How search insights complement social media data
Search and social data go hand in hand when it comes to understanding customer preferences, moments and interests, helping to spotlight different stripes of behaviour.
Social data provides a record of public conversation. These explicit signals differ across social networks, which host distinct communities and means of expression that can include:
- news articles
- instant reactions
- debates around topics
- expressions of strong emotions
- expressions of tastes and preferences
- brags and status-signalling
- call to actions (sign petition, donate etc)
And so on. Viewing these different platforms in conjunction provides an in-depth understanding of the trends, issues and audiences galvanising society.
If social data arises from individuals establishing and reinforcing their identities by publicly expressing their opinions and ideas, then search data, which we’ve expanded and further integrated in-platform, provides a record of a society’s needs. These implicit signals can manifest as:
- intent to buy
- interest in a topic
- localized searches
- navigating to frequently visited websites
- queries resulting from educational syllabuses and assignments
Take MacBooks as one example.
On Twitter, the leading MacBook posts lean on its role as a status symbol. Owning one, or receiving one, is a signifier of either luxury or a complex, desirable job.
To be an excellent developer you !need:
– CS degree
– $3000 Macbook
– $1500 iPhone
– 3 Monitors
– Drink coffee
– Mechanical keyboard
– RGB lights
– Standing desk
– $1500 iPad Pro
– Code at least 10 / day
– No social life
– No hobbies
– Hate PHP
– Eat junk food
– Work at FAANG
— Raf Rasenberg (@rafrasenberg) January 20, 2021
Over on TikTok, meanwhile, the single most engaged-with post outlines how viewers can customise their MacBooks with small ‘super aesthetic’ alterations.
There is a clear affinity on the platform for posters who ‘edit’ their MacBooks without amending hardware. That #hacksforcollege is a hashtag that reoccurs across these posts provides a strong indication to the behaviour of a particular age and education profile.
Search offers separate but complementary insight.
On the TRAC interface, these different types of query are split according to their formulation, meaning that queries beginning ‘When’ are grouped together, for instance. In this case, these queries revolve around release dates, providing a signal of consumer intent. At the same time, queries beginning with ‘How’ tend to relate to upkeep queries, with ‘Can’ expressions bridging the gap between these two query types.
Exploring these web search results can help to illustrate where to look within a wider Pulsar search.
Remaining with MacBooks, we see the search string ‘which MacBook is best for college’, which offers strong signals as to buyer profile.
We have already seen that the most popular MacBook content on TikTok is college-related, but does it follow, to flip the equation, that TikTok is where most college-related MacBook conversation takes place?
Filtering the social data using the keyword ‘college’ shows that the most common platform in which potential students debate whether to buy a MacBook or not occur is, in fact, Reddit. The subreddits these take place in range from the specialised (r/macbookpro), to the mainstream (r/askreddit), to the illegal (r/piracy).
The site appears to be the online platform where decisions among college students are formed with regard to laptop purchases, before these float upstream and are expressed as expressions of status or humour on the likes of Twitter.
And, taking a wider view beyond students, we see Reddit account for large swathes of the total MacBook conversation.
Not that search data only aids analysis. It can also inform how a Pulsar search is set-up in the first place. Any Pulsar TRAC search, on any topic, allows you to discover how people are interacting with that topic across search engines.
TRAC provides Twitter, Facebook, Instagram, TikTok and numerous others, such as doctor’s social network Sermo, as social data sources. And now, it combines these with Google web search data, which allows a user to search for both individual URLs, keywords and phrases using Boolean logic.
Discover what queries are being made, use these to create a representative social search, and segment the audiences engaging with a topic, all in one tool.