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Emotion analysis: the feature that outperformed “Sentiment analysis”

What the difference between Emotion analysis and Sentiment analysis is

Both Emotion analysis and Sentiment Analysis are used to uncover and quantify the way in which people respond to new products.

Whether it’s measuring the response to a new TV series or event, Emotion and Sentiment Analytics can help “content creators” respond with tailor-made offerings. These custom products can enhance Brand Reputation.

It may seem that Emotion and Sentiment Analytics are the same, but the two techniques have some important differences. Both techniques have a desire to better understand the needs of the consumer, but they achieve this in different ways.

In today’s Internet-based world, a video or show can have millions of views, without really telling us anything about the way in which viewers responded to it.

Software can analyse the words and emojis used in text. This information is used to understand and track people’s emotional fluctuations during ads.

Emojis can be broken down to uncover how males and females differ in emotion, which emotions spike overnight, which brands are most popular as well as times, locations and negative responses. The words used in texts on web sites can also be analysed.

Do you want to know why large enterprises prefer Digital Consumer Intelligence? Watch our Digital Consumer Adventures webinars.

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Experts from large companies, such as Deloitte, Group M and ENI have already found an innovative way to use this technological cookieless model for their creative strategies.

Sentiment Analysis

Sentiment Analytics tries to understand the general feelings and emotions experienced by a viewer or customer.

It doesn’t zone in on specific details or emotions that are experienced. Instead, it uses the contrast of positive and negative experiences to determine a broad response.

For example, using sentiment ratings, a production company may determine that 70% of viewers enjoyed the season finale of a TV series. This enables them to evaluate the response as positive. This allows for a general understanding of the success of the product. In short, it can determine if your product and the consumer are a good match.

This type of opinion analysis is widely used as part of Digital Consumer Intelligence and forms part of gaining a better understanding of the public reception of a movie or TV series, new business or the launch of a new product.

Using opinion mining to study and analyse affective states and associated information is a great way to learn more about what consumers want.

Emotion Analysis

Emotional Analysis uses a complex system to understand consumer responses. While Sentiment statistics monitor simplified positive or negative markers, the latter deeply analyses a full range of human emotions and sensitivities.

This method measures the differences in the feelings that various viewers or buyers express -using emoji and text analysis. This is a thorough look into the emotions and intensities experienced as emotions are felt and change.

Unlike sentiment analysis, the emotional analysis includes the subtleties within human emotion. So, for example with the positive spectrum, we may find emotions like happiness, excitement, elation or contentment.

The emotional analysis also looks into the motives and impulses of a viewer, buyer or reader. These exact insights can be very revealing and are easily translated into actions.

Even uncovering a confused response can reveal that your content is too complicated and you need to deliver clearer content. On the other hand, if the dominant feeling is boredom, you’ll need to liven things up with humor or creative content or a cliffhanger moment.

Why Emotion Analysis is the winner

1. It uncovers complex emotions and motivations

We all know that human emotions are complex and never as clear cut as either positive or negative. If you want to strategize marketing in a powerful way and enhance your brand reputation, you need a holistic view of your audience’s feedback.

While Sentiment Analytics divides opinion into negative, positive or neutral, this is only a shallow understanding. To truly design content that meets the deeper needs of the consumer, the motivations and emotional blocks of your customers must be understood.

2. It provides deeper insights

Sentiment analytics gives you basic responses but in-depth Emotional Analytics aims to give an understanding of people’s actions and the motivations behind those actions.

To understand why a viewer or reader skipped over the content or closed the post or didn’t finish a TV series you’ll need more than a negative/positive percentage. This is the only way to truly get a grasp of how to tweak the content.

While sentiment analysis helps you gauge how content performs, emotional analysis helps you to understand why. Emotion analytics embraces the full spectrum of human emotions.

3. Turn Insight into action

By analyzing viewer responses more deeply than just “positive” and “negative” it is possible to fine-tune content to create a premium experience for the consumer.

When you discover each and every emotion being experienced, you can take the best course of action. Emotional responses become part of the metrics you track and it can be used to inspire consumers with content that feeds into their needs.

Methods and techniques for Emotion Analysis

There are a lot of services available for tracking the thoughts and feelings of consumers, for example, consumer panels and viral video services like Unruly‘s ‘Future Video Lab’. Video Lab gives marketers real-time data on consumer’s emotional triggers that help determine how videos should be distributed.

It can be applied to drive content into the sphere where it is likely to be most pleasing to the consumer and therefore increase sales and popularity. For example, positive consumer reactions to a trailer or snippet can encourage producers to create similar, but longer, content.

Knowing how customers respond to content or products enables marketers to make predictions about how new content should be structured, which aspects to avoid and which to enhance. While some content may need minor tweaking, others should be stopped completely if it evokes a negative reaction. This can help avoid costly campaigns that hold little value.

Even if an ad is, initially, received positively, it may have a short shelf life and this is where emotion analytics can help determine when an ad should be removed from view.

Today, thanks to Digital Consumer Intelligence technology, it is possible to classify different emotions (from joy to admiration, from fear to sadness), in order to have different audience segments, profiling strategically and having a full vision of the potential target.

In addition, the AI technological models can go beyond the text, managing to “read” what is written in the images, obtaining a classification for Image Recognition.

The Future of Emotional Analysis

Tracking public emotion is set to become the wave of the future. At Birmingham’s New Street Train Station, Ocean Outdoor placed digital screens to track the age and sex of people that walk by in order to show more relevant advertising on nearby billboards. It is likely that in the future we will be seeing this technology applied in hundreds of different ways.

And that’s not all! 

In the next months, KPI6 will release a lot of new disrupting features that will radically change the Market Research industry forever. So, keep in touch with us.

Have you enjoyed this article and do you want to know more? Watch our Digital Consumer Adventures webinars.

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Experts from large companies, such as Deloitte, Group M and ENI have already found an innovative way to use this technological cookieless model for their creative strategies.