Cutting-edge technological advancements in artificial intelligence (AI) and its associated technologies such as machine learning (ML) and deep learning (DL) have garnered immense attention from academia and the industry due to their ability to automate tasks previously executed by human beings in various sectors of the economy. In the business sector, there's an increased interest to explore how these emerging technologies can be deployed to enhance engagements between businesses and clients by automating their interactions. Fueled by the prevalence of a plethora of digital marketing data, these data-driven techniques can help businesses to provide faster and more efficient customer support, while also freeing up human staff to focus on more complex business-related issues. Accordingly, this chapter seeks to provide a compact review of the different AI-powered techniques and their applications in scaling up customer support, highlight existing challenges in their usage in the marketing domain along with proposing future research directions.
TopIntroduction
The extensive innovations in information and communication technologies (ICT) in recent years have transformed many sectors of the economy such as agriculture, business, healthcare, education, and manufacturing among many others. In the business sector, ICT usage has resulted in the emergence of a digital marketing domain which is powered by the predominance of a wealth of marketing data. (Shah & Murthi, 2021). To generate meaningful insights from this ocean of data, different firms have been compelled to adopt cutting-edge and data-oriented techniques that can process and analyze these data to make effective and informed marketing decisions. Such techniques include AI, ML and DL which, unlike the old-school statistical and econometrical models, have the potential to be automated to process, analyze, and extract meaningful insights from huge volumes of marketing data, leading to the accelerated success of many local and global businesses (Chintalapati & Pandey, 2022). Accordingly, the popularity of such techniques in powering digital marketing has been drawing tremendous attention from academia and the industry with many businesses now spending billions in investments to benefit from their usage. This has led to a projected increase rate of 39.4%, equivalent to expenditures ranging between $17.1B to $90.1B by the year 2026 (BCC Research, 2022). Additionally, this spending frenzy has been attributed to the increasing computing power, reducing hardware costs as well as consumer and business interactions becoming more personalized and pervasive with several digital footprints left behind that can be harvested and exploited by firms to improve customer loyalty and retention.
Currently, this AI-powered marketing paradigm together with key support technologies such as the internet and big data are largely based on the deployment of AI techniques to enhance the cognitive and behavioral facets of the customer experience. Recent research shows a deepening of their application in building meaningful relationships between clients and businesses (Ngai & Wu, 2022). While these techniques have increased the number of ways that consumers may interact with businesses and brands, they have equally made it feasible for customer interactions to be automated by businesses. For instance, AI-powered algorithms that propel recommender systems, as evidenced by their prevalence in prominent online commerce (e-commerce) web portals and content platforms, including Alibaba, Amazon, Netflix, and eBay among others; self-serving portals powered by ML/DL algorithms enable customers to find solutions to their problems without human intervention; automated bidding algorithms operate at a millisecond timescale to evaluate a web surfer's profile and optimize ad delivery bids. Meanwhile, chatbots simulate human-like conversations with clients to cultivate loyalty and sustain relationships (Ma & Sun, 2020). These technologies, alongside numerous other applications including customer churn prevention, sentiment analysis, and social media mining exemplify the proficiency of AI agents powered by ML/DL algorithms. Such agents efficiently process vast quantities of unstructured data in real time and yield dependable and precise predictions, facilitating informed marketing decisions. Moreover, every facet of today’s corporate business performance has increased dramatically as a result of these endeavors which has resulted in enhanced customer loyalty, retention and sales (Kaartemo & Nyström, 2021).
Amidst the increasing sophistication of traditional statistical and econometric models in marketing and customer support (Dixon et al., 2020; Ma & Sun, 2020), various studies have been undertaken to investigate how different AI-based techniques can serve as better alternatives. AI-powered techniques such as chatbots, Natural Language Processing (NLP), predictive modelling, recommender systems, sentiment analysis, and self-serving portals, among others, have been deployed to predict and gain insights into customer behavior. Moreover, such techniques have found more success in scenarios where traditional methods do not provide a good fit. Overall, AI has the potential to revolutionize the customer care experience by providing personalized, efficient, and effective service that meets the needs of today's tech-savvy customers. On that basis, therefore, we provide the main contributions of this chapter.