Market Research Information Needs in Industrial B2B Sales : Artificial intelligence systems architecture conceptualization

Abstract

With the recent development of AI systems and the large number of online sources providing industrial B2B sales data, it has become apparent that the internet as a source of sales information could be used more effectively than it currently is. This thesis is based on a case study of a Finnish industrial manufacturing company’s sales department, in which the need for more efficient use of secondary data in market research tasks was identified as a key interest. The current AI solutions have demonstrated the ability to understand, collect, and structure web data, making them a viable option for supporting secondary market research tasks. This research first examines current secondary information research habits and builds a case for the current situation by leveraging sales domain knowledge through survey and interview results. Secondly, an analysis is conducted using both quantitative and qualitative data, enabling the identification of the key information needs for the AI system to satisfy. Thirdly, three separate AI system architectures are generated based on the information needs identified within the sales department, the technically available options, and systems engineering practices. As a result, a justified direction for future AI systems development within the company and sales department is offered, while being supported by the empirical findings of the study.