et al, 2022), but only few studies provide a structured framework for AI decisions in marketing (Wu and Monfort, 2022), and to the best of our knowledge, there is no comparative assessment of existing AI support for concrete use cases in price management. Several classification criteria of AI systems have been used (by type of AI, industry usage, mode of human-AI cooperation, primary role of AI, etc.)…”