It\\\’s Good To Listen

Sending a letter across the world once took 10 weeks. Today we can send a WhatsApp message the same distance within 10 seconds. But as the communication evolution continues, the huge volume of digital engagements consumers have on a daily basis makes it difficult for us to capture their attention, both from a research and a marketing perspective.

It’s acknowledged in the insight industry that response rates to traditional approaches – like surveys – are in decline. This is no real surprise when the research experiences people have differ drastically from how they communicate in their day-to-day lives. A 20-question ‘wordy survey’ feels very different from a quick Snapchat or Instagram post.

All of this plays a role in why we are seeing an increase in the use of observational data. The development of social listening platforms allows us to tap into the public conversations people are having, as another form of data, without the issue of response rates.


Social data allows us to access consumers’ spontaneous discussions of brands and services, or of the hot topics of the nation, from budgets to politics, festivals to celebrities. Consumers are discussing what they care about most, which allows us to understand what’s important to them in a way that isn’t influenced by researcher thinking or questionnaires.

In addition, it’s all ‘in the moment’, so we can capture the emotions involved more effectively, since we’re not relying on post-rationalised recollection. But, as researchers, we’re not making the most of this data.

While social listening is well established in the world of marketing and advertising, research has been slower on the uptake, albeit with good reason. Social listening platforms were developed for advertisers; there are a lot of ‘what’ metrics, but there’s very little ‘why’.

Social listening has weaknesses too, of course. Many users become unstuck when they do not look beyond the metrics provided by the platforms – they rely on these but don’t delve deeper into the data. When you do delve deeper, social listening can open up a world of data, and with the right approach it can be extremely rich.

Social listening doesn’t have all the answers; you can’t ask questions, so, of course, you have gaps. Also, different streams of data can give you different results. For example, a record label looking to launch a Katy Perry album can rely on Facebook topic data alone, but Spotify data will give them a different picture – it will reveal the gap between what people say they do vs what they actually do. In this case, men don’t talk on Facebook about liking Katy Perry’s music, but they do listen (a lot) on Spotify!


As researchers, we’re uniquely placed to apply our existing research thinking and to combine this with the data and metrics provided by social listening platforms, analysing the data qualitatively, at scale. We’ve developed an approach to building code frames driven by the data; the story we derive is therefore built from the data, rather than necessarily from us going in to prove a theory.

As this data is at scale, we can then start to build what we call ‘big qual data’, and use machine learning to help us

analyse it en masse, across multiple markets, quickly and cost effectively. Digital listening projects can be launched on all sides of the globe within an hour; data can be accessed almost immediately and provide a snapshot of findings not long after – something that’s very difficult to do with a primary research approach.

What this data doesn’t provide us with is some of the cultural context behind a ‘why’. Overlaying culture and trends, as well as primary research techniques, allows us to better understand the behavioural differences we see by market.


Social listening gives us a knowledge base, on top of which we can then layer other forms of research – like asking questions – to build on that knowledge. This means that we can be much more focused on asking a smaller number of questions to help us fill in the gaps, rather than having to ask consumers everything. This not only speeds up the primary research process and reduces the costs, it also reflects the way consumers are interacting online – i.e. in short, concise bursts of communication.

As a result, we end up with more agile and effective research. By embracing the new available data sources, we can make research more relevant in today’s market, where agility and speed are becoming ever more important.

By Karen Schofield, Managing Director,  Join the Dots Singapore