Aiming for the Heart: Why Brands are Paying Attention to Customer Emotions

By Mayank Trivedi » 6 min read

Remember the last time you made a purchase that made you happy? Ask yourself – was your purchase decision steered by your needs? Or was it influenced by a sense of desire?

Buying decisions are closely linked to our emotions. The way we feel can often impact our purchase intent. Brands are realizing this and paying attention to measuring the emotional input that goes into decision-making.

This is where emotional analytics comes in. Emotional analytics is aimed at understanding how customers communicate both verbally and nonverbally. Brands believe that once they gain insight into a customer’s emotional response, they can customize their brand experiences in completely new ways. This means they can change the messaging, the language, and the overall tone of their campaign to make it resonate with customers.

Emotional analytics primarily includes emotion-sensing technology, such as facial recognition software, along with digital assistant applications. According to Gartner, by 2022, people’s smart devices may know more about their emotional state than their own families. Shocking, but true.

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Why is emotional analytics important?

In today’s internet world, humans express their sentiments and feelings via emojis, likes, dislikes, texts, and videos. Understanding the underlying meanings behind these electronic symbols is crucial for businesses. A recent study revealed that emotions data plays a crucial role in deciding whether a prospect wants to do business with a company or not. In fact, consumers that have a positive emotional experience with a company are upto fifteen times more likely to recommend the company than those with a negative experience. This difference can mean a win or loss for a business.

By using emotional analytics software, companies can collect crucial data on how a person communicates verbally and nonverbally. This data can provide insights into how a customer perceives a product, and how they interact with a customer service representative. Just as with other data on customer experience, emotions data is used to create strategies that will improve the business’s customer relationship management (CRM).

How emotional analytics is improving customer experiences

Emotional analytics is emerging as the gateway technology to the next level of personalization, also known as individualization. This is expected to grow and provide brands a deeper way of understanding and targeting customers. Sounds great in theory, but how will this work?

An Australian shopping center giant recently installed cameras atop advertising screens to detect individual faces and record the gender, age, and mood of shoppers. This helps them collect data to effectively manage and operate their stores. By capturing and analyzing emotions data of customers, the company is able to better understand customers’ needs and connect them with retailers.

Another big global corporation, an automobile manufacturer is effectively using emotional analytics by developing a sentiment analysis application that collects data from publicly available word-of-mouth information. The app uses AI to categorize customer voices into more than a thousand types of topics and emotions. It also breaks down the traditional positive, negative, and neutral categories of sentiment into specific subcategories, such as ‘satisfaction’ and ‘disappointment’, to provide a better understanding of what’s being said.

Insights are also gathered via text analytics, which examine word choices in comments or messages to determine different sentiments about a product or service. In addition, insights are gathered from biometrics, facial expressions, and vocal tones, gathered by microphones and video cameras contained within a particular device, be that a phone, computer, or even an ATM. Even though emotional analytics is in its infancy, it’s becoming an increasingly common consideration for gaining an even deeper understanding of the customer.

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A balanced approach to emotional analytics

The key to emotional analytics’ success is the ability to tap into customers’ key characteristics in a non-intrusive way. The most effective way to do that is by making the content relevant to the individual.

Say you bought a gift for your golf-loving friend. Based on this purchase, you start to receive ‘personalized’ ads for golf. That becomes tedious. Instead, the shift to individualization takes a fuller look at me. It can recognize that perhaps I’m not that interested in golf, based on my more holistic online interactions. The next generation of consumers will be digitally mature, and they expect not just personalized, but individualized experiences. By understanding consumer characteristics, emotional analytics can help organizations make an individualized interaction with each customer.

How emotional analytics will shape customer experiences

Smart devices are already capable of predicting our next moves before we make them. When we run a simple Google search for ‘hiking poles’, our Facebook profiles carry advertisements for hiking poles when we login seconds later. In the same way, when we forget to attach a document, Outlook reminds us to attach it before we press enter. Emotional analytics expands this concept, allowing companies to understand what engages their consumers and provides insight. By giving marketers unprecedented insights into what customers really want, emotional analytics is expected to transform individualized experiences.

Large global corporations such as Unilever are utilizing emotional analytics by developing software that recognizes facial expressions. The market for emotional analytics is expected to grow rapidly.

Recent research predicts that facial recognition and emotions analytics technology market will grow from $3.37 billion in 2016 to $7.76 billion by 2022.

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Mayank Trivedi

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