Advancements in digital areas resulted in an opportunity for consumers to share their experiences in easy, quick, and through various means. Experiential marketing research focuses on how to get consumer insights from product and service experiences of consumers on social media and how to transform these insights into profits for companies. This chapter discusses the netnography technique based on the gathering and interpretation of posts of consumers shared about their product/service experiences in digital environments and the marketing practices of this technique. On the other hand, considering the volume and variety of data that are increasing in digital areas, it also dwells on the practical benefits of the use of mixed research models that would allow getting benefit from both the in-depth information potential offered by the lithography technique and the dynamic structure of quantitative data analysis techniques.
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Postmodernism has offered a significant theoretical infrastructure to understand consumer behaviors and consumption-related phenomena. Considering these theoretical foundations, experiential marketing research has focused on emotional people who use consumption as a way of constructing meaningful experiences (Skandalis et al., 2019; Pine & Gilmore, 2011).
A marketing manager needs to know when and how their brands are used, which emotions and contexts are associated with their brands, and which social experiences take place when their brands are consumed. To correctly interpret what consumers want from their experiences, managers need a collective understanding of methods addressing the dynamic nature of the lifestyles of consumers. For example, it is possible to make an observation on consumers who use Harley-Davidson motorcycles and analyze the experience they gain based on this observation. What is lacking methodologically is an empathetic understanding concerning the social interactions of motorcyclists in the context of their identities, spirits, and daily lives. Filing this gap requires an analysis of those who use Harley-Davidson motorcycles in their social environments (Schouten & Mcalexander, 1995).
On the other hand, the improvement of digital areas has made it possible for consumers to gain their consumption experiences through various methods including virtual reality. And thus, social media has become an area to experience new products for consumers. In addition, consumers are given a chance of sharing the experiences they gain in easy, fast, and various methods. While consumers share their experiences of products and services with various contents in a very large digital area, sharing platforms may also significantly vary. Widespread adoption of social media tools results in the production of a high amount of textual data. Researchers are able to conduct analyses with a great number of data structures such as videos, pictures, photographs, and audio records.
Businesses may analyze this high amount of social media data to discover new information and interesting patterns and understand what their competitors are doing and how the industry is changing to gain a competitive advantage over their competitors. Decision-makers may use the findings to develop new products/services and take efficient strategic and operational decisions. It is believed that this makes it possible that competitive intelligence may help businesses become aware of their strengths and weaknesses and increase the level of business efficiency and customer satisfaction (He et al., 2013).
Mixed quantitative data analysis methods such as data mining, machine learning, and deep learning are frequently used to analyze data obtained from the Internet. However, considering the typical ways of communication and cultural structures of social media communities, it is seen that what is required is such techniques that are more explanatory and interpretative. Therefore, new internet research methods are needed for experiential marketing research to use the data from digital areas for the benefits of businesses (Sharma et al., 2018). From this point of view, it is focused on the netnography technique as one of the most important application techniques of experiential marketing and, the mixed techniques where it is possible to use the netnography technique together with big data analysis techniques such as machine learning, data mining and text mining.