What is Emotion Analytics and how it can improve user engagement metrics

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Human facial expressions align with the proverb – It’s not what you say, but how you say it.

Emotion is a strong feeling which nurtures human relations. Maybe, that’s why we deliberately learn to express our feelings through emotions.

Human emotions are broadly categorized as 6 types – Anger, joy, love, sadness, fear, and surprise. Industries are leveraging human emotions to understand their customers and their journeys through the product and services.

With progressive technology, a large amount of customer data is produced every year. It is practically impossible to analyze voluminous data without the help of automation.

Automatic data analysis has empowered many technologies such as Deep Learning, Convolutional Neural Network, and Machine Learning. On the other hand, Automated Emotion Analysis has given rise to a newborn technology, which takes its roots from automated data analysis.

It’s pretty soon to talk about Emotion Analytics. But so was Artificial Intelligence in the early 90s. 

The exemplary growth of AI since then is a great story in itself. Thanks to A.I., machines can do manual tasks with unfathomable speed and accuracy. For example, Recognizing and counting humans, animals, and objects in the crowd, translating languages, predicting Football & NBA games outcomes, create memes, and many more.

What is Emotion Analytics?

Imagine you get a call from your boss on a weekend. He tells you to set up milestones & deadlines for your next project because the client meeting which was scheduled for next Tuesday is rescheduled to Monday.

With a heavy heart, you open your laptop and log in through facial recognition. The app sees the sadness on your face. 

After a couple of hours, during a break, you tell your machine to play songs. Surprisingly it starts playing peppy songs to make up your mood. That’s a small example of how Emotion Analytics software can help users with a better experience.

Emotion Analytics is a state-of-the-art technology that identifies and analyzes the band of the human emotional spectrum including moods, attitude, way of talking, and personality.

How Emotion Analytics can help humans

“There are two sides to Emotion Analytics”, says S. Anand, the CEO, and Co-founder of Gramener. “One is detecting emotion, and the other is conveying data and information through emotions.”

Let’s talk about Emotion Detection first.

Facial detection is the first step in detecting emotions on a human face. Facial detection is the ability to detect the location of the face in any input image or frame. The output is the bounding box coordinates of the detected faces.

Here’s an example of how emotion detection can help a retail business. The retail industry, like any other industry, grows with happy customers. Happy customers recommend products and also buy more. Bottom line – Happy Emotions, Happy Customers, Happy Business.

The cameras placed in retail stores can capture multiple emotions and expressions of the customers. 

• Do they smile while seeing a product?
• Do they give curious expressions?
• Are they disappointed in the product?

These are some thoughts that come out as expressions. Furthermore, the cameras can also capture the time the customer spends with a product. Analysis of such data can offer retail executives strategies for better customer experience.

Currently, we know what users want – their expectations from a product. Emotion Analytics would help us understand if we stand by the customers’ expectations. 

For example, you go to a retail store to purchase a baby food product. Amongst a bunch of brands, you select Brand-A. Now, if you purchase the same brand every week, a camera placed near the product aisle can detect your expressions every time you pick that product and let the producer know of your experience. It’s a simple case of understanding the customer journey experience.

Side-B of Emotion Analytics – Conveying data and information through emotions.

Usually, we present insights blandly to our stakeholders or clients. What if we add a flavor of emotion to it? Wouldn’t it make a good story and easy to consume?

Brand marketing has understood the importance of emotions. Marketers use Emojis inside emails to make communication with subscribers a bit more personal and engaging. What if we could similarly convey data insights using emoticons or emotional stories?

People relate to emotions. Using stories is the most comprehensive way to convey data insights. Emotional stories, on the other hand, add more value to the content altogether. It shows the emotions of the presenter associated with data. It will also give the viewer a perception to look at data, e:g; happily, sadly, disappointingly, surprisingly and make inferences out of it.

Emotion Data Analysis Algorithms Need Huge Training Data

Oh! But for emotion recognition we would need huge data of expressions and sentiments, right? 

Well, with people doing multiple activities with a camera such as a video call, or facial login, there’s plenty of emotion data available online. There are companies who, after taking user’s consent, use their facial expressions for training and testing algorithms. The data can further be fed to Machine Learning algorithms, which eventually can train to identify expressions, tones, and other characteristics that correlate to specific emotions.

Enterprise applications and Use Cases of Emotion Analytics

The emotion of a customer is the most valuable insight. There are many algorithm heavy and low-code machine learning solutions that make good use cases for emotion analytics.

Sales & Marketing guys are already combining the sentiment analysis insights with their existing CRM data. It is giving them a holistic view of consumers to offer personalized targeted ad campaigns.

Apart from that, many industries can reap the benefits of Emotion Analytics. In fact, Gramener’s Machine Learning Consulting is focused to help such business domains and users who want to scale rapidly by leveraging effective AI and ML Solutions.

1. Emotion Analytics in Retail Industry

Retail sectors can use facial recognition systems to know the customer experience and draw insights for better CX.

We presented a prototype of a facial recognition application during the Gartner Data and Analytics Summit, Orlando. When people walked by, the system counted the number of people smiling followed by their gender. Surprisingly, 7% of men smiled compared to 20% of women seeing their age live on screen. Retail industry business users can track customer emotions to know the performance of their products.

2. Emotion Analytics in Media Industry

The media industry can certainly improvise the content and marketing strategies knowing the sentiments of their consumers. Market research in the media industry is qualitative. Disney is using Emotion Analytics techniques to know the audience’s experience. Reportedly, the A.I. powered algorithms can identify complex expressions and even predict the upcoming emotions. 

In an experiment, a software captured people’s faces using infrared cameras during movie screenings including ‘The Jungle Book’ and ‘Star Wars: The Force Awakens’.

After just a few minutes of tracking facial behavior, the algorithm was advanced enough to be able to predict when they would smile or laugh (in relation to specific moments in the movies).

3. Emotion Analytics in Human Resource

HR executives can analyze the emotions of employees walking in and out of various rooms in the office. It would help them identify spots in offices where employees go in a happy mood, e:g; games room, pantry, canteen, etc. Further, HR executives can take important opinions and meetings at places where employees are happy and much active to make a conversation.

4. Emotion Analytics in Academia

Colleges and universities can identify students’ emotions in computer-enabled classrooms and respond actively to the affective state of students. It is also called as affect detection. The algorithm can detect boredom, delight, frustration, and the affect-sensitive interfaces can endow exciting possibilities of making education more student-friendly.

Conclusion

Research says, “The global Emotion Analytics Market is expected to grow at USD ~25 billion by 2023 at a CAGR of ~17% during the forecast period 2017-2023.”

Globally leading technology companies such as Apple and Microsoft have their own Emotion Analytics software. It’s time for more companies including SMEs to invest in the Emotion Analytics software market to improve their marketing, sales, services, and customer experience.