Social Media Analytics For Marketing: Literature Review
Many research works have been accomplished to determine how social media data analytics impact on business projects positively. Subsequently, many authors have published adequate research articles, presenting the findings of these exercises to inform the business world and society as a whole on the significance of the concept in the marketing context. Consequently, the past and the present research are sufficient to inform future studies on the same problem. This section presents a review of various works of literature that have bee published concerning the need for social media marketing in business.
Social media is an important point of convergence of resourceful data with unlimited value to the marketers. The data represents the views of shares and user engagement among themselves and how they may affect the business activities. However, Abaido (2019) argued that while the data are useful to various business persons or individuals, at the raw nature, they may not be of any importance to the targeted user. On this note, Van Hee et al. (2018) observed that there is need to embrace tracking and analysis of the data; Stieglitz et al. (2018) added that the intervention constitutes the concept of metrics and can be applied as a strategic approach towards informing a successful marketing plan. On this note, Lou (2017) argued that social data gathered and analyzed in this manner can help to disclose certain trends with critical use to the marketers and ultimately the business activities associated with them. Moreover, Jacobson et al (2020) suggested that the data can be used for the strategic identification of the high-performing content. Nevertheless, Garett et al. (2016) felt that the identification of the content is not a very easy task, therefore; the associated task of analysis should be closely done considering the specific audience, different trends on the various platforms, and the effectiveness of the social media activity of the brand in question. It, therefore, follows that there many ways through which social media analytics for marketing is of a great benefit to business organizations. However, Felt (2016) added that the organization involved should take the exercise to another level by applying certain strategies to ensure that social media analytics to help shape your brand’s marketing strategy.
Beneficial Strategies of Data Analytics for Marketing
Identification and Tracking of Key Performance Index
An organization needs to determine what they consider as their social media key performance indicators. In an ideal situation, the identified KPIs should have a meaningful relationship with the overall strategy at which a business organization embraces (Van Hee et al., 2018). Subsequently, the targeted success can only be achieved when an appropriate tool is utilized for this purpose (Stieglitz et al., 2018). The choice of the right tool is an important strategy towards guaranteeing a proper collection and analysis of data efficiently and effectively. The tools deemed to have the best features manifest through the ease of use, having a user-friendly interface that can be easy to manage.
On the same note, the use of social media analytics allows marketers to rationalize returns on investments, as a result of which they manage to justify the value of the platform of social media that they prefer (Lou, 2017). Identification and prioritizing of the KPIs are important activities for enabling brands to leverage only the most applicable data for the measurement and the optimization of social media posting (Jacobson et al., 2020). The resulting data can is important to guarantee the information to the marketers. For instance, they manage to understand the platforms which best match their brand. Additionally, they also serve to evaluate the content having the largest reach.
Target Content Based on the Interests and Trends
Social media content exists in various forms and there are many users each having different needs. This implies that their interests also vary (Abaido, 2019). Besides, trends also determine the type of content that should be targeted to a given group of users. According to Felt (2016), social analytics can serve as important strategies towards providing marketers with resourceful insights that are associated with the right products, content, and advertisements. As a result, it becomes relatively easier to create the most interest and buzz online (Garett et al., 2016). Brands subsequently emerge with the potential of leveraging this information for the need of identifying the top content as well as the stakeholder who intervene ad serve as brand influencers.
Marketers have the obligation of ensuring that they work closely in partnership with stakeholders who have influencing effects on the operations towards the communication about a brand that an organization wants to be known. According to Jacobson (2020), to ensure this, marketers must play the role of ensuring a consistent intervention that tracks all chatter which is identified with their brand on the online platforms in some way. Achieving the desired goal on the same requires the need to recognize concepts like sentiment, keywords, and language, and they should be used correctly (Lou, 2017). Closely related to the strategy is the measurement of the actual perception which is subsequently compared against the desired perception. The resulting data is useful for marketers who can use it to recognize the positioning of a brand in the market (Stieglitz et al., 2017). Afterward, the organization doing the marketing project in the social media can determine whether the current situation is associated with a wrong or right position.
Social Media Analytics and Cybercrime
While social media analytics is of importance to organizations in the event of a marketing brand, it has been long connected with cybercrime. For instance, it is associated with a damaged reputation. Stieglitz et al. (2018) argued that an organization can experience irrevocable harm when their system is hacked. Lou (2017) noted that it is quite hard to change the influence made, therefore, the organization and the associated brand may not easily become favorites among the public. While they depend on the details of the customer, handle challenging them may and may get into the wrong hands when the intervention of bad actors is experienced.
Additionally, Abaido (2019) emphasized that the organization conducting the marketing communications by analyzing the huge amounts of data have the potential of losing their customers to the competitors. Van Hee et al. (2020) added that he occurs when they are no longer anywhere leaders in any given market segment irrespective of their target for the same. Cybercrime may prevent the selling of new products and services when a hacker gets the opportunity to manipulate the data for their gain (Jacobson et al., 2020). They may be motivated by the competitors who may focus on ensuring that they gain profit off of the hard ideas and work of victim of the attack (Garett et al., 2016). While there are legal interventional reactive measures, the organization affected ay take adequately long to recover the losses caused.
References
Abaido, G. (2019). Cyberbullying on social media platforms among university students in the United Arab Emirates. International Journal Of Adolescence And Youth, 25(1), 407-420. https://doi.org/10.1080/02673843.2019.1669059
Felt, M. (2016). Social media and the social sciences: How researchers employ Big Data analytics. Big Data & Society, 3(1), 205395171664582. https://doi.org/10.1177/2053951716645828
Garett, R., Lord, L., & Young, S. (2016). Associations between social media and cyberbullying: a review of the literature. Mhealth, 2, 46-46. https://doi.org/10.21037/mhealth.2016.12.01
Jacobson, J., Gruzd, A., & Hernández-García, Á. (2020). Social media marketing: Who is watching the watchers?. Journal Of Retailing And Consumer Services, 53. https://doi.org/10.1016/j.jretconser.2019.03.001
Lou, S. (2017). Applying Data Analytics to Social Media Advertising: A Twitter Advertising Campaign Case Study. Journal Of Advertising Education, 21(1), 26-32. https://doi.org/10.1177/109804821702100106
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. https://doi.org/10.1016/j.ijinfomgt.2017.12.002
Van Hee, C., Jacobs, G., Emmery, C., Desmet, B., Lefever, E., & Verhoeven, B. et al. (2018). Automatic detection of cyberbullying in a social media text. PLOS ONE, 13(10), e0203794. https://doi.org/10.1371/journal.pone.0203794