The objective of this chapter is to explore publications that have addressed the applicability of AI to boost customer acquisition, via bibliometric analysis via VOSViewer. These results reveal an acceleration of research since 2018, a predominance of American institutions, a concentration of publications in marketing and computer science journals, a trend towards the decentralization of research on the uses of AI in customer acquisition and an absence of the emergence of predominant author groups.
Top1. Introduction
Recent AI advances provide excellent opportunities for marketing practitioners and academic researchers. They have transformed marketing practices (Rangaswamy et al., 2020). These increasingly rely on AI algorithms that mimic human cognitive abilities (Huang & Rust, 2018). These applications bring significant benefits in terms of cost reduction, diversification of service channels, personalization of offers, and innovative, user-friendly solutions (Haenlein & Kaplan, 2019).
In addition, the digitalization of marketing has led to the creation of centralized databases containing structured data (e.g., sales data, customer information) and unstructured data (e.g., videos, images) that require advanced AI models to analyze them (Paschen et al., 2020). Thanks to this ability to obtain and exploit massive and in-depth customer data, AI has become an essential tool in customer relationship management, which has moved from a transactional perspective to a relational one in which customer acquisition plays a fundamental role (Pansari & Kumar, 2017).
Customer acquisition is seeking a new customer, i.e., selling a product or service to someone for the first time (Peltokoski, 2022). Acquiring new customers is the backbone of any commercial enterprise (Gopalakrishnan et al., 2022). It has received significant attention in various fields, including marketing, organizational behavior, consumer behavior, and service management (Kumar et al., 2010). The literature often mentions it as a critical element of customer lifetime value (Zheng et al., 2022).
AI is widely used to acquire customers. It provides information to identify their needs, segment them into homogeneous groups, anticipate their concerns, and provide personalized experiences. Overall, AI improves the efficiency and effectiveness of customer acquisition strategies (Castro et al., 2023).
Academically, AI in customer acquisition is an emerging field of research that is developing very rapidly (Feng, 2021). In its early stages, the topics addressed are varied and scattered, such as the use of Big Data as a decision support system for customer acquisition (Alla et al., 2022), applications of various machine learning techniques (Salminen et al.,2019), the use of AI strategically to acquire new customers (Berger et al., 2019), dynamic online pricing (Misra et al., 2019), the automation of acquisition tasks by AI (Thomaz et al., 2020), the impact of AI on retailing (Davenport et al., 2020), the study of psychological and cultural barriers to consumer adoption of autonomous shopping systems (De Bellis & Venkataramani, 2020), the development of explainable automatic product recommendation techniques (Marchand & Marx, 2020), and the impact of AI on customer journeys (Puntoni et al., 2018).