Truth-seeking in Research: Why turn to Search as a Data Source
If you’ve ever googled your own name, what made you do it?
Millions of people use Search every day. Multiple times. This search activity can range from the commonplace, like looking for a fail-proof banana bread recipe, to the utterly bizarre, like checking up on an old conspiracy theory you remembered while in the shower. Search activity ranges from broad (‘best running shoes’), to specific (‘adidas Ultraboost’).
What does Search represent?
What underpins these searches? Simply put, Curiosity. Not so simply put, our searches are micro-investigations we conduct for ourselves. They are indications of people acting on their concerns, desires, whims, and fancies.
Since the Internet’s invention several decades ago, the nature of human interaction with it has changed drastically, especially so in the last two decades. We observe a willingness to share more online, seen in the use of social media platforms for microblogging, sharing of personal thoughts and experiences.
We see people crowdsourcing advice and feedback on public forums like Reddit and Quora. In all these instances, one’s digital output is tied to an account identity — a post on Instagram requires owning an account, participation in a reddit thread necessitates the existence of a username, whether that’s a fictional name or some permutation of alphabets in your given birth name.
What makes search different is that search is, arguably, anonymous. Typing a query into the google search bar doesn’t require an account name. These queries do not have their digital presence etched onto public social feeds or post threads either. Precisely because Search is private, people confide more in it. For some consumers turning to search, asking Google their potentially embarrassing questions incur less of a social cost than asking their family doctor.
Studying Claimed VS. True Consumer Behavior Data
From a research standpoint, Search represents a source of truth, and is an expression of unstated behavior. It is an honest snapshot of the minds of consumers, revealing what would typically fall through the cracks of traditional survey-based research.
Unlike survey-based data, which reflects fixed time, consumer claims within structures of questionnaire design and — dare we say it- responder integrity, a strong case for studying search as a data source would be that search data reflects real-time consumer opinions, formed and put out without the presence of an intermediary, thus more accurately reflecting uncensored, private inclinations.
Identifying New Ground with Search
In forecasting research, Search presents itself as a useful tool for identifying emerging trends and consumer behaviors, precisely because search queries have the potential to indicate the breadth and depth of consumer intent to experiment — whether that manifests as navigational searches for retro fashion pinterest boards or upcycling tutorials on youtube, or searching for raw brown sugar for making homemade, DIY wellness face masks.
Uniquely, examining search data enables researchers to gain a deeper understanding of brand-agnostic consumers, identifying potential routes to engaging this group of consumers through mapping, analysing and categorising their search intent and journey.
Share of Search is an indicator of Engagement
Situating the Share of Search metric against traditional research metrics like Share of Voice, the latter is ultimately a metric related to advertising, a measure of spending, indicating the percentage of ad spend a brand has compared to the total media expenditure for the industry it’s in.
On the other hand, we see Share of Search as an indicator of consumer engagement — showing a brand’s presence in the consumer’s mindspace, and how top of mind they are when consumers search for a product or service.
Of course, Search is not our only data stream, and cannot be. Culturally, while search represents consumer truth, social data represents how consumer thinking is manifested, indicating ‘ideal’ self-representation. Public forum data too, represents truth, albeit carefully put. Understanding consumer intent requires gleaning insights from multiple facets of a consumer, private and public, curated and uncurated.
At Quilt.AI, we spend our time interpreting search, social and public data through lenses of our Machine Learning Cultural Models and Human Insight (informed by anthropology and semiotics. It is this combination of data sources and sense-making tools which provides a unique approach to understanding Consumer Segments in this age of constant disruption and rapid change.
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Read more about how we use our AI capabilities in research here.