Emotions analytics (EA) is a new very fashionable term in content marketing, so I thought I’d give it a spin in this article. What does it mean? Well, in the old days (not too long ago) we content-marketers used to research target audiences both quantitatively and qualitatively, but not really get a measure of how they are emotionally impacted by our content. Now it appears there are ways to scientifically measure emotions in content-consuming audiences. There is now software that collects data on how a person communicates verbally and non-verbally and thus “understands” the person’s mood or attitude. Using these insights, content can be closely customized to our target audiences, in the many interactions they have with our content … especially in areas like customer relationship management (CRM) and customer experience (CX) management.
As with anything new in the area of Content Analytics & ROI, there are proponents and opponents. Proponents of Emotional Analytics say that these scientific programs are more valuable and granular than the information obtained through mere customer satisfaction surveys. Opponents say that even if Emotional Analytics continues to advance and develop, there can really be no measurement more refined or developed than our own gut feelings. The jury is still out on this one …
How does emotional analytics work? A behind-the-scenes glimpse …
Every day, millions and billions of people post their emotions on the social media – on Snapchat, Facebook, Twitter and Instagram, as well as on websites and in videos. So there is plenty of “emotions data” already out there, available to the new breed of EA software vendors, to analyze and arrive at insights.
Essentially these vendors have the onerous task of sifting through gazillions of human-content interactions to see the resultant actions people took – and to see if there is clear correlation between their emotional cues (words they used, their facial expressions or their voice inflexions) and their final actions. To an extent Emotional Analytics does overlap the Facial Recognition technology, but is not restricted to it. In fact, it may well go far beyond it.
The “emotions data” is obtainable from video cameras that capture facial expressions during chats, microphones that collect data on tones of voice, or samples of text in blogs and articles and social updates. Of course, we also have the emojis that people like to annotate their text with. All this data, fed into machine learning algorithms, can help us learn to recognize expressions, tones and other characteristics that correlate to specific emotions.
Although, today’s emotional range may cover typical broad categories of emotions – like anger, contempt, confusion, disgust, fear, frustration, joy, sadness or surprise – we may have finer shades or nuances of emotions recognized by machine learning. The end result we expect to see is not just what emotions people had at the time they typed text, or spoke via voice or video, but what actions they took immediately after. That is the point that marketers are more interested in.
I found it very interesting to find this exercise by TechTarget on identifying a basic set of facial expressions that are most used in videos and images as reflecting some clear emotions. Clearly businesses are analyzing emotions with a lot of gusto.
The technology by which emotional data collection is done is now referred to as as “text mining”, “audio mining” or “video-mining”. Soon there are likely to be dashboards where we can, as marketers, see the pattern waves of emotions in our target audiences in real-time, and be able to predict their subsequent engagement behavior.
We may thus be able to shape our content to create fluctuations in emotions that can alter buying behavior. That is the general objective all experts are working towards.
Emotional Analytics market expected to grow to $7.76 billion by 2022!
Recent research appears to predict that the Emotional Analytics technology market will grow from the $3.37 billion it was in 2016-2017 to almost $7.76 billion by 2022. That’s a huge leap forward, if it happens.
The larger area of Emotional Analytics may well also become more segmented into clear sub-areas such as sentiment analysis, quantification of mood, or emotional variance by digital media types like images, video, audio, and text.
If you take a look at the vendor market, three distinct types of vendors are already in existence, doing trailblazing work. They include these players …
How content marketers can use emotion analytics, and what benefits they will gain!
One key element, that is just beginning to be explored by content-marketers, is how to use Emotional Analytics to successfully attract their ideal consumer base. For example, when creating content, marketers may believe users will be highly attracted to humour. But after measuring user sentiments, they may well find that their consumers would rather prefer content with a serious tone. Changing the content tone of voice may yield results in terms of better user engagement or attention.
Emotion analytics can also help decide on the timing and length of content. Emotion becomes more visible when attention lingers on a piece of content long enough. There could be a below-threshold level where attention exists, but is so low that no emotion become palpable. Knowing this could help content-marketers better shape content length. Today, we only gauge attention … but do not have the means to go beyond attention to understand the emotion experienced while paying attention.
A statistic that may be of great interest to content-marketers, is that consumers who have a positive emotional experience with a company are 15 times more likely to recommend the company, than customers with a negative experience, according to a 2016 study by customer experience firm Temkin Group. Customers with positive experiences are also six times more likely to forgive a company if it made a mistake, compared to those who had a negative experience. The need for measuring the “positive” and “negative” emotions thus becomes paramount.
Another area that may eventually score above all other benefits is the correlation between emotion and subsequent actions. I believe content-marketers will eventually be able to study, discover patterns in behavior, and then be able to predict the impact of different emotion-eliciting types of content.
Finally, there may also be a lot of value in being able to see the relationships between certain types of content and the associated emotion-triggers, and whether using content of certain types can help reshape emotions and moods, and if so how.
Discovering pain-points using emotion-revealing tactics – using a slightly less-scientific way
We also need to appreciate that knowing “customer emotional turbulence” can have immense value when we are trying to locate our customers’ pain-points. As marketers, we all know that we have to serve content to customers that solves their most distressing problems. That is how our content can have the impact it is created for.
As we all wait to see the more scientific approaches to Emotional Analytics evolve, and start becoming part of our toolkits, I have found one less-scientific approach that works for me quite well. It helps me in gauging customer emotions to any piece of content. I like to call this my “Sentimeter Tactic”.
I pick an article from my niche topic carefully, to see if it has covered a number of sub-topics. I then give my research respondents (usually a focus group) a whole bunch of emoticons with all kinds of expressions. I tell them I don’t want any verbal explanations from them.
I just want them to do two things … for the overall “emotional feel” of the article topic, they have to choose the emoticon that best matches the feelings they have. And then, they have to take the separate sub-topics of the article, and slap on the emoticons for each area, to show how they feel when they see and read that part of the article.
See below how a sample exercise would look like after the users have had a “emotional-expression session” …
Emoticons are immensely useful in helping people express what they feel without the rationality of words and their justifications. If the focus group is well-chosen, from a good representative sample of the target audiences, the results of this kind of informal emotion-research seems to work surprisingly well.
So what are your thoughts on this topic? Do share!
This post is incomplete without your input. The community of aspiring digital solopreneurs would feel galvanized to hear from you … so do share your thoughts on this topic with us in the comments field below this post.
Other articles in our series “Content Analytics & ROI”:
- 6 User Behavior Metrics That Affirm Your Content’s Engagement Power!
- Calculating Content ROI Accurately Is Tedious – But Worth The Time!