The future of social media research: or how to re-invent social media listening in 10 steps (full text)

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Social media monitoring is a growing industry but one that is stuck in its old ways. And in need a of a urgent re-think.

Since it emerged 15 years ago, the industry has been largely responsible for driving some of the most interesting evolutions in the research space, such as the democratisation of text mining and computational approaches to mining qualitative information.

This is an approach that for the first time enables both a granular and a birds-eye view of the data, making it possible to produce qualitative observations on a mass scale. A new perspective that is blurring the lines between qualitative and quantitative thinking.

And with computation also comes the ability to mine larger (and messier) datasets, which is in turn steadily shifting the focus of what we call knowledge, from understanding causation to identifying correlations.

However, despite the broad impact of social data on the market research industry and the evolution of its infrastructure, the social media monitoring toolbox hasn’t evolved much in 15 years.

While the web, its users, the brands and the advertising strategies have changed dramatically, most social media monitoring platforms today still do exactly what they used to do when the industry first emerged.

The more than 480 platforms currently available on the market all tend to be affected by similar issues: the obsession with shallow volume-led metrics, the inability to measure exposure, the lack of context to the social data, no understanding of the audience and poor data manipulation and visualization interfaces.

Add to this the more systemic revolutions the industry is facing, such as the visualization of social media, which is going to pose huge challenges to an industry that’s been entirely built on text mining.

Most of these issues can be ascribed to the lack of research thinking in the design of the tools we use today. The majority of the companies that established the frameworks in the monitoring space have come at it from a web analytics perspective. Which has lead to favouring the monitoring and analytics framework rather than the insight and intelligence framework for studying online social interactions.

But the analytics approach has also lead to another big misconception. Social data is not quantitative data, rather qualitative data on a quantitative scale. This might sound like a very byzantine distinction to some but over the past ten years this approach has had huge implications on the way social data has been modelled, analysed, sold, delivered and used by organisations worldwide. 

Now with social data intelligence becoming central to many organizations and brands, researchers can and have to play a more active role in shaping the tools for the job. So how can we help disrupting a shallow social media monitoring model to make it more powerful and relevant to the way the web works today?

  1. Introducing new ways of sampling social data beyond keyword tracking: Audience Mapping (harvesting content from a set of users) Content Diffusion (harvesting content that contains specific URLs), Social Simulations (agent based simulation based on social data), MROC Augmentation;

  2. Shifting the focus from the content of the conversations to everything around it: context, behaviors, social graphs and interest graphs;

  3. Implementing solid analysis frameworks to move away from basic analytics towards intelligence, for example embedding techniques to make the most of social data such as social network analysis, discourse analysis, reach analysis, attribution analysis; 

  4. Opening up the social silo by connecting social data with other datasets such as sales, NPS, stock trading, media exposure;

  5. Introducing scalable human analysis alongside algorithmic coding by crowdsourcing parts of the research process;

  6. Making machine learning ubiquitous to capitalize on the benefits of human coding;

  7. Improving the Data User Experience to support intuitive data manipulation and delivery at different levels of the company and across multiple devices;

  8. Help re-design the company decision-making process: organizations are learning that they have to re-engineer the way they make decisions in order to make the most of real-time intelligence; there’s no point in delivering real-time intelligence if your client can’t make real-time decisions;

  9. Create smart research products based on integrating traditional methods and social data: live segments, social panels, social surveys;

  10. Making Research Programmable: shape the transition from monitoring platforms to social data driven business applications as social data becomes invisible and embedded into the way any organization creates new products, plans advertising or stocks their stores. Move away from dashboards and Powerpoint reports and plug the data and the intelligence into the products and services we deliver.

Time for the researchers to roll up their sleeves.

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