Using sensors to read human emotional cues like facial expressions or tone of voice, computers collect data about feelings that can be analyzed and used in various applications. Eventually, computer programming that learns non-verbal communication would be able to read human actions and recognize emotions.

Better Customer Service Protocols

There are already customer service programs designed to interact with humans conversationally, but their limitations are the same: they can’t anticipate and respond accordingly to a mood. While smart phone assistants like iPhone’s Siri can figure out mood based on the word choices, it can’t detect frustration or feelings that indicate urgency. Certainly, automated phone systems and chatbots can’t recognize and field angry customers.  

For consumers, is there anything worse to a frustrated customer then a cheerful, automated, ‘I’m here to help you!’? Customers quickly move from impatient to frustration and then disgust; they give up or bring their business somewhere else.

In any industry with customer-facing artificial intelligence programs at work, adding emotional intelligence to the mix would change the game considerably. Reading the emotional voice cues of a tone or recognizing the mood of a customer could retain business and make it easier to service customers. Even by implementing a program that filters angry customers from an automated call to a live call center would eliminate lost business.

Accurate Marketing With Emotional Intelligence

In marketing, the basic premise of ad campaigns is to target emotions and spur action, but many of the current tools are only guessing at human behavior. Imagine the increase in effectiveness of advertising if data could accurately predict and trigger the right feelings and create a reaction?

Emotional intelligence programming capable of mining data from scans of people can detect nuances in human faces and recognize changes in body language during viewing or listening to an ad. Analyzing the data could reveal preferences, attraction, rejection, or other types of reactions and accurately assess the ad’s effectiveness. By programming computers to read these emotional cues, advertisers could better test their schemes and fine-tune messages to greater control the desired outcome

One Size Does Not Fit All

One roadblock for developers of artificial emotional intelligence programs is accounting for differences. While emotional responses are universal, facial features vary between races. This presents a unique challenge for companies developing artificial emotional intelligence because they must pattern from multiple sample sets to account for these variations. Finally, speech dialects and accents from different geographical regions could affect tone of voice. Nevertheless, enough common denominators exist to make development of these programs worthwhile, and as emotional intelligence capability improves, so will the experiences of consumers.