Assessing the intention to adopt computational intelligence in interactive marketing

Abstract

Interactive marketing (IM) can be used by e-commerce businesses to provide interactive and personalised experiences to e-customers by building sustainable relationships and delivering value. Computational intelligence (CI) is the ability of a machine to learn specific tasks via data or experimental observation for understanding and analysing customer behavioural patterns. Thus, this study explores how e-customers may intend to adopt CI in e-commerce within the boundaries of IM. Using online surveys, the primary data were collected from 315 e-customers of e-commerce businesses. Subsequently, the quantitative approach was used to analyse the data. The finding reveals that using a variety of techniques such as fuzzy logic, learning theory, evolutionary computation, genetic algorithms, and deep learning, CI predicts e-customer behaviour in a changing environment. Such a prediction results in desirable impacts, including more successful IM campaigns and retention actions. Further, CI uses a computational thinking approach, including the specification of the problem, algorithmic expression, solution implementation, and solution evaluation, for the identification and classification of stock-keeping units. This allows e-customers to compare the attributes of similar products.