“According to sample online social data, what are some
ways that the COVID-19 crisis may affect a particular brand?”
Especially in view of the last two questions, as of March 2020, the
keywords ‘panic buying’ and ‘panic shopping’ and their derivatives
become significant search terms and hashtags on Twitter (Chen, 2020).
After analysing social listening data for a case study, aspects such as
the possible causes, reactions, advantages, and drawbacks can be
discussed to better understand the business context. This research also
proposes strategies for different business scenarios over time to take
advantage of the social listening applications and data for companies.
- Literature Review and Theoretical
Perspectives: Social Listening
Concepts
A definition of social listening is given in Anderson et al (2017),
whereby it is described as a process to identify and analyse big picture
information about a company, product, brand, or individual from online
social media. By contrast, social monitoring is an endeavour that
focuses on the daily interaction and the details (Cuttica, 2016).
Crawford (2009) gave a broader description by categorising social
listening to three types, which are background listening, reciprocal
listening, and delegated listening. These can involve different
listeners such as individuals, politicians, and corporations. Background
listening is a method that generally scans the social media, where
content continuously flows in the background, without an intention to
interfere (Crawford, 2009). Reciprocal listening is a method that uses a
broadcast approach to respond to the comments and direct messages in
social media, which is commonly used in politics. The delegated
listening conducted by corporations, enables others listeners to take
part and observes the participants and data in a controllable manner.
Likewise, Paine (2011) emphasises that companies should focus on their
content and the publicly available data about the customers rather than
exhaust their own resources trying to monitor everything.
Social Listening Factors and
Benefits
Social listening can bring multiple advantages to companies. According
to Crawford, companies can have three benefits from social listening:
hearing opinions, leveraging customer support, and increasing the
brand’s influence (2009). According to Patrick (2017), Clutch surveyed
300 social listening software users who worked at companies with more
than 100 employees. This survey indicated that improving products and
services, attracting new customers, and improving customer service are
considered the most beneficial aspects to companies (respectively 25%,
24%, and 21% of responses). Also, the survey indicated other benefits
of social listening, such as monitoring content performance, recruiting
and hiring new employees, and learning about the competition. In
practice, 42% of the participating companies used these tools to
improve the relationship with their customers by adopting various social
listening strategies. The data from Clutch’s 2017 social listening
survey also showed that 86% of the participants monitor customer
requests, questions, and concerns, while 77% monitor their competition.
In the contexts of the company brand, a company’s interests are likely
to be greatly impacted by any snowball effects that may begin with a
single post. Identifying and coping with a crisis is vital for companies
(Paine, 2011); Magdalena Urbaniak, the brand manager of “Brand24”
company, said“With a social media monitoring tool, we are able to
respond first and nip a potential social media crisis in the bud before
it can escalate.”
In regards to social listening for market competition, brands and
products need to be positioned flexibly and be able to respond and
thrive in the market (Paine, 2011). There are so-called hot buttons or
factors and different dimensions such as price, value, delivery, and
variability. The positioning is to find what strategic approach a
company might take, e.g. being a leader, follower, or laggard. For
example, a company can listen to the comment of their rivals’ customers
for what drives their consumption decisions and then make their own
marketing strategies (Paine, 2011).
Social Listening
Measurement
Measurement is a critical part of social listening. From the perspective
of business functions, social listening can be applied to public
relations, marketing, and advertising fields (Paine, 2011). The data for
measurement can be based on public data, such as the number of mentions
and followers, or a group of intelligence systems (Kietzmann et al.,
2011). Optional metrics for measuring social listening mentions are
strength, sentiment, passion, and reach (Kietzmann et al., 2011). The
strength metric is the number of times a company or product are
mentioned in the selected channel. The sentiment metric is the
proportion of mentions i.e. positive versus negative. The passion metric
is the frequency in which certain users mention a company or a product.
The reach metric is the distinct users in mentions divided by the
strength metrics (Kietzmann et al., 2011).
As a particular metric, sentiment measurement is a
media-content-analysis-based method to understand how the image of a
company projects in the outside environment (Paine, 2011). Through media
content analysis, companies can try to measure the positivity of their
image in the media and better understand their audience. Paine (2011)
points out that there are several key aspects of sentiment measurement.
One is the main subject for the sentiment. The second is the type of the
measured item, such as opinion, feature story, Q&A, and customer
feedback. The third is the visibility of companies in the measured item,
which means whether the images of companies can be identified within
public content. The fourth is the people who make the posts. The fifth
is the tonality, which means whether a reader seems to have a conflict
with the company. The sixth is the type of media in which the item
appeared. The seventh is the key message in the item. The eighth is how
customer satisfaction and responsiveness to customers appear in the
content.
Social media software and data are quite accessible online to users
around the world; therefore, technology transfer and exchange of new
ideas can take place (Erturk, 2009). For social media sentiment
listening, manual analysis may be impractical due to the enormous amount
of data (Schweidel & Moe, 2014). By contrast, software-automated
sentiment analysis is a viable way, both faster and easier (Paine,
2011). Also, Hopkins and King (2010) suggest a hybrid sentiment analysis
by combing manual analysis and software. For example, in many cases, a
human can detect the sentiments toward the price of a product which
automated analysis cannot detect. Furthermore, most systems cannot
identify the irony and sarcasm in a common conversation, which causes
the vast majority of automatic sentiment analysis systems to only detect
correctly at a rate of 50% or less (Paine, 2011).
Methodology
This research adopts a case study methodology. Since this research
focuses on the strategies of social listening and monitoring practices,
looking at multiple snapshots in time has its advantage of conducting
in-depth research by seeing variations under different scenarios.
In terms of the data sources, this research aims to find data from
Facebook, Twitter, and YouTube, which are mainstream social media.
However, for those users who comment a company or product using multiple
accounts across different channels, the results of calculating mentions
may seem duplicated, i.e. they may overstate certain outcomes such as
consumer sentiments. However, this research advocates a multi-channel
approach review the influences of brands and trends. For 2020, during
the period influenced by COVID, the paper uses data and keywords mainly
from Google Trends as well as Wordtracker.com.
As to the social listening tool, this paper adopts SentiOne as the
social listening tool. SentiOne is a professional social listening tool
which can be used for monitoring and analysing various social media in
26 languages and can detect trends (Kucharska, Brunetti, Confente, &
Mladenović, 2018). In this research, SentiOne is leveraged to perform
the data gathering and present results in a dashboard. In addition, this
research has used other tools such as Microsoft Power BI to visualise
some of the findings.
Pak’nSave is a well-known discount food warehouse chain in New Zealand.
This paper studies social listening or monitoring, including brand image
reviewing and crisis detecting. Pak’nSave is a good example of a company
influenced by social media trends.
The first step of conducting a social listening is keyword selection.
For a company or a product, there may be probably multiple names or
spellings. For example, the company registered name is the Pak’nSave,
while on the Internet, users are likely to type it in other ways, as a
result of typos, or easier ways of typing, or naturally-pronunciations,
such as “Pak n save”,” pack and save”, or “Pak and save”. To find
appropriate search keywords, this paper leverages Google AdWords, which
provides keyword suggestions based on the target market. Table 1 is the
search criteria of the subjects. The table lists major related names for
the brands or products in this research. The AdWords tools calculated 12
months’ search results and provided the times of search for relevant
keywords which are organised and listed for a “keyword ideas”. This
research manually filtered out irrelevant keywords and focused on the
most relevant brands and product synonyms and selected the most-searched
keywords in the keyword ideas list.
Table 1: Keyword searches for three scenarios