“The task is…not so much to see what no one has yet seen; but to think what nobody has yet thought, about that which everybody sees.” – Erwin Schrödinger
Last week I joined panelists from Unilever and Microsoft at a Brandwatch event to discuss using data to uncover actionable insights.
One of the questions we were asked was whether there was a better way for researchers and strategists to identify insights, particularly given the growing number of data sources now available to us.
Before I address that, I think it’s worth clarifying what we mean by an insight.
The Oxford Dictionary defines it as the capacity to gain an accurate and deep understanding of someone or something. It can relate to a wide variety of things including the brand, category, customer or culture more broadly. At its heart, whether you work for an agency or a client, an insight should always enable you to do something as a result. They are always a means, never an end in themselves.
In the words of Saatchi & Saatchi’s CSO, Richard Huntington, “trends are trendy, insights get shit done”.
As W+K’s Martin Weigel has also highlighted, we cannot “mine” for insights. They’re not waiting to be uncovered. They’re the product of thought and analysis.
That said, there are two broad frameworks that can help guide the process of identifying insights and both can make use of social data.
Combining facts with observations is one of the most effective ways to reach an insight.
In planning, facts generally come from quantitative research, but equally can be as a simple as a thing that is known to be true: ‘your luggage travels separately to you’.
Observations are subjective statements based on something we’ve seen or heard: ‘people get stressed when their hand luggage is taken off them’.
For creative purposes, insights only matter if they inform ideas or lead to great execution. In this example ‘your luggage has a harder time travelling than you’, the insight informed some brilliant creative work for Samsonite.
Looking for disconnects in the data can also be an effective way of identifying insights.
This could be a conflicting attitude held by the same group of people.
A straightforward example of this is the fact the majority of the country are in favour of the royal family but want the throne to pass straight to Prince William bypassing Prince Charles; seemingly failing to grasp the central concept of a monarchy in the process.
Or it could be two data points that demonstrate an inherent tension a brand could exploit. For example, Generation C might be more digitally connected than ever before, but they are also more physically confined.
Unless the context of the finding also relates specifically to social media, social media research does not lead to facts in the context of being representative of what the population thinks or does.
The majority of social media conversation about a topic may be on Twitter, but the majority of conversation almost certainly is not.
Generally speaking, those contextual ‘facts’ that can be identified through other forms of big data are usually truisms: the bigger the dataset, the more mundane the findings. An example being, Twitter data demonstrates people wake later at the weekend.
If we accept the purpose of social media research is not to generate facts, beyond a specific context, we can begin to understand its true value and utilise it more effectively.
As planners increasingly draw upon the same sources for facts (TGI, Mintel, etc), observations become a more valuable way of differentiating your approach by grounding your strategy in stronger insights. And, in that regard, good advertising must be consistently distinctive from what a brand’s competitors are producing; otherwise it simply advertises the category, or worse, a rival.
Following the same data trails can easily lead to a homogeneity that fails to address the need to make communications memorable and distinctive from the category in which a brand competes.
Social data provides the potential to derive observations at a greater scale than traditional qualitative approaches like focus groups (although, it should be noted, rarely in the same depth).
It’s also more subjective in requiring someone to interpret it. In that regard, the same dataset can lead to two different findings.
Starting with the facts can provide a framework for exploring different hypotheses within social media conversations. Although the subsequent danger lies in framing what you find through the search terms, conducted properly, social media research presents the opportunity to identify more robust observations.
Ultimately, this means arriving at better insights too.
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