AI-powered technologies allow online B2B companies to serve their customers with accurate and relevant information, 24/7. For example, they experience an increase in requests for information from customers on such aspects as product availability, features, or other services. The chapter aims to explore artificial intelligence in B2B business. The study employed qualitative research, and the data was collected through a focus group for data collection. An AI-powered chatbot enhanced with natural language processing and understanding conversationally-worded requests could instantaneously provide this information without a human representative. This is vital as the added uncertainty around the pandemic means business customers seek real answers and ways to adapt and fast. The findings suggest the critical success factors of AI-driven CRM in B2B markets. The limitations of the study include the data collection being restricted to one B2B company. The implications are that further study can be extended for exploring AI-based CRM in B2B markets.
TopTheoretical Background
Relationship marketing includes affinity marketing, loyalty marketing, cross-selling, up-selling, co-branding, co-marketing, and customer-supplier partnering as per the pioneer study (Sheth&Parvatiyar,2000).90% of the B2B professional services sector marketersrefer that AI is advantageousover competitors (Ransbotham et al., 2019).Gartner (2018) predicted that one-third of all B2B marketers would AI-powered technologies, such as virtual customer assistants and Chatbots, and drive marketing automation by 2020. Siemens AG, an industrial manufacturing company in Europe, uses machine learning to analyze supplierinvitations to support decision-making. 20-30% acceleration of the tendering process resulted from the efforts (Ransbotham et al., 2019).
Marketing has become increasingly interested in AI's capabilities in imitating humans (Vlaˇci´c et al., 2021).Academics and practitioners recognize various benefits of using AI techniques in B2B settings. These entail increasing sales(Syam& Sharma, 2018), improve the buyer and supplier relationship (Gordini &Veglio, 2017), target B2B marketing campaigns (Liu, 2019), and deliver support in managers'decisions (Jabbar et al., 2019). The transformative benefits of AI adoption arelimited to a limited number group of B2B marketers. The majority of the intensive literature focuses on the technological understanding of AI (Dwivedi et al., 2021), the knowledge of the implications of AI for B2B is far from conclusive.
The research topics are driven by the challenges of AI implementation. Artificial intelligence's economic value depends on having access to high-quality and quantity data and data management properly. However, data for B2B marketing are scarce, and value extraction is difficult (Lilien, 2016). With the lack of precious data and the fact that collected datawith less essential and poorly managed, B2B marketers miss out on actionable insight, leading to nonreal marketing and sales strategies. The lack of data management is a priority for AI use in B2B marketing.