Social Media Analytics: Contextualizing Data for Meaningful Insights

Virginia B. Bautista, Quality Control (QC) Team Lead

iSentia Brandtology

February 2014

Figure 2

Providing online intelligence is a serious business. Analysts constantly deal with tons of unstructured data waiting

to be discovered, interpreted and communicated. At first glance, the contents of social media conversations seem

nothing new – similar types of voices surface on a daily basis: complaining, complimenting, announcing, liking,

sharing, asking, seeking advice, or simply commenting for the sake of commenting. Many times, social media

chatter seems more like noise than conversation, and hence, does not warrant any attention.

The challenge for analysts is how to avoid simply dumping data on PowerPoint slides. How can analysts translate a

huge amount of data to actionable insights? How should they frame stories to guarantee that companies would

make logical decisions using online intelligence? The secret is with the context, and with big data, context is big

deal.

This article gives analysts some tips on how to formulate meaningful insights derived from careful planning,

organizing, and contextualizing of available data from various social media channels.

How to Contextualize Data for Meaningful Insights

Making Relevant Comparisons

The amount of buzz about a brand

will not make much sense without

relevant comparisons. For

example, if Brand A garners 1,758

buzz on December, what does that

mean? Its significance can only be

explained with proper

comparisons, so we also look at

Brand A’s Share of Buzz (SOB)

compared with its competitors

and compared against the entire industry. If the closest competitor

has about 4,000 buzz, then Brand A is quite behind (Fig 1). If the

industry buzz is more than 20,000, then Brand A is nowhere in

consumers’ minds (Fig 1 and 2). In short, the number of buzz alone,

without an analysis of the brand’s competitive and industry

positioning, does not yield anything meaningful.

Figure 1

Figure 4

Figure 3

Figure 5

Analyzing Trend

Certainly, discovering consumer insights for

the current month is good (Fig 3). Looking,

however, at top conversation themes about

a brand or industry throughout the last 3 to

6 months or from the previous year to date

is a smart move (Fig 4). Through month-on-

month, year-over-year or year-to-date

analysis, analysts can help companies

predict the next big thing in the industry. By

understanding the key conversations in the

past and the

current events that

trigger buzz,

companies are

certain to make

informed decisions

for the future.

Correlating Key Social Metrics

Having the largest SOB against industry

competitors is not a reason for a company

to automatically rejoice. For proper

context, Social Buzz could be correlated

with Social Sentiment and Social

Engagement.

The most favorable market position would

be to have the largest SOB, and the

highest net sentiment (Figure 5). A lot of

netizens talking about a brand could be an

indication of the need for prompt action if sentiments are negative. Equally important is the

engagement vis-à-vis buzz. Is the buzz concentrated among few voices? How many people like, share

or comment on Brand A’s social media posts? Which particular posts across brands’ social assets

resonate the most with fans or followers?

Figure 6

Analysts should be able to identify the top

themes, the key positive and negative

sentiment drivers, especially those that

need Brand A’s attention, and the type of

posts that are likely to lead to high

engagement. The findings may not

highlight causal effect, but correlational

relationships between and among buzz,

sentiments and engagement could be

established for deep dive analysis.

Analyzing Channels

At times, what netizens say is

as important as where they

share their views. Instead of

simply finding out the top

channels where Brand A is

mentioned, analysts should

contextualize by looking at how

social media conversations on

particular channels start, and

how other netizens react to the

points raised by the thread

starter. Examining and

comparing top channels across

competitors and in the industry could also bring new perspectives. For example, is Brand A discussed

in major industry channels where most netizens exchange their views on top brands and issues? In

channels where netizens compare and contrast brands, insights could also be extracted based on co-

mentions and frequently cited attributes within the industry (Fig 7).

Discovering Patterns in Social Asset Performance

Aside from listening to social

media conversations, analysts

also have to be adept in

observing how brands and

their competitors make use of

their social assets, e.g. on

Facebook, Twitter, Sina Weibo,

etc. Among the aspects that

could be unveiled include:

Figure 7

Figure 8

Figure 9 Figure 10

Figure 11

How does Brand A fare compared with its competitors in terms of fan size and growth?

Which Facebook posts are likely to gain high social engagement? (Fig 8)

At what time do Brand A and its competitors post updates on its social assets?

At what time are the netizens most likely to comment on or retweet the brands’ posts? (Fig 9 and 10)

How often and how soon do Brand A and

its competitors respond to consumers’

posts/inquiries on its own social assets?

(Fig 11)

What is Brand A’s shelf life and half-life?

The insights to these questions could help

companies make informed decisions on

the best time to post on their social assets, on how often to post updates, and on how soon to respond

to consumers’ queries, etc. Without knowledge on how best to use social assets, getting the message

across would seem impossible.

Identifying Key Opinion Leaders or Influencers

In many instances, the choice of

comments or insights to highlight

depends not only on the relevance of

posts, but also on the sources of buzz. Is

the netizen a key opinion leader (KOL) or

influencer in the industry? How does that

KOL impact engagement rate of Brand A’s

posts? Identifying KOLs provides

companies a basis in deciding whether to

engage KOLs or not, for what purpose,

and how it could be effectively done.

Decoding Native Language

Netizens do talk to each other using their mother tongue. Extracting

insights without decoding native languages including local jargons, can

lead to misleading findings. Analysts are ideally native speakers of a

particular language who can read between and beyond words and can

make sense on whether the netizens are being sarcastic in their posts,

or if they are sincere. With language context, companies are assured

that analysts consider critical factors distinct to a language, e.g. local

expressions and tones in the formulation of insights.

Adding Business Sense

With so many

conversations going on

in real time across

various social media

channels, choosing the

right insights to highlight

is crucial. Analysts have

to be aware that insights

are meant to be utilized

in businesses’ success in

the industry. With clear

understanding of how

businesses work and

how various industries

operate, combined with

knowledge in related fields like marketing, business development, branding, public relations (PR),

advertising, public governance, and customer service management (CRM), analysts could add business

sense in how they collect essential information to translate to actionable insights. By wearing a

‘business hat’, distinguishing useful insights from mere noise could be less tricky.

Zooming in on Demographics and Psychographics

For insights to be utilized effectively for well-targeted marketing efforts, zooming

in on demographics or psychographics vis-à-vis key metrics like buzz and

sentiments is a useful strategy. Information on netizens’ demographics including

age, gender, location, etc., and psychographic descriptions including values,

attitudes and behaviour can provide a lot of opportunities to target the right

market segment.

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