Geoff Simmons
Queen's University Belfast
Dr Simmons is a Senior Lecturer in Management at Queen's Management School, Queen's University Belfast. His research interests are in the impact of digital technology innovation on marketing, with a particular interest in small firms.
Background
Social media has shifted power from marketers to a plethora of diverse communities of interest. Communities using social media technology consist of groups of people from different backgrounds and histories that probably have never met yet are held together by a common interest or goal. While recognising that online social networks revolve around a group of people one already knows or has already met, in this paper we define social media communities as compartmentalizing and retaining groups of relative strangers as well as pre-established interpersonal connections. For example, Facebook while incorporating strong social networks also includes the forming of social bonds around common topics of interest.
These communities using social media applications such as Snapchat and Twitter generate and share content pertaining to products and brands that marketers previously controlled. Marketing departments in response are employing data scientists to mine big data from social media communities. They employ social media analytics involving social network analysis, machine learning, data mining, information retrieval (IR), and natural language processing (NLP). The ultimate objective of social media analytics is the automation of the human-intensive process of detecting and summarizing patterns that are emerging in social media. For example, machine learning algorithms automatically apply complex mathematical calculations to everyday conversations culled from social media communities – over and over, faster and faster.
While social media analytics can play an important role in connecting marketers with social media communities, it can be dangerous in isolation. Taken to excess, firms retreat into abstraction and conservatism that relies obsessively on numbers, analysis, and reports – what she terms ‘paralysis by analysis’. For example, everyday conversations on applications such as Facebook have embedded biases. These hidden biases inevitably are captured by data scientists employing machine learning, with the true meaning of what people in social media communities are saying often being lost in translation.
Data scientists with ‘hard’ data mining skills need to be integrated in marketing departments alongside marketers with ‘soft’ interpretive skills. Quantitative analytics techniques in marketing departments should not diminish the importance of interpretive market research to understand the meanings behind what people in social media communities are saying and sharing. Interpretive research approaches position the meaning-making practices of human actions as social constructions by human actors that applies equally to researchers.
Aim and Objectives
Interpretive research is challenged in this big data obsessed era by a perceived lack of rigour and robustness in its approach to understanding peoples’ behaviours in social media communities. This paper aims to counter that on three levels: 1) promote the value of interpretive market research approaches in social media communities: 2) conceptualise the marketer personality traits integral to netnography as an interpretive market research approach with significant potential; 3) put forward ideas for how marketing departments can integrate interpretive market researchers with data scientists.
