Why Context Matters for Visualization of Social Media Data
At Visible Technologies, we believe the definition of visualization is misunderstood. Anyone can create a bar chart or pie graph that reflects data trends, but real visualization is piecing together a narrative that helps brand managers or media buyers understand a linear path from start to finish. That story and depiction through words and images can help them make a business decision about their next product launch – or where to shift advertising dollars to be more effective.
Context really matters. Without it your social media data just doesn’t makes sense.
SOCIAL MEDIA DATA: OPENING THE DOOR TO NEW WAYS OF THINKING
It is important to understand that social media data is irregular. Brands have bursts of conversations instead of predictable streams and trends, so traditional quantitative research methods don’t work. Yet, brand managers deal with millions of records which constitutes big data, so traditional qualitative methods don’t directly apply either. While this might seem challenging to some, to Visible it provides a perfect vehicle to rethink the notion of visualization and address context
Just because a brand experiences a 100% spike in volume one day doesn’t mean anything good or bad because there’s no context to explain the spike. Finding out what that spike represents and whether it has visibility is important, as well as who saw it.
Did it all happen in forums?
Was it genuine author conversation back and forth about a certain topic?
Or was it just spam bots on Twitter?
The biggest stance brand managers and media buyers should ask themselves when building context and a story is “how do you get to meaningful data in the first place?” If brands can’t get to the right data then they are not going to find anything insightful about it. Sometimes getting to the needle – showing how that needle was found – is the most difficult part..
A KALEIDOSCOPE OF SOCIAL SENTIMENT
An advertising agency we’ve worked with likened the analysis of social media to a kaleidoscope; an analogy we love and continue to share. In this scenario, just as a kaleidoscope segments images a general set of information about a brand is segmented or sliced of its characteristics. Brands don’t want to report on one slice alone – rather understand how those slices fit and interact together. It’s the interaction of elements that tell a story, and that’s ultimately what brand managers and media buyers are seeking.
For example, do people complain about the gas mileage of a car at the same time that they mention its ugly interior design? Analyzing the data to find correlation (or not) is important, and visualization has to be able to explain that. A volume chart on one data element does not tell you that. It’s important to show the kaleidoscope and layers of intersection visually. To do this the slices have to be shown together because that’s where the insights are. Maybe some layers have nothing to do with each other, but maybe they have everything to do with each other. The methodology that’s important is how to identify the layers and piece them together so a customer can see the full kaleidoscope view.
VISUALIZATION: THE MARRIAGE OF QUANT AND QUAL
Anytime someone is dealing with big data, they have a high possibility of getting lost in pure numbers, and quantitative data alone can drive marketers crazy. They see a spike in social media data for example and know that more people are talking about a particular subject, but they don’t know what that means. Is it good or bad? How does it compare with other brands and their spikes? These are frequent questions that are asked.
Over time, we’ve learned that showing marketers a visual combination of qualitative and quantitative insights helps them to get to a business result and come to an end of the story. Due to this increasing demand, there is a growing need for data analysts and insights services to help companies look at the huge streams of data and trends, and give brands that qualitative visual view of the story backed up by big data. The combination gives marketers the confidence to understand what the data means and what they should do about it.
When beginning to look for this information, marketers should consciously think about the brand and what context will matter the most to end the story. This might mean comparing the brand to several key competitors or showcasing activity with trends in the industry. Through knowing what to extract from big data and understanding the brand, applying meaningful pictures helps marketers to understand what is really emerging from a trend perspective with their brand.
At the end of the day, digging deep into data and helping the facts tell a compelling story are crucial to understanding the story and visualization.
Big Data + Visualization = Contextual Brand Stories That Move Business Forward. It’s really that simple.
This article was originally posted to the WTIA (Washington Technology Industry Association) blog, found here.
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