Social Listening

Overview

When corporations monitor and analyze discussions of their products and services online, they are participating in the process of “social listening.” This practice has gained importance as consumers take to social media to discuss products and services. Social listening is sometimes confused with social monitoring. Both are similar in that they collect data from online platforms. However, social media monitoring rarely goes beyond the collecting of data. Social listening is the process of analysis of these social media posts. Through this analysis corporations are better able to understand how they can adjust their marketing and operating strategies to best meet the needs of their customers (Russell, 2014).

Marketing teams and outside analysts collect information about these comments and determine the best ways in which a corporation can respond. This response might come in the form of a direct response on the same platform, a direct mail or backchannel response, a news campaign, or ignoring the comment. What is important to corporations is the ability to know what is being said about them in nearly real time and to respond before an isolated event becomes a viral event.

When collecting and analyzing data from social media, social listening monitors focus on individual comments. They do not assess the ways that products and services have been reviewed by outside organizations unless those outsider reviews have become the topic of online discussion. This means that if the magazine Consumer Reports releases a list of the top 100 products of the year, social listening monitors will not pay attention to the list unless it appears on individual comments and discussion boards. They are interested only in the ways that individuals are discussing goods and services.

A number of companies have been developed to conduct analyses of social media post data for corporations. One of the hardest tasks for these companies is to separate comments that are meaningless from comments that indicate how the public feels about a company’s products or services. These services are the listeners; corporations often outsource this work and receive the analytical reports that have been produced after the listening is complete.

Further Insights

Determining what information needs to be collected and analyzed is one of the hardest aspects of social listening. For some products, particularly those that are popular among social media users, there are millions of posts each day. On Twitter, it is estimated that more than 25 billion posts can be made in one day. It would be impossible to manually process each of those posts, and prohibitively expensive to hire a team to comb through the data to determine which posts need to be assessed. Instead, computer programmers have worked to design bots that can collect posts using algorithms to find the information that needs to be analyzed.

The information, which is collected, should help the corporation to understand the overall brand health. This information is needed to know if the brand is framed in a positive light. If commenters and opinion leaders are making derogatory statements about the brand, and if any one is saying that the brand is out of date, unethical, or otherwise problematic.

Information about specific marketing events is often also collected. For example, if a new product was recently released, social listeners will search to find out if this new product had an effect on discussions about the product or company—for example, whether the product prompted new discussions, garnered attention, or went unnoticed. Of special interest is whether online discussion matched sales or helped to inform sales. All of these questions are addressed through sentiment analysis, which addresses the ways that consumers express their relationship with the corporation or brand. For example, if a product sold fewer units than expected, social listening might be used to determine why, and may reveal insights that allow corporations to sell as expected, or at least to correct their mistakes so that future products sell better.

Sentiment analysis was used for quite other reasons by Cole-Lewis et al. (2015) in their analysis of e-cigarette product sales and discussions. This study was supported by the National Cancer Institute’s Tobacco Control Research Board as a way to better understand how consumers are discussing e-cigarettes, the ways that they are using these products, and the effects of e-cigarettes on future smoking habits. From this information, the government can then better prepare anti-smoking campaigns aimed at improving public health. Similarly, Anderson et al. (2017) analyzed social media posts to learn about the ways that antidepressants are used for non-medical reasons. This kind of data about drug usage would be impossible for doctors or health officials to find out about on their own.

This process of post-marketing social listening is often used by manufacturers of pharmaceutical drugs. Researchers such as Powell et al. (2016) have argued that drug companies need to participate in post-marketing safety surveillance to determine the ways that their products are used and the existence of any potential side effects. Doctors and pharmaceutical companies already do this through interviews, doctor’s reports, and a review of medical literature. However, there is a rich and seldom analyzed set of information on social media platforms such as Facebook where patients comment on how well their new medications are working and any problems they are having.

Keller, Mosadeghi, Cohen, Kwan & Spiegel (2018) found that medical social listening is particularly important for patients who are experiencing multiple medical issues, such as women who want to have children but suffer from inflammatory bowel disease. These women sometimes post to social media discussing their concerns about drugs used to treat inflammatory bowel disease and their concerns. From this data, researchers are able to understand how messages about inflammatory bowel disease are reaching the public. Doctors ask for certain information when they are meeting with their patients, but they do not always have detailed conversions with patients. However, patients are often happy to have more candid and detailed discussions online with their friends and families. Facebook and other social media sites allow doctors to have a window into these discussions—for example, whether patients are concerned about topics they had not anticipated, or how accurately patients actually understand their medical condition and treatment. From this information, doctors can better serve their patients and researchers can develop drugs that help to treat patients with minimal side effects.

Social listening will reveal if there are specific events that need to be responded to. For example, a restaurant may track comments made about their service. They may find that one diner had a terrible meal, that their food was burnt or served cold. The restaurant can directly respond to these consumers, offering their apologies and perhaps a free meal. However, if the restaurant begins to see a consistent trend of complaints about the food on Thursday evenings, they know that there is a larger problem than one burnt meal. From this information, which is gathered and assessed through social listening, they will be able to respond by making changes to the ways that their food is prepared, possibly hiring a new cook or wait staff. They may also continue to respond to individual comments, but at the social listening stage, attention is placed on the overarching analysis and response to trends. Companies gain information on what consumers like, but they pay most attention to what are known as “pain points” or locations where consumers are making complaints or experiencing difficulty. Social listening lets companies know what is said, when it is said, and helps them to understand why those comments are being made. This allows for immediate, successful responses.

Many companies will use social listening as part of their social media strategy. This means that web content developers will begin by producing new messages and information based on market research as well as the successes and failure of past campaigns. When the new campaign or online media is released the social media team will continue assessing the effect of their work. Through social listening, the social media team will be able to assess the success of their work and make changes accordingly. Social listening can also be used as a type of advertising. Playstation uses social listening to find conversations about gaming. It invents witty responses, encouraging gamers to play on the PS console (Chia, 2021).

Issues

Social listening can uncover unexpected trends. They may find that their products are being used in an imitative way, are popular among an untargeted demographic, or present in discussions where the company was previously absent. They may also find that their product has become the center of user-generated content, such as videos, songs, or other types of productions that review, celebrate, use, or mock commercial products. Paying attention to user-generated content can give corporations an idea of the ways their goods function in consumers’ lives. They can also help consumers think of new ways to market and sell their products or services.

Social listening is used not only for brand analysis but also to understand the ways that political policies have been enacted and discussed, as well as the ways that the public responds to new threats and crisis events. For example, Kim and Hastak (2018) assess the ways that social media monitoring can inform the activities of first responders during moments of crisis. Additionally, Sakamoto, Matsushita, Noda and Tsuda (2018) demonstrate how social listening can be used to assist politicians’ efforts to be informed about hot topics of discussion and respond to these trends in ways that help them to better connect with their constituents. Programmers are working to better understand the needs of political officials and the way that they might use social listening tools as opposed to traditional government surveys and analysis tools. For example, Hollander et al. (2016) asks how the data that can be collected through social listening differs from the information that has been traditionally collected from the national housing survey. They found that the data collected from Twitter was able to provide new insights into the ways that community members were discussing topics such as schools, zoning, health, and parks. This study was then used to prepare better tools for urban planners to understand and respond to the needs of their communities.

Many policy makers have applauded these efforts as a way to better connect with voters. By receiving quick reports on issues that voters are concerned about, politicians can spend time analyzing the data, searching for solutions, and proposing policies. However, skeptics warn that this information can also be used for nefarious purposes. For example, Woolley (2016) assesses the ways that information collected through social listening can be used by social bots to affect opinion and commentary. Social bots are computer programs designed to generate comments on social media in a way that mimics human behavior. These bots make posts to social media platforms such as Reddit and Twitter. To the untrained analyst, some of these bots are difficult to identify because they have been trained to comment just as humans do, including voicing opinions, using grammatical errors, or in other ways appearing to be authentic. Woolley warns that these bots are being analyzed in ways that will allow them to post more broadly. This produces a problem for those who engage in social listening because the purpose is to assess the real, human-posted comments. Auto-generated bot comments contaminate the data by creating a false impression that a large proportion of commentary is in line with that of the bot owner.

Allem, Ferrara, Uppu, Cruz and Unger (2017) warn that the inability to distinguish between bot and human postings has already affected the ability of researchers to understand social media posts about tobacco use. In their study, Allem et al. attempted to differentiate between Tweets posted by humans and those that were posted by social bots. They found that at least for their team it was possible to differentiate between the two, and they argue that the continued capability to make this differentiation is critical for consumers’ ability to engage in meaningful decision making. The potential problem that they are trying to avoid is that of consumers looking for smoking-cessation information who receive information from social bots designed to convince consumers that e-cigarettes are a healthy, risk-free alternative.

Artificial intelligence (AI) has changed social listening by automating and streamlining it. AI is capable of collecting enormous amounts of social media and online data. However, businesses must evaluate data to avoid misleading customers or taking action on rumors.

Bibliography

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