Social Media Analytics
Social Media is an advancement in the technology and innovation that has evolved over the last decade. In the contemporary world, it has become an essential driver for obtaining and distribution of information in diverse fields like entertainment, business, and more. Social media refers to the web-based and mobile-based internet applications that enable the formation, access, and exchange of content produced and generated by the users and are universally accessible. There are numerous platforms concerned with social media like Facebook, Instagram, Twitter, and more through which a billion people interact with each other. Moreover, the term social media analytics refers to the platform that aids in creating, accepting, and then leveraging individuals for the business proposing and communal actions. Usually, this approach collects the data from social media sites and blogs and then assesses the data to use business decisions (Leidig, 2018). The approach of social media analytics has been used by numerous businesses to attain the competitive advantage of the firm. This report discusses the literature review on the topic of “social media analytics”. Moreover, the literature review entails the studies conducted by numerous scholars on this particular topic.
The study conducted by Fan & Gordon (2014) described all the usage of social media analytics in the business scenario. They demonstrated the stages and techniques of social media analytics. Therefore, this source for the literature review and the research is suitable as it gives complete information about social media analytics. Moreover, it provided accurate data with respect to social media analytics taken from the relevant sources. Furthermore, the study conducted by Brooker, Barnett & Cribbin (2016) discussed the shortage of methodological strategies for managing the social media data. Their study has used the data collection approach and conduct an empirical study due to which their study can be a reliable source for the conduction of research on social media analytics as it provides real-time data that will be accurate.
There are numerous studies that have been steered for describing the idea of social media analytics by abundant social scientists. This literature review discusses the meaning, advantages, disadvantages, applications, and more of the social media analytics in the contemporary environment. Additionally, this literature review helps in studying diverse researchers’ study on the identical topic.
According to a study by Batrinca & Treleaven (2015) described social media as the internet-based application that enables formation and exchange of data or content produced by the users. Their study suggested that social media data is the major, ironic, and most vigorous indication of social behavior, creating novel prospects to know the people, groups, and societies. Moreover, they discussed that social media analytics in the business scenario was initially adopted by the retail and finance companies. The finance business utilizes social media analytics in order to assess market sentimentality. On the other hand, the retail business utilizes this analytics in order to exploit their product awareness, customer service enhancement, network arrangement examination, fraud detection, and more. The study of Singh et al. (2019) demonstrated the techniques of the social media analytics that are responsible for determining the nature of the presenter by their response. Their study highlighted two techniques namely computational science technique, and sentiments analysis. The former technique is centered on machine learning for solving the issues of an enormous amount of data. Further, it is focused on resampling methods like principal component analysis, local regression, and more. The later technique that is sentimental analysis is based on the context, subjectivity, and polarity. Furthermore, the study by Ruhi (2014) described social media analytics as to the practice of outlining and accepting the accurate metrics for assessing the achievement of the social media ingenuities in the companies. Her study suggested that social media analytics software uses the algorithms reliant on the social network evaluation, data mining methods, and difficult event processing. These algorithms further offer a wide variety of analysis competencies for the management to originate essential business perceptions and to make effective decisions. Businesses practice social media analytics for BI solutions (business intelligence). They use business intelligence tools in order to collect, analyze, and interpret social media data. Oh, Sasser & Almahmoud (2015) suggested that social media are a business of web communication expertise that converts web-based statement into collaborating social podiums like Facebook and Twitter. Moreover, it suggested that social media analytics represents a competitive advantage to those who have skills and competency to incorporate it suitably. The study by Chen, Lin & Yuan (2017, June) demonstrated that the key parts of the social media are its users and their conduct as they are one who post and repost the messages that contains media, text, and more. The data generated by users on the social media is stored and have numerous patterns that can only be analyzed and measured using the social media analytics. Fan & Gordon (2014) reflected the process of the social media analytics using the stages namely capture, understand, and present. This process is known as CUP in which capture means getting the suitable social media insight by observing and listening to numerous sources of social media, archiving the suitable data, and mining the appropriate information. It is also that not all the data captured in the first stage is useful. This stage aids in identification of discussions on the social media sources linked with its actions and doings. Moreover, the second stage is understand in which there is selection of suitable data for the purpose of modeling and at the same time evading the low excellence data, can be done using the numerous analytics techniques on the data. In this stage, the data collected is to be assessed and produce metrics that is helpful for the decision making. Furthermore, the last stage that is present focuses on the display of results from the second stage in an expressive manner. In this stage, visualization methods are utilized for the presentation of the useful results that is visual dashboard could be used that sums and showcase the information from various sources. Furthermore, their study indicated the use of sentiment analytics for the monitoring of the social media systems and the applications of the trend analysis.
The study conducted by Bekmamedova & Shanks (2014) demonstrated how social media analytics brings a competitive advantage in the business. It indicated that the early adopters of social media analytics are more inclined to attain a competitive advantage. Their study provided the example of a bank named Bankco that used these analytics in order to understand the impact of the campaign of marketing on the consumers specifically among the youngsters. Moreover, this enabled the bank to compete with other big and famous banks. The social media analytics also helped Bankco to recognize the key novel consumers for its products and services. Furthermore, the study conducted by He et al (2015) suggested that social media analytics has huge potential to deliver convenient information, actionable facts, and essential visions for the businesses to improve competitiveness and resolve corporate problems. Their study also reflected that the substantial increase of the social media data makes a key prospect for businesses to influence data analytics solutions to exploit consumer insights and better understand the people’s vision on a particular topic or product. Moreover, social media analytics helps companies to be remain informed of the latest activities of their competitors so that they could know the reaction and perception of consumers for their products and services
Stieglitz et al (2018) demonstrated the benefits of social media analytics to the business as a firm make use of social media data for numerous motives. It can be helpful in detecting the novel trends in the communiqué that could contain irrepressible wicked publicity. Moreover, it assists businesses as a channel to collaborate with the clients. Companies also take advantage of this analytics in the decision making of the company. Furthermore, the study by Holsapple, Hsiao & Pakath (2014) suggested that businesses can get benefit from SMA (social media analytics) by measuring the amount of user-produced thrill for a service or product. This information about consumer perception assists them in informing and improving the marketing strategy. Hazarika & Nag (2014) suggested that social media analytics aids banks to achieve their business objectives. Banks can apply SMA to the fruitful datasets reflected in posts, blogs, and the tweets to originate the customer intellect, apprehend the necessity for specific services, and products in the particular segments of customers. Moreover, it helps in establishing marketing approaches for a novel product launch and handle credit evasions and threats. Furthermore, it assists banks in transforming normal customer relationship management into social CRM. The key benefits that have been mentioned in their study are: better reputation management and product awareness; innovation of goods; judicious novel business prospects; business procedure enhancements; superior client engagement; and enhanced marketing approaches. These are explained below:
According to Hausman (2014), there are numerous challenges that are posed by social media analytics. One of the challenges is that real-time SMA requires decision-makers to have some analytical competencies for the analysis purpose. However, not all organizations have experts or specialists who have those qualities, and hence it is a challenge for the business posed by SMA. One such example of a company using real-time social media analytics is 'Uber' that uses the same for motivating numerous drivers to offer services by increasing the price of the ride. Furthermore, unstructured data is another challenge as the data consists of words despite numeral and thus text analytics pauses seriously behind the numeric study. This further creates a problem in the analysis of the real-time unstructured data.
According to Hu et al (2019), it is true that SMA has developed from just being a tool for gathering client likes and commentaries to a prospect to attain important business visions and create prompt & effective decisions. The companies can predict the desires of their customers more precisely by expanding the SMA with the predictive competencies. This further entails the utilization of the regression technique and progressive tools to offer comprehensive information of the clients and their future actions based on social media. The areas in which SMA has a huge impact are as follows:
Karim, A., Khan, N., & Khan, G. F. (2016) suggested that the entire social media analytics has its competencies in diverse areas like technical, environmental, cultural, governance, and people. These are described below:
Technical competency: In order to conduct the SMA, there are certain tools like text mining, data mining, and sentiment analysis. Data mining is the technique that is focused on determining understanding from large databases. It entails key algorithms that allow one to attain the important visions and knowledge from the enormous data. Moreover, text mining is the tool that mines expressive data from formless written social media information. Earlier, businesses used to follow the traditional content analysis to collect valuable information from big datasets of the social media that was not much effective. They must use automatic computer tools that robotically mine visions, trends, and knowledge from written information. Furthermore, there is a sentiment analysis that is a special application of text mining. It is focused on the spontaneous mining of optimistic or negative visions from the textual information. This technique is widely being used by businesses in the contemporary environment for mining user-generated social media information (Chang et al, 2019).
Cultural competency: The cultural competency entails the approach and obvious business norms, standards, and social data that lead to organized ways of collecting, examining, and distributing the data. The cultural competencies of the SMA are innovativeness, business supportiveness, harmony, and leadership. The harmony is the cultural competency as it is essential for the staff members to have some harmony for the decision making linked with social media data. Moreover, the data of social media analytics needs to be analyzed by a person who is analytical as well as innovative who further has the capabilities to take risks and discover novel prospects. Furthermore, to collect and analyze the SMA data, business support is the essential thing that will drive the employees in doing the same.
People competency: The people capability areas entail training & authorization; business skills & familiarity; and cordiality & communication skills. Since the social media data is extremely unstructured in nature and hence it is required to be analyzed by a trained individual so that some useful information could be produced from such data. Moreover, business skills and familiarity is needed to evaluate such data. Furthermore, individuals need robust social and communication skills for getting significant insights from social media analytics.
Governance competency: The capabilities in this respect are decision making; adaptability & flexibility; strategic alignment; and change management. The strategic alignment is essential for aligning the business goals with the SMA goals in order to bring consistency in the company. Moreover, a business must have the ability to adjust to the changes that occur in order to take complete benefit from social media analytics.
Environmental competency: They are connected to the entire business understanding and consciousness linked with social media data guidelines and privacy issues. The businesses have to obey the laws and regulations about the availability of community data in social media analytics. Furthermore, they also need to consider certain privacy issues while extracting data from social media.
There are numerous analytical tools that are in-built in SMA. These are as follows:
Facebook Analytics: It represents the performance of the posts and the conduct of the fans of business products. Furthermore, it identifies the best time of day to post, the most famous type of content, and the best day of the week to post. In addition, one can be able to analyze likes, people, posts, page views, reach, who likes, and more from the past seven days, or 28 days.
LinkedIn Analytics: It offers metrics and tendencies about the business page. Data can be found in respect of updates, extent, and commitment. Moreover, it shows information regarding followers such as demographics, type, and trends. Furthermore, it analyzes the data about visitors in terms of career page clicks, demographics, unique visitors, and page views.
Twitter Analytics: It represents the performance on twitter by outlining the performance for 28 days. The essential and core things that it overviews are total impression, novel followers, number of tweets, top media tweet, a top tweet by received impressions, profile visits, and more.
YouTube Analytics: This analytics enables the company to monitor the performance of the network and videos. It also demonstrates the information about ads generating revenue for the company.
Instagram Analytics: It offers data regarding the followers, their online status, and the posts viewers. It considers the demographics like gender, age, and location of the followers. In addition, it shows the reach, impressions, website clicks, and follower activities (Ayodeji & Kumar, 2019).
From the above literature review on the topic “social media analytics”, it can be concluded that social media analytics is the procedure of gathering unseen visions from the social data that can be both unstructured and structures in order to allow the informed decisions. Moreover, it can be inferred that it has numerous advantages to the business organizations like it aids them in creating attractive social media campaigns, it increased the returns on investment, identify the trending or ongoing matters, discovers the best inspirations for the brand, and more. In addition, it found that the early adopters of social media analytics are more inclined to attain a competitive advantage. It helps businesses to improve customer engagement and customer service. In addition, it assists businesses in creating more successful marketing strategies that can fulfill the desires of the customers and it aids in making the tailored-made products for the clients.
Ayodeji, O. G., & Kumar, V. (2019). Social media analytics: a tool for the success of the online retail industry. International Journal of Services Operations and Informatics, 10(1), 79-95.
Batrinca, B., & Treleaven, P. C. (2015). Social media analytics: a survey of techniques, tools, and platforms. Ai & Society, 30(1), 89-116.
Bekmamedova, N., & Shanks, G. (2014, January). Social media analytics and business value: a theoretical framework and case study. In 2014 47th Hawaii international conference on system sciences (pp. 3728-3737). IEEE.
Brooker, P., Barnett, J., & Cribbin, T. (2016). Doing social media analytics. Big Data & Society, 3(2), 2053951716658060.
Chang, Y. C., Ku, C. H., & Chen, C. H. (2019). Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. International Journal of Information Management, 48, 263-279.
Fan, W., & Gordon, M. D. (2014). The power of social media analytics. Communications of the ACM, 57(6), 74-81.
Hausman, A. (2014). 5 challenges of real-time social media analytics. Retrieved from https://www.hausmanmarketingletter.com/challenges-real-time-social-media-analytics/
Hazarika, D., & Nag, S. (2014). How Banks Can Use Social Media Analytics to Drive Business Advantage. publisher Cognizant, available at https://www. cognizant. com/InsightsWhitepapers/How-Banks-Can-Use-Social-Media-Analytics-To-Drive-Business-Advantage. pdf (20 May 2017).
He, W., Shen, J., Tian, X., Li, Y., Akula, V., Yan, G., & Tao, R. (2015). Gaining competitive intelligence from social media data. Industrial management & data systems, 115(9), 1622.
Holsapple, C., Hsiao, S. H., & Pakath, R. (2014). Business social media analytics: definition, benefits, and challenges.
Hu, Y., Xu, A., Hong, Y., Gal, D., Sinha, V., & Akkiraju, R. (2019). Generating business intelligence through social media analytics: measuring brand personality with consumer-, employee-, and firm-generated content. Journal of Management Information Systems, 36(3), 893-930.
Karim, A., Khan, N., & Khan, G. F. (2016). A Social Media Analytics Capability Framework For Firm’s Competitive Advantage. In Pacific Asia Conference On Information Systems (PACIS). Association For Information System.
Leidig, P. (2018). What is social media analytics & why is it important. Retrieved from https://www.netbase.com/blog/what-is-social-media-analytics-why-is-it-important/
Ruhi, U. (2014). Social Media Analytics as a business intelligence practice: current landscape & future prospects. Umar Ruhi (2014)," Social Media Analytics as a Business Intelligence Practice: Current Landscape & Future Prospects", Journal of Internet Social Networking & Virtual Communities, 2014.
Singh, S., Arya, P., Patel, A., & Tiwari, A. K. (2019). Social Media Analysis through Big Data Analytics: A Survey. Available at SSRN 3349561.
Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics–Challenges in topic discovery, data collection, and data preparation. International journal of information management, 39, 156-168.
Oh, C., Sasser, S., & Almahmoud, S. (2015). Social media analytics framework: the case of Twitter and Super Bowl ads. Journal of Information Technology Management, 26(1), 1-18.
Chen, S., Lin, L., & Yuan, X. (2017, June). Social media visual analytics. In Computer Graphics Forum (Vol. 36, No. 3, pp. 563-587).
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