Beyond Social Listening, with AI Enabled Consumer Intelligence

Media Technology

When the first social media listening platforms became available, from around 2007, they represented a rapid and significant change in the way businesses were able to gather consumer intelligence.

woman with hand cupped to ear

All of a sudden they could get access to millions of spontaneous, unprompted discussions relevant to their brand, in close to real-time, it was like nothing that had ever existed before. But although this created a lot of new opportunities, social listening platforms still had certain limitations.

Users can perform quite complex Boolean searches to filter out the tweets, blog posts, discussion forum posts and so forth that are relevant to the topic of interest, but the platform returns a lot of unstructured data and it’s up to the user to interpret it all. This is fine if the user has the appropriate skillset to get what they need from the data, but it’s fair to say that most of the marketers who use such platforms are not data scientists or market research professionals.

It’s easy for the data to be misinterpreted, or useful insights to be completely overlooked. And while there’s certainly a role for conventional social listening platforms, it’s clear that there’s a need for something to help businesses make sense of all this social data.

This is the problem addressed by a new breed of social listening technology that industry analysts have described as AI Enabled Consumer Intelligence.

This technology builds upon conventional social listening in two ways. Firstly, advances in machine learning and Artificial Intelligence (AI) mean that computers can now help us find deeper meaning in these huge volumes of social data. Secondly, human expertise in areas like data science, market research, and domain knowledge of specific industries, can all be applied to interrogate the data in more sophisticated ways.

Improving Data Quality

Social data consists of many individual tweets, posts, comments and more from a wide range of sources. It would be impractical to manually classify even a small dataset of a few thousand pieces of content, never mind the much larger volumes that are more commonly generated.

But with AI we can now automatically classify huge numbers of social posts, to several levels of specificity (e.g. sports – > football – > Manchester City Football Club) so that the data is intelligently organised, making analysis easier and more effective. In real terms, that means researchers and marketers can uncover behaviours and patterns that they simply hadn’t anticipated. To use an old adage, you don’t know what you don’t know; but AI Enabled Consumer Intelligence helps you to see what you don’t know, and look for answers. 

Another relatively recent advance is that AI is now capable of converting audio files into text, and even creating text descriptions of the contents of image files. This means that photographs posted to social media, and discussions taking place in podcasts, can now be analysed in the same way as text content, adding a much more diverse range of data into the mix.

Practical Benefits

So what does all of this mean in real terms for marketing professionals? Let’s use Scotch whisky as an example:

Social Listening might simply tell you that from October to February people talk more about Scotch whisky on social media. This is an interesting observation, but not particularly useful in isolation, and could be interpreted in a number of (possibly incorrect) ways.

AI Enabled Consumer Intelligence, on the other hand, could tell you that people enjoy whisky for its warming quality during the cold winter months, and that they’re also interested in finding new whisky cocktail recipes. Furthermore, in the run-up to Christmas, people need help choosing the best brand as a gift for the Scotch aficionado in their life. These are much more useful insights that can be used to craft more effective marketing campaigns.

Another strength is an enhanced ability to detect potential new trends that might not be obvious to the untrained eye. AI’s ability to find patterns hidden in huge swathes of data, in combination with the skills of human experts to interpret those patterns, makes it easier to spot the early signs of a potential change in behaviours or attitudes. Importantly, this can happen early enough for businesses to take action while there is still time. 

While conventional social media listening still serves a useful purpose, with AI Enabled Consumer Intelligence, we’re much closer to the Holy Grail of uncovering actionable insights in social data. 

Jenny Force is the VP Marketing for Linkfluence, an AI Enabled Consumer Intelligence Platform, and part of the Meltwater Group.