For years consumer research was done by conducting surveys and organizing focus groups to derive insights. It was the advent of social analytics and new machine learning technologies that allowed brands and agencies to do this in a much more automated way and limit the need for these traditional methods.
In my previous blog post, I spoke about how technology sometimes comes full circle and that this trend is often driven by core human characteristics. In the early days of man, we recorded information and communicated very visually. Paleolithic cave drawings and hieroglyphics are two such examples.
We moved to a more text based form of communication as the need for efficiently recording information became more pressing. Drawing was just too slow of a process. However, new technologies like emoji keyboards, camera enabled smart phones, and GIFs have made visual communication more efficient again and we have found ourselves reverting back to our old ways.
We saw a similar trend in phone communication. There was an efficiency gained when you could talk to someone on the phone from miles away without having to physically be near them and as technology progressed we moved closer to recreating the experience of being in a room with someone(e.g. telepresence). Once again gravitating towards our innate human desires.
So how does this relate to social analytics? We have found a new way to analyze consumer opinion in a more efficient way, but we as humans are fundamentally wired to learn about and interact with other humans not via a screen or a fancy software platform, but face to face. We ask questions and they answer.
The question I’d like to pose here is what is the social analytics equivalent of emoji keyboards? What if we had the power to generate artificial focus groups from social data that marketers and researchers can interact with? The social analytics interface would no longer be just a collection of buttons, filters, and features. The interface would be a person or group of persons.
Using the power of technologies like augmented reality, holographic computers like Hololens, and breakthroughs in AI, I believe this is possible.
In theory, with image recognition we have the ability to analyze biometrics and variety of facial attributes of a particular audience and generate an artificial representation of what your target audience looks like. We can then derive interests, personality traits, and opinions on certain topics from social media that we can then attribute to those visual representations. You could ask questions of certain demographic subset of your audience like young mothers how they feel about a certain product. They might respond with “Angry” using our emotional analysis and when asked “Why?” or “How can we improve the experience?” respond with the topics from our topic analysis that contributed to the Angry emotion.
There are obvious challenges with developing this type of capability as there are with any sort of innovative technology, but this could be something we see in the near future for data analytics.
For additional insight into image recognition technology and social data, watch the recording of our image analytics webinar with guest speaker Susan Etlinger from Altimeter.