Impact of Social Media on Organizational Customer Sentiment

Impact of Social Media on Organizational Customer Sentiment

Task 2: Social Media Analytics

Social media analytics refers to collecting data on social media platforms and analyzing social media analytics tools to help make informed business decisions. Social media analytics is used mostly in mining customer sentiment that helps support customer service and marketing activities. The first approach towards using social media intelligence is identifying the business goals that will benefit from the data collection and analysis. Some organizational objectives are getting feedback on services and products, reducing costs related to customer service, increasing revenues, and improving public opinion on business vision or specific product (Choi et al. 2020, p. 804). Businesses use different key performance indicators (KPIs) to evaluate these activities. For instance, they evaluate customer engagement using the number of followers on Twitter, mentions of the company, and retweets from customers. They also monitor social media activities such as sharing and liking the company’s posts on Facebook accounts. There are four types of social media analytics: descriptive analytics, diagnostic, predictive, and prescriptive analytics. According to Choi et al. (2020, p. 800), the main advantages of using social media analytic tools include creating a competitive advantage over others through a better understanding of brands. It also allows businesses to learn from customers, getting effective solutions to organizational problems, and help to improve on services and products. The insights gained from SMA provide customer sentiment, which explains customers’ experience with services and products and helps organizations perform better.

2.1. Impact of social media on organizational customer sentiment

First, customer sentiment relates to the context, feeling, and tone of customers’ actions and emotions related to their actions, such as mentioning the organization, completing a purchase, or leaving a review. The findings of Fiaidhi et al. (2019, p. 9) reveal that tracking customer sentiment allows organizations to get insights into general customer satisfaction, engagement, and loyalty. Social media platforms such as Facebook and Twitter have a great impact on organizations’ customer sentiment. Amazon, an online giant retailer in the United States, is the best example to quote regarding social media and customer sentiment. The company utilizes Facebook and Twitter, including Pinterest, for most of its customer engagement. Presently, Amazon has recorded over 28.7 million likes on its official Facebook page. The figures are so high since the company ensures customer engagement by checking customers’ feedback and responding to an enormous number of comments (He et al. 2019, p. 154). Social media platforms, mainly Facebook and Twitter, help Amazon learn much about customer sentiment and take the correct actions to improve their satisfaction and loyalty. Amazon uses the platforms to reply to customer comments politely and quickly, provides useful links and information, and other activities which increase customer satisfaction elevating loyalty (Fiaidhi et al. 2019, p. 10). Organizations can measure the effect of social media on customer sentiment using analytic tools such as Hootsuite Analytics which analyses all customer opinions and reactions on organizational social media platforms. Hootsuite Analytics provides a general overview of the clicks, comments, shares, video views, and reach, which dictates customer engagement and reveals their loyalty and satisfaction.

Task 3: Big Data Technologies

Big data technology refers to software utility designed to extract, process. It analyzes data and information from large and complex data sets that traditional Software for data processing cannot handle. The three leading big data technologies include; MapReduce, Hadoop, and NoSQL. MapReduce is a programming model and a connected implementation for generating and processing big data arrays with distributed, parallel algorithms (Maleki et al. 2019, p. 6980). Besides, it consists of a mapping procedure that performs sorting and filtering, including a reduced method summarizing operations. The technology processes and distributes huge and multi-structured data collections over a large cluster of ordinary processors or machines. Its main goal is to achieve high performance using a ‘simple computer.’ Hadoop refers to the open-source framework used to store and analyze huge amounts of unstructured and distributed data. Hadoop technology framework stores and analyze data in various machines at low costs and high speeds. Hadoop comprises various components/elements that facilitate its functionalities, such as Hadoop Distributed File System (HDFS), Name Node, Job Tracker, Slaves Nodes, and more (Jeong et al. 2017, p. 335). The technology helps to access the semi-structured and unstructured data such as social media feeds log files. It also helps break up data into parts then load it into its file system components such as HDFS (Kumar et al. 2014, p. 32). Hadoop also replicates each part of the data several times before loading it in the file system for failsafe processing. NoSQL is a big data technology that integrates various unique database technologies in continuous development and helps design modern applications. It reflects the non-relational database that plays data retrieval and accumulation and is deployed in big data analytics and instantaneous web applications. According to Kumar et al (2014, p. 29) the technology is used to store various data characterized by quick performance and flexibility when dealing with a wide range of data types. The examples of NoSQL include Redis, Cassandra, and MongoDB.

Task 3.1. The role of Big Data Technologies

In terms of use application for data management, big data technologies play very vital roles. The main function of the NoSQL is to store data in its database and helps organizations to properly store huge data in different kinds of storage models such as Columnar, documents, graphs, and more. In organizations, the main role of MapReduce is to analyze and process huge volumes of multi-structured data faster and timely. Some of its tasks include text analysis, machine learning, graph analysis, and more. The technology also provides great control when it comes to data analytics in organizations. Finally, Hadoop plays the role of storing and analyzing huge amounts of data characterized by its low costs and high speeds (Chávez et al. 2018, p. 716). An example organization using Hadoop is LinkedIn Company. It’s used in storing and processing huge amounts of data from the company’s customers. The technology is good at storage, and LinkedIn Company uses Hadoop HDFS reliably in data storage.

Task 4: Ethical Issues in the use of Analytics

Privacy

The use of data analytics technologies and a data-driven approach in decision-making is a boon in business and raises ethical issues related to privacy. For example, through people analytics, active monitoring systems collect information in organizations which helps to facilitate productivity, improve performance accuracy in measurements, and reduce operating costs. However, these actions conflict with employees’ rights, such as freedom from being listened to watched in the workplace and right to solitude, hence ending up as unpleasant, unethical, and affecting employees’ morale and health (Someh et al. 2019, p. 723). Recent research reveals that the modern data sources in Human Resource Management (HRM) such as email data, behavioral and performance monitoring, video surveillance, messages, and social media profiles are problematic.  The data collection from employees and customers is a great idea as it helps in predictive analytics and business decision-making. Still, the problem comes from the exposure of personal information. The data is transferred between systems and stored in data pools for analysis; information breaches by hackers could cause great privacy risks to an unknown number of people and their private lives (Cimato & Damiani 2018, p. 115).

Transparency

According to Turilli and Floridi (2009, p. 110), “transparency refers to the possibility of accessing information, intentions or behaviors that have been intentionally revealed through a process of disclosure.” The authors also argue that transparency can be seen as a pro-ethical condition that impairs or enables other ethical principles and practices. Ethical issues arise from both intended and unintended use of the information contained in the data analytic tools of organizations (Turilli & Floridi 2009, p. 107). According to employees’ perspectives, it’s hard to tell whether organizations use the collected information and data for promoting personal interests or organizations’ goals and objectives. The data analytics provide data for the organizations and used for research, handled by several administrators, and other reasons that violate the transparency rules of employees’ data and information in the organization.

Bias and Discrimination

Quoting from the Oxford English Dictionary defines discrimination as “treating a person or particular group of people differently, especially in a worse way from how you treat other people, because of their skin color, sex, sexuality, etc.” Discrimination and bias are totally against the law, and the chance that data-driven analytics and technologies could arise to discrimination is a question and debate of concern. Some of the challenges raised from big data analytics in discrimination and bias include; intended and unintended data bias, advertisements and offers, the digital divide, and more. Misinterpretations, shortcomings, or errors in data analytics cause unintended bias in predictions or released statements in decision making (Slade & Prinsloo 2013, p. 1518). Besides, data bias can start from biases in data collection processes such as biased designs or surveys, biases in collecting data sources, or insufficient data collection. The digital divide observed in data analytics, and data-driven technology is a significant issue of discrimination. The working environment consists of people of different ages, incomes, gender, race, educational level, etc. People in the workplace have different knowledge in terms of technology, and such technologies are complicated. For instance, people with less knowledge in technology and computers would question personalized email messages from an organization and personal or confidential information besides the fact that it’s essential in promoting the organizational goals.

Governance and Accountability

Data governance refers to the formal standards, policies, and accountabilities regarding data. Some of the aspects of governance that could result in unethical issues include establishing procedures and rules, developing ethical norms, or internalizing costs (Tooby 2019, p. 42). Accountability in the organizational and data context refers to the party of the individual response to the actions and handling of data around the organization’s activities. As mentioned earlier in this report, accountability in data analytics is still a problem due to the enormous amounts of data collected, how they are handled, and the fact that data analytics is still a new field without policies and standards covering all the aspects. The organization handles and takes over the governance stipulating rules and regulations or standards to govern how and which data is collected. There arise problems in governance where the standards and rules laid by the organizations do not cover all technical aspects of ethics. Besides, it’s unfortunate that accountability in data analytics can be difficult sometimes. For instance, a data breach common in technology could lead to security and privacy risks for individuals and organizations. It could also be difficult to determine the persons responsible if the involved team followed all the standards, regulations, and correct procedures (Martinez-Martin & Kreitmair 2018, p. 32).

Task 4.1: How organizations can address the ethical Issues in data analytics

Regarding privacy issues, organizations need to observe human rights, ensure data confidentiality and collect only relevant data from employees and customers. It will reduce the exposure of confidential information and violation of human rights. In the attempt to mitigate the ethical issues related to transparency of information in the era of information technology, Turilli and Floridi (2009, p. 111) suggested that organizations should use information management systems to help store, communicate, and validate the information. Organizations should ensure that employees are educated on how the data is used to improve their trust in the company and ensure they feel safe. Discrimination in data technologies and analytics can be complex to mitigate. Still, it requires organizations to collect the correct data and information from their employees and customers to avoid non-rational decisions dominated by bias and discrimination (Veale & Binns 2017, p. 25). Educating the employees in the workplace on how the technologies work would be better; hence employees will know how the technology helps to push the organizational goals. Finally, addressing accountability and governance issues would require organizations to deeply understand all the data and data analytics concepts to put forward laws and procedures in all aspects and areas. They should also lay out clear lines of accountability to make the people handling data in the organization exercise more care, hence mitigating ethical issues. Organizations must adhere to all governmental laws and regulations regarding the privacy of individuals, transparency, bias, and discrimination and have their regulations to ensure good governance and complete accountability.

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