Journal of Fintech and Sustainable Innovation
Vol. II. 2017 Submitted: Apr. 2017- Approved: Aug. 2017
Article updated Dec, 2024
Andreou, Elena
Master programme in Social Information System, Faculty of Applied Sciences, Open University of Cyprus
Achieving competitive advantages in the Digital Era became a war against an invisible giant since the element of the traditional marketing mix “place” has lost its meaning being almost replaced by the term “presence”. Nowadays, innovative media such as blogs, wikis, forums and social networks are widely acknowledged as the new hybrid that joined the marketing mix.
Competitors analysis was never an easy task but now how can an organization monitor and analyze the “presence” of its competitors since there are so many mediums and techniques that a competitor can use to deploy content over the web? And a viral content doesn’t always mean a positive content. Many studies have proven that a negative content goes viral faster than a positive content.
This phenomena of information overload has created the need of finding ways to automate the process of data mining and sentiment analysis (the analysis of influence that this data has on the market, positive or negative) in order to develop a competitive strategy (Liu et al. 2011). This study provides an answer on how an organization can achieve faster and at a “very low cost” competitive advantages using R programming to collect and analyze big data and through sentiment analysis to retrieve the highest volume of positive keywords to create competitive content.
Keywords: competitive intelligence, sentiment analysis, social media analysis, R programming
The explosion of social media channels has drastically changed the rules of the game for all businesses and organizations. Even businesses with a conservative attitude and late adopters of changes are forced to recognize that conventional methods and marketing strategies became progressively less effective and that greater importance should be paid to the social media marketing.
According to a study made by Maki (2016), social media, smartphones and iPads have created a new reality of digital communication, which requires a new kind of know-how and that companies often lack coherent vision and strategy when it comes to social media marketing. However, increased competition in the service sector and globalization phenomena has created a sense of urgency for communications within a strategic framework.
The worst error in strategy is to compete with rivals on the same dimensions. (Michael Porter)
But to understand a company’s competitive environment and develop a digital strategy is not an easy pitch. In today’s fast paced and rapidly changing business environment, simple information regarding competition, like their market share and offer are not anymore satisfactory volume of information. Competitive intelligence activities help gather, analyze and disseminate this information which is important in gaining a competitive advantage (Gracanin, Kalac and Jovanovic, 2015).
Despite the relative novelty of social media marketing use in business, according to Kaplan and Haenlein (2010) this concept is top on the agenda for many business executives today. Business analysts and decision makers know that the costs of navigating without a social-intelligence map can be substantial and they are using rich real-time data from YouTube, Facebook, Second Life, and Twitter to gain fresh strategic insight and build strong strategies (Lampe et al., 2012; Bhagwat and Goutam, 2013; Junaid et al., 2017).
It has already been demonstrated and proven by many studies and research that the use of social media for business purposes today, is the ‘best opportunities available’ due to their features since they can enhance communication, interaction, learning and collaboration (Levinson and Gibson, 2010; Neti, 2011; Chi, 2011; Arca, 2012; Cox, 2012; Gratto and Gratton, 2012; Lampe et al., 2012; Bhagwat and Goutam, 2013; Junaid et al., 2017)
Moreover, many studies and research have proven that applying sentiment analysis technique is one of the best methods of data analysis to create competitive intelligence and its correct assessment and management can open huge opportunities to businesses (Pang and Lee, 2008; Ferguson et al., 2009; O’Connor et al., 2010; Krishna, 2014; Isah, Trundle and Neagu, 2015; Badita, 2016; Beigi et al, 2016; Leidig, 2016; Mahabal, 2016; Junaid et al., 2017)
However, it was also stressed that in this period where the major e-business pressures are labeled by the 3Cs: competition, customers, and change, there is an imperative need of finding ways to automate these processes (Liu, Cao and He, 2011).
This paper attempts to build a new analytical framework of a supposed organization’s competitor using R code to retrieve and analyze unstructured content from Facebook, YouTube and website, in order to create the most competitive strategy.
‘If you are ignorant of both your enemy and yourself, then you are a fool and certain to be defeated in every battle. If you know yourself, but not your enemy, for every battle won, you will suffer a loss. If you know your enemy and yourself, you will win every battle.’ Chinese military theorist Sun Tzu (2001: The Way of Strategy).
According to Wilson (1994), to increase competitive advantage, competitor analysis should be a
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central element of the company’s marketing plan, with detailed attention being paid to each competitor and their marketing strategies.
The purpose of doing competitor analysis is to identify competitors’ strengths and weaknesses in order to realize the opportunities and threats for the company’s business.
Weiss (2002) classified competitors in four broaden categories, putting on the first place organizations offering the same product or service with the company, second, organizations offering similar products or services, third, organizations that could offer the same or similar products or services in the future and last but not least, organizations that could remove the need of the company’s product or service.
The problem nowadays, as well stressed by Weiss (2000) is that the impact of e-commerce is changing the ways companies do business, and the way they sell, promote and advertise their products or services and he underlined that the correct approach is to identify trends before they become obvious and the real battle to win is: NOT TO BE JUST LIKE EVERYONE ELSE.
Here is where competitive intelligence can lead to an effective long-term strategic decision. The Society of Competitive Intelligence Professionals (SCIP) defines competitive intelligence as ‘a systematic and ethical program for gathering, analyzing and managing external information that can affect your company’s plans, decisions and operations.’(Perry and Ross, 2008; Stenberg and Vu-Thi, 2017)
Basically, competitive intelligence, or CI, means legally collection of information from competitors’ open sources publicly available such as websites and social media (Weiss, 2002). The purpose is to analyze this information and find out what they are doing right but also what they are doing wrong as to help company’s decision makers to ascertain threats but most important, to identify opportunities. By having identified potential threads or opportunities, the firm can take the necessary actions which will lead to achieving competitive advantage.
According to Sebastiani (2002) the term “text mining” (also referred to as text data mining) is commonly used to denote the process of deriving high-quality information from a text in order to capture key concepts, to detect lexical or linguistic usage patterns and uncover hidden relationships and trends.
Nowadays, when business environment is so unstable and it can change fast and unpredictably, especially for the worse, data mining became a matter of considerable importance due to its role in strategic planning, since when viewed and managed from a strategic perspective, can ingrain an organization with an enormous competitive advantage. Many studies have indicated marked performance growth in companies that have strong business analytics capabilities (Zikopoulos et al.)
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Data mining is not an easy task. Much of the information that is being produced by collectors of data lacks the structure to make it suitable for storage and analysis in traditional databases. (Zikopoulos et al.) And gathering and proper storing data is not the only difficulty that an organization faces these days, data governance and analysis to ensure that the business strategy is prepared to put up with market treads and challenges is even harder (Nelson and Dodson).
Since we are living in a fast technological era where changes on business environment can occur faster than can be managed and controlled, organizations must act on the analytics data rapidly before the data become obsolete.
The evolution of social media and their deep implication in the business world led to the need of development of opinion-oriented information-access services as underlined by Pang and Lee (2008).
Opinion oriented information is not a new concept that evolved out of web widespread. During the decision-making process from old times people were seeking for opinions from priests, relatives or friends. Later on, through the 20th century, the need of opinion oriented information created a new field of business specialists who have developed surveys and polling methodology to gather opinion oriented marketing information (Krosnick, Judd, and Wittenbrink 2005).
Nowadays, the explosion of Social Media gave people a voice and a medium to express their opinions about products, services, political views and preferences as well as to distribute news alerts. Since all this information is now available for free over the web, marketers try to find new cheaper ways to use this information on business decision making.
The process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral was defined as sentiment analysis or opinion mining.
According to O’Connor et al. (2010) it can be analyzed publicly available data to get insights on consumers’ attitudes in the same manner that public opinion was gained from population with different questioners or interviews and that this could be a faster and less expensive alternative to traditional polls.
Many researchers and academics have proved that sentiment analysis techniques can be applied to a text in order to gain insights on people’s attitude towards different products or services. I.e Ferguson et al. (2009) used sentiment analysis method to label a text within financial blogs, Sprenger and Welpe (2010) used sentiment analysis from text created by Tweets to determine sentiment towards individual stocks, and Bollen, Mao and Pepe (2010) have conducted a study using sentiment analysis of Tweets to determine whether events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood.
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The data used in this research is not publicly available but can be requested from the author. Please reach out to Elena Andreou at ea@elenaandreou.com
With the explosion of reviews, ratings, recommendations, comments and critiques, online opinion has turned into a kind of virtual currency for organizations looking to market their products/services, identify new opportunities or threads and manage their reputation. It is clear to all marketers that people want participation, not propaganda.
Marketing on the web is not to be compared with the traditional marketing where an advertisement was about glitzy and colorful illustrations for the cover page of a magazine or a short commercial during the 8 o’clock news on TV. Marketing on the web is about creating valuable content which will have a positive impact on the targeted audience, understanding the keywords and concepts customers are looking for, and use those keywords to drive them to specialized links to provide the information sought after.
It is well recognized that machine learning techniques can be used to infer sentiments over social media data and these techniques are cheaper and faster than other methods and can help decision makers to take fast actions. When the firm gains the real intelligence – the strategic wisdom – that allows decision makers to make the right decisions, then the organization can properly be prepared to stand against competition and turn the threads into opportunities.
The data used in this research is not publicly available but can be requested from the author. Please reach out to Elena Andreou at ea@elenaandreou.com