Natalie Jane de Vries

A Data-Driven Approach to Reverse Engineering Customer Engagement Models: Towards Functional Constructs

Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The ‘communities’ of questionnaire items that emerge from our community detection method form possible ‘functional constructs’ inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such ‘functional constructs’ suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling.

Customer engagement behaviours in social media: capturing innovation opportunities

Purpose Social media brand pages have become instrumental in enabling customers to voluntarily participate in providing feedback/ideas for improvement and collaboration with others that contribute to the innovation effort of brands. However, research on mechanisms which harness these specific customer engagement behaviours (CEB) in branded social media platforms is limited. Based on the stimulus–organism–response paradigm, this study investigates how specific online-service design characteristics in social media brand pages induce customer-perceived value perceptions, which in turn, stimulate feedback and collaboration intentions with customers. Design/methodology/approach Data collected from 654 US consumers of brand pages on Facebook were used to empirically test the proposed framework via structural equation modelling. Findings The theoretical framework found support for most hypothesized relationships showing how online-service design characteristics induce an identified set of customer value perceptions that influence customer feedback and collaboration intentions. Research limitations/implications The sample is restricted to customer evaluations of brand pages on Facebook in the USA. Practitioners are advised to maximize online-service design characteristics of content quality, brand page interactivity, sociability and customer contact quality as stimulants that induce brand learning value, entitativity value and hedonic value. This then translates to customer feedback and collaboration intentions towards the brand page. Originality/value The findings have important implications for the design and optimization of online services in the customer engagement-innovation interface to harness CEBs for innovation performance.

Where Does My Brand End? An Overlapping Community Approach

In this paper, we present a new Memetic Algorithm for overlapping community detection. We use a link-based clustering approach to detect the communities of edges in complex networks. To assess the quality of our method, we present experimental results for benchmark networks in comparison to other state-of-the-art algorithms. In addition, we present a case study of a co-purchasing product network from a brand-centric point of view to show the real-life utility of this new Memetic Algorithm.