Social media have profoundly changed our lives and how we interact with one another and the world around us (Qualman, 2009 and Safko and Brake, 2009). Recent research indicates that more and more people are using social media applications such as Facebook and Twitters for various reasons such as making new friends, socializing with old friends, receiving information, and entertaining themselves (Kaplan and Haenlein, 2010, Keckley and Hoffman, 2010, Park et al., 2009, Raacke and Bonds-Raacke, 2008 and Shih, 2009). As a result, many large companies are adopting social media to accommodate this growing trend in order to gain business values such as driving customer traffic, increasing customer loyalty and retention, increasing sales and revenues, improving customer satisfaction, creating brand awareness and building reputation (Culnan et al., 2010, Kietzmann et al., 2011, Sinderen and Almeida, 2011 and Weber, 2009). Typical activities supported by social media applications include branding (advertising, marketing, and content delivery), sales, customer care and support, product development and innovation (Culnan et al., 2010 and Di Gangi et al., 2010). An example is that many hotel chains such as Starwood Hotels and Resorts have been leveraging the power of social media in recent years to stay connected with guests, seek feedback from guests on their service, address customers’ complaints and issues, and help potential guests make their travel decision (Lanz et al., 2010, Lollis, 2011 and Müller, 2011).
The wide adoption of social media tools has generated a wealth of textual data, which contain hidden knowledge for businesses to leverage for a competitive edge. In particular, marketers can dig into the vast amount of social media data to detect and discover new knowledge (e.g., brand popularity) and interesting patterns, understand what their competitors are doing and how the industry is changing, and use the findings and improved understanding to achieve competitive advantage against their competitors (Dey et al., 2011 and Governatori and Iannella, 2011). Decision makers can also use the findings to develop new products or services and make informed strategic and operational decisions. It is believed that competitive intelligence can help organizations to realize strengths and weaknesses, enhance business effectiveness, and improve customer satisfaction (Lau, Lee & Ho, 2005). Competitive intelligence is defined to be “the art of defining, gathering and analyzing intelligence about competitor's products, promotions, sales etc. from external sources” (Dey, Haque, Khurdiya, & Shroff, 2011). A successful organization should have the ability to process all available information (e.g., customers’ opinions, product prices from competitors, reviews of services and products), identify what has happened and predict what will happen in the immediate future. As many companies are not familiar with social media competitive intelligence (Dai, Kakkonen, & Sutinen, 2011) and analysis and lack enough understanding of the process of mining social media data, the authors conducted a case study to illustrate how social media data can be transformed into knowledge through text mining.
The remainder of the paper is organized as follows. Section 2 is a brief review of text mining. Section 3 explains the research questions explored in this paper, the context of the study, details its methodological approach (samples and procedures) as well as the key findings. Section 4 discusses the findings in depth. Section 5 discusses the implications and recommendation for social media competitive analysis. Section 6 concludes with suggestions for future research.
As social media have become a topic of interest for many industries, it is important to understand how social media data can be harvested for decision making at the industry level. Currently, the majority of social media studies focus on individual companies or organizations. There are few studies performing social media competitive analysis on the leading companies in an industry in a systemic way. As an exploratory study, this case study made a contribution by using text mining to perform competitive analysis for the user-generated data on Twitter and Facebook in three major pizza chains. Results from the text mining and social media competitive analysis show that these pizza chains actively engaged their customers in social media such as Twitter and Facebook. They used the social media not only to promote their services, but also to bond with their customers. Findings from this study suggest that social media plays an important role in sustaining a positive relationship with customers.
Future research will focus on finding innovative ways to turn businesses’ social media fans from “like” to “buy”. For example, pizzerias will have to provide consumers easy ways to purchase pizzas inside social media from “selecting pizza, adding their selections to shopping carts, and completing purchases through payment with credit cards and points” (Anderson, Sims, Price, & Brusa, 2011). To reduce the gap from “like” to “buy”, multiple types of customer-related data such as purchase, sales, behavioral, and demographic data need to be collected to form sociographic data. Businesses also need to track not only what consumers buy, but also what their friends buy (Anderson, Sims, Price, & Brusa, 2011). Thus, a future research area is to track real-time data and apply data mining and text mining to analyze all these data in order to acquire better competitive intelligence. Such efforts could lead to more personalized, differentiated and specific services to customers.
