Artificial intelligence ecosystems for marketing communications

Abstract

The goal of this article is to help advertising scholars, students and practitioners understand and anticipate the effects of artificial intelligence (AI) and machine learning (ML) on advertising and, more generally, marketing communications (Marcom). While many discussions of AI centre on algorithms and models, we argue that to understand AI in Marcom, one must consider the broader ecosystem in which these algorithms operate. This article develops a framework that shows the Marcom-AI ecosystem and its outcomes, consisting of the following mutually reinforcing components: (1) algorithms and models, (2) customer data (3) digital environments (e.g. mobile devices, digital signage), (4) digital content assets (e.g. images, videos, copy) and (5) information technology infrastructure. We briefly sketch the uses of AI within Marcom. Most or all components of the ecosystem are usually necessary for AI to address Marcom opportunities and challenges. In conjunction with these components, the ecosystem comprises a broad set of stakeholders: consumers, influencers, brands/advertisers, media and messaging platforms, data platforms, publishers and content creators, MarTech/AdTech vendors, AI/ML service providers, device manufacturers and regulators. The combination of these components and stakeholders enables marketers to optimize touchpoints through targeting and choice architectures, create platforms for testing, derive insights from data, and support marketing processes and workflows. Building from the framework, we close by identifying future research directions for advertising scholars, including understanding consumer response to AI touchpoints, privacy, interactions between stakeholders, and how the ecosystem will evolve.

Keywords:

• machine learning algorithms
• customer data
• digital environments
• digital content assets
• IT infrastructure

Disclosure statement

The authors declare that there are no funding sources nor conflicts of interest. No data were analyzed in preparing this manuscript.

Notes

1 There have been two major AI winters and several smaller ones, including the failure of machine translation in 1966, DARPA’s frustration with the Speech Understanding Research program in the first half of the 1970s, the end of the Fifth Generation Computer Systems in the 1990s, etc.

2 Wikipedia defines an algorithm as “a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation.” The term model means different things to different communities, but we are thinking of a statistical model, where there is a formal specification of both the signal and noise describing how the data were generated. For example, logistic regression and Gaussian mixtures models (GMM) are defined by statistical models and the model parameters must be estimated with some algorithm. In contrast, methods like k-means clustering, CART, and random forests are defined as algorithms, without a statistical model.

Additional information

Notes on contributors

Edward Malthouse

Edward C. Malthouse is the Erastus Otis Haven Professor of Integrated Marketing Communications and Professor of Industrial Engineering and Management Science at Northwestern University. He is the Research Director for the Spiegel Center for Digital and Database Marketing and a researcher for the Local News Initiative, both at Northwestern University. He is the co-editor of the Journal of Service Research and associate Editor for Frontiers in Big Data-Recommender Systems. He was the co-editor of the Journal of Interactive Marketing between 2005-2011 and has co-edited two special issues for the Journal of Advertising. His research interests center on customer engagement and experiences; digital, social and mobile media; media management; big data; customer relationship management and lifetime value models; recommender systems; and predictive analytics.

Jonathan Copulsky

Jonathan Copulsky is Senior Lecturer of Integrated Marketing Communications and Academic Director of the Business Marketing Strategy program at Northwestern University. Prior to joining the faculty at Northwestern, he was a senior partner and Chief Marketing Officer at Deloitte Consulting and Chief Marketing and Sales Officer at CCH Incorporated. He is the author of Brand Resilience and co-author of The Technology Fallacy and The Transformation Myth. His research interests focus on digital transformation, marketing technology, conversational marketing, customer engagement, brand risk, and the marketing of new products and services..