Hybrid intelligence: human–AI collaboration in marketing analytics

Hybrid intelligence, also known as human–AI (artificial intelligence) collaboration, is a concept that combines the strengths of both human intelligence and artificial intelligence to achieve better outcomes than either could achieve alone (Andonians 2023; Chowdhury 2022; Huang and Rust 2022). In marketing, hybrid intelligence can bring numerous benefits and is even more essential in the context of modern advancements in generative AI (Davenport and Mittal 2022). One way hybrid intelligence can benefit marketing is by enhancing customer experience and personalization. AI can analyze customer data, preferences, and behavior to provide insights, while human judgment can be used to create relevant and engaging content, offers, and interactions (Verhoef et al. 2015). This combination allows for a more personalized and tailored approach to marketing.

Another benefit of hybrid intelligence in marketing is the improvement of campaign management and analytics (Andonians 2023). AI can automate tasks, optimize decisions, and provide insights, while human expertise is crucial in designing strategies, interpreting results, and taking action. This collaboration allows for more efficient and effective campaign management (Davenport 2018; Huang and Rust 2022). Hybrid intelligence also plays a role in addressing data privacy and ethical issues in marketing and advancing the marketing analytics field (SAS 2020). AI can be used to comply with regulations, protect customer data, and detect fraud, while human values should ensure transparency, accountability, and trust. This combination ensures that marketing practices aim to be ethical and in line with privacy regulations.

The collaboration between human intelligence and AI brings significant advantages to the marketing domain. By combining human intuition and creativity with AI’s computational capabilities, marketing strategies can be driven, campaign performances can be improved, and customer experiences can be personalized (Davenport 2018; Huang and Rust 2018). Hybrid intelligence is a powerful concept that can revolutionize the field of marketing, making it essential to explore its benefits and challenges for marketing strategy and analytics.

Bibliometric analysis

For this purpose, we performed a bibliometric analysis to evaluate the current state of knowledge regarding hybrid intelligence in marketing. We incorporated in the analysis marketing-related studies discovered through Web of Science by searching for the following keywords: hybrid intelligence, human–machine, human–robot, robot–human, retail, advertising, marketing, consumer, customer, and sales. After eliminating unrelated topics, the final dataset included 134 studies from ABDC, ABS, and Scimago ranked journals (“Appendix”). The results of the bibliometric analysis in VOSViewer are presented in Fig. 1.

Fig. 1

Key research themes regarding hybrid intelligence

As reflected in Fig. 1, cluster 1 focuses on AI acceptance, interaction, and responses. Terms such as “acceptance,” “chatbot,” “consumers,” and “human–machine interaction” dominate the studies examined. This cluster is especially dominant in the literature and reflects the significant focus on robots, human–robot interaction, and specific areas of marketing research, especially services. Cluster 2 appears to revolve around behavior, emotions, and the broader service sector, including “hospitality” and “tourism.” The inclusion of “big data” and “machine learning” suggests an emphasis on data-driven approaches in these sectors. Cluster 3 is about technology adoption and its implications. This is evident from terms like “adoption,” “attitudes,” “information technology,” “technology acceptance model,” and “user acceptance.” Cluster 4 includes terms that focus on anthropomorphic aspects of technology, such as “anthropomorphism” and “human–robot interaction,” highlighting studies about human-like AI and robots.

The quantitative results of the analysis in Table 1 include term weights (total link strength represents how strongly a term is associated with other terms) and keyword co-occurrences (the frequency of the term). High occurrence and link strength for terms like “artificial intelligence,” “technology,” and “anthropomorphism” showcase their central role in the discourse. Also, terms such as “machine learning” and “robots” have very high average citations, suggesting that research discussing these topics are both seminal and influential. With significant concepts such as “customer experience,” “experience,” “service robot,” and “social presence,” it is evident that there is a considerable focus on understanding the user or customer’s experience and feelings when interacting with AI-driven systems or robots.

Table 1 Bibliometric results

Future research

Considering these findings, the convergence of human cognition and AI capabilities in the field of marketing presents a promising area for both academic and practitioner research. This fusion of human–AI collaboration in marketing encompasses various aspects that warrant investigation, offering insights into optimal strategies, obstacles, and prospects for the industry. We therefore propose the following research topics as a starting point for future research in the realm of hybrid intelligence in marketing:

• The evolution of marketing roles with AI integration: How are job profiles, responsibilities, and requisite skills of marketing professionals evolving due to AI augmentation?

• Ethical considerations in hybrid intelligence: What are the ethical implications of using AI in marketing, particularly in data collection, personalization, and targeting? How does the human role intersect with these ethical considerations?

• Human–AI collaboration in marketing decision-making: How effective is collaborative decision-making between AI and humans versus purely human or AI-driven decisions?

• Consumer perceptions of AI-driven marketing: How do consumers perceive and react to marketing initiatives that AI visibly drives? Is there a difference in trust or engagement when they are aware of AI involvement?

• Hybrid intelligence in creativity and content generation: How does human creativity and innovation balance with AI-driven content? In what ways can this combination maximize engagement and relevance?

• Emotion recognition and hybrid intelligence: How do AI’s emotion recognition capabilities and human intuition enhance marketing strategies?

• AI’s role in global marketing strategies: How can hybrid intelligence aid businesses in tailoring their marketing strategies for different cultures and regions?

• Hybrid intelligence in crisis management: How can AI provide real-time assistance to predict, monitor, and manage public relations crises, and how humans can leverage these data to develop strategic and timely responses?

• Optimizing consumer journeys with hybrid intelligence: What is the role of AI in mapping and predicting customer journeys and how can human insights augment these pathways?

• Hybrid intelligence in predictive analytics versus actual outcomes: How accurate are AI-driven predictive models in marketing when combined with human intuition? How often do these predictions align with actual outcomes?

• The role of hybrid intelligence in building brand loyalty: How can AI-driven insights, when combined with human-driven brand strategies, influence brand loyalty and customer lifetime value?

• Training needs for marketers in a hybrid intelligence environment: What skills and training are required for modern marketers to effectively collaborate with AI tools?

• The impact of hybrid intelligence on return on marketing investment: How does the synergy between humans and AI impact marketing return on investment (ROI) across various industries and campaigns.

• Challenges in integrating AI in traditional marketing firms: What types of problems do traditional marketing agencies face when integrating AI?

Given this list of potential topics, Journal of Marketing Analytics welcomes cutting-edge, interdisciplinary, and data-driven research on hybrid intelligence.

Appendix

Author	Title	Journal	Year
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